forked from moreo/QuaPy
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23 Commits
| Author | SHA1 | Date |
|---|---|---|
 Alejandro Moreo Fernandez
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9555c4a731 | |
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Alejandro Moreo Fernandez
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49a8cf3b0d | |
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Alejandro Moreo Fernandez
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a04723a976 | |
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Alejandro Moreo Fernandez
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472e49047e | |
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Alejandro Moreo Fernandez
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aa894a3472 | |
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Alejandro Moreo Fernandez
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36ac6db27d | |
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Alejandro Moreo Fernandez
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6ca89d0e55 | |
Paweł Czyż
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2db7cf20bd | |
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Paweł Czyż
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5cdd158fcc | |
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Paweł Czyż
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d34b086a76 | |
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Paweł Czyż
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4dd66b1921 | |
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Paweł Czyż
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020530e14f | |
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Alejandro Moreo Fernandez
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25baae643b | |
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Alejandro Moreo Fernandez
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f674151eba | |
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Alejandro Moreo Fernandez
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3921b8368e | |
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Paweł Czyż
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2cc4908326 | |
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Paweł Czyż
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3705264529 | |
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Alejandro Moreo Fernandez
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448d60ac42 | |
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Alejandro Moreo Fernandez
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b43eafa36f | |
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Alejandro Moreo Fernandez
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b3ccf71edb | |
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Alejandro Moreo Fernandez
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320b3eac38 | |
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Alejandro Moreo Fernandez
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9542eaee61 | |
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Alejandro Moreo Fernandez
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d50a86daf4 |
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@ -70,8 +70,7 @@ instance/
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.scrapy
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# Sphinx documentation
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docs/_build/doctest
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docs/_build/doctrees
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docs/_build/
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# PyBuilder
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target/
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@ -0,0 +1,3 @@
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[submodule "result_table"]
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path = result_table
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url = gitea@gitea-s2i2s.isti.cnr.it:moreo/result_table.git
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@ -1,3 +1,11 @@
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Change Log 0.1.9
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----------------
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- Added Bayesian CC method (thanks to Pawel Czyz!). The method is described in detail in the paper
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Ziegler, Albert, and Paweł Czyż. "Bayesian Quantification with Black-Box Estimators."
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arXiv preprint arXiv:2302.09159 (2023).
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Change Log 0.1.8
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----------------
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16
README.md
16
README.md
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@ -14,7 +14,6 @@ for facilitating the analysis and interpretation of the experimental results.
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### Last updates:
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* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
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* A detailed wiki is available [here](https://github.com/HLT-ISTI/QuaPy/wiki)
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* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
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### Installation
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@ -78,12 +77,10 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
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* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
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* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
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* Datasets frequently used in quantification (textual and numeric), including:
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* 32 UCI Machine Learning binary datasets.
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* 5 UCI Machine Learning multiclass datasets (_new in v0.1.8!_).
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* 32 UCI Machine Learning datasets.
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* 11 Twitter quantification-by-sentiment datasets.
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* 3 product reviews quantification-by-sentiment datasets.
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* 4 tasks from LeQua competition (_new in v0.1.7!_)
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* IFCB dataset of plankton water samples (_new in v0.1.8!_).
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* Native support for binary and single-label multiclass quantification scenarios.
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* Model selection functionality that minimizes quantification-oriented loss functions.
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* Visualization tools for analysing the experimental results.
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@ -97,7 +94,6 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
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* tqdm
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* pandas, xlrd
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* matplotlib
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* ucimlrepo
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## Contributing
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@ -105,7 +101,7 @@ In case you want to contribute improvements to quapy, please generate pull reque
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## Documentation
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The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html).
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The [developer API documentation](https://hlt-isti.github.io/QuaPy/build/html/modules.html) is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html).
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Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples
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are provided:
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@ -120,10 +116,4 @@ are provided:
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## Acknowledgments:
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<img src="logo/SoBigData.png" alt="SoBigData++" width="250"/>
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<img src="logo/LogoQuaDaSh.png" alt="QuaDaSh" width="250"/>
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<img src="logo/NextGenerationEU.jpg" alt="QuaDaSh" width="250"/>
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<img src="SoBigData.png" alt="SoBigData++" width="250"/>
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Before Width: | Height: | Size: 128 KiB After Width: | Height: | Size: 128 KiB |
95
TODO.txt
95
TODO.txt
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@ -1,95 +0,0 @@
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ensembles seem to be broken; they have an internal model selection which takes the parameters, but since quapy now
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works with protocols it would need to know the validation set in order to pass something like
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"protocol: APP(val, etc.)"
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sample_size should not be mandatory when qp.environ['SAMPLE_SIZE'] has been specified
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clean all the cumbersome methods that have to be implemented for new quantifiers (e.g., n_classes_ prop, etc.)
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make truly parallel the GridSearchQ
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make more examples in the "examples" directory
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merge with master, because I had to fix some problems with QuaNet due to an issue notified via GitHub!
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added cross_val_predict in qp.model_selection (i.e., a cross_val_predict for quantification) --would be nice to have
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it parallelized
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check the OneVsAll module(s)
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check the set_params de neural.py, because the separation of estimator__<param> is not implemented; see also
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__check_params_colision
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HDy can be customized so that the number of bins is specified, instead of explored within the fit method
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Packaging:
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==========================================
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Document methods with paper references
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unit-tests
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clean wiki_examples!
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Refactor:
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==========================================
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Unify ThresholdOptimization methods, as an extension of PACC (and not ACC), the fit methods are almost identical and
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use a prob classifier (take into account that PACC uses pcc internally, whereas the threshold methods use cc
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instead). The fit method of ACC and PACC has a block for estimating the validation estimates that should be unified
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as well...
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Refactor protocols. APP and NPP related functionalities are duplicated in functional, LabelledCollection, and evaluation
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New features:
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==========================================
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Add "measures for evaluating ordinal"?
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Add datasets for topic.
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Do we want to cover cross-lingual quantification natively in QuaPy, or does it make more sense as an application on top?
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Current issues:
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==========================================
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Revise the class structure of quantification methods and the methods they inherit... There is some confusion regarding
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methods isbinary, isprobabilistic, and the like. The attribute "learner_" in aggregative quantifiers is also
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confusing, since there is a getter and a setter.
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Remove the "deep" in get_params. There is no real compatibility with scikit-learn as for now.
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SVMperf-based learners do not remove temp files in __del__?
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In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
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negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
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an instance of single-label with 2 labels. Check
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Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
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OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
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Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
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Add random seed management to support replicability (see temp_seed in util.py).
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GridSearchQ is not trully parallelized. It only parallelizes on the predictions.
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In the context of a quantifier (e.g., QuaNet or CC), the parameters of the learner should be prefixed with "estimator__",
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in QuaNet this is resolved with a __check_params_colision, but this should be improved. It might be cumbersome to
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impose the "estimator__" prefix for, e.g., quantifiers like CC though... This should be changed everywhere...
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QuaNet needs refactoring. The base quantifiers ACC and PACC receive val_data with instances already transformed. This
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issue is due to a bad design.
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Improvements:
|
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==========================================
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Explore the hyperparameter "number of bins" in HDy
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Rename EMQ to SLD ?
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Parallelize the kFCV in ACC and PACC?
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Parallelize model selection trainings
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We might want to think of (improving and) adding the class Tabular (it is defined and used on branch tweetsent). A more
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recent version is in the project ql4facct. This class is meant to generate latex tables from results (highligting
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best results, computing statistical tests, colouring cells, producing rankings, producing averages, etc.). Trying
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||||
to generate tables is typically a bad idea, but in this specific case we do have pretty good control of what an
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experiment looks like. (Do we want to abstract experimental results? this could be useful not only for tables but
|
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also for plots).
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Add proper logging system. Currently we use print
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It might be good to simplify the number of methods that have to be implemented for any new Quantifier. At the moment,
|
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there are many functions like get_params, set_params, and, specially, @property classes_, which are cumbersome to
|
||||
implement for quick experiments. A possible solution is to impose get_params and set_params only in cases in which
|
||||
the model extends some "ModelSelectable" interface only. The classes_ should have a default implementation.
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|
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Checks:
|
||||
==========================================
|
||||
How many times is the system of equations for ACC and PACC not solved? How many times is it clipped? Do they sum up
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||||
to one always?
|
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Re-check how hyperparameters from the quantifier and hyperparameters from the classifier (in aggregative quantifiers)
|
||||
is handled. In scikit-learn the hyperparameters from a wrapper method are indicated directly whereas the hyperparams
|
||||
from the internal learner are prefixed with "estimator__". In QuaPy, combinations having to do with the classifier
|
||||
can be computed at the begining, and then in an internal loop the hyperparams of the quantifier can be explored,
|
||||
passing fit_learner=False.
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Re-check Ensembles. As for now, they are strongly tied to aggregative quantifiers.
|
||||
Re-think the environment variables. Maybe add new ones (like, for example, parameters for the plots)
|
||||
Do we want to wrap prevalences (currently simple np.ndarray) as a class? This might be convenient for some interfaces
|
||||
(e.g., for specifying artificial prevalences in samplings, for printing them -- currently supported through
|
||||
F.strprev(), etc.). This might however add some overload, and prevent/difficult post processing with numpy.
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Would be nice to get a better integration with sklearn.
|
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|
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|
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@ -1,123 +0,0 @@
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<ul><li><a href="quapy/classification/calibration.html">quapy.classification.calibration</a></li>
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<h1>Source code for quapy.classification.calibration</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">abstention.calibration</span> <span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
|
||||
<span class="c1"># Wrappers of calibration defined by Alexandari et al. in paper <http://proceedings.mlr.press/v119/alexandari20a.html></span>
|
||||
<span class="c1"># requires "pip install abstension"</span>
|
||||
<span class="c1"># see https://github.com/kundajelab/abstention</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifier">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
|
||||
<span class="sd"> is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.</span>
|
||||
<span class="sd"> <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies a (re)calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_.</span>
|
||||
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
|
||||
<span class="sd"> :param calibrator: the calibration object (an instance of abstention.calibration.CalibratorFactory)</span>
|
||||
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p</span>
|
||||
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
|
||||
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
|
||||
<span class="sd"> training set afterwards. Default value is 5.</span>
|
||||
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None</span>
|
||||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">calibrator</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the calibration for the probabilistic classifier.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'wrong value for val_split: the number of folds must be > 2'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_cv</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">0</span> <span class="o"><</span> <span class="n">k</span> <span class="o"><</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'wrong value for val_split: the proportion of validation documents must be in (0,1)'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_tr_val</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all</span>
|
||||
<span class="sd"> training instances via cross-validation, and then retrains the classifier on all training instances.</span>
|
||||
<span class="sd"> The posterior probabilities thus generated are used for calibrating the outputs of the classifier.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">cross_val_predict</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'predict_proba'</span>
|
||||
<span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a</span>
|
||||
<span class="sd"> training and a validation set, and then uses the training samples to learn classifier which is then used</span>
|
||||
<span class="sd"> to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate</span>
|
||||
<span class="sd"> the classifier. The classifier is not retrained on the whole dataset.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">Xtr</span><span class="p">,</span> <span class="n">Xva</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">yva</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">)</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">Xva</span><span class="p">)</span>
|
||||
<span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">yva</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts class labels for the data instances in `X`</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples,)` with the class label predictions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates posterior probabilities for the data instances in `X`</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the classes on which the classifier has been trained on</span>
|
||||
|
||||
<span class="sd"> :return: array-like of shape `(n_classes)`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NBVSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
|
||||
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
|
||||
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
|
||||
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
|
||||
<span class="sd"> training set afterwards. Default value is 5.</span>
|
||||
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
|
||||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">NoBiasVectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BCTSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
|
||||
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
|
||||
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
|
||||
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
|
||||
<span class="sd"> training set afterwards. Default value is 5.</span>
|
||||
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
|
||||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span> <span class="n">bias_positions</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
|
||||
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
|
||||
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
|
||||
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
|
||||
<span class="sd"> training set afterwards. Default value is 5.</span>
|
||||
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
|
||||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="VSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
|
||||
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
|
||||
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
|
||||
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
|
||||
<span class="sd"> training set afterwards. Default value is 5.</span>
|
||||
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
|
||||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">VectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
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<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">TruncatedSVD</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)</span>
|
||||
<span class="sd"> that also generates embedded inputs (i.e., that implements `transform`), as those required for</span>
|
||||
<span class="sd"> :class:`quapy.method.neural.QuaNet`. This is a mock method to allow for easily instantiating</span>
|
||||
<span class="sd"> :class:`quapy.method.neural.QuaNet` on array-like real-valued instances.</span>
|
||||
<span class="sd"> The transformation consists of applying :class:`sklearn.decomposition.TruncatedSVD`</span>
|
||||
<span class="sd"> while classification is performed using :class:`sklearn.linear_model.LogisticRegression` on the low-rank space.</span>
|
||||
|
||||
<span class="sd"> :param n_components: the number of principal components to retain</span>
|
||||
<span class="sd"> :param kwargs: parameters for the</span>
|
||||
<span class="sd"> `Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ classifier</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator.</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_components'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">}</span>
|
||||
<span class="n">params</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">params</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Set the parameters of this estimator.</span>
|
||||
|
||||
<span class="sd"> :param parameters: a `**kwargs` dictionary with the estimator parameters for</span>
|
||||
<span class="sd"> `Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__</span>
|
||||
<span class="sd"> and eventually also `n_components` for `TruncatedSVD`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">params_</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="s1">'n_components'</span> <span class="ow">in</span> <span class="n">params_</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">params_</span><span class="p">[</span><span class="s1">'n_components'</span><span class="p">]</span>
|
||||
<span class="k">del</span> <span class="n">params_</span><span class="p">[</span><span class="s1">'n_components'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params_</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fit the model according to the given training data. The fit consists of</span>
|
||||
<span class="sd"> fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples, n_classes)` with the class labels</span>
|
||||
<span class="sd"> :return: `self`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">nF</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">nF</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="o">=</span> <span class="n">TruncatedSVD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances `X` embedded into the low-rank space.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
|
||||
<span class="sd"> :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
|
||||
<span class="sd"> instances in `X`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.transform">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if</span>
|
||||
<span class="sd"> `n_components` >= `X.shape[1]`.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to embed</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_components)` with the embedded instances</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">X</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
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|
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|
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<h1>Source code for quapy.classification.neural</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
|
||||
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
|
||||
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span>
|
||||
<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">pad_sequence</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">NeuralClassifierTrainer</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains a neural network for text classification.</span>
|
||||
|
||||
<span class="sd"> :param net: an instance of `TextClassifierNet` implementing the forward pass</span>
|
||||
<span class="sd"> :param lr: learning rate (default 1e-3)</span>
|
||||
<span class="sd"> :param weight_decay: weight decay (default 0)</span>
|
||||
<span class="sd"> :param patience: number of epochs that do not show any improvement in validation</span>
|
||||
<span class="sd"> to wait before applying early stop (default 10)</span>
|
||||
<span class="sd"> :param epochs: maximum number of training epochs (default 200)</span>
|
||||
<span class="sd"> :param batch_size: batch size for training (default 64)</span>
|
||||
<span class="sd"> :param batch_size_test: batch size for test (default 512)</span>
|
||||
<span class="sd"> :param padding_length: maximum number of tokens to consider in a document (default 300)</span>
|
||||
<span class="sd"> :param device: specify 'cpu' (default) or 'cuda' for enabling gpu</span>
|
||||
<span class="sd"> :param checkpointpath: where to store the parameters of the best model found so far</span>
|
||||
<span class="sd"> according to the evaluation in the held-out validation split (default '../checkpoint/classifier_net.dat')</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">net</span><span class="p">:</span> <span class="s1">'TextClassifierNet'</span><span class="p">,</span>
|
||||
<span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
|
||||
<span class="n">weight_decay</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">epochs</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
|
||||
<span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
|
||||
<span class="n">batch_size_test</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
|
||||
<span class="n">padding_length</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
|
||||
<span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">,</span>
|
||||
<span class="n">checkpointpath</span><span class="o">=</span><span class="s1">'../checkpoint/classifier_net.dat'</span><span class="p">):</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">TextClassifierNet</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'net is not an instance of </span><span class="si">{</span><span class="n">TextClassifierNet</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">vocabulary_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span><span class="o">=</span><span class="p">{</span>
|
||||
<span class="s1">'lr'</span><span class="p">:</span> <span class="n">lr</span><span class="p">,</span>
|
||||
<span class="s1">'weight_decay'</span><span class="p">:</span> <span class="n">weight_decay</span><span class="p">,</span>
|
||||
<span class="s1">'patience'</span><span class="p">:</span> <span class="n">patience</span><span class="p">,</span>
|
||||
<span class="s1">'epochs'</span><span class="p">:</span> <span class="n">epochs</span><span class="p">,</span>
|
||||
<span class="s1">'batch_size'</span><span class="p">:</span> <span class="n">batch_size</span><span class="p">,</span>
|
||||
<span class="s1">'batch_size_test'</span><span class="p">:</span> <span class="n">batch_size_test</span><span class="p">,</span>
|
||||
<span class="s1">'padding_length'</span><span class="p">:</span> <span class="n">padding_length</span><span class="p">,</span>
|
||||
<span class="s1">'device'</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">learner_hyperparams</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">checkpointpath</span> <span class="o">=</span> <span class="n">checkpointpath</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[NeuralNetwork running on </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s1">]'</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">checkpointpath</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.reset_net_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Reinitialize the network parameters</span>
|
||||
|
||||
<span class="sd"> :param vocab_size: the size of the vocabulary</span>
|
||||
<span class="sd"> :param n_classes: the number of target classes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">learner_hyperparams</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span><span class="p">[</span><span class="s1">'device'</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">xavier_uniform</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Get hyper-parameters for this estimator</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">get_params</span><span class="p">(),</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span><span class="p">}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Set the parameters of this trainer and the learner it is training.</span>
|
||||
<span class="sd"> In this current version, parameter names for the trainer and learner should</span>
|
||||
<span class="sd"> be disjoint.</span>
|
||||
|
||||
<span class="sd"> :param params: a `**kwargs` dictionary with the parameters</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">trainer_hyperparams</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span>
|
||||
<span class="n">learner_hyperparams</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">trainer_hyperparams</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">learner_hyperparams</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the use of parameter </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s1"> is ambiguous since it can refer to '</span>
|
||||
<span class="sa">f</span><span class="s1">'a parameters of the Trainer or the learner </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">trainer_hyperparams</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">learner_hyperparams</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'parameter </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s1"> is not valid'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">trainer_hyperparams</span><span class="p">:</span>
|
||||
<span class="n">trainer_hyperparams</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">learner_hyperparams</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span> <span class="o">=</span> <span class="n">trainer_hyperparams</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">learner_hyperparams</span> <span class="o">=</span> <span class="n">learner_hyperparams</span> </div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">""" Gets the device in which the network is allocated</span>
|
||||
|
||||
<span class="sd"> :return: device</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_train_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
||||
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
|
||||
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">xi</span><span class="p">,</span> <span class="n">yi</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
|
||||
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
|
||||
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">yi</span><span class="p">)</span>
|
||||
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
|
||||
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"loss"</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
|
||||
<span class="n">predictions</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
|
||||
<span class="n">true_labels</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">yi</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"f1"</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">'macro'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_test_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
|
||||
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
<span class="k">for</span> <span class="n">xi</span><span class="p">,</span> <span class="n">yi</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
|
||||
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
|
||||
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">yi</span><span class="p">)</span>
|
||||
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">predictions</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
|
||||
<span class="n">true_labels</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">yi</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
|
||||
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"loss"</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
|
||||
<span class="n">status</span><span class="p">[</span><span class="s2">"f1"</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">'macro'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__update_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="n">pbar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">] training epoch=</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'tr-loss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"tr"</span><span class="p">][</span><span class="s2">"loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'tr-acc=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"tr"</span><span class="p">][</span><span class="s2">"acc"</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% '</span>
|
||||
<span class="sa">f</span><span class="s1">'tr-macroF1=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"tr"</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% '</span>
|
||||
<span class="sa">f</span><span class="s1">'patience=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">patience</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'val-loss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"va"</span><span class="p">][</span><span class="s2">"loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'val-acc=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"va"</span><span class="p">][</span><span class="s2">"acc"</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% '</span>
|
||||
<span class="sa">f</span><span class="s1">'macroF1=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"va"</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">%'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the model according to the given training data.</span>
|
||||
|
||||
<span class="sd"> :param instances: list of lists of indexed tokens</span>
|
||||
<span class="sd"> :param labels: array-like of shape `(n_samples, n_classes)` with the class labels</span>
|
||||
<span class="sd"> :param val_split: proportion of training documents to be taken as the validation set (default 0.3)</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span>
|
||||
<span class="n">checkpoint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">checkpointpath</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">train</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="n">train_generator</span> <span class="o">=</span> <span class="n">TorchDataset</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">train</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">asDataloader</span><span class="p">(</span>
|
||||
<span class="n">opt</span><span class="p">[</span><span class="s1">'batch_size'</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pad_length</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'padding_length'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'device'</span><span class="p">])</span>
|
||||
<span class="n">valid_generator</span> <span class="o">=</span> <span class="n">TorchDataset</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">val</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">asDataloader</span><span class="p">(</span>
|
||||
<span class="n">opt</span><span class="p">[</span><span class="s1">'batch_size_test'</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pad_length</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'padding_length'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'device'</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'tr'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'loss'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'acc'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">},</span>
|
||||
<span class="s1">'va'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'loss'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'acc'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}}</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">],</span> <span class="n">weight_decay</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'weight_decay'</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span> <span class="o">=</span> <span class="n">EarlyStop</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">'patience'</span><span class="p">],</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">'epochs'</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">pbar</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_train_epoch</span><span class="p">(</span><span class="n">train_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'tr'</span><span class="p">],</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_test_epoch</span><span class="p">(</span><span class="n">valid_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'va'</span><span class="p">],</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'va'</span><span class="p">][</span><span class="s1">'f1'</span><span class="p">],</span> <span class="n">epoch</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
|
||||
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">checkpoint</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">STOP</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'training ended by patience exhasted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'for epoch </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">))</span>
|
||||
<span class="k">break</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'performing one training pass over the validation set...'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_train_epoch</span><span class="p">(</span><span class="n">valid_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'tr'</span><span class="p">],</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[done]'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances</span>
|
||||
|
||||
<span class="sd"> :param instances: list of lists of indexed tokens</span>
|
||||
<span class="sd"> :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
|
||||
<span class="sd"> instances in `X`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">instances</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">TorchDataset</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span><span class="o">.</span><span class="n">asDataloader</span><span class="p">(</span>
|
||||
<span class="n">opt</span><span class="p">[</span><span class="s1">'batch_size_test'</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pad_length</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'padding_length'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'device'</span><span class="p">]):</span>
|
||||
<span class="n">posteriors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">xi</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NeuralClassifierTrainer.transform">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the embeddings of the instances</span>
|
||||
|
||||
<span class="sd"> :param instances: list of lists of indexed tokens</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, embed_size)` with the embedded instances,</span>
|
||||
<span class="sd"> where `embed_size` is defined by the classification network</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer_hyperparams</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
<span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">TorchDataset</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span><span class="o">.</span><span class="n">asDataloader</span><span class="p">(</span>
|
||||
<span class="n">opt</span><span class="p">[</span><span class="s1">'batch_size_test'</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pad_length</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'padding_length'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">opt</span><span class="p">[</span><span class="s1">'device'</span><span class="p">]):</span>
|
||||
<span class="n">embeddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">document_embedding</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TorchDataset">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">TorchDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms labelled instances into a Torch's :class:`torch.utils.data.DataLoader` object</span>
|
||||
|
||||
<span class="sd"> :param instances: list of lists of indexed tokens</span>
|
||||
<span class="sd"> :param labels: array-like of shape `(n_samples, n_classes)` with the class labels</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">{</span><span class="s1">'doc'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="s1">'label'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">}</span>
|
||||
|
||||
<div class="viewcode-block" id="TorchDataset.asDataloader">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">asDataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">pad_length</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Converts the labelled collection into a Torch DataLoader with dynamic padding for</span>
|
||||
<span class="sd"> the batch</span>
|
||||
|
||||
<span class="sd"> :param batch_size: batch size</span>
|
||||
<span class="sd"> :param shuffle: whether or not to shuffle instances</span>
|
||||
<span class="sd"> :param pad_length: the maximum length for the list of tokens (dynamic padding is</span>
|
||||
<span class="sd"> applied, meaning that if the longest document in the batch is shorter than</span>
|
||||
<span class="sd"> `pad_length`, then the batch is padded up to its length, and not to `pad_length`.</span>
|
||||
<span class="sd"> :param device: whether to allocate tensors in cpu or in cuda</span>
|
||||
<span class="sd"> :return: a :class:`torch.utils.data.DataLoader` object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="s1">'doc'</span><span class="p">][:</span><span class="n">pad_length</span><span class="p">])</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">pad_sequence</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">padding_value</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PAD_INDEX'</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s1">'label'</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">targets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">data</span><span class="p">,</span> <span class="n">targets</span><span class="p">]</span>
|
||||
|
||||
<span class="n">torchDataset</span> <span class="o">=</span> <span class="n">TorchDataset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">torchDataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="n">shuffle</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">TextClassifierNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract Text classifier (`torch.nn.Module`)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.document_embedding">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
<span class="sd"> :param x: a batch of instances, typically generated by a torch's `DataLoader`</span>
|
||||
<span class="sd"> instance (see :class:`quapy.classification.neural.TorchDataset`)</span>
|
||||
<span class="sd"> :return: a torch tensor of shape `(n_samples, n_dimensions)`, where</span>
|
||||
<span class="sd"> `n_samples` is the number of documents, and `n_dimensions` is the</span>
|
||||
<span class="sd"> dimensionality of the embedding</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.forward">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Performs the forward pass.</span>
|
||||
|
||||
<span class="sd"> :param x: a batch of instances, typically generated by a torch's `DataLoader`</span>
|
||||
<span class="sd"> instance (see :class:`quapy.classification.neural.TorchDataset`)</span>
|
||||
<span class="sd"> :return: a tensor of shape `(n_instances, n_classes)` with the decision scores</span>
|
||||
<span class="sd"> for each of the instances and classes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">doc_embedded</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">document_embedding</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">doc_embedded</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.dimensions">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Gets the number of dimensions of the embedding space</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances in `x`</span>
|
||||
|
||||
<span class="sd"> :param x: a torch tensor of indexed tokens with shape `(n_instances, pad_length)`</span>
|
||||
<span class="sd"> where `n_instances` is the number of instances in the batch, and `pad_length`</span>
|
||||
<span class="sd"> is length of the pad in the batch</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.xavier_uniform">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">xavier_uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Performs Xavier initialization of the network parameters</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
|
||||
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">p</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TextClassifierNet.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LSTMnet">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">LSTMnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
|
||||
<span class="sd"> Long Short Term Memory networks.</span>
|
||||
|
||||
<span class="sd"> :param vocabulary_size: the size of the vocabulary</span>
|
||||
<span class="sd"> :param n_classes: number of target classes</span>
|
||||
<span class="sd"> :param embedding_size: the dimensionality of the word embeddings space (default 100)</span>
|
||||
<span class="sd"> :param hidden_size: the dimensionality of the hidden space (default 256)</span>
|
||||
<span class="sd"> :param repr_size: the dimensionality of the document embeddings space (default 100)</span>
|
||||
<span class="sd"> :param lstm_class_nlayers: number of LSTM layers (default 1)</span>
|
||||
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size_</span> <span class="o">=</span> <span class="n">vocabulary_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span><span class="o">=</span><span class="p">{</span>
|
||||
<span class="s1">'embedding_size'</span><span class="p">:</span> <span class="n">embedding_size</span><span class="p">,</span>
|
||||
<span class="s1">'hidden_size'</span><span class="p">:</span> <span class="n">hidden_size</span><span class="p">,</span>
|
||||
<span class="s1">'repr_size'</span><span class="p">:</span> <span class="n">repr_size</span><span class="p">,</span>
|
||||
<span class="s1">'lstm_class_nlayers'</span><span class="p">:</span> <span class="n">lstm_class_nlayers</span><span class="p">,</span>
|
||||
<span class="s1">'drop_p'</span><span class="p">:</span> <span class="n">drop_p</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">embedding_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">embedding_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">drop_p</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">repr_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">set_size</span><span class="p">):</span>
|
||||
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span>
|
||||
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">'lstm_class_nlayers'</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">'hidden_size'</span><span class="p">])</span>
|
||||
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">'lstm_class_nlayers'</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">'hidden_size'</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
|
||||
<span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span> <span class="o">=</span> <span class="n">var_hidden</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">var_cell</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span>
|
||||
|
||||
<div class="viewcode-block" id="LSTMnet.document_embedding">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
<span class="sd"> :param x: a batch of instances, typically generated by a torch's `DataLoader`</span>
|
||||
<span class="sd"> instance (see :class:`quapy.classification.neural.TorchDataset`)</span>
|
||||
<span class="sd"> :return: a torch tensor of shape `(n_samples, n_dimensions)`, where</span>
|
||||
<span class="sd"> `n_samples` is the number of documents, and `n_dimensions` is the</span>
|
||||
<span class="sd"> dimensionality of the embedding</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">embedded</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embedding</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">rnn_output</span><span class="p">,</span> <span class="n">rnn_hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="p">(</span><span class="n">embedded</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init_hidden</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]))</span>
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">rnn_hidden</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">abstracted</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LSTMnet.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size_</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="CNNnet">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">CNNnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
|
||||
<span class="sd"> Convolutional Neural Networks.</span>
|
||||
|
||||
<span class="sd"> :param vocabulary_size: the size of the vocabulary</span>
|
||||
<span class="sd"> :param n_classes: number of target classes</span>
|
||||
<span class="sd"> :param embedding_size: the dimensionality of the word embeddings space (default 100)</span>
|
||||
<span class="sd"> :param hidden_size: the dimensionality of the hidden space (default 256)</span>
|
||||
<span class="sd"> :param repr_size: the dimensionality of the document embeddings space (default 100)</span>
|
||||
<span class="sd"> :param kernel_heights: list of kernel lengths (default [3,5,7]), i.e., the number of</span>
|
||||
<span class="sd"> consecutive tokens that each kernel covers</span>
|
||||
<span class="sd"> :param stride: convolutional stride (default 1)</span>
|
||||
<span class="sd"> :param stride: convolutional pad (default 0)</span>
|
||||
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
|
||||
<span class="n">kernel_heights</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">CNNnet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size_</span> <span class="o">=</span> <span class="n">vocabulary_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span><span class="o">=</span><span class="p">{</span>
|
||||
<span class="s1">'embedding_size'</span><span class="p">:</span> <span class="n">embedding_size</span><span class="p">,</span>
|
||||
<span class="s1">'hidden_size'</span><span class="p">:</span> <span class="n">hidden_size</span><span class="p">,</span>
|
||||
<span class="s1">'repr_size'</span><span class="p">:</span> <span class="n">repr_size</span><span class="p">,</span>
|
||||
<span class="s1">'kernel_heights'</span><span class="p">:</span><span class="n">kernel_heights</span><span class="p">,</span>
|
||||
<span class="s1">'stride'</span><span class="p">:</span> <span class="n">stride</span><span class="p">,</span>
|
||||
<span class="s1">'drop_p'</span><span class="p">:</span> <span class="n">drop_p</span>
|
||||
<span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">embedding_size</span><span class="p">)</span>
|
||||
<span class="n">in_channels</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="p">(</span><span class="n">kernel_heights</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">embedding_size</span><span class="p">),</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="p">(</span><span class="n">kernel_heights</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">embedding_size</span><span class="p">),</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="p">(</span><span class="n">kernel_heights</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">embedding_size</span><span class="p">),</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">repr_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_heights</span><span class="p">)</span> <span class="o">*</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">conv_layer</span><span class="p">):</span>
|
||||
<span class="n">conv_out</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># conv_out.size() = (batch_size, out_channels, dim, 1)</span>
|
||||
<span class="n">activation</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv_out</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># activation.size() = (batch_size, out_channels, dim1)</span>
|
||||
<span class="n">max_out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">activation</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># maxpool_out.size() = (batch_size, out_channels)</span>
|
||||
<span class="k">return</span> <span class="n">max_out</span>
|
||||
|
||||
<div class="viewcode-block" id="CNNnet.document_embedding">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
<span class="sd"> :param input: a batch of instances, typically generated by a torch's `DataLoader`</span>
|
||||
<span class="sd"> instance (see :class:`quapy.classification.neural.TorchDataset`)</span>
|
||||
<span class="sd"> :return: a torch tensor of shape `(n_samples, n_dimensions)`, where</span>
|
||||
<span class="sd"> `n_samples` is the number of documents, and `n_dimensions` is the</span>
|
||||
<span class="sd"> dimensionality of the embedding</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embedding</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
|
||||
<span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># input.size() = (batch_size, 1, num_seq, embedding_length)</span>
|
||||
|
||||
<span class="n">max_out1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__conv_block</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">)</span>
|
||||
<span class="n">max_out2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__conv_block</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">)</span>
|
||||
<span class="n">max_out3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__conv_block</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">)</span>
|
||||
|
||||
<span class="n">all_out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">max_out1</span><span class="p">,</span> <span class="n">max_out2</span><span class="p">,</span> <span class="n">max_out3</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># all_out.size() = (batch_size, num_kernels*out_channels)</span>
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">all_out</span><span class="p">))</span> <span class="c1"># (batch_size, num_kernels*out_channels)</span>
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">abstracted</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="CNNnet.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size_</span></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
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|
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<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">random</span>
|
||||
<span class="kn">import</span> <span class="nn">shutil</span>
|
||||
<span class="kn">import</span> <span class="nn">subprocess</span>
|
||||
<span class="kn">import</span> <span class="nn">tempfile</span>
|
||||
<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
|
||||
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
|
||||
<span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">dump_svmlight_file</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SVMperf">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""A wrapper for the `SVM-perf package <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`__ by Thorsten Joachims.</span>
|
||||
<span class="sd"> When using losses for quantification, the source code has to be patched. See</span>
|
||||
<span class="sd"> the `installation documentation <https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses>`__</span>
|
||||
<span class="sd"> for further details.</span>
|
||||
|
||||
<span class="sd"> References:</span>
|
||||
|
||||
<span class="sd"> * `Esuli et al.2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`__</span>
|
||||
<span class="sd"> * `Barranquero et al.2015 <https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X>`__</span>
|
||||
|
||||
<span class="sd"> :param svmperf_base: path to directory containing the binary files `svm_perf_learn` and `svm_perf_classify`</span>
|
||||
<span class="sd"> :param C: trade-off between training error and margin (default 0.01)</span>
|
||||
<span class="sd"> :param verbose: set to True to print svm-perf std outputs</span>
|
||||
<span class="sd"> :param loss: the loss to optimize for. Available losses are "01", "f1", "kld", "nkld", "q", "qacc", "qf1", "qgm", "mae", "mrae".</span>
|
||||
<span class="sd"> :param host_folder: directory where to store the trained model; set to None (default) for using a tmp directory</span>
|
||||
<span class="sd"> (temporal directories are automatically deleted)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="c1"># losses with their respective codes in svm_perf implementation</span>
|
||||
<span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'01'</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'kld'</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">'nkld'</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">'q'</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">'qacc'</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">'qf1'</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">'qgm'</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">'mae'</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">'mrae'</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'01'</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span>
|
||||
|
||||
<span class="c1"># def set_params(self, **parameters):</span>
|
||||
<span class="c1"># """</span>
|
||||
<span class="c1"># Set the hyper-parameters for svm-perf. Currently, only the `C` and `loss` parameters are supported</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># :param parameters: a `**kwargs` dictionary `{'C': <float>}`</span>
|
||||
<span class="c1"># """</span>
|
||||
<span class="c1"># assert sorted(list(parameters.keys())) == ['C', 'loss'], \</span>
|
||||
<span class="c1"># 'currently, only the C and loss parameters are supported'</span>
|
||||
<span class="c1"># self.C = parameters.get('C', self.C)</span>
|
||||
<span class="c1"># self.loss = parameters.get('loss', self.loss)</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># def get_params(self, deep=True):</span>
|
||||
<span class="c1"># return {'C': self.C, 'loss': self.loss}</span>
|
||||
|
||||
<div class="viewcode-block" id="SVMperf.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains the SVM for the multivariate performance loss</span>
|
||||
|
||||
<span class="sd"> :param X: training instances</span>
|
||||
<span class="sd"> :param y: a binary vector of labels</span>
|
||||
<span class="sd"> :return: `self`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="ow">in</span> <span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unsupported loss </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="si">}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s1">'</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">'svm_perf_learn'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">'svm_perf_classify'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span> <span class="o">=</span> <span class="s1">'-w 3 -l '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span> <span class="o">=</span> <span class="s1">'-c '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_classes_</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
|
||||
<span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
|
||||
<span class="c1"># this would allow to run parallel instances of predict</span>
|
||||
<span class="n">random_code</span> <span class="o">=</span> <span class="s1">'svmperfprocess'</span><span class="o">+</span><span class="s1">'-'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="c1"># tmp dir are removed after the fit terminates in multiprocessing...</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">(</span><span class="n">suffix</span><span class="o">=</span><span class="n">random_code</span><span class="p">)</span><span class="o">.</span><span class="n">name</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span><span class="p">,</span> <span class="s1">'.'</span> <span class="o">+</span> <span class="n">random_code</span><span class="p">)</span>
|
||||
<span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'model-'</span><span class="o">+</span><span class="n">random_code</span><span class="p">)</span>
|
||||
<span class="n">traindat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'train-</span><span class="si">{</span><span class="n">random_code</span><span class="si">}</span><span class="s1">.dat'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dump_svmlight_file</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">traindat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cmd</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span><span class="p">,</span> <span class="n">traindat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[Running]'</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
|
||||
<span class="n">remove</span><span class="p">(</span><span class="n">traindat</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SVMperf.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances `X`</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
|
||||
<span class="sd"> :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
|
||||
<span class="sd"> instances in `X`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">confidence_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">confidence_scores</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">predictions</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SVMperf.decision_function">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Evaluate the decision function for the samples in `X`.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` containing the instances to classify</span>
|
||||
<span class="sd"> :param y: unused</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples,)` containing the decision scores of the instances</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'tmpdir'</span><span class="p">),</span> <span class="s1">'predict called before fit'</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">'model directory corrupted'</span>
|
||||
<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">),</span> <span class="s1">'model not found'</span>
|
||||
<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="c1"># in order to allow for parallel runs of predict, a random code is assigned</span>
|
||||
<span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
|
||||
<span class="n">random_code</span> <span class="o">=</span> <span class="s1">'-'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
|
||||
<span class="n">predictions_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'predictions'</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">'.dat'</span><span class="p">)</span>
|
||||
<span class="n">testdat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'test'</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">'.dat'</span><span class="p">)</span>
|
||||
<span class="n">dump_svmlight_file</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">testdat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cmd</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span><span class="p">,</span> <span class="n">testdat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions_path</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[Running]'</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
|
||||
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
|
||||
<span class="n">remove</span><span class="p">(</span><span class="n">testdat</span><span class="p">)</span>
|
||||
<span class="n">remove</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">scores</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'tmpdir'</span><span class="p">):</span>
|
||||
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
<hr/>
|
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|
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<div role="contentinfo">
|
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<p>© Copyright 2024, Alejandro Moreo.</p>
|
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<h1>Source code for quapy.data._ifcb</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span>
|
||||
|
||||
<div class="viewcode-block" id="IFCBTrainSamplesFromDir">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTrainSamplesFromDir">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">IFCBTrainSamplesFromDir</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="n">classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">path_dir</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'.csv'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span><span class="n">sample</span><span class="p">))</span>
|
||||
<span class="c1"># all columns but the first where we get the class</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
|
||||
<span class="k">yield</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
|
||||
|
||||
<div class="viewcode-block" id="IFCBTrainSamplesFromDir.total">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTrainSamplesFromDir.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the total number of samples that the protocol generates.</span>
|
||||
|
||||
<span class="sd"> :return: The number of training samples to generate.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IFCBTestSamples">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTestSamples">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">IFCBTestSamples</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">test_prevalences_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">test_prevalences_path</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">test_sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
|
||||
<span class="c1">#Load the sample from disk</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span><span class="n">test_sample</span><span class="p">[</span><span class="s1">'sample'</span><span class="p">]</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">))</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">test_sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">yield</span> <span class="n">X</span><span class="p">,</span> <span class="n">prevalences</span>
|
||||
|
||||
<div class="viewcode-block" id="IFCBTestSamples.total">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTestSamples.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the total number of samples that the protocol generates.</span>
|
||||
|
||||
<span class="sd"> :return: The number of test samples to generate.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span><span class="o">.</span><span class="n">index</span><span class="p">)</span></div>
|
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<h1>Source code for quapy.data._lequa2022</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span>
|
||||
|
||||
<span class="n">DEV_SAMPLES</span> <span class="o">=</span> <span class="mi">1000</span>
|
||||
<span class="n">TEST_SAMPLES</span> <span class="o">=</span> <span class="mi">5000</span>
|
||||
|
||||
<span class="n">ERROR_TOL</span> <span class="o">=</span> <span class="mf">1E-3</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="load_category_map">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_category_map">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">load_category_map</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="n">cat2code</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fin</span><span class="p">:</span>
|
||||
<span class="n">category</span><span class="p">,</span> <span class="n">code</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="n">cat2code</span><span class="p">[</span><span class="n">category</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">code</span><span class="p">)</span>
|
||||
<span class="n">code2cat</span> <span class="o">=</span> <span class="p">[</span><span class="n">cat</span> <span class="k">for</span> <span class="n">cat</span><span class="p">,</span> <span class="n">code</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">cat2code</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
|
||||
<span class="k">return</span> <span class="n">cat2code</span><span class="p">,</span> <span class="n">code2cat</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="load_raw_documents">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_raw_documents">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">load_raw_documents</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
|
||||
<span class="n">documents</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"text"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="n">labels</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="s2">"label"</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
|
||||
<span class="n">labels</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">documents</span><span class="p">,</span> <span class="n">labels</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="load_vector_documents">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_vector_documents">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">load_vector_documents</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="n">D</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="n">labelled</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">301</span>
|
||||
<span class="k">if</span> <span class="n">labelled</span><span class="p">:</span>
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">D</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">D</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">D</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SamplesFromDir">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.SamplesFromDir">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">SamplesFromDir</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">ground_truth_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">load_fn</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">load_fn</span> <span class="o">=</span> <span class="n">load_fn</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">true_prevs</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">ground_truth_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">prevalence</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_prevs</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
|
||||
<span class="n">sample</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">load_fn</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="si">}</span><span class="s1">.txt'</span><span class="p">))</span>
|
||||
<span class="k">yield</span> <span class="n">sample</span><span class="p">,</span> <span class="n">prevalence</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">ResultSubmission</span><span class="p">:</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init_df</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">categories</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">categories</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">categories</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'wrong format for categories: an int (>=2) was expected'</span><span class="p">)</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">categories</span><span class="p">)))</span>
|
||||
<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">set_names</span><span class="p">(</span><span class="s1">'id'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">n_categories</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.add">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.add">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">prevalence_values</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_id</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: expected int for sample_sample, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">sample_id</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: expected np.ndarray for prevalence_values, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__init_df</span><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">sample_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: prevalence values for "</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">" already added'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">prevalence_values</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">prevalence_values</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_categories</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: wrong shape found for prevalence vector </span><span class="si">{</span><span class="n">prevalence_values</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="p">(</span><span class="n">prevalence_values</span> <span class="o"><</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">or</span> <span class="p">(</span><span class="n">prevalence_values</span> <span class="o">></span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: prevalence values out of range [0,1] for "</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">"'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">prevalence_values</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">></span> <span class="n">ERROR_TOL</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error: prevalence values do not sum up to one for "</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">"'</span>
|
||||
<span class="sa">f</span><span class="s1">'(error tolerance </span><span class="si">{</span><span class="n">ERROR_TOL</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">prevalence_values</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.load">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.load">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="s1">'ResultSubmission'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_file_format</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="p">()</span>
|
||||
<span class="n">r</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
|
||||
<span class="k">return</span> <span class="n">r</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.dump">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.dump">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_dataframe_format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.prevalence">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="n">sel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample_id</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">sel</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">sel</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.iterrows">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.iterrows">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">iterrows</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
|
||||
<span class="n">prevalence</span> <span class="o">=</span> <span class="n">row</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="k">yield</span> <span class="n">index</span><span class="p">,</span> <span class="n">prevalence</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.check_file_format">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.check_file_format">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">check_file_format</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the file </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1"> does not seem to be a valid csv file. '</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_dataframe_format</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ResultSubmission.check_dataframe_format">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.check_dataframe_format">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">check_dataframe_format</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
|
||||
<span class="n">hint_path</span> <span class="o">=</span> <span class="s1">''</span> <span class="c1"># if given, show the data path in the error message</span>
|
||||
<span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">hint_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">' in </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">'</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="s1">'id'</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'wrong header</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">, '</span>
|
||||
<span class="sa">f</span><span class="s1">'the format of the header should be "id,0,...,n-1", '</span>
|
||||
<span class="sa">f</span><span class="s1">'where n is the number of categories'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span> <span class="k">for</span> <span class="n">ci</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">))):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'wrong header</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">, category ids should be 0,1,2,...,n-1, '</span>
|
||||
<span class="sa">f</span><span class="s1">'where n is the number of categories'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: results file is empty'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">!=</span> <span class="n">DEV_SAMPLES</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">!=</span> <span class="n">TEST_SAMPLES</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'wrong number of prevalence values found</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">; '</span>
|
||||
<span class="sa">f</span><span class="s1">'expected </span><span class="si">{</span><span class="n">DEV_SAMPLES</span><span class="si">}</span><span class="s1"> for development sets and '</span>
|
||||
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">TEST_SAMPLES</span><span class="si">}</span><span class="s1"> for test sets; found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ids</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="n">expected_ids</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)))</span>
|
||||
<span class="k">if</span> <span class="n">ids</span> <span class="o">!=</span> <span class="n">expected_ids</span><span class="p">:</span>
|
||||
<span class="n">missing</span> <span class="o">=</span> <span class="n">expected_ids</span> <span class="o">-</span> <span class="n">ids</span>
|
||||
<span class="k">if</span> <span class="n">missing</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'there are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1"> missing ids</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">sorted</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">unexpected</span> <span class="o">=</span> <span class="n">ids</span> <span class="o">-</span> <span class="n">expected_ids</span>
|
||||
<span class="k">if</span> <span class="n">unexpected</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'there are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1"> unexpected ids</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">sorted</span><span class="p">(</span><span class="n">unexpected</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">category_id</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">category_id</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">or</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">category_id</span><span class="p">]</span> <span class="o">></span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1"> column "</span><span class="si">{</span><span class="n">category_id</span><span class="si">}</span><span class="s1">" contains values out of range [0,1]'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">round_errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">prevs</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">></span> <span class="n">ERROR_TOL</span>
|
||||
<span class="k">if</span> <span class="n">round_errors</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'warning: prevalence values in rows with id </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">round_errors</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'do not sum up to 1 (error tolerance </span><span class="si">{</span><span class="n">ERROR_TOL</span><span class="si">}</span><span class="s1">), '</span>
|
||||
<span class="sa">f</span><span class="s1">'probably due to some rounding errors.'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">df</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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||||
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||||
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<title>quapy.data.base — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
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<h1>Source code for quapy.data.base</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">issparse</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">vstack</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RepeatedStratifiedKFold</span>
|
||||
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">RandomState</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">strprev</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">temp_seed</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">LabelledCollection</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A LabelledCollection is a set of objects each with a label attached to each of them. </span>
|
||||
<span class="sd"> This class implements several sampling routines and other utilities.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> :param instances: array-like (np.ndarray, list, or csr_matrix are supported)</span>
|
||||
<span class="sd"> :param labels: array-like with the same length of instances</span>
|
||||
<span class="sd"> :param classes: optional, list of classes from which labels are taken. If not specified, the classes are inferred</span>
|
||||
<span class="sd"> from the labels. The classes must be indicated in cases in which some of the labels might have no examples</span>
|
||||
<span class="sd"> (i.e., a prevalence of 0)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">instances</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="c1"># lists of strings occupy too much as ndarrays (although python-objects add a heavy overload)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="n">n_docs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)))</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'labels (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="si">}</span><span class="s1">) contain values not included in classes_ (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_docs</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.load">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.load">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge</span>
|
||||
<span class="sd"> of reading the instances must be specified. This function can be a custom one, or any of the reading functions</span>
|
||||
<span class="sd"> defined in :mod:`quapy.data.reader` module.</span>
|
||||
|
||||
<span class="sd"> :param path: string, the path to the file containing the labelled instances</span>
|
||||
<span class="sd"> :param loader_func: a custom function that implements the data loader and returns a tuple with instances and</span>
|
||||
<span class="sd"> labels</span>
|
||||
<span class="sd"> :param classes: array-like, the classes according to which the instances are labelled</span>
|
||||
<span class="sd"> :param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances, i.e.,</span>
|
||||
<span class="sd"> these arguments are used to call `loader_func(path, **loader_kwargs)`</span>
|
||||
<span class="sd"> :return: a :class:`LabelledCollection` object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">),</span> <span class="n">classes</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the length of this collection (number of labelled instances)</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.prevalence">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span>
|
||||
|
||||
<span class="sd"> :return: a np.ndarray of shape `(n_classes)` with the relative frequencies of each class, in the same order</span>
|
||||
<span class="sd"> as listed by `self.classes_`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts</span><span class="p">()</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.counts">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of instances for each of the classes in the codeframe.</span>
|
||||
|
||||
<span class="sd"> :return: a np.ndarray of shape `(n_classes)` with the number of instances of each class, in the same order</span>
|
||||
<span class="sd"> as listed by `self.classes_`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">])</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">])</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The number of classes</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns True if the number of classes is 2</span>
|
||||
|
||||
<span class="sd"> :return: boolean</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">==</span> <span class="mi">2</span>
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.sampling_index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the</span>
|
||||
<span class="sd"> prevalence values are not specified, then returns the index of a uniform sampling.</span>
|
||||
<span class="sd"> For each class, the sampling is drawn with replacement if the requested prevalence is larger than</span>
|
||||
<span class="sd"> the actual prevalence of the class, or without replacement otherwise.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
|
||||
<span class="sd"> it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in</span>
|
||||
<span class="sd"> `self.classes_` can be specified, while the other class takes prevalence value `1-p`</span>
|
||||
<span class="sd"> :param shuffle: if set to True (default), shuffles the index before returning it</span>
|
||||
<span class="sd"> :param random_state: seed for reproducing sampling</span>
|
||||
<span class="sd"> :return: a np.ndarray of shape `(size)` with the indexes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># no prevalence was indicated; returns an index for uniform sampling</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">uniform_sampling_index</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),)</span>
|
||||
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="s1">'unexpected number of prevalences'</span>
|
||||
<span class="k">assert</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'prevalences (</span><span class="si">{</span><span class="n">prevs</span><span class="si">}</span><span class="s1">) wrong range (sum=</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
|
||||
<span class="c1"># Decide how many instances should be taken for each class in order to satisfy the requested prevalence</span>
|
||||
<span class="c1"># accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is</span>
|
||||
<span class="c1"># <= size * prevs[i]) examples are drawn from class i, there could be a remainder number of instances to take</span>
|
||||
<span class="c1"># to satisfy the size constrain. The remainder is distributed along the classes with probability = prevs.</span>
|
||||
<span class="c1"># (This aims at avoiding the remainder to be placed in a class for which the prevalence requested is 0.)</span>
|
||||
<span class="n">n_requests</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="n">prevs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">class_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)}</span>
|
||||
<span class="n">remainder</span> <span class="o">=</span> <span class="n">size</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">n_requests</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
|
||||
<span class="k">with</span> <span class="n">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="c1"># due to rounding, the remainder can be 0, >0, or <0</span>
|
||||
<span class="k">if</span> <span class="n">remainder</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="c1"># when the remainder is >0 we randomly add 1 to the requests for each class;</span>
|
||||
<span class="c1"># more prevalent classes are more likely to be taken in order to minimize the impact in the final prevalence</span>
|
||||
<span class="k">for</span> <span class="n">rand_class</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">remainder</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">prevs</span><span class="p">):</span>
|
||||
<span class="n">n_requests</span><span class="p">[</span><span class="n">rand_class</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="n">remainder</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="c1"># when the remainder is <0 we randomly remove 1 from the requests, unless the request is 0 for a chosen</span>
|
||||
<span class="c1"># class; we repeat until remainder==0</span>
|
||||
<span class="k">while</span> <span class="n">remainder</span><span class="o">!=</span><span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">rand_class</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">prevs</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">n_requests</span><span class="p">[</span><span class="n">rand_class</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">n_requests</span><span class="p">[</span><span class="n">rand_class</span><span class="p">]</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
<span class="n">remainder</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">indexes_sample</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">class_</span><span class="p">,</span> <span class="n">n_requested</span> <span class="ow">in</span> <span class="n">n_requests</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="n">n_candidates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">])</span>
|
||||
<span class="n">index_sample</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">][</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_candidates</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_requested</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="p">(</span><span class="n">n_requested</span> <span class="o">></span> <span class="n">n_candidates</span><span class="p">))</span>
|
||||
<span class="p">]</span> <span class="k">if</span> <span class="n">n_requested</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">indexes_sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index_sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">indexes_sample</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">indexes_sample</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">shuffle</span><span class="p">:</span>
|
||||
<span class="n">indexes_sample</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">indexes_sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">indexes_sample</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.uniform_sampling_index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn</span>
|
||||
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span>
|
||||
<span class="sd"> otherwise.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the size of the uniform sample</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
<span class="sd"> :return: a np.ndarray of shape `(size)` with the indexes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ng</span> <span class="o">=</span> <span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span>
|
||||
<span class="k">return</span> <span class="n">ng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="n">size</span> <span class="o">></span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.sampling">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence</span>
|
||||
<span class="sd"> values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than</span>
|
||||
<span class="sd"> the actual prevalence of the class, or with replacement otherwise.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
|
||||
<span class="sd"> it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in</span>
|
||||
<span class="sd"> `self.classes_` can be specified, while the other class takes prevalence value `1-p`</span>
|
||||
<span class="sd"> :param shuffle: if set to True (default), shuffles the index before returning it</span>
|
||||
<span class="sd"> :param random_state: seed for reproducing sampling</span>
|
||||
<span class="sd"> :return: an instance of :class:`LabelledCollection` with length == `size` and prevalence close to `prevs` (or</span>
|
||||
<span class="sd"> prevalence == `prevs` if the exact prevalence values can be met as proportions of instances)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prev_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="n">shuffle</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">prev_index</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.uniform_sampling">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn</span>
|
||||
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span>
|
||||
<span class="sd"> otherwise.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
<span class="sd"> :return: an instance of :class:`LabelledCollection` with length == `size`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">unif_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">uniform_sampling_index</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">unif_index</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.sampling_from_index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the</span>
|
||||
<span class="sd"> index.</span>
|
||||
|
||||
<span class="sd"> :param index: np.ndarray</span>
|
||||
<span class="sd"> :return: an instance of :class:`LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">documents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
|
||||
<span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.split_stratified">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired</span>
|
||||
<span class="sd"> proportion.</span>
|
||||
|
||||
<span class="sd"> :param train_prop: the proportion of elements to include in the left-most returned collection (typically used</span>
|
||||
<span class="sd"> as the training collection). The rest of elements are included in the right-most returned collection</span>
|
||||
<span class="sd"> (typically used as a test collection).</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
<span class="sd"> :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the</span>
|
||||
<span class="sd"> second one with `1-train_prop` elements</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">tr_docs</span><span class="p">,</span> <span class="n">te_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">te_labels</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="n">train_prop</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">tr_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">te_docs</span><span class="p">,</span> <span class="n">te_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.split_random">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_random">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired</span>
|
||||
<span class="sd"> proportion.</span>
|
||||
|
||||
<span class="sd"> :param train_prop: the proportion of elements to include in the left-most returned collection (typically used</span>
|
||||
<span class="sd"> as the training collection). The rest of elements are included in the right-most returned collection</span>
|
||||
<span class="sd"> (typically used as a test collection).</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
<span class="sd"> :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the</span>
|
||||
<span class="sd"> second one with `1-train_prop` elements</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_prop</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">train_prop</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> \
|
||||
<span class="s1">'argument train_prop cannot be greater than the number of elements in the collection'</span>
|
||||
<span class="n">splitpoint</span> <span class="o">=</span> <span class="n">train_prop</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_prop</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">train_prop</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> \
|
||||
<span class="s1">'argument train_prop out of range (0,1)'</span>
|
||||
<span class="n">splitpoint</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">*</span><span class="n">train_prop</span><span class="p">))</span>
|
||||
<span class="n">left</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="n">indexes</span><span class="p">[:</span><span class="n">splitpoint</span><span class="p">],</span> <span class="n">indexes</span><span class="p">[</span><span class="n">splitpoint</span><span class="p">:]</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">left</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">right</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of this collection with another collection.</span>
|
||||
<span class="sd"> Both labelled collections must have the same classes.</span>
|
||||
|
||||
<span class="sd"> :param other: another :class:`LabelledCollection`</span>
|
||||
<span class="sd"> :return: a :class:`LabelledCollection` representing the union of both collections</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">classes_</span><span class="p">)):</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'unsupported operation for collections on different classes; '</span>
|
||||
<span class="sa">f</span><span class="s1">'expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">other</span><span class="o">.</span><span class="n">classes_</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.join">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.join">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">'LabelledCollection'</span><span class="p">]):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of the collections given in input.</span>
|
||||
|
||||
<span class="sd"> :param args: instances of :class:`LabelledCollection`</span>
|
||||
<span class="sd"> :return: a :class:`LabelledCollection` representing the union of both collections</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">args</span> <span class="o">=</span> <span class="p">[</span><span class="n">lc</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span> <span class="k">if</span> <span class="n">lc</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
|
||||
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'empty list is not allowed for mix'</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">lc</span><span class="p">,</span> <span class="n">LabelledCollection</span><span class="p">)</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]),</span> \
|
||||
<span class="s1">'only instances of LabelledCollection allowed'</span>
|
||||
|
||||
<span class="n">first_instances</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">instances</span>
|
||||
<span class="n">first_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">first_instances</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="nb">type</span><span class="p">(</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span><span class="o">==</span><span class="n">first_type</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]),</span> \
|
||||
<span class="s1">'not all the collections are of instances of the same type'</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">first_instances</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">first_ndim</span> <span class="o">=</span> <span class="n">first_instances</span><span class="o">.</span><span class="n">ndim</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="n">first_ndim</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]),</span> \
|
||||
<span class="s1">'not all the ndarrays are of the same dimension'</span>
|
||||
<span class="k">if</span> <span class="n">first_ndim</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">first_shape</span> <span class="o">=</span> <span class="n">first_instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="n">first_shape</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]),</span> \
|
||||
<span class="s1">'not all the ndarrays are of the same shape'</span>
|
||||
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">first_instances</span><span class="p">):</span>
|
||||
<span class="n">instances</span> <span class="o">=</span> <span class="n">vstack</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_instances</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">instances</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="p">(</span><span class="n">lc</span><span class="o">.</span><span class="n">instances</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'unsupported operation for collection types'</span><span class="p">)</span>
|
||||
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">labels</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">])</span>
|
||||
<span class="n">classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span>
|
||||
|
||||
<span class="sd"> >>> svm = LinearSVC().fit(*my_collection.Xy)</span>
|
||||
|
||||
<span class="sd"> :return: a tuple `(instances, labels)` from this collection</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from</span>
|
||||
<span class="sd"> a :class:`LabelledCollection` object.</span>
|
||||
|
||||
<span class="sd"> :return: a tuple `(instances, prevalence)` from this collection</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.instances</span>
|
||||
|
||||
<span class="sd"> :return: self.instances</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.labels</span>
|
||||
|
||||
<span class="sd"> :return: self.labels</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.prevalence()</span>
|
||||
|
||||
<span class="sd"> :return: self.prevalence()</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.stats">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.stats">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</span>
|
||||
|
||||
<span class="sd"> >>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)</span>
|
||||
<span class="sd"> >>> data.training.stats()</span>
|
||||
<span class="sd"> >>> #instances=3821, type=<class 'scipy.sparse.csr.csr_matrix'>, #features=4403, #classes=[0 1], prevs=[0.081, 0.919]</span>
|
||||
|
||||
<span class="sd"> :param show: if set to True (default), prints the stats in standard output</span>
|
||||
<span class="sd"> :return: a dictionary containing some stats of this collection. Keys include `#instances` (the number of</span>
|
||||
<span class="sd"> instances), `type` (the type representing the instances), `#features` (the number of features, if the</span>
|
||||
<span class="sd"> instances are in array-like format), `#classes` (the classes of the collection), `prevs` (the prevalence</span>
|
||||
<span class="sd"> values for each class)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">ninstances</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
<span class="n">instance_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="n">instance_type</span> <span class="o">==</span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="n">nfeats</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="n">instance_type</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="ow">or</span> <span class="n">issparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">nfeats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">nfeats</span> <span class="o">=</span> <span class="s1">'?'</span>
|
||||
<span class="n">stats_</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'instances'</span><span class="p">:</span> <span class="n">ninstances</span><span class="p">,</span>
|
||||
<span class="s1">'type'</span><span class="p">:</span> <span class="n">instance_type</span><span class="p">,</span>
|
||||
<span class="s1">'features'</span><span class="p">:</span> <span class="n">nfeats</span><span class="p">,</span>
|
||||
<span class="s1">'classes'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span>
|
||||
<span class="s1">'prevs'</span><span class="p">:</span> <span class="n">strprev</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())}</span>
|
||||
<span class="k">if</span> <span class="n">show</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'#instances=</span><span class="si">{</span><span class="n">stats_</span><span class="p">[</span><span class="s2">"instances"</span><span class="p">]</span><span class="si">}</span><span class="s1">, type=</span><span class="si">{</span><span class="n">stats_</span><span class="p">[</span><span class="s2">"type"</span><span class="p">]</span><span class="si">}</span><span class="s1">, #features=</span><span class="si">{</span><span class="n">stats_</span><span class="p">[</span><span class="s2">"features"</span><span class="p">]</span><span class="si">}</span><span class="s1">, '</span>
|
||||
<span class="sa">f</span><span class="s1">'#classes=</span><span class="si">{</span><span class="n">stats_</span><span class="p">[</span><span class="s2">"classes"</span><span class="p">]</span><span class="si">}</span><span class="s1">, prevs=</span><span class="si">{</span><span class="n">stats_</span><span class="p">[</span><span class="s2">"prevs"</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">stats_</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.kFCV">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation.</span>
|
||||
|
||||
<span class="sd"> :param nfolds: integer (default 5), the number of folds to generate</span>
|
||||
<span class="sd"> :param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run</span>
|
||||
<span class="sd"> :param random_state: integer (default 0), guarantees that the folds generated are reproducible</span>
|
||||
<span class="sd"> :return: yields `nfolds * nrepeats` folds for k-fold cross validation</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">kf</span> <span class="o">=</span> <span class="n">RepeatedStratifiedKFold</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="n">nrepeats</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">train_index</span><span class="p">,</span> <span class="n">test_index</span> <span class="ow">in</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">Xy</span><span class="p">):</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">train_index</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">test_index</span><span class="p">)</span>
|
||||
<span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Dataset">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span>
|
||||
|
||||
<span class="sd"> :param training: a :class:`LabelledCollection` instance</span>
|
||||
<span class="sd"> :param test: a :class:`LabelledCollection` instance</span>
|
||||
<span class="sd"> :param vocabulary: if indicated, is a dictionary of the terms used in this textual dataset</span>
|
||||
<span class="sd"> :param name: a string representing the name of the dataset</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">'incompatible labels in training and test collections'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">training</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">test</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">vocabulary</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
|
||||
|
||||
<div class="viewcode-block" id="Dataset.SplitStratified">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.SplitStratified">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.</span>
|
||||
<span class="sd"> See :meth:`LabelledCollection.split_stratified`</span>
|
||||
|
||||
<span class="sd"> :param collection: :class:`LabelledCollection`</span>
|
||||
<span class="sd"> :param train_size: the proportion of training documents (the rest conforms the test split)</span>
|
||||
<span class="sd"> :return: an instance of :class:`Dataset`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="o">*</span><span class="n">collection</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="n">train_size</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The classes according to which the training collection is labelled</span>
|
||||
|
||||
<span class="sd"> :return: The classes according to which the training collection is labelled</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The number of classes according to which the training collection is labelled</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns True if the training collection is labelled according to two classes</span>
|
||||
|
||||
<span class="sd"> :return: boolean</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">binary</span>
|
||||
|
||||
<div class="viewcode-block" id="Dataset.load">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.load">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.</span>
|
||||
<span class="sd"> The function in charge of reading the instances must be specified. This function can be a custom one, or any of</span>
|
||||
<span class="sd"> the reading functions defined in :mod:`quapy.data.reader` module.</span>
|
||||
|
||||
<span class="sd"> :param train_path: string, the path to the file containing the training instances</span>
|
||||
<span class="sd"> :param test_path: string, the path to the file containing the test instances</span>
|
||||
<span class="sd"> :param loader_func: a custom function that implements the data loader and returns a tuple with instances and</span>
|
||||
<span class="sd"> labels</span>
|
||||
<span class="sd"> :param classes: array-like, the classes according to which the instances are labelled</span>
|
||||
<span class="sd"> :param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances.</span>
|
||||
<span class="sd"> See :meth:`LabelledCollection.load` for further details.</span>
|
||||
<span class="sd"> :return: a :class:`Dataset` object</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">train_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Alias to `self.training` and `self.test`</span>
|
||||
|
||||
<span class="sd"> :return: the training and test collections</span>
|
||||
<span class="sd"> :return: the training and test collections</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span>
|
||||
|
||||
<div class="viewcode-block" id="Dataset.stats">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.stats">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</span>
|
||||
|
||||
<span class="sd"> >>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)</span>
|
||||
<span class="sd"> >>> data.stats()</span>
|
||||
<span class="sd"> >>> Dataset=kindle #tr-instances=3821, #te-instances=21591, type=<class 'scipy.sparse.csr.csr_matrix'>, #features=4403, #classes=[0 1], tr-prevs=[0.081, 0.919], te-prevs=[0.063, 0.937]</span>
|
||||
|
||||
<span class="sd"> :param show: if set to True (default), prints the stats in standard output</span>
|
||||
<span class="sd"> :return: a dictionary containing some stats of this collection for the training and test collections. The keys</span>
|
||||
<span class="sd"> are `train` and `test`, and point to dedicated dictionaries of stats, for each collection, with keys</span>
|
||||
<span class="sd"> `#instances` (the number of instances), `type` (the type representing the instances),</span>
|
||||
<span class="sd"> `#features` (the number of features, if the instances are in array-like format), `#classes` (the classes of</span>
|
||||
<span class="sd"> the collection), `prevs` (the prevalence values for each class)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">tr_stats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">(</span><span class="n">show</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">te_stats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">stats</span><span class="p">(</span><span class="n">show</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">show</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dataset=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s1"> #tr-instances=</span><span class="si">{</span><span class="n">tr_stats</span><span class="p">[</span><span class="s2">"instances"</span><span class="p">]</span><span class="si">}</span><span class="s1">, #te-instances=</span><span class="si">{</span><span class="n">te_stats</span><span class="p">[</span><span class="s2">"instances"</span><span class="p">]</span><span class="si">}</span><span class="s1">, '</span>
|
||||
<span class="sa">f</span><span class="s1">'type=</span><span class="si">{</span><span class="n">tr_stats</span><span class="p">[</span><span class="s2">"type"</span><span class="p">]</span><span class="si">}</span><span class="s1">, #features=</span><span class="si">{</span><span class="n">tr_stats</span><span class="p">[</span><span class="s2">"features"</span><span class="p">]</span><span class="si">}</span><span class="s1">, #classes=</span><span class="si">{</span><span class="n">tr_stats</span><span class="p">[</span><span class="s2">"classes"</span><span class="p">]</span><span class="si">}</span><span class="s1">, '</span>
|
||||
<span class="sa">f</span><span class="s1">'tr-prevs=</span><span class="si">{</span><span class="n">tr_stats</span><span class="p">[</span><span class="s2">"prevs"</span><span class="p">]</span><span class="si">}</span><span class="s1">, te-prevs=</span><span class="si">{</span><span class="n">te_stats</span><span class="p">[</span><span class="s2">"prevs"</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">{</span><span class="s1">'train'</span><span class="p">:</span> <span class="n">tr_stats</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">:</span> <span class="n">te_stats</span><span class="p">}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Dataset.kFCV">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.kFCV">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around</span>
|
||||
<span class="sd"> :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.</span>
|
||||
|
||||
<span class="sd"> :param nfolds: integer (default 5), the number of folds to generate</span>
|
||||
<span class="sd"> :param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run</span>
|
||||
<span class="sd"> :param random_state: integer (default 0), guarantees that the folds generated are reproducible</span>
|
||||
<span class="sd"> :return: yields `nfolds * nrepeats` folds for k-fold cross validation as instances of :class:`Dataset`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="n">nrepeats</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)):</span>
|
||||
<span class="k">yield</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s1">'fold </span><span class="si">{</span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="o">%</span><span class="w"> </span><span class="n">nfolds</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">nfolds</span><span class="si">}</span><span class="s1"> (round=</span><span class="si">{</span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="o">//</span><span class="w"> </span><span class="n">nfolds</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Dataset.reduce">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.reduce">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span>
|
||||
|
||||
<span class="sd"> :param n_train: number of training documents to keep (default 100)</span>
|
||||
<span class="sd"> :param n_test: number of test documents to keep (default 100)</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_train</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_test</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
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|
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<h1>Source code for quapy.data.datasets</h1><div class="highlight"><pre>
|
||||
<div class="viewcode-block" id="warn">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.warn">[docs]</a>
|
||||
<span></span><span class="k">def</span> <span class="nf">warn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">warnings</span>
|
||||
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span> <span class="o">=</span> <span class="n">warn</span>
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">zipfile</span>
|
||||
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="kn">from</span> <span class="nn">ucimlrepo</span> <span class="kn">import</span> <span class="n">fetch_ucirepo</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.preprocessing</span> <span class="kn">import</span> <span class="n">text2tfidf</span><span class="p">,</span> <span class="n">reduce_columns</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.reader</span> <span class="kn">import</span> <span class="o">*</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">download_file_if_not_exists</span><span class="p">,</span> <span class="n">download_file</span><span class="p">,</span> <span class="n">get_quapy_home</span><span class="p">,</span> <span class="n">pickled_resource</span>
|
||||
|
||||
|
||||
<span class="n">REVIEWS_SENTIMENT_DATASETS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'hp'</span><span class="p">,</span> <span class="s1">'kindle'</span><span class="p">,</span> <span class="s1">'imdb'</span><span class="p">]</span>
|
||||
<span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'gasp'</span><span class="p">,</span> <span class="s1">'hcr'</span><span class="p">,</span> <span class="s1">'omd'</span><span class="p">,</span> <span class="s1">'sanders'</span><span class="p">,</span>
|
||||
<span class="s1">'semeval13'</span><span class="p">,</span> <span class="s1">'semeval14'</span><span class="p">,</span> <span class="s1">'semeval15'</span><span class="p">,</span> <span class="s1">'semeval16'</span><span class="p">,</span>
|
||||
<span class="s1">'sst'</span><span class="p">,</span> <span class="s1">'wa'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">]</span>
|
||||
<span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'gasp'</span><span class="p">,</span> <span class="s1">'hcr'</span><span class="p">,</span> <span class="s1">'omd'</span><span class="p">,</span> <span class="s1">'sanders'</span><span class="p">,</span>
|
||||
<span class="s1">'semeval'</span><span class="p">,</span> <span class="s1">'semeval16'</span><span class="p">,</span>
|
||||
<span class="s1">'sst'</span><span class="p">,</span> <span class="s1">'wa'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">]</span>
|
||||
<span class="n">UCI_BINARY_DATASETS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'acute.a'</span><span class="p">,</span> <span class="s1">'acute.b'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.1'</span><span class="p">,</span> <span class="s1">'balance.2'</span><span class="p">,</span> <span class="s1">'balance.3'</span><span class="p">,</span>
|
||||
<span class="s1">'breast-cancer'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.1'</span><span class="p">,</span> <span class="s1">'cmc.2'</span><span class="p">,</span> <span class="s1">'cmc.3'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.1'</span><span class="p">,</span> <span class="s1">'ctg.2'</span><span class="p">,</span> <span class="s1">'ctg.3'</span><span class="p">,</span>
|
||||
<span class="c1">#'diabetes', # <-- I haven't found this one...</span>
|
||||
<span class="s1">'german'</span><span class="p">,</span>
|
||||
<span class="s1">'haberman'</span><span class="p">,</span>
|
||||
<span class="s1">'ionosphere'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.1'</span><span class="p">,</span> <span class="s1">'iris.2'</span><span class="p">,</span> <span class="s1">'iris.3'</span><span class="p">,</span>
|
||||
<span class="s1">'mammographic'</span><span class="p">,</span>
|
||||
<span class="s1">'pageblocks.5'</span><span class="p">,</span>
|
||||
<span class="c1">#'phoneme', # <-- I haven't found this one...</span>
|
||||
<span class="s1">'semeion'</span><span class="p">,</span>
|
||||
<span class="s1">'sonar'</span><span class="p">,</span>
|
||||
<span class="s1">'spambase'</span><span class="p">,</span>
|
||||
<span class="s1">'spectf'</span><span class="p">,</span>
|
||||
<span class="s1">'tictactoe'</span><span class="p">,</span>
|
||||
<span class="s1">'transfusion'</span><span class="p">,</span>
|
||||
<span class="s1">'wdbc'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.1'</span><span class="p">,</span> <span class="s1">'wine.2'</span><span class="p">,</span> <span class="s1">'wine.3'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-q-red'</span><span class="p">,</span> <span class="s1">'wine-q-white'</span><span class="p">,</span>
|
||||
<span class="s1">'yeast'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">UCI_MULTICLASS_DATASETS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'dry-bean'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-quality'</span><span class="p">,</span>
|
||||
<span class="s1">'academic-success'</span><span class="p">,</span>
|
||||
<span class="s1">'digits'</span><span class="p">,</span>
|
||||
<span class="s1">'letter'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">LEQUA2022_TASKS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'T1A'</span><span class="p">,</span> <span class="s1">'T1B'</span><span class="p">,</span> <span class="s1">'T2A'</span><span class="p">,</span> <span class="s1">'T2B'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">_TXA_SAMPLE_SIZE</span> <span class="o">=</span> <span class="mi">250</span>
|
||||
<span class="n">_TXB_SAMPLE_SIZE</span> <span class="o">=</span> <span class="mi">1000</span>
|
||||
|
||||
<span class="n">LEQUA2022_SAMPLE_SIZE</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'TXA'</span><span class="p">:</span> <span class="n">_TXA_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'TXB'</span><span class="p">:</span> <span class="n">_TXB_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'T1A'</span><span class="p">:</span> <span class="n">_TXA_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'T1B'</span><span class="p">:</span> <span class="n">_TXB_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'T2A'</span><span class="p">:</span> <span class="n">_TXA_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'T2B'</span><span class="p">:</span> <span class="n">_TXB_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'binary'</span><span class="p">:</span> <span class="n">_TXA_SAMPLE_SIZE</span><span class="p">,</span>
|
||||
<span class="s1">'multiclass'</span><span class="p">:</span> <span class="n">_TXB_SAMPLE_SIZE</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_reviews">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_reviews">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a Reviews dataset as a Dataset instance, as used in</span>
|
||||
<span class="sd"> `Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."</span>
|
||||
<span class="sd"> Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.</span>
|
||||
<span class="sd"> The list of valid dataset names can be accessed in `quapy.data.datasets.REVIEWS_SENTIMENT_DATASETS`</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb'</span>
|
||||
<span class="sd"> :param tfidf: set to True to transform the raw documents into tfidf weighted matrices</span>
|
||||
<span class="sd"> :param min_df: minimun number of documents that should contain a term in order for the term to be</span>
|
||||
<span class="sd"> kept (ignored if tfidf==False)</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for</span>
|
||||
<span class="sd"> faster subsequent invokations</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.Dataset` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> does not match any known dataset for sentiment reviews. '</span> \
|
||||
<span class="sa">f</span><span class="s1">'Valid ones are </span><span class="si">{</span><span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">URL_TRAIN</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'https://zenodo.org/record/4117827/files/</span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">_train.txt'</span>
|
||||
<span class="n">URL_TEST</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'https://zenodo.org/record/4117827/files/</span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">_test.txt'</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'reviews'</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">train_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'reviews'</span><span class="p">,</span> <span class="n">dataset_name</span><span class="p">,</span> <span class="s1">'train.txt'</span><span class="p">)</span>
|
||||
<span class="n">test_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'reviews'</span><span class="p">,</span> <span class="n">dataset_name</span><span class="p">,</span> <span class="s1">'test.txt'</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">URL_TRAIN</span><span class="p">,</span> <span class="n">train_path</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">URL_TEST</span><span class="p">,</span> <span class="n">test_path</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pickle_path</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">pickle</span><span class="p">:</span>
|
||||
<span class="n">pickle_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'reviews'</span><span class="p">,</span> <span class="s1">'pickle'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">.pkl'</span><span class="p">)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">load</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">from_text</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">tfidf</span><span class="p">:</span>
|
||||
<span class="n">text2tfidf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">min_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">reduce_columns</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">dataset_name</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_twitter">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_twitter">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a Twitter dataset as a :class:`quapy.data.base.Dataset` instance, as used in:</span>
|
||||
<span class="sd"> `Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.</span>
|
||||
<span class="sd"> Social Network Analysis and Mining6(19), 1–22 (2016) <https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf>`_</span>
|
||||
<span class="sd"> Note that the datasets 'semeval13', 'semeval14', 'semeval15' share the same training set.</span>
|
||||
<span class="sd"> The list of valid dataset names corresponding to training sets can be accessed in</span>
|
||||
<span class="sd"> `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN`, while the test sets can be accessed in</span>
|
||||
<span class="sd"> `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TEST`</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: the name of the dataset: valid ones are 'gasp', 'hcr', 'omd', 'sanders', 'semeval13',</span>
|
||||
<span class="sd"> 'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'</span>
|
||||
<span class="sd"> :param for_model_selection: if True, then returns the train split as the training set and the devel split</span>
|
||||
<span class="sd"> as the test set; if False, then returns the train+devel split as the training set and the test set as the</span>
|
||||
<span class="sd"> test set</span>
|
||||
<span class="sd"> :param min_df: minimun number of documents that should contain a term in order for the term to be kept</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for</span>
|
||||
<span class="sd"> faster subsequent invokations</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.Dataset` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span> <span class="o">+</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> does not match any known dataset for sentiment twitter. '</span> \
|
||||
<span class="sa">f</span><span class="s1">'Valid ones are </span><span class="si">{</span><span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span><span class="si">}</span><span class="s1"> for model selection and '</span> \
|
||||
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span><span class="si">}</span><span class="s1"> for test (datasets "semeval14", "semeval15", "semeval16" share '</span> \
|
||||
<span class="sa">f</span><span class="s1">'a common training set "semeval")'</span>
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">URL</span> <span class="o">=</span> <span class="s1">'https://zenodo.org/record/4255764/files/tweet_sentiment_quantification_snam.zip'</span>
|
||||
<span class="n">unzipped_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'tweet_sentiment_quantification_snam'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">):</span>
|
||||
<span class="n">downloaded_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'tweet_sentiment_quantification_snam.zip'</span><span class="p">)</span>
|
||||
<span class="n">download_file</span><span class="p">(</span><span class="n">URL</span><span class="p">,</span> <span class="n">downloaded_path</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">downloaded_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">file</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">data_home</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">downloaded_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">'semeval13'</span><span class="p">,</span> <span class="s1">'semeval14'</span><span class="p">,</span> <span class="s1">'semeval15'</span><span class="p">}:</span>
|
||||
<span class="n">trainset_name</span> <span class="o">=</span> <span class="s1">'semeval'</span>
|
||||
<span class="n">testset_name</span> <span class="o">=</span> <span class="s1">'semeval'</span> <span class="k">if</span> <span class="n">for_model_selection</span> <span class="k">else</span> <span class="n">dataset_name</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"the training and development sets for datasets 'semeval13', 'semeval14', 'semeval15' are common "</span>
|
||||
<span class="sa">f</span><span class="s2">"(called 'semeval'); returning trainin-set='</span><span class="si">{</span><span class="n">trainset_name</span><span class="si">}</span><span class="s2">' and test-set=</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'semeval'</span> <span class="ow">and</span> <span class="n">for_model_selection</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'dataset "semeval" can only be used for model selection. '</span>
|
||||
<span class="s1">'Use "semeval13", "semeval14", or "semeval15" for model evaluation.'</span><span class="p">)</span>
|
||||
<span class="n">trainset_name</span> <span class="o">=</span> <span class="n">testset_name</span> <span class="o">=</span> <span class="n">dataset_name</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">for_model_selection</span><span class="p">:</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">trainset_name</span><span class="si">}</span><span class="s1">.train.feature.txt'</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s1">.dev.feature.txt'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">trainset_name</span><span class="si">}</span><span class="s1">.train+dev.feature.txt'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'semeval16'</span><span class="p">:</span> <span class="c1"># there is a different test name in the case of semeval16 only</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s1">.dev-test.feature.txt'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s1">.test.feature.txt'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pickle_path</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">pickle</span><span class="p">:</span>
|
||||
<span class="n">mode</span> <span class="o">=</span> <span class="s2">"train-dev"</span> <span class="k">if</span> <span class="n">for_model_selection</span> <span class="k">else</span> <span class="s2">"train+dev-test"</span>
|
||||
<span class="n">pickle_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="s1">'pickle'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">mode</span><span class="si">}</span><span class="s1">.pkl'</span><span class="p">)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">load</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">from_sparse</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">min_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">reduce_columns</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">dataset_name</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_UCIBinaryDataset">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryDataset">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a UCI dataset as an instance of :class:`quapy.data.base.Dataset`, as used in</span>
|
||||
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).</span>
|
||||
<span class="sd"> Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.</span>
|
||||
<span class="sd"> Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_</span>
|
||||
<span class="sd"> and</span>
|
||||
<span class="sd"> `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).</span>
|
||||
<span class="sd"> Dynamic ensemble selection for quantification tasks.</span>
|
||||
<span class="sd"> Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
<span class="sd"> The datasets do not come with a predefined train-test split (see :meth:`fetch_UCILabelledCollection` for further</span>
|
||||
<span class="sd"> information on how to use these collections), and so a train-test split is generated at desired proportion.</span>
|
||||
<span class="sd"> The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: a dataset name</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param test_split: proportion of documents to be included in the test set. The rest conforms the training set</span>
|
||||
<span class="sd"> :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.Dataset` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">test_split</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_UCIBinaryLabelledCollection">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryLabelledCollection">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">LabelledCollection</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a UCI collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in</span>
|
||||
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).</span>
|
||||
<span class="sd"> Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.</span>
|
||||
<span class="sd"> Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_</span>
|
||||
<span class="sd"> and</span>
|
||||
<span class="sd"> `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).</span>
|
||||
<span class="sd"> Dynamic ensemble selection for quantification tasks.</span>
|
||||
<span class="sd"> Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
<span class="sd"> The datasets do not come with a predefined train-test split, and so Pérez-Gállego et al. adopted a 5FCVx2 evaluation</span>
|
||||
<span class="sd"> protocol, meaning that each collection was used to generate two rounds (hence the x2) of 5 fold cross validation.</span>
|
||||
<span class="sd"> This can be reproduced by using :meth:`quapy.data.base.Dataset.kFCV`, e.g.:</span>
|
||||
|
||||
<span class="sd"> >>> import quapy as qp</span>
|
||||
<span class="sd"> >>> collection = qp.datasets.fetch_UCIBinaryLabelledCollection("yeast")</span>
|
||||
<span class="sd"> >>> for data in qp.train.Dataset.kFCV(collection, nfolds=5, nrepeats=2):</span>
|
||||
<span class="sd"> >>> ...</span>
|
||||
|
||||
<span class="sd"> The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: a dataset name</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param test_split: proportion of documents to be included in the test set. The rest conforms the training set</span>
|
||||
<span class="sd"> :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.LabelledCollection` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="n">UCI_BINARY_DATASETS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> does not match any known dataset from the UCI Machine Learning datasets repository. '</span> \
|
||||
<span class="sa">f</span><span class="s1">'Valid ones are </span><span class="si">{</span><span class="n">UCI_BINARY_DATASETS</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">dataset_fullname</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'acute.a'</span><span class="p">:</span> <span class="s1">'Acute Inflammations (urinary bladder)'</span><span class="p">,</span>
|
||||
<span class="s1">'acute.b'</span><span class="p">:</span> <span class="s1">'Acute Inflammations (renal pelvis)'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.1'</span><span class="p">:</span> <span class="s1">'Balance Scale Weight & Distance Database (left)'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.2'</span><span class="p">:</span> <span class="s1">'Balance Scale Weight & Distance Database (balanced)'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.3'</span><span class="p">:</span> <span class="s1">'Balance Scale Weight & Distance Database (right)'</span><span class="p">,</span>
|
||||
<span class="s1">'breast-cancer'</span><span class="p">:</span> <span class="s1">'Breast Cancer Wisconsin (Original)'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.1'</span><span class="p">:</span> <span class="s1">'Contraceptive Method Choice (no use)'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.2'</span><span class="p">:</span> <span class="s1">'Contraceptive Method Choice (long term)'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.3'</span><span class="p">:</span> <span class="s1">'Contraceptive Method Choice (short term)'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.1'</span><span class="p">:</span> <span class="s1">'Cardiotocography Data Set (normal)'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.2'</span><span class="p">:</span> <span class="s1">'Cardiotocography Data Set (suspect)'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.3'</span><span class="p">:</span> <span class="s1">'Cardiotocography Data Set (pathologic)'</span><span class="p">,</span>
|
||||
<span class="s1">'german'</span><span class="p">:</span> <span class="s1">'Statlog German Credit Data'</span><span class="p">,</span>
|
||||
<span class="s1">'haberman'</span><span class="p">:</span> <span class="s2">"Haberman's Survival Data"</span><span class="p">,</span>
|
||||
<span class="s1">'ionosphere'</span><span class="p">:</span> <span class="s1">'Johns Hopkins University Ionosphere DB'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.1'</span><span class="p">:</span> <span class="s1">'Iris Plants Database(x)'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.2'</span><span class="p">:</span> <span class="s1">'Iris Plants Database(versicolour)'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.3'</span><span class="p">:</span> <span class="s1">'Iris Plants Database(virginica)'</span><span class="p">,</span>
|
||||
<span class="s1">'mammographic'</span><span class="p">:</span> <span class="s1">'Mammographic Mass'</span><span class="p">,</span>
|
||||
<span class="s1">'pageblocks.5'</span><span class="p">:</span> <span class="s1">'Page Blocks Classification (5)'</span><span class="p">,</span>
|
||||
<span class="s1">'semeion'</span><span class="p">:</span> <span class="s1">'Semeion Handwritten Digit (8)'</span><span class="p">,</span>
|
||||
<span class="s1">'sonar'</span><span class="p">:</span> <span class="s1">'Sonar, Mines vs. Rocks'</span><span class="p">,</span>
|
||||
<span class="s1">'spambase'</span><span class="p">:</span> <span class="s1">'Spambase Data Set'</span><span class="p">,</span>
|
||||
<span class="s1">'spectf'</span><span class="p">:</span> <span class="s1">'SPECTF Heart Data'</span><span class="p">,</span>
|
||||
<span class="s1">'tictactoe'</span><span class="p">:</span> <span class="s1">'Tic-Tac-Toe Endgame Database'</span><span class="p">,</span>
|
||||
<span class="s1">'transfusion'</span><span class="p">:</span> <span class="s1">'Blood Transfusion Service Center Data Set'</span><span class="p">,</span>
|
||||
<span class="s1">'wdbc'</span><span class="p">:</span> <span class="s1">'Wisconsin Diagnostic Breast Cancer'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.1'</span><span class="p">:</span> <span class="s1">'Wine Recognition Data (1)'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.2'</span><span class="p">:</span> <span class="s1">'Wine Recognition Data (2)'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.3'</span><span class="p">:</span> <span class="s1">'Wine Recognition Data (3)'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-q-red'</span><span class="p">:</span> <span class="s1">'Wine Quality Red (6-10)'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-q-white'</span><span class="p">:</span> <span class="s1">'Wine Quality White (6-10)'</span><span class="p">,</span>
|
||||
<span class="s1">'yeast'</span><span class="p">:</span> <span class="s1">'Yeast'</span><span class="p">,</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="c1"># the identifier is an alias for the dataset group, it's part of the url data-folder, and is the name we use</span>
|
||||
<span class="c1"># to download the raw dataset</span>
|
||||
<span class="n">identifier_map</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'acute.a'</span><span class="p">:</span> <span class="s1">'acute'</span><span class="p">,</span>
|
||||
<span class="s1">'acute.b'</span><span class="p">:</span> <span class="s1">'acute'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.1'</span><span class="p">:</span> <span class="s1">'balance-scale'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.2'</span><span class="p">:</span> <span class="s1">'balance-scale'</span><span class="p">,</span>
|
||||
<span class="s1">'balance.3'</span><span class="p">:</span> <span class="s1">'balance-scale'</span><span class="p">,</span>
|
||||
<span class="s1">'breast-cancer'</span><span class="p">:</span> <span class="s1">'breast-cancer-wisconsin'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.1'</span><span class="p">:</span> <span class="s1">'cmc'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.2'</span><span class="p">:</span> <span class="s1">'cmc'</span><span class="p">,</span>
|
||||
<span class="s1">'cmc.3'</span><span class="p">:</span> <span class="s1">'cmc'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.1'</span><span class="p">:</span> <span class="s1">'00193'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.2'</span><span class="p">:</span> <span class="s1">'00193'</span><span class="p">,</span>
|
||||
<span class="s1">'ctg.3'</span><span class="p">:</span> <span class="s1">'00193'</span><span class="p">,</span>
|
||||
<span class="s1">'german'</span><span class="p">:</span> <span class="s1">'statlog/german'</span><span class="p">,</span>
|
||||
<span class="s1">'haberman'</span><span class="p">:</span> <span class="s1">'haberman'</span><span class="p">,</span>
|
||||
<span class="s1">'ionosphere'</span><span class="p">:</span> <span class="s1">'ionosphere'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.1'</span><span class="p">:</span> <span class="s1">'iris'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.2'</span><span class="p">:</span> <span class="s1">'iris'</span><span class="p">,</span>
|
||||
<span class="s1">'iris.3'</span><span class="p">:</span> <span class="s1">'iris'</span><span class="p">,</span>
|
||||
<span class="s1">'mammographic'</span><span class="p">:</span> <span class="s1">'mammographic-masses'</span><span class="p">,</span>
|
||||
<span class="s1">'pageblocks.5'</span><span class="p">:</span> <span class="s1">'page-blocks'</span><span class="p">,</span>
|
||||
<span class="s1">'semeion'</span><span class="p">:</span> <span class="s1">'semeion'</span><span class="p">,</span>
|
||||
<span class="s1">'sonar'</span><span class="p">:</span> <span class="s1">'undocumented/connectionist-bench/sonar'</span><span class="p">,</span>
|
||||
<span class="s1">'spambase'</span><span class="p">:</span> <span class="s1">'spambase'</span><span class="p">,</span>
|
||||
<span class="s1">'spectf'</span><span class="p">:</span> <span class="s1">'spect'</span><span class="p">,</span>
|
||||
<span class="s1">'tictactoe'</span><span class="p">:</span> <span class="s1">'tic-tac-toe'</span><span class="p">,</span>
|
||||
<span class="s1">'transfusion'</span><span class="p">:</span> <span class="s1">'blood-transfusion'</span><span class="p">,</span>
|
||||
<span class="s1">'wdbc'</span><span class="p">:</span> <span class="s1">'breast-cancer-wisconsin'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-q-red'</span><span class="p">:</span> <span class="s1">'wine-quality'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-q-white'</span><span class="p">:</span> <span class="s1">'wine-quality'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.1'</span><span class="p">:</span> <span class="s1">'wine'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.2'</span><span class="p">:</span> <span class="s1">'wine'</span><span class="p">,</span>
|
||||
<span class="s1">'wine.3'</span><span class="p">:</span> <span class="s1">'wine'</span><span class="p">,</span>
|
||||
<span class="s1">'yeast'</span><span class="p">:</span> <span class="s1">'yeast'</span><span class="p">,</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="c1"># the filename is the name of the file within the data_folder indexed by the identifier</span>
|
||||
<span class="n">file_name</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'acute'</span><span class="p">:</span> <span class="s1">'diagnosis.data'</span><span class="p">,</span>
|
||||
<span class="s1">'00193'</span><span class="p">:</span> <span class="s1">'CTG.xls'</span><span class="p">,</span>
|
||||
<span class="s1">'statlog/german'</span><span class="p">:</span> <span class="s1">'german.data-numeric'</span><span class="p">,</span>
|
||||
<span class="s1">'mammographic-masses'</span><span class="p">:</span> <span class="s1">'mammographic_masses.data'</span><span class="p">,</span>
|
||||
<span class="s1">'page-blocks'</span><span class="p">:</span> <span class="s1">'page-blocks.data.Z'</span><span class="p">,</span>
|
||||
<span class="s1">'undocumented/connectionist-bench/sonar'</span><span class="p">:</span> <span class="s1">'sonar.all-data'</span><span class="p">,</span>
|
||||
<span class="s1">'spect'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'SPECTF.train'</span><span class="p">,</span> <span class="s1">'SPECTF.test'</span><span class="p">],</span>
|
||||
<span class="s1">'blood-transfusion'</span><span class="p">:</span> <span class="s1">'transfusion.data'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-quality'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'winequality-red.csv'</span><span class="p">,</span> <span class="s1">'winequality-white.csv'</span><span class="p">],</span>
|
||||
<span class="s1">'breast-cancer-wisconsin'</span><span class="p">:</span> <span class="s1">'breast-cancer-wisconsin.data'</span> <span class="k">if</span> <span class="n">dataset_name</span><span class="o">==</span><span class="s1">'breast-cancer'</span> <span class="k">else</span> <span class="s1">'wdbc.data'</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="c1"># the filename containing the dataset description (if any)</span>
|
||||
<span class="n">desc_name</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'acute'</span><span class="p">:</span> <span class="s1">'diagnosis.names'</span><span class="p">,</span>
|
||||
<span class="s1">'00193'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
||||
<span class="s1">'statlog/german'</span><span class="p">:</span> <span class="s1">'german.doc'</span><span class="p">,</span>
|
||||
<span class="s1">'mammographic-masses'</span><span class="p">:</span> <span class="s1">'mammographic_masses.names'</span><span class="p">,</span>
|
||||
<span class="s1">'undocumented/connectionist-bench/sonar'</span><span class="p">:</span> <span class="s1">'sonar.names'</span><span class="p">,</span>
|
||||
<span class="s1">'spect'</span><span class="p">:</span> <span class="s1">'SPECTF.names'</span><span class="p">,</span>
|
||||
<span class="s1">'blood-transfusion'</span><span class="p">:</span> <span class="s1">'transfusion.names'</span><span class="p">,</span>
|
||||
<span class="s1">'wine-quality'</span><span class="p">:</span> <span class="s1">'winequality.names'</span><span class="p">,</span>
|
||||
<span class="s1">'breast-cancer-wisconsin'</span><span class="p">:</span> <span class="s1">'breast-cancer-wisconsin.names'</span> <span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'breast-cancer'</span> <span class="k">else</span> <span class="s1">'wdbc.names'</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="n">identifier</span> <span class="o">=</span> <span class="n">identifier_map</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
|
||||
<span class="n">filename</span> <span class="o">=</span> <span class="n">file_name</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">identifier</span><span class="si">}</span><span class="s1">.data'</span><span class="p">)</span>
|
||||
<span class="n">descfile</span> <span class="o">=</span> <span class="n">desc_name</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">identifier</span><span class="si">}</span><span class="s1">.names'</span><span class="p">)</span>
|
||||
<span class="n">fullname</span> <span class="o">=</span> <span class="n">dataset_fullname</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
|
||||
|
||||
<span class="n">URL</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'http://archive.ics.uci.edu/ml/machine-learning-databases/</span><span class="si">{</span><span class="n">identifier</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="n">data_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'uci_datasets'</span><span class="p">,</span> <span class="n">identifier</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> <span class="c1"># filename could be a list of files, in which case it will be processed later</span>
|
||||
<span class="n">data_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">URL</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">data_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">descfile</span><span class="p">:</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">URL</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">descfile</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">descfile</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">descfile</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'could not read the description file'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'no file description available'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Loading </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> (</span><span class="si">{</span><span class="n">fullname</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'acute'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-16'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">','</span><span class="p">,</span> <span class="s1">'.'</span><span class="p">)))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="p">[</span><span class="n">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)]</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'acute.a'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'yes'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'acute.b'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'yes'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'balance-scale'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'balance.1'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'L'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'balance.2'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'B'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'balance.3'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'R'</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'breast-cancer-wisconsin'</span> <span class="ow">and</span> <span class="n">dataset_name</span><span class="o">==</span><span class="s1">'breast-cancer'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">Xy</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
|
||||
<span class="n">Xy</span><span class="p">[</span><span class="n">Xy</span><span class="o">==</span><span class="s1">'?'</span><span class="p">]</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="n">Xy</span> <span class="o">=</span> <span class="n">Xy</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">Xy</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">Xy</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'breast-cancer-wisconsin'</span> <span class="ow">and</span> <span class="n">dataset_name</span><span class="o">==</span><span class="s1">'wdbc'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">32</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'M'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'cmc'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">8</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'cmc.1'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'cmc.2'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'cmc.3'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'00193'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">'Data'</span><span class="p">,</span> <span class="n">skipfooter</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">24</span><span class="p">))]</span> <span class="c1"># select columns numbered (number 23 is the target label)</span>
|
||||
<span class="c1"># replaces the header with the first row</span>
|
||||
<span class="n">new_header</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># grab the first row for the header</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="c1"># take the data less the header row</span>
|
||||
<span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">new_header</span> <span class="c1"># set the header row as the df header</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">22</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'NSP'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'ctg.1'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># 1==Normal</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'ctg.2'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># 2==Suspect</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'ctg.3'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="c1"># 3==Pathologic</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'statlog/german'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">24</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">24</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'haberman'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'ionosphere'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">34</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">34</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'b'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'iris'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'iris.1'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'Iris-setosa'</span><span class="p">)</span> <span class="c1"># 1==Setosa</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'iris.2'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'Iris-versicolor'</span><span class="p">)</span> <span class="c1"># 2==Versicolor</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'iris.3'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'Iris-virginica'</span><span class="p">)</span> <span class="c1"># 3==Virginica</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'mammographic-masses'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="n">df</span> <span class="o">==</span> <span class="s1">'?'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="n">Xy</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">Xy</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">Xy</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">5</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'page-blocks'</span><span class="p">:</span>
|
||||
<span class="n">data_path_</span> <span class="o">=</span> <span class="n">data_path</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'.Z'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">data_path_</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Warning: file </span><span class="si">{</span><span class="n">data_path_</span><span class="si">}</span><span class="s1"> does not exist. If this is the first time you '</span>
|
||||
<span class="sa">f</span><span class="s1">'attempt to load this dataset, then you have to manually unzip the </span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'and name the extracted file </span><span class="si">{</span><span class="n">data_path_</span><span class="si">}</span><span class="s1"> (unfortunately, neither zipfile, nor '</span>
|
||||
<span class="sa">f</span><span class="s1">'gzip can handle unix compressed files automatically -- there is a repo in GitHub '</span>
|
||||
<span class="sa">f</span><span class="s1">'https://github.com/umeat/unlzw where the problem seems to be solved anyway).'</span><span class="p">)</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path_</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> <span class="c1"># 5==block "graphic"</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'semeion'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="kc">True</span> <span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">256</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">263</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># 263 stands for digit 8 (labels are one-hot vectors from col 256-266)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'undocumented/connectionist-bench/sonar'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">60</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">60</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'R'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'spambase'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">57</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">57</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'spect'</span><span class="p">:</span>
|
||||
<span class="n">dfs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">filename</span><span class="p">:</span>
|
||||
<span class="n">data_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">URL</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">file</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">data_path</span><span class="p">)</span>
|
||||
<span class="n">dfs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">))</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">45</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'tic-tac-toe'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'o'</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'b'</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'x'</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'negative'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'blood-transfusion'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'wine'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">14</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'wine.1'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'wine.2'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'wine.3'</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'wine-quality'</span><span class="p">:</span>
|
||||
<span class="n">filename</span> <span class="o">=</span> <span class="n">filename</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">dataset_name</span><span class="o">==</span><span class="s1">'wine-q-red'</span> <span class="k">else</span> <span class="n">filename</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">data_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">URL</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">data_path</span><span class="p">)</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">';'</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">11</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">11</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">></span> <span class="mi">5</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">identifier</span> <span class="o">==</span> <span class="s1">'yeast'</span><span class="p">:</span>
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="s1">'NUC'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_UCIMulticlassDataset">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassDataset">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`. </span>
|
||||
|
||||
<span class="sd"> The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria:</span>
|
||||
<span class="sd"> - It has more than 1000 instances</span>
|
||||
<span class="sd"> - It is suited for classification</span>
|
||||
<span class="sd"> - It has more than two classes</span>
|
||||
<span class="sd"> - It is available for Python import (requires ucimlrepo package)</span>
|
||||
|
||||
<span class="sd"> >>> import quapy as qp</span>
|
||||
<span class="sd"> >>> dataset = qp.datasets.fetch_UCIMulticlassDataset("dry-bean")</span>
|
||||
<span class="sd"> >>> train, test = dataset.train_test</span>
|
||||
<span class="sd"> >>> ...</span>
|
||||
|
||||
<span class="sd"> The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`</span>
|
||||
|
||||
<span class="sd"> The datasets are downloaded only once and pickled into disk, saving time for consecutive calls.</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: a dataset name</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param test_split: proportion of documents to be included in the test set. The rest conforms the training set</span>
|
||||
<span class="sd"> :param verbose: set to True (default is False) to get information (stats) about the dataset</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.Dataset` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">test_split</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_UCIMulticlassLabelledCollection">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="n">LabelledCollection</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.</span>
|
||||
|
||||
<span class="sd"> The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria:</span>
|
||||
<span class="sd"> - It has more than 1000 instances</span>
|
||||
<span class="sd"> - It is suited for classification</span>
|
||||
<span class="sd"> - It has more than two classes</span>
|
||||
<span class="sd"> - It is available for Python import (requires ucimlrepo package)</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> >>> import quapy as qp</span>
|
||||
<span class="sd"> >>> collection = qp.datasets.fetch_UCIMulticlassLabelledCollection("dry-bean")</span>
|
||||
<span class="sd"> >>> X, y = collection.Xy</span>
|
||||
<span class="sd"> >>> ...</span>
|
||||
|
||||
<span class="sd"> The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`</span>
|
||||
|
||||
<span class="sd"> The datasets are downloaded only once and pickled into disk, saving time for consecutive calls.</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: a dataset name</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where the dataset will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :param test_split: proportion of documents to be included in the test set. The rest conforms the training set</span>
|
||||
<span class="sd"> :param verbose: set to True (default is False) to get information (stats) about the dataset</span>
|
||||
<span class="sd"> :return: a :class:`quapy.data.base.LabelledCollection` instance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="n">UCI_MULTICLASS_DATASETS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> does not match any known dataset from the '</span> \
|
||||
<span class="sa">f</span><span class="s1">'UCI Machine Learning datasets repository (multiclass). '</span> \
|
||||
<span class="sa">f</span><span class="s1">'Valid ones are </span><span class="si">{</span><span class="n">UCI_MULTICLASS_DATASETS</span><span class="si">}</span><span class="s1">'</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">identifiers</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s2">"dry-bean"</span><span class="p">:</span> <span class="mi">602</span><span class="p">,</span>
|
||||
<span class="s2">"wine-quality"</span><span class="p">:</span> <span class="mi">186</span><span class="p">,</span>
|
||||
<span class="s2">"academic-success"</span><span class="p">:</span> <span class="mi">697</span><span class="p">,</span>
|
||||
<span class="s2">"digits"</span><span class="p">:</span> <span class="mi">80</span><span class="p">,</span>
|
||||
<span class="s2">"letter"</span><span class="p">:</span> <span class="mi">59</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="n">full_names</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s2">"dry-bean"</span><span class="p">:</span> <span class="s2">"Dry Bean Dataset"</span><span class="p">,</span>
|
||||
<span class="s2">"wine-quality"</span><span class="p">:</span> <span class="s2">"Wine Quality"</span><span class="p">,</span>
|
||||
<span class="s2">"academic-success"</span><span class="p">:</span> <span class="s2">"Predict students' dropout and academic success"</span><span class="p">,</span>
|
||||
<span class="s2">"digits"</span><span class="p">:</span> <span class="s2">"Optical Recognition of Handwritten Digits"</span><span class="p">,</span>
|
||||
<span class="s2">"letter"</span><span class="p">:</span> <span class="s2">"Letter Recognition"</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="n">identifier</span> <span class="o">=</span> <span class="n">identifiers</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
|
||||
<span class="n">fullname</span> <span class="o">=</span> <span class="n">full_names</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Loading UCI Muticlass </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> (</span><span class="si">{</span><span class="n">fullname</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">file</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'uci_multiclass'</span><span class="p">,</span> <span class="n">dataset_name</span><span class="o">+</span><span class="s1">'.pkl'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">fetch_ucirepo</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="nb">id</span><span class="p">)</span>
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'data'</span><span class="p">][</span><span class="s1">'features'</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(),</span> <span class="n">data</span><span class="p">[</span><span class="s1">'data'</span><span class="p">][</span><span class="s1">'targets'</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
|
||||
<span class="n">classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">pickled_resource</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">download</span><span class="p">,</span> <span class="n">identifier</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s1">'yes'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'no'</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span> <span class="n">astype</span><span class="o">=</span><span class="nb">float</span><span class="p">):</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">repl</span><span class="p">[</span><span class="n">x</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">astype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_lequa2022">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_lequa2022">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_lequa2022</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads the official datasets provided for the `LeQua <https://lequa2022.github.io/index>`_ competition.</span>
|
||||
<span class="sd"> In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification</span>
|
||||
<span class="sd"> problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead.</span>
|
||||
<span class="sd"> Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B are multiclass quantification</span>
|
||||
<span class="sd"> problems consisting of estimating the class prevalence values of 28 different merchandise products.</span>
|
||||
<span class="sd"> We refer to the `Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).</span>
|
||||
<span class="sd"> A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.</span>
|
||||
<span class="sd"> <https://ceur-ws.org/Vol-3180/paper-146.pdf>`_ for a detailed description</span>
|
||||
<span class="sd"> on the tasks and datasets.</span>
|
||||
|
||||
<span class="sd"> The datasets are downloaded only once, and stored for fast reuse.</span>
|
||||
|
||||
<span class="sd"> See `lequa2022_experiments.py` provided in the example folder, that can serve as a guide on how to use these</span>
|
||||
<span class="sd"> datasets.</span>
|
||||
|
||||
|
||||
<span class="sd"> :param task: a string representing the task name; valid ones are T1A, T1B, T2A, and T2B</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :return: a tuple `(train, val_gen, test_gen)` where `train` is an instance of</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection`, `val_gen` and `test_gen` are instances of</span>
|
||||
<span class="sd"> :class:`quapy.data._lequa2022.SamplesFromDir`, a subclass of :class:`quapy.protocol.AbstractProtocol`,</span>
|
||||
<span class="sd"> that return a series of samples stored in a directory which are labelled by prevalence.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.data._lequa2022</span> <span class="kn">import</span> <span class="n">load_raw_documents</span><span class="p">,</span> <span class="n">load_vector_documents</span><span class="p">,</span> <span class="n">SamplesFromDir</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">LEQUA2022_TASKS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Unknown task </span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">. Valid ones are </span><span class="si">{</span><span class="n">LEQUA2022_TASKS</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">URL_TRAINDEV</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/record/6546188/files/</span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">.train_dev.zip'</span>
|
||||
<span class="n">URL_TEST</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/record/6546188/files/</span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">.test.zip'</span>
|
||||
<span class="n">URL_TEST_PREV</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/record/6546188/files/</span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">.test_prevalences.zip'</span>
|
||||
|
||||
<span class="n">lequa_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'lequa2022'</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
|
||||
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span> <span class="o">+</span> <span class="s1">'_tmp.zip'</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">file</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">)):</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">URL_TRAINDEV</span><span class="p">)</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">URL_TEST</span><span class="p">)</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">URL_TEST_PREV</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">task</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'T1A'</span><span class="p">,</span> <span class="s1">'T1B'</span><span class="p">]:</span>
|
||||
<span class="n">load_fn</span> <span class="o">=</span> <span class="n">load_vector_documents</span>
|
||||
<span class="k">elif</span> <span class="n">task</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'T2A'</span><span class="p">,</span> <span class="s1">'T2B'</span><span class="p">]:</span>
|
||||
<span class="n">load_fn</span> <span class="o">=</span> <span class="n">load_raw_documents</span>
|
||||
|
||||
<span class="n">tr_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="s1">'public'</span><span class="p">,</span> <span class="s1">'training_data.txt'</span><span class="p">)</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">tr_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="o">=</span><span class="n">load_fn</span><span class="p">)</span>
|
||||
|
||||
<span class="n">val_samples_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="s1">'public'</span><span class="p">,</span> <span class="s1">'dev_samples'</span><span class="p">)</span>
|
||||
<span class="n">val_true_prev_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="s1">'public'</span><span class="p">,</span> <span class="s1">'dev_prevalences.txt'</span><span class="p">)</span>
|
||||
<span class="n">val_gen</span> <span class="o">=</span> <span class="n">SamplesFromDir</span><span class="p">(</span><span class="n">val_samples_path</span><span class="p">,</span> <span class="n">val_true_prev_path</span><span class="p">,</span> <span class="n">load_fn</span><span class="o">=</span><span class="n">load_fn</span><span class="p">)</span>
|
||||
|
||||
<span class="n">test_samples_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="s1">'public'</span><span class="p">,</span> <span class="s1">'test_samples'</span><span class="p">)</span>
|
||||
<span class="n">test_true_prev_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="s1">'public'</span><span class="p">,</span> <span class="s1">'test_prevalences.txt'</span><span class="p">)</span>
|
||||
<span class="n">test_gen</span> <span class="o">=</span> <span class="n">SamplesFromDir</span><span class="p">(</span><span class="n">test_samples_path</span><span class="p">,</span> <span class="n">test_true_prev_path</span><span class="p">,</span> <span class="n">load_fn</span><span class="o">=</span><span class="n">load_fn</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">train</span><span class="p">,</span> <span class="n">val_gen</span><span class="p">,</span> <span class="n">test_gen</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_IFCB">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_IFCB">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fetch_IFCB</span><span class="p">(</span><span class="n">single_sample_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads the IFCB dataset for quantification from `Zenodo <https://zenodo.org/records/10036244>`_ (for more</span>
|
||||
<span class="sd"> information on this dataset, please follow the zenodo link).</span>
|
||||
<span class="sd"> This dataset is based on the data available publicly at</span>
|
||||
<span class="sd"> `WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>`_.</span>
|
||||
<span class="sd"> The scripts for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.</span>
|
||||
<span class="sd"> Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.</span>
|
||||
|
||||
<span class="sd"> The datasets are downloaded only once, and stored for fast reuse.</span>
|
||||
|
||||
<span class="sd"> :param single_sample_train: a boolean. If true, it will return the train dataset as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (all examples together).</span>
|
||||
<span class="sd"> If false, a generator of training samples will be returned. Each example in the training set has an individual label.</span>
|
||||
<span class="sd"> :param for_model_selection: if True, then returns a split 30% of the training set (86 out of 286 samples) to be used for model selection; </span>
|
||||
<span class="sd"> if False, then returns the full training set as training set and the test set as the test set</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :return: a tuple `(train, test_gen)` where `train` is an instance of</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection`, if `single_sample_train` is true or</span>
|
||||
<span class="sd"> :class:`quapy.data._ifcb.IFCBTrainSamplesFromDir`, i.e. a sampling protocol that returns a series of samples</span>
|
||||
<span class="sd"> labelled example by example. test_gen will be a :class:`quapy.data._ifcb.IFCBTestSamples`, </span>
|
||||
<span class="sd"> i.e., a sampling protocol that returns a series of samples labelled by prevalence.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.data._ifcb</span> <span class="kn">import</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">,</span> <span class="n">IFCBTestSamples</span><span class="p">,</span> <span class="n">get_sample_list</span><span class="p">,</span> <span class="n">generate_modelselection_split</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
|
||||
|
||||
<span class="n">URL_TRAIN</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/records/10036244/files/IFCB.train.zip'</span>
|
||||
<span class="n">URL_TEST</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/records/10036244/files/IFCB.test.zip'</span>
|
||||
<span class="n">URL_TEST_PREV</span><span class="o">=</span><span class="sa">f</span><span class="s1">'https://zenodo.org/records/10036244/files/IFCB.test_prevalences.zip'</span>
|
||||
|
||||
<span class="n">ifcb_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">'ifcb'</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
|
||||
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="s1">'ifcb_tmp.zip'</span><span class="p">)</span>
|
||||
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">file</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span><span class="s1">'train'</span><span class="p">)):</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="n">URL_TRAIN</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span><span class="s1">'test'</span><span class="p">)):</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="n">URL_TEST</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span><span class="s1">'test_prevalences.csv'</span><span class="p">)):</span>
|
||||
<span class="n">download_unzip_and_remove</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="n">URL_TEST_PREV</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Load test prevalences and classes</span>
|
||||
<span class="n">test_true_prev_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="s1">'test_prevalences.csv'</span><span class="p">)</span>
|
||||
<span class="n">test_true_prev</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">test_true_prev_path</span><span class="p">)</span>
|
||||
<span class="n">classes</span> <span class="o">=</span> <span class="n">test_true_prev</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
|
||||
|
||||
<span class="c1">#Load train and test samples</span>
|
||||
<span class="n">train_samples_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span><span class="s1">'train'</span><span class="p">)</span>
|
||||
<span class="n">test_samples_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span><span class="s1">'test'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">for_model_selection</span><span class="p">:</span>
|
||||
<span class="c1"># In this case, return 70% of training data as the training set and 30% as the test set</span>
|
||||
<span class="n">samples</span> <span class="o">=</span> <span class="n">get_sample_list</span><span class="p">(</span><span class="n">train_samples_path</span><span class="p">)</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">generate_modelselection_split</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
|
||||
<span class="n">train_gen</span> <span class="o">=</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">(</span><span class="n">path_dir</span><span class="o">=</span><span class="n">train_samples_path</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span> <span class="n">samples</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Test prevalence is computed from class labels</span>
|
||||
<span class="n">test_gen</span> <span class="o">=</span> <span class="n">IFCBTestSamples</span><span class="p">(</span><span class="n">path_dir</span><span class="o">=</span><span class="n">train_samples_path</span><span class="p">,</span> <span class="n">test_prevalences</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">samples</span><span class="o">=</span><span class="n">test</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># In this case, we use all training samples as the training set and the test samples as the test set</span>
|
||||
<span class="n">train_gen</span> <span class="o">=</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">(</span><span class="n">path_dir</span><span class="o">=</span><span class="n">train_samples_path</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">classes</span><span class="p">)</span>
|
||||
<span class="n">test_gen</span> <span class="o">=</span> <span class="n">IFCBTestSamples</span><span class="p">(</span><span class="n">path_dir</span><span class="o">=</span><span class="n">test_samples_path</span><span class="p">,</span> <span class="n">test_prevalences</span><span class="o">=</span><span class="n">test_true_prev</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># In the case the user wants it, join all the train samples in one LabelledCollection</span>
|
||||
<span class="k">if</span> <span class="n">single_sample_train</span><span class="p">:</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">lc</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">train_gen</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="n">train</span><span class="p">,</span> <span class="n">test_gen</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">train_gen</span><span class="p">,</span> <span class="n">test_gen</span></div>
|
||||
|
||||
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|
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<h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">spmatrix</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">Dataset</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">map_parallel</span>
|
||||
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="text2tfidf">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.text2tfidf">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of</span>
|
||||
<span class="sd"> tfidf weighted sparse vectors</span>
|
||||
|
||||
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are</span>
|
||||
<span class="sd"> lists of str</span>
|
||||
<span class="sd"> :param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary (default 3)</span>
|
||||
<span class="sd"> :param sublinear_tf: whether or not to apply the log scalling to the tf counters (default True)</span>
|
||||
<span class="sd"> :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
|
||||
<span class="sd"> :param kwargs: the rest of parameters of the transformation (as for sklearn's</span>
|
||||
<span class="sd"> `TfidfVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html>`_)</span>
|
||||
<span class="sd"> :return: a new :class:`quapy.data.base.Dataset` in `csr_matrix` format (if inplace=False) or a reference to the</span>
|
||||
<span class="sd"> current Dataset (if inplace=True) where the instances are stored in a `csr_matrix` of real-valued tfidf scores</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
||||
|
||||
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="n">sublinear_tf</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">training_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="reduce_columns">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.reduce_columns">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of</span>
|
||||
<span class="sd"> `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present</span>
|
||||
<span class="sd"> in at least `min_df` instances in the training set</span>
|
||||
|
||||
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` in which instances are represented in sparse format (any</span>
|
||||
<span class="sd"> subtype of scipy.sparse.spmatrix)</span>
|
||||
<span class="sd"> :param min_df: integer, minimum number of instances below which the columns are removed</span>
|
||||
<span class="sd"> :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
|
||||
<span class="sd"> :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current</span>
|
||||
<span class="sd"> :class:`quapy.data.base.Dataset` (inplace=True) where the dimensions corresponding to infrequent terms</span>
|
||||
<span class="sd"> in the training set have been removed</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">'unaligned vector spaces'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
|
||||
<span class="n">column_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">((</span><span class="n">X</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="n">take_columns</span> <span class="o">=</span> <span class="n">column_prevalence</span> <span class="o">>=</span> <span class="n">min_df</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
|
||||
<span class="n">W</span> <span class="o">=</span> <span class="n">W</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">W</span>
|
||||
|
||||
<span class="n">Xtr</span><span class="p">,</span> <span class="n">Xte</span> <span class="o">=</span> <span class="n">filter_by_occurrences</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">Xtr</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">Xte</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">Xtr</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">Xte</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="standardize">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.standardize">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.</span>
|
||||
<span class="sd"> Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the</span>
|
||||
<span class="sd"> standard deviation.</span>
|
||||
|
||||
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` object</span>
|
||||
<span class="sd"> :param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new</span>
|
||||
<span class="sd"> :class:`quapy.data.base.Dataset` is to be returned</span>
|
||||
<span class="sd"> :return: an instance of :class:`quapy.data.base.Dataset`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="ow">not</span> <span class="n">inplace</span><span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.</span>
|
||||
<span class="sd"> To index a document means to replace each different token by a unique numerical index.</span>
|
||||
<span class="sd"> Rare words (i.e., words occurring less than `min_df` times) are replaced by a special token `UNK`</span>
|
||||
|
||||
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` object where the instances of training and test documents</span>
|
||||
<span class="sd"> are lists of str</span>
|
||||
<span class="sd"> :param min_df: minimum number of occurrences below which the term is replaced by a `UNK` index</span>
|
||||
<span class="sd"> :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
|
||||
<span class="sd"> :param kwargs: the rest of parameters of the transformation (as for sklearn's</span>
|
||||
<span class="sd"> `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>_`)</span>
|
||||
<span class="sd"> :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current</span>
|
||||
<span class="sd"> :class:`quapy.data.base.Dataset` (inplace=True) consisting of lists of integer values representing indices.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
||||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
||||
|
||||
<span class="n">indexer</span> <span class="o">=</span> <span class="n">IndexTransformer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">training_index</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test_index</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">training_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
|
||||
<span class="n">test_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">indexer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">container_type</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'unexpected type of container (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
<span class="k">if</span> <span class="n">element_type</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">element_type</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'unexpected type of element (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">IndexTransformer</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This class implements a sklearn's-style transformer that indexes text as numerical ids for the tokens it</span>
|
||||
<span class="sd"> contains, and that would be generated by sklearn's</span>
|
||||
<span class="sd"> `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_</span>
|
||||
|
||||
<span class="sd"> :param kwargs: keyworded arguments from</span>
|
||||
<span class="sd"> `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># a valid index is assigned after fit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span> <span class="c1"># a valid index is assigned after fit</span>
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</span>
|
||||
|
||||
<span class="sd"> :param X: a list of strings</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">build_analyzer</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'UNK_TOKEN'</span><span class="p">],</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'UNK_INDEX'</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PAD_TOKEN'</span><span class="p">],</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PAD_INDEX'</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.transform">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms the strings in `X` as lists of numerical ids</span>
|
||||
|
||||
<span class="sd"> :param X: a list of strings</span>
|
||||
<span class="sd"> :param n_jobs: the number of parallel workers to carry out this task</span>
|
||||
<span class="sd"> :return: a `np.ndarray` of numerical ids</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># given the number of tasks and the number of jobs, generates the slices for the parallel processes</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'transform called before fit'</span>
|
||||
<span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_index</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span>
|
||||
<span class="n">vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="p">[[</span><span class="n">vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span><span class="p">)</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span><span class="p">(</span><span class="n">doc</span><span class="p">)]</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="s1">'indexing'</span><span class="p">)]</span>
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.fit_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the transform on `X` and transforms it.</span>
|
||||
|
||||
<span class="sd"> :param X: a list of strings</span>
|
||||
<span class="sd"> :param n_jobs: the number of parallel workers to carry out this task</span>
|
||||
<span class="sd"> :return: a `np.ndarray` of numerical ids</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.vocabulary_size">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the length of the vocabulary according to which the document tokens have been indexed</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.add_word">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.</span>
|
||||
<span class="sd"> Useful to define special tokens for codifying unknown words, or padding tokens.</span>
|
||||
|
||||
<span class="sd"> :param word: string, surface form of the token</span>
|
||||
<span class="sd"> :param id: integer, numerical value to assign to the token (leave as None for indicating the next valid id,</span>
|
||||
<span class="sd"> default)</span>
|
||||
<span class="sd"> :param nogaps: if set to True (default) asserts that the id indicated leads to no numerical gaps with</span>
|
||||
<span class="sd"> precedent ids stored so far</span>
|
||||
<span class="sd"> :return: integer, the numerical id for the new token</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'word </span><span class="si">{</span><span class="n">word</span><span class="si">}</span><span class="s1"> already in dictionary'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="c1"># add the word with the next id</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">id2word</span> <span class="o">=</span> <span class="p">{</span><span class="n">id_</span><span class="p">:</span><span class="n">word_</span> <span class="k">for</span> <span class="n">word_</span><span class="p">,</span> <span class="n">id_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
|
||||
<span class="k">if</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">id2word</span><span class="p">:</span>
|
||||
<span class="n">old_word</span> <span class="o">=</span> <span class="n">id2word</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">id</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">old_word</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">old_word</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">nogaps</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">id</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'word </span><span class="si">{</span><span class="n">word</span><span class="si">}</span><span class="s1"> added with id </span><span class="si">{</span><span class="nb">id</span><span class="si">}</span><span class="s1">, while the current vocabulary size '</span>
|
||||
<span class="sa">f</span><span class="s1">'is of </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">()</span><span class="si">}</span><span class="s1">, and id gaps are not allowed'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
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|
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|
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|
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<h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">dok_matrix</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="from_text">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reads a labelled colletion of documents.</span>
|
||||
<span class="sd"> File fomart <0 or 1>\t<document>\n</span>
|
||||
|
||||
<span class="sd"> :param path: path to the labelled collection</span>
|
||||
<span class="sd"> :param encoding: the text encoding used to open the file</span>
|
||||
<span class="sd"> :param verbose: if >0 (default) shows some progress information in standard output</span>
|
||||
<span class="sd"> :return: a list of sentences, and a list of labels</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">all_sentences</span><span class="p">,</span> <span class="n">all_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="o">></span><span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">file</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="sa">f</span><span class="s1">'loading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">line</span><span class="p">:</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">label</span><span class="p">,</span> <span class="n">sentence</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">sentence</span> <span class="o">=</span> <span class="n">sentence</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">class2int</span><span class="p">:</span>
|
||||
<span class="n">label</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">sentence</span><span class="p">:</span>
|
||||
<span class="n">all_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
|
||||
<span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'format error in </span><span class="si">{</span><span class="n">line</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">all_sentences</span><span class="p">,</span> <span class="n">all_labels</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="from_sparse">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reads a labelled collection of real-valued instances expressed in sparse format</span>
|
||||
<span class="sd"> File format <-1 or 0 or 1>[\s col(int):val(float)]\n</span>
|
||||
|
||||
<span class="sd"> :param path: path to the labelled collection</span>
|
||||
<span class="sd"> :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
|
||||
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">':'</span><span class="p">)</span>
|
||||
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
|
||||
|
||||
<span class="n">all_documents</span><span class="p">,</span> <span class="n">all_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="n">max_col</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="sa">f</span><span class="s1">'loading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">'</span><span class="p">):</span>
|
||||
<span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">parts</span><span class="p">:</span>
|
||||
<span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parts</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
|
||||
<span class="n">cols</span><span class="p">,</span> <span class="n">vals</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">)</span> <span class="k">for</span> <span class="n">col_val</span> <span class="ow">in</span> <span class="n">parts</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
|
||||
<span class="n">cols</span><span class="p">,</span> <span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">cols</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
|
||||
<span class="n">max_col</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_col</span><span class="p">,</span> <span class="n">cols</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
|
||||
<span class="n">all_documents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">cols</span><span class="p">,</span> <span class="n">vals</span><span class="p">))</span>
|
||||
<span class="n">n_docs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_labels</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">dok_matrix</span><span class="p">((</span><span class="n">n_docs</span><span class="p">,</span> <span class="n">max_col</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">cols</span><span class="p">,</span> <span class="n">vals</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">all_documents</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">all_documents</span><span class="p">),</span>
|
||||
<span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s1">'\-- filling matrix of shape </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span><span class="p">):</span>
|
||||
<span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">all_labels</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="from_csv">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reads a csv file in which columns are separated by ','.</span>
|
||||
<span class="sd"> File format <label>,<feat1>,<feat2>,...,<featn>\n</span>
|
||||
|
||||
<span class="sd"> :param path: path to the csv file</span>
|
||||
<span class="sd"> :param encoding: the text encoding used to open the file</span>
|
||||
<span class="sd"> :return: a np.ndarray for the labels and a ndarray (float) for the covariates</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s1">'reading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">'</span><span class="p">):</span>
|
||||
<span class="n">yi</span><span class="p">,</span> <span class="o">*</span><span class="n">xi</span> <span class="o">=</span> <span class="n">instance</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span>
|
||||
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span><span class="n">xi</span><span class="p">)))</span>
|
||||
<span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">yi</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="reindex_labels">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.</span>
|
||||
<span class="sd"> E.g.:</span>
|
||||
|
||||
<span class="sd"> >>> reindex_labels(['B', 'B', 'A', 'C'])</span>
|
||||
<span class="sd"> >>> (array([1, 1, 0, 2]), array(['A', 'B', 'C'], dtype='<U1'))</span>
|
||||
|
||||
<span class="sd"> :param y: the list or array of original labels</span>
|
||||
<span class="sd"> :return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">classnames</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span>
|
||||
<span class="n">label2index</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classnames</span><span class="p">)}</span>
|
||||
<span class="n">indexed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">classnames</span><span class="p">:</span>
|
||||
<span class="n">indexed</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">label2index</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">indexed</span><span class="p">,</span> <span class="n">classnames</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binarize">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span>
|
||||
|
||||
<span class="sd"> >>> binarize([1, 2, 3, 1, 1, 0], pos_class=2)</span>
|
||||
<span class="sd"> >>> array([0, 1, 0, 0, 0, 0])</span>
|
||||
|
||||
<span class="sd"> :param y: array-like of labels</span>
|
||||
<span class="sd"> :param pos_class: integer, the positive class</span>
|
||||
<span class="sd"> :return: a binary np.ndarray, in which values 1 corresponds to positions in whcih `y` had `pos_class` labels, and</span>
|
||||
<span class="sd"> 0 otherwise</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">ybin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
|
||||
<span class="n">ybin</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">pos_class</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">ybin</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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<h1>Source code for quapy.error</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""Implementation of error measures used for quantification"""</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="from_name">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.from_name">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Gets an error function from its name. E.g., `from_name("mae")`</span>
|
||||
<span class="sd"> will return function :meth:`quapy.error.mae`</span>
|
||||
|
||||
<span class="sd"> :param err_name: string, the error name</span>
|
||||
<span class="sd"> :return: a callable implementing the requested error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">err_name</span> <span class="ow">in</span> <span class="n">ERROR_NAMES</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'unknown error </span><span class="si">{</span><span class="n">err_name</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="n">callable_error</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()[</span><span class="n">err_name</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">callable_error</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="f1e">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.f1e">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">f1e</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""F1 error: simply computes the error in terms of macro :math:`F_1`, i.e.,</span>
|
||||
<span class="sd"> :math:`1-F_1^M`, where :math:`F_1` is the harmonic mean of precision and recall,</span>
|
||||
<span class="sd"> defined as :math:`\\frac{2tp}{2tp+fp+fn}`, with `tp`, `fp`, and `fn` standing</span>
|
||||
<span class="sd"> for true positives, false positives, and false negatives, respectively.</span>
|
||||
<span class="sd"> `Macro` averaging means the :math:`F_1` is computed for each category independently,</span>
|
||||
<span class="sd"> and then averaged.</span>
|
||||
|
||||
<span class="sd"> :param y_true: array-like of true labels</span>
|
||||
<span class="sd"> :param y_pred: array-like of predicted labels</span>
|
||||
<span class="sd"> :return: :math:`1-F_1^M`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">'macro'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="acce">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.acce">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">acce</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the error in terms of 1-accuracy. The accuracy is computed as</span>
|
||||
<span class="sd"> :math:`\\frac{tp+tn}{tp+fp+fn+tn}`, with `tp`, `fp`, `fn`, and `tn` standing</span>
|
||||
<span class="sd"> for true positives, false positives, false negatives, and true negatives,</span>
|
||||
<span class="sd"> respectively</span>
|
||||
|
||||
<span class="sd"> :param y_true: array-like of true labels</span>
|
||||
<span class="sd"> :param y_pred: array-like of predicted labels</span>
|
||||
<span class="sd"> :return: 1-accuracy</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="n">y_pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean absolute error (see :meth:`quapy.error.ae`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :return: mean absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.ae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the absolute error between the two prevalence vectors.</span>
|
||||
<span class="sd"> Absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
|
||||
<span class="sd"> :math:`AE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}|\\hat{p}(y)-p(y)|`,</span>
|
||||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the normalized absolute error between the two prevalence vectors.</span>
|
||||
<span class="sd"> Normalized absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
|
||||
<span class="sd"> :math:`NAE(p,\\hat{p})=\\frac{AE(p,\\hat{p})}{z_{AE}}`,</span>
|
||||
<span class="sd"> where :math:`z_{AE}=\\frac{2(1-\\min_{y\\in \\mathcal{Y}} p(y))}{|\\mathcal{Y}|}`, and :math:`\\mathcal{Y}`</span>
|
||||
<span class="sd"> are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: normalized absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mnae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean normalized absolute error (see :meth:`quapy.error.nae`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :return: mean normalized absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mse">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mse">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mse</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean squared error (see :meth:`quapy.error.se`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the</span>
|
||||
<span class="sd"> true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the</span>
|
||||
<span class="sd"> predicted prevalence values</span>
|
||||
<span class="sd"> :return: mean squared error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="se">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.se">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the squared error between the two prevalence vectors.</span>
|
||||
<span class="sd"> Squared error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
|
||||
<span class="sd"> :math:`SE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}(\\hat{p}(y)-p(y))^2`,</span>
|
||||
<span class="sd"> where</span>
|
||||
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">((</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mkld">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean Kullback-Leibler divergence (see :meth:`quapy.error.kld`) across the</span>
|
||||
<span class="sd"> sample pairs. The distributions are smoothed using the `eps` factor</span>
|
||||
<span class="sd"> (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. KLD is not defined in cases in which the distributions contain</span>
|
||||
<span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span>
|
||||
<span class="sd"> If `eps=None`, the sample size will be taken from the environment variable `SAMPLE_SIZE`</span>
|
||||
<span class="sd"> (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean Kullback-Leibler distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="kld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.kld">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the Kullback-Leibler divergence between the two prevalence distributions.</span>
|
||||
<span class="sd"> Kullback-Leibler divergence between two prevalence distributions :math:`p` and :math:`\\hat{p}`</span>
|
||||
<span class="sd"> is computed as</span>
|
||||
<span class="sd"> :math:`KLD(p,\\hat{p})=D_{KL}(p||\\hat{p})=</span>
|
||||
<span class="sd"> \\sum_{y\\in \\mathcal{Y}} p(y)\\log\\frac{p(y)}{\\hat{p}(y)}`,</span>
|
||||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. KLD is not defined in cases in which the distributions contain</span>
|
||||
<span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span>
|
||||
<span class="sd"> If `eps=None`, the sample size will be taken from the environment variable `SAMPLE_SIZE`</span>
|
||||
<span class="sd"> (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: Kullback-Leibler divergence between the two distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">smooth_prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span>
|
||||
<span class="n">smooth_prevs_hat</span> <span class="o">=</span> <span class="n">prevs_hat</span> <span class="o">+</span> <span class="n">eps</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">smooth_prevs</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">smooth_prevs</span><span class="o">/</span><span class="n">smooth_prevs_hat</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnkld">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mnkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean Normalized Kullback-Leibler divergence (see :meth:`quapy.error.nkld`)</span>
|
||||
<span class="sd"> across the sample pairs. The distributions are smoothed using the `eps` factor</span>
|
||||
<span class="sd"> (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions contain</span>
|
||||
<span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span>
|
||||
<span class="sd"> If `eps=None`, the sample size will be taken from the environment variable `SAMPLE_SIZE`</span>
|
||||
<span class="sd"> (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean Normalized Kullback-Leibler distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nkld">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the Normalized Kullback-Leibler divergence between the two prevalence distributions.</span>
|
||||
<span class="sd"> Normalized Kullback-Leibler divergence between two prevalence distributions :math:`p` and</span>
|
||||
<span class="sd"> :math:`\\hat{p}` is computed as</span>
|
||||
<span class="sd"> math:`NKLD(p,\\hat{p}) = 2\\frac{e^{KLD(p,\\hat{p})}}{e^{KLD(p,\\hat{p})}+1}-1`,</span>
|
||||
<span class="sd"> where</span>
|
||||
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions</span>
|
||||
<span class="sd"> contain zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample</span>
|
||||
<span class="sd"> size. If `eps=None`, the sample size will be taken from the environment variable</span>
|
||||
<span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: Normalized Kullback-Leibler divergence between the two distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">ekld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">ekld</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">ekld</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mrae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean relative absolute error (see :meth:`quapy.error.rae`) across</span>
|
||||
<span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
|
||||
<span class="sd"> :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `mrae` is not defined in cases in which the true</span>
|
||||
<span class="sd"> distribution contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`,</span>
|
||||
<span class="sd"> with :math:`T` the sample size. If `eps=None`, the sample size will be taken from</span>
|
||||
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="rae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.rae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the absolute relative error between the two prevalence vectors.</span>
|
||||
<span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
|
||||
<span class="sd"> is computed as</span>
|
||||
<span class="sd"> :math:`RAE(p,\\hat{p})=</span>
|
||||
<span class="sd"> \\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}\\frac{|\\hat{p}(y)-p(y)|}{p(y)}`,</span>
|
||||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `rae` is not defined in cases in which the true distribution</span>
|
||||
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
|
||||
<span class="sd"> sample size. If `eps=None`, the sample size will be taken from the environment variable</span>
|
||||
<span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nrae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the normalized absolute relative error between the two prevalence vectors.</span>
|
||||
<span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
|
||||
<span class="sd"> is computed as</span>
|
||||
<span class="sd"> :math:`NRAE(p,\\hat{p})= \\frac{RAE(p,\\hat{p})}{z_{RAE}}`,</span>
|
||||
<span class="sd"> where</span>
|
||||
<span class="sd"> :math:`z_{RAE} = \\frac{|\\mathcal{Y}|-1+\\frac{1-\\min_{y\\in \\mathcal{Y}} p(y)}{\\min_{y\\in \\mathcal{Y}} p(y)}}{|\\mathcal{Y}|}`</span>
|
||||
<span class="sd"> and :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `nrae` is not defined in cases in which the true distribution</span>
|
||||
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
|
||||
<span class="sd"> sample size. If `eps=None`, the sample size will be taken from the environment variable</span>
|
||||
<span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: normalized relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">min_p</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">min_p</span><span class="p">)</span><span class="o">/</span><span class="n">min_p</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnrae">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mnrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean normalized relative absolute error (see :meth:`quapy.error.nrae`) across</span>
|
||||
<span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
|
||||
<span class="sd"> :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `mnrae` is not defined in cases in which the true</span>
|
||||
<span class="sd"> distribution contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`,</span>
|
||||
<span class="sd"> with :math:`T` the sample size. If `eps=None`, the sample size will be taken from</span>
|
||||
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean normalized relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="smooth">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.smooth">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">""" Smooths a prevalence distribution with :math:`\\epsilon` (`eps`) as:</span>
|
||||
<span class="sd"> :math:`\\underline{p}(y)=\\frac{\\epsilon+p(y)}{\\epsilon|\\mathcal{Y}|+</span>
|
||||
<span class="sd"> \\displaystyle\\sum_{y\\in \\mathcal{Y}}p(y)}`</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_classes,)` with the smoothed distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">eps</span> <span class="o">*</span> <span class="n">n_classes</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">eps</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'eps was not defined, and qp.environ["SAMPLE_SIZE"] was not set'</span><span class="p">)</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">eps</span>
|
||||
|
||||
|
||||
<span class="n">CLASSIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">f1e</span><span class="p">,</span> <span class="n">acce</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">mae</span><span class="p">,</span> <span class="n">mnae</span><span class="p">,</span> <span class="n">mrae</span><span class="p">,</span> <span class="n">mnrae</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SINGLE</span> <span class="o">=</span> <span class="p">{</span><span class="n">ae</span><span class="p">,</span> <span class="n">nae</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SMOOTH</span> <span class="o">=</span> <span class="p">{</span><span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">mrae</span><span class="p">}</span>
|
||||
<span class="n">CLASSIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">CLASSIFICATION_ERROR</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR_SINGLE</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SMOOTH_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR_SMOOTH</span><span class="p">}</span>
|
||||
<span class="n">ERROR_NAMES</span> <span class="o">=</span> \
|
||||
<span class="n">CLASSIFICATION_ERROR_NAMES</span> <span class="o">|</span> <span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="o">|</span> <span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span>
|
||||
|
||||
<span class="n">f1_error</span> <span class="o">=</span> <span class="n">f1e</span>
|
||||
<span class="n">acc_error</span> <span class="o">=</span> <span class="n">acce</span>
|
||||
<span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mae</span>
|
||||
<span class="n">absolute_error</span> <span class="o">=</span> <span class="n">ae</span>
|
||||
<span class="n">mean_relative_absolute_error</span> <span class="o">=</span> <span class="n">mrae</span>
|
||||
<span class="n">relative_absolute_error</span> <span class="o">=</span> <span class="n">rae</span>
|
||||
<span class="n">normalized_absolute_error</span> <span class="o">=</span> <span class="n">nae</span>
|
||||
<span class="n">normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">nrae</span>
|
||||
<span class="n">mean_normalized_absolute_error</span> <span class="o">=</span> <span class="n">mnae</span>
|
||||
<span class="n">mean_normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">mnrae</span>
|
||||
</pre></div>
|
||||
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<a href="../../index.html">QuaPy: A Python-based open-source framework for quantification</a>
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<li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
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<h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="prediction">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prediction</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Uses a quantification model to generate predictions for the samples generated via a specific protocol.</span>
|
||||
<span class="sd"> This function is central to all evaluation processes, and is endowed with an optimization to speed-up the</span>
|
||||
<span class="sd"> prediction of protocols that generate samples from a large collection. The optimization applies to aggregative</span>
|
||||
<span class="sd"> quantifiers only, and to OnLabelledCollectionProtocol protocols, and comes down to generating the classification</span>
|
||||
<span class="sd"> predictions once and for all, and then generating samples over the classification predictions (instead of over</span>
|
||||
<span class="sd"> the raw instances), so that the classifier prediction is never called again. This behaviour is obtained by</span>
|
||||
<span class="sd"> setting `aggr_speedup` to 'auto' or True, and is only carried out if the overall process is convenient in terms</span>
|
||||
<span class="sd"> of computations (e.g., if the number of classification predictions needed for the original collection exceed the</span>
|
||||
<span class="sd"> number of classification predictions needed for all samples, then the optimization is not undertaken).</span>
|
||||
|
||||
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
|
||||
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
|
||||
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
|
||||
<span class="sd"> in charge of generating the samples for which the model has to issue class prevalence predictions.</span>
|
||||
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
|
||||
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
|
||||
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
|
||||
<span class="sd"> convenient or not. Set to False to deactivate.</span>
|
||||
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
|
||||
<span class="sd"> :return: a tuple `(true_prevs, estim_prevs)` in which each element in the tuple is an array of shape</span>
|
||||
<span class="sd"> `(n_samples, n_classes)` containing the true, or predicted, prevalence values for each sample</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'auto'</span><span class="p">,</span> <span class="s1">'force'</span><span class="p">],</span> <span class="s1">'invalid value for aggr_speedup'</span>
|
||||
|
||||
<span class="n">sout</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="kc">None</span>
|
||||
|
||||
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="s1">'auto'</span><span class="p">,</span> <span class="s1">'force'</span><span class="p">]:</span>
|
||||
<span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span>
|
||||
<span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span>
|
||||
<span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">'force'</span><span class="p">:</span>
|
||||
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'forcing aggregative speedup'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="s1">'sample_size'</span><span class="p">):</span>
|
||||
<span class="n">nD</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span>
|
||||
<span class="n">samplesD</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">()</span> <span class="o">*</span> <span class="n">protocol</span><span class="o">.</span><span class="n">sample_size</span>
|
||||
<span class="k">if</span> <span class="n">nD</span> <span class="o"><</span> <span class="n">samplesD</span><span class="p">:</span>
|
||||
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'speeding up the prediction for the aggregative quantifier, '</span>
|
||||
<span class="sa">f</span><span class="s1">'total classifications </span><span class="si">{</span><span class="n">nD</span><span class="si">}</span><span class="s1"> instead of </span><span class="si">{</span><span class="n">samplesD</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">apply_optimization</span><span class="p">:</span>
|
||||
<span class="n">pre_classified</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">'predicting'</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span>
|
||||
<span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span>
|
||||
<span class="n">true_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample_prev</span><span class="p">)</span>
|
||||
|
||||
<span class="n">true_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">)</span>
|
||||
<span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">estim_prevs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluation_report">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">,</span>
|
||||
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates a report (a pandas' DataFrame) containing information of the evaluation of the model as according</span>
|
||||
<span class="sd"> to a specific protocol and in terms of one or more evaluation metrics (errors).</span>
|
||||
|
||||
|
||||
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
|
||||
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
|
||||
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
|
||||
<span class="sd"> in charge of generating the samples in which the model is evaluated.</span>
|
||||
<span class="sd"> :param error_metrics: a string, or list of strings, representing the name(s) of an error function in `qp.error`</span>
|
||||
<span class="sd"> (e.g., 'mae', the default value), or a callable function, or a list of callable functions, implementing</span>
|
||||
<span class="sd"> the error function itself.</span>
|
||||
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
|
||||
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
|
||||
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
|
||||
<span class="sd"> convenient or not. Set to False to deactivate.</span>
|
||||
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
|
||||
<span class="sd"> :return: a pandas' DataFrame containing the columns 'true-prev' (the true prevalence of each sample),</span>
|
||||
<span class="sd"> 'estim-prev' (the prevalence estimated by the model for each sample), and as many columns as error metrics</span>
|
||||
<span class="sd"> have been indicated, each displaying the score in terms of that metric for every sample.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span>
|
||||
|
||||
<span class="n">error_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">e</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_metrics</span><span class="p">]</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="s1">'__call__'</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">),</span> <span class="s1">'invalid error functions'</span>
|
||||
<span class="n">error_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">]</span>
|
||||
|
||||
<span class="n">row_entries</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">series</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'true-prev'</span><span class="p">:</span> <span class="n">true_prev</span><span class="p">,</span> <span class="s1">'estim-prev'</span><span class="p">:</span> <span class="n">estim_prev</span><span class="p">}</span>
|
||||
<span class="k">for</span> <span class="n">error_name</span><span class="p">,</span> <span class="n">error_metric</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">error_names</span><span class="p">,</span> <span class="n">error_funcs</span><span class="p">):</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">)</span>
|
||||
<span class="n">series</span><span class="p">[</span><span class="n">error_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
|
||||
<span class="n">row_entries</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
|
||||
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span><span class="n">row_entries</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">df</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluate">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
|
||||
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Evaluates a quantification model according to a specific sample generation protocol and in terms of one</span>
|
||||
<span class="sd"> evaluation metric (error).</span>
|
||||
|
||||
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
|
||||
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
|
||||
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the</span>
|
||||
<span class="sd"> protocol in charge of generating the samples in which the model is evaluated.</span>
|
||||
<span class="sd"> :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
|
||||
<span class="sd"> (e.g., 'mae'), or a callable function implementing the error function itself.</span>
|
||||
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
|
||||
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
|
||||
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
|
||||
<span class="sd"> convenient or not. Set to False to deactivate.</span>
|
||||
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
|
||||
<span class="sd"> :return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with</span>
|
||||
<span class="sd"> the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns</span>
|
||||
<span class="sd"> a single float</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metric</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">error_metric</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_metric</span><span class="p">)</span>
|
||||
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluate_on_samples">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">evaluate_on_samples</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span>
|
||||
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Evaluates a quantification model on a given set of samples and in terms of one evaluation metric (error).</span>
|
||||
|
||||
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
|
||||
<span class="sd"> :param samples: a list of samples on which the quantifier is to be evaluated</span>
|
||||
<span class="sd"> :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
|
||||
<span class="sd"> (e.g., 'mae'), or a callable function implementing the error function itself.</span>
|
||||
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
|
||||
<span class="sd"> :return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with</span>
|
||||
<span class="sd"> the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns</span>
|
||||
<span class="sd"> a single float</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">IterateProtocol</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="n">error_metric</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
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|
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<h1>Source code for quapy.functional</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">scipy</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="prevalence_linspace">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_linspace">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence_linspace</span><span class="p">(</span><span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Produces an array of uniformly separated values of prevalence.</span>
|
||||
<span class="sd"> By default, produces an array of 21 prevalence values, with</span>
|
||||
<span class="sd"> step 0.05 and with the limits smoothed, i.e.:</span>
|
||||
<span class="sd"> [0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]</span>
|
||||
|
||||
<span class="sd"> :param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)</span>
|
||||
<span class="sd"> :param repeats: number of times each prevalence is to be repeated (defaults to 1)</span>
|
||||
<span class="sd"> :param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1</span>
|
||||
<span class="sd"> :return: an array of uniformly separated prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">smooth_limits_epsilon</span>
|
||||
<span class="n">p</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-=</span> <span class="n">smooth_limits_epsilon</span>
|
||||
<span class="k">if</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the smoothing in the limits is greater than the prevalence step'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">repeats</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">repeats</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">p</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="prevalence_from_labels">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_labels">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computed the prevalence values from a vector of labels.</span>
|
||||
|
||||
<span class="sd"> :param labels: array-like of shape `(n_instances)` with the label for each instance</span>
|
||||
<span class="sd"> :param classes: the class labels. This is needed in order to correctly compute the prevalence vector even when</span>
|
||||
<span class="sd"> some classes have no examples.</span>
|
||||
<span class="sd"> :return: an ndarray of shape `(len(classes))` with the class prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">labels</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'param labels does not seem to be a ndarray of label predictions'</span><span class="p">)</span>
|
||||
<span class="n">unique</span><span class="p">,</span> <span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">by_class</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">unique</span><span class="p">,</span> <span class="n">counts</span><span class="p">)))</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">by_class</span><span class="p">[</span><span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="n">prevalences</span> <span class="o">/=</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="prevalence_from_probabilities">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_probabilities">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence_from_probabilities</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">binarize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a vector of prevalence values from a matrix of posterior probabilities.</span>
|
||||
|
||||
<span class="sd"> :param posteriors: array-like of shape `(n_instances, n_classes,)` with posterior probabilities for each class</span>
|
||||
<span class="sd"> :param binarize: set to True (default is False) for computing the prevalence values on crisp decisions (i.e.,</span>
|
||||
<span class="sd"> converting the vectors of posterior probabilities into class indices, by taking the argmax).</span>
|
||||
<span class="sd"> :return: array of shape `(n_classes,)` containing the prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'param posteriors does not seem to be a ndarray of posteior probabilities'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">binarize</span><span class="p">:</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevalence_from_labels</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">posteriors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">prevalences</span> <span class="o">/=</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="as_binary_prevalence">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.as_binary_prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">as_binary_prevalence</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two</span>
|
||||
<span class="sd"> values representing a binary distribution.</span>
|
||||
|
||||
<span class="sd"> :param positive_prevalence: prevalence for the positive class</span>
|
||||
<span class="sd"> :param clip_if_necessary: if True, clips the value in [0,1] in order to guarantee the resulting distribution</span>
|
||||
<span class="sd"> is valid. If False, it then checks that the value is in the valid range, and raises an error if not.</span>
|
||||
<span class="sd"> :return: np.ndarray of shape `(2,)`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">clip_if_necessary</span><span class="p">:</span>
|
||||
<span class="n">positive_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">positive_prevalence</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'the value provided is not a valid prevalence for the positive class'</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">1</span><span class="o">-</span><span class="n">positive_prevalence</span><span class="p">,</span> <span class="n">positive_prevalence</span><span class="p">])</span><span class="o">.</span><span class="n">T</span></div>
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HellingerDistance">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.HellingerDistance">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">HellingerDistance</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Q</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.</span>
|
||||
<span class="sd"> The HD for two discrete distributions of `k` bins is defined as:</span>
|
||||
|
||||
<span class="sd"> .. math::</span>
|
||||
<span class="sd"> HD(P,Q) = \\frac{ 1 }{ \\sqrt{ 2 } } \\sqrt{ \\sum_{i=1}^k ( \\sqrt{p_i} - \\sqrt{q_i} )^2 }</span>
|
||||
|
||||
<span class="sd"> :param P: real-valued array-like of shape `(k,)` representing a discrete distribution</span>
|
||||
<span class="sd"> :param Q: real-valued array-like of shape `(k,)` representing a discrete distribution</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">P</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">Q</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TopsoeDistance">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.TopsoeDistance">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">TopsoeDistance</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-20</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Topsoe distance between two (discretized) distributions `P` and `Q`.</span>
|
||||
<span class="sd"> The Topsoe distance for two discrete distributions of `k` bins is defined as:</span>
|
||||
|
||||
<span class="sd"> .. math::</span>
|
||||
<span class="sd"> Topsoe(P,Q) = \\sum_{i=1}^k \\left( p_i \\log\\left(\\frac{ 2 p_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) +</span>
|
||||
<span class="sd"> q_i \\log\\left(\\frac{ 2 q_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) \\right)</span>
|
||||
|
||||
<span class="sd"> :param P: real-valued array-like of shape `(k,)` representing a discrete distribution</span>
|
||||
<span class="sd"> :param Q: real-valued array-like of shape `(k,)` representing a discrete distribution</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">P</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">P</span><span class="o">+</span><span class="n">epsilon</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">P</span><span class="o">+</span><span class="n">Q</span><span class="o">+</span><span class="n">epsilon</span><span class="p">))</span> <span class="o">+</span> <span class="n">Q</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">Q</span><span class="o">+</span><span class="n">epsilon</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">P</span><span class="o">+</span><span class="n">Q</span><span class="o">+</span><span class="n">epsilon</span><span class="p">)))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="uniform_prevalence_sampling">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.uniform_prevalence_sampling">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">uniform_prevalence_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the `Kraemer algorithm <http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf>`_</span>
|
||||
<span class="sd"> for sampling uniformly at random from the unit simplex. This implementation is adapted from this</span>
|
||||
<span class="sd"> `post <https://cs.stackexchange.com/questions/3227/uniform-sampling-from-a-simplex>_`.</span>
|
||||
|
||||
<span class="sd"> :param n_classes: integer, number of classes (dimensionality of the simplex)</span>
|
||||
<span class="sd"> :param size: number of samples to return</span>
|
||||
<span class="sd"> :return: `np.ndarray` of shape `(size, n_classes,)` if `size>1`, or of shape `(n_classes,)` otherwise</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">n_classes</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="mi">1</span><span class="o">-</span><span class="n">u</span><span class="p">,</span> <span class="n">u</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">u</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">_0s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">_1s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">_0s</span><span class="p">,</span> <span class="n">u</span><span class="p">])</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">u</span><span class="p">,</span> <span class="n">_1s</span><span class="p">])</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">b</span><span class="o">-</span><span class="n">a</span>
|
||||
<span class="k">if</span> <span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">u</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="n">uniform_simplex_sampling</span> <span class="o">=</span> <span class="n">uniform_prevalence_sampling</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="strprev">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.strprev">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">strprev</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a string representation for a prevalence vector. E.g.,</span>
|
||||
|
||||
<span class="sd"> >>> strprev([1/3, 2/3], prec=2)</span>
|
||||
<span class="sd"> >>> '[0.33, 0.67]'</span>
|
||||
|
||||
<span class="sd"> :param prevalences: a vector of prevalence values</span>
|
||||
<span class="sd"> :param prec: float precision</span>
|
||||
<span class="sd"> :return: string</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="s1">'['</span><span class="o">+</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">p</span><span class="si">:</span><span class="s1">.</span><span class="si">{</span><span class="n">prec</span><span class="si">}</span><span class="s1">f</span><span class="si">}</span><span class="s1">'</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">prevalences</span><span class="p">])</span> <span class="o">+</span> <span class="s1">']'</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="adjusted_quantification">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.adjusted_quantification">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">adjusted_quantification</span><span class="p">(</span><span class="n">prevalence_estim</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the</span>
|
||||
<span class="sd"> positive class `p` comes down to computing:</span>
|
||||
|
||||
<span class="sd"> .. math::</span>
|
||||
<span class="sd"> ACC(p) = \\frac{ p - fpr }{ tpr - fpr }</span>
|
||||
|
||||
<span class="sd"> :param prevalence_estim: float, the estimated value for the positive class</span>
|
||||
<span class="sd"> :param tpr: float, the true positive rate of the classifier</span>
|
||||
<span class="sd"> :param fpr: float, the false positive rate of the classifier</span>
|
||||
<span class="sd"> :param clip: set to True (default) to clip values that might exceed the range [0,1]</span>
|
||||
<span class="sd"> :return: float, the adjusted count</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">den</span> <span class="o">=</span> <span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span>
|
||||
<span class="k">if</span> <span class="n">den</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">den</span> <span class="o">+=</span> <span class="mf">1e-8</span>
|
||||
<span class="n">adjusted</span> <span class="o">=</span> <span class="p">(</span><span class="n">prevalence_estim</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">/</span> <span class="n">den</span>
|
||||
<span class="k">if</span> <span class="n">clip</span><span class="p">:</span>
|
||||
<span class="n">adjusted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">adjusted</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">adjusted</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="normalize_prevalence">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.normalize_prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Normalize a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in</span>
|
||||
<span class="sd"> cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in</span>
|
||||
<span class="sd"> cases in which all values are zero.</span>
|
||||
|
||||
<span class="sd"> :param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values</span>
|
||||
<span class="sd"> :return: a normalized vector or matrix of prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">accum</span> <span class="o">=</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">true_divide</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">accum</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">accum</span><span class="o">></span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">allzeros</span> <span class="o">=</span> <span class="n">accum</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">==</span><span class="mi">0</span>
|
||||
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">allzeros</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">1.</span><span class="o">/</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span><span class="p">[</span><span class="n">accum</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">1.</span><span class="o">/</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__num_prevalence_combinations_depr</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the number of prevalence combinations in the n_classes-dimensional simplex if `nprevpoints` equally distant</span>
|
||||
<span class="sd"> prevalence values are generated and `n_repeats` repetitions are requested.</span>
|
||||
|
||||
<span class="sd"> :param n_classes: integer, number of classes</span>
|
||||
<span class="sd"> :param n_prevpoints: integer, number of prevalence points.</span>
|
||||
<span class="sd"> :param n_repeats: integer, number of repetitions for each prevalence combination</span>
|
||||
<span class="sd"> :return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the</span>
|
||||
<span class="sd"> number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">__cache</span><span class="o">=</span><span class="p">{}</span>
|
||||
<span class="k">def</span> <span class="nf">__f</span><span class="p">(</span><span class="n">nc</span><span class="p">,</span><span class="n">np</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="p">(</span><span class="n">nc</span><span class="p">,</span><span class="n">np</span><span class="p">)</span> <span class="ow">in</span> <span class="n">__cache</span><span class="p">:</span> <span class="c1"># cached result</span>
|
||||
<span class="k">return</span> <span class="n">__cache</span><span class="p">[(</span><span class="n">nc</span><span class="p">,</span><span class="n">np</span><span class="p">)]</span>
|
||||
<span class="k">if</span> <span class="n">nc</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span> <span class="c1"># stop condition</span>
|
||||
<span class="k">return</span> <span class="mi">1</span>
|
||||
<span class="k">else</span><span class="p">:</span> <span class="c1"># recursive call</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">__f</span><span class="p">(</span><span class="n">nc</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">-</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">np</span><span class="p">)])</span>
|
||||
<span class="n">__cache</span><span class="p">[(</span><span class="n">nc</span><span class="p">,</span><span class="n">np</span><span class="p">)]</span> <span class="o">=</span> <span class="n">x</span>
|
||||
<span class="k">return</span> <span class="n">x</span>
|
||||
<span class="k">return</span> <span class="n">__f</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">n_prevpoints</span><span class="p">)</span> <span class="o">*</span> <span class="n">n_repeats</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="num_prevalence_combinations">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.num_prevalence_combinations">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally</span>
|
||||
<span class="sd"> distant prevalence values are generated and `n_repeats` repetitions are requested.</span>
|
||||
<span class="sd"> The computation comes down to calculating:</span>
|
||||
|
||||
<span class="sd"> .. math::</span>
|
||||
<span class="sd"> \\binom{N+C-1}{C-1} \\times r</span>
|
||||
|
||||
<span class="sd"> where `N` is `n_prevpoints-1`, i.e., the number of probability mass blocks to allocate, `C` is the number of</span>
|
||||
<span class="sd"> classes, and `r` is `n_repeats`. This solution comes from the</span>
|
||||
<span class="sd"> `Stars and Bars <https://brilliant.org/wiki/integer-equations-star-and-bars/>`_ problem.</span>
|
||||
|
||||
<span class="sd"> :param n_classes: integer, number of classes</span>
|
||||
<span class="sd"> :param n_prevpoints: integer, number of prevalence points.</span>
|
||||
<span class="sd"> :param n_repeats: integer, number of repetitions for each prevalence combination</span>
|
||||
<span class="sd"> :return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the</span>
|
||||
<span class="sd"> number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">N</span> <span class="o">=</span> <span class="n">n_prevpoints</span><span class="o">-</span><span class="mi">1</span>
|
||||
<span class="n">C</span> <span class="o">=</span> <span class="n">n_classes</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">n_repeats</span>
|
||||
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">binom</span><span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="n">C</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">C</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_nprevpoints_approximation">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_nprevpoints_approximation">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">combinations_budget</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so</span>
|
||||
<span class="sd"> that the number of valid prevalence values generated as combinations of prevalence points (points in a</span>
|
||||
<span class="sd"> `n_classes`-dimensional simplex) do not exceed combinations_budget.</span>
|
||||
|
||||
<span class="sd"> :param combinations_budget: integer, maximum number of combinations allowed</span>
|
||||
<span class="sd"> :param n_classes: integer, number of classes</span>
|
||||
<span class="sd"> :param n_repeats: integer, number of repetitions for each prevalence combination</span>
|
||||
<span class="sd"> :return: the largest number of prevalence points that generate less than combinations_budget valid prevalences</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">n_classes</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">n_repeats</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">combinations_budget</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'parameters must be positive integers'</span>
|
||||
<span class="n">n_prevpoints</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
|
||||
<span class="n">combinations</span> <span class="o">=</span> <span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">combinations</span> <span class="o">></span> <span class="n">combinations_budget</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">n_prevpoints</span><span class="o">-</span><span class="mi">1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">n_prevpoints</span> <span class="o">+=</span> <span class="mi">1</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="check_prevalence_vector">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.check_prevalence_vector">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">check_prevalence_vector</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">raise_exception</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">toleranze</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1.</span>
|
||||
|
||||
<span class="sd"> :param p: the prevalence vector to check</span>
|
||||
<span class="sd"> :return: True if `p` is valid, False otherwise</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">p</span><span class="o">>=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">raise_exception</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'the prevalence vector contains negative numbers'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">p</span><span class="o"><=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">raise_exception</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'the prevalence vector contains values >1'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">toleranze</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">raise_exception</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'the prevalence vector does not sum up to 1'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="k">return</span> <span class="kc">True</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_divergence">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_divergence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_divergence</span><span class="p">(</span><span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">divergence</span><span class="o">==</span><span class="s1">'HD'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">HellingerDistance</span>
|
||||
<span class="k">elif</span> <span class="n">divergence</span><span class="o">==</span><span class="s1">'topsoe'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">TopsoeDistance</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'unknown divergence </span><span class="si">{</span><span class="n">divergence</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">divergence</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">divergence</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'argument "divergence" not understood; use a str or a callable function'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="argmin_prevalence">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.argmin_prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'optim_minimize'</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'optim_minimize'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'linear_search'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'ternary_search'</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="optim_minimize">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.optim_minimize">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex</span>
|
||||
<span class="sd"> that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's</span>
|
||||
<span class="sd"> SLSQP routine.</span>
|
||||
|
||||
<span class="sd"> :param loss: (callable) the function to minimize</span>
|
||||
<span class="sd"> :param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector</span>
|
||||
<span class="sd"> :return: (ndarray) the best prevalence vector found</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
|
||||
|
||||
<span class="c1"># the initial point is set as the uniform distribution</span>
|
||||
<span class="n">uniform_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,))</span>
|
||||
|
||||
<span class="c1"># solutions are bounded to those contained in the unit-simplex</span>
|
||||
<span class="n">bounds</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">))</span> <span class="c1"># values in [0,1]</span>
|
||||
<span class="n">constraints</span> <span class="o">=</span> <span class="p">({</span><span class="s1">'type'</span><span class="p">:</span> <span class="s1">'eq'</span><span class="p">,</span> <span class="s1">'fun'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)})</span> <span class="c1"># values summing up to 1</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">x0</span><span class="o">=</span><span class="n">uniform_distribution</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'SLSQP'</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="n">bounds</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraints</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">r</span><span class="o">.</span><span class="n">x</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="linear_search">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.functional.linear_search">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring</span>
|
||||
<span class="sd"> the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the</span>
|
||||
<span class="sd"> early methods in quantification literature used it, e.g., HDy). A most powerful alternative is `optim_minimize`.</span>
|
||||
|
||||
<span class="sd"> :param loss: (callable) the function to minimize</span>
|
||||
<span class="sd"> :param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector</span>
|
||||
<span class="sd"> :return: (ndarray) the best prevalence vector found</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">n_classes</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="s1">'linear search is only available for binary problems'</span>
|
||||
|
||||
<span class="n">prev_selected</span><span class="p">,</span> <span class="n">min_score</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="k">for</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">prevalence_linspace</span><span class="p">(</span><span class="n">n_prevalences</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">loss</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">prev</span><span class="p">,</span> <span class="n">prev</span><span class="p">]))</span>
|
||||
<span class="k">if</span> <span class="n">min_score</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">score</span> <span class="o"><</span> <span class="n">min_score</span><span class="p">:</span>
|
||||
<span class="n">prev_selected</span><span class="p">,</span> <span class="n">min_score</span> <span class="o">=</span> <span class="n">prev</span><span class="p">,</span> <span class="n">score</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">prev_selected</span><span class="p">,</span> <span class="n">prev_selected</span><span class="p">])</span></div>
|
||||
|
||||
</pre></div>
|
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<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KernelDensity</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeSoftQuantifier</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics.pairwise</span> <span class="kn">import</span> <span class="n">rbf_kernel</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">KDEBase</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Common ancestor for KDE-based methods. Implements some common routines.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">BANDWIDTH_METHOD</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'scott'</span><span class="p">,</span> <span class="s1">'silverman'</span><span class="p">]</span>
|
||||
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Checks that the bandwidth parameter is correct</span>
|
||||
|
||||
<span class="sd"> :param bandwidth: either a string (see BANDWIDTH_METHOD) or a float</span>
|
||||
<span class="sd"> :return: nothing, but raises an exception for invalid values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'invalid bandwidth, valid ones are </span><span class="si">{</span><span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span><span class="si">}</span><span class="s1"> or float values'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">bandwidth</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"the bandwith for KDEy should be in (0,1), since this method models the unit simplex"</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_kde_function">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Wraps the KDE function from scikit-learn.</span>
|
||||
|
||||
<span class="sd"> :param X: data for which the density function is to be estimated</span>
|
||||
<span class="sd"> :param bandwidth: the bandwidth of the kernel</span>
|
||||
<span class="sd"> :return: a scikit-learn's KernelDensity object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">KernelDensity</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.pdf">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Wraps the density evalution of scikit-learn's KDE. Scikit-learn returns log-scores (s), so this</span>
|
||||
<span class="sd"> function returns :math:`e^{s}`</span>
|
||||
|
||||
<span class="sd"> :param kde: a previously fit KDE function</span>
|
||||
<span class="sd"> :param X: the data for which the density is to be estimated</span>
|
||||
<span class="sd"> :return: np.ndarray with the densities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_mixture_components">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an array containing the mixture components, i.e., the KDE functions for each class.</span>
|
||||
|
||||
<span class="sd"> :param X: the data containing the covariates</span>
|
||||
<span class="sd"> :param y: the class labels</span>
|
||||
<span class="sd"> :param n_classes: integer, the number of classes</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the kernel</span>
|
||||
<span class="sd"> :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">cat</span><span class="p">],</span> <span class="n">bandwidth</span><span class="p">)</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyML">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> the authors show that minimizing the distribution mathing criterion for KLD is akin to performing</span>
|
||||
<span class="sd"> maximum likelihood (ML).</span>
|
||||
|
||||
<span class="sd"> The distribution matching optimization problem comes down to solving:</span>
|
||||
|
||||
<span class="sd"> :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
|
||||
<span class="sd"> :math:`\\alpha` defined by</span>
|
||||
|
||||
<span class="sd"> :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
|
||||
<span class="sd"> KDE function that uses the datapoints in X as the kernel centers.</span>
|
||||
|
||||
<span class="sd"> In KDEy-ML, the divergence is taken to be the Kullback-Leibler Divergence. This is equivalent to solving:</span>
|
||||
<span class="sd"> :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} -</span>
|
||||
<span class="sd"> \\mathbb{E}_{q_{\\widetilde{U}}} \\left[ \\log \\boldsymbol{p}_{\\alpha}(\\widetilde{x}) \\right]`</span>
|
||||
|
||||
<span class="sd"> which corresponds to the maximum likelihood estimate.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyML.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyML.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood</span>
|
||||
<span class="sd"> of the data (i.e., that minimizes the negative log-likelihood)</span>
|
||||
|
||||
<span class="sd"> :param posteriors: instances in the sample converted into posterior probabilities</span>
|
||||
<span class="sd"> :return: a vector of class prevalence estimates</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-10</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
|
||||
<span class="n">test_densities</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prev_i</span> <span class="o">*</span> <span class="n">dens_i</span> <span class="k">for</span> <span class="n">prev_i</span><span class="p">,</span> <span class="n">dens_i</span> <span class="ow">in</span> <span class="nb">zip</span> <span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">test_densities</span><span class="p">))</span>
|
||||
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
|
||||
|
||||
<span class="sd"> The distribution matching optimization problem comes down to solving:</span>
|
||||
|
||||
<span class="sd"> :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
|
||||
<span class="sd"> :math:`\\alpha` defined by</span>
|
||||
|
||||
<span class="sd"> :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
|
||||
<span class="sd"> KDE function that uses the datapoints in X as the kernel centers.</span>
|
||||
|
||||
<span class="sd"> In KDEy-HD, the divergence is taken to be the squared Hellinger Distance, an f-divergence with corresponding</span>
|
||||
<span class="sd"> f-generator function given by:</span>
|
||||
|
||||
<span class="sd"> :math:`f(u)=(\\sqrt{u}-1)^2`</span>
|
||||
|
||||
<span class="sd"> The authors proposed a Monte Carlo solution that relies on importance sampling:</span>
|
||||
|
||||
<span class="sd"> :math:`\\hat{D}_f(p||q)= \\frac{1}{t} \\sum_{i=1}^t f\\left(\\frac{p(x_i)}{q(x_i)}\\right) \\frac{q(x_i)}{r(x_i)}`</span>
|
||||
|
||||
<span class="sd"> where the datapoints (trials) :math:`x_1,\\ldots,x_t\\sim_{\\mathrm{iid}} r` with :math:`r` the</span>
|
||||
<span class="sd"> uniform distribution.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> :param montecarlo_trials: number of Monte Carlo trials (default 10000)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">'HD'</span><span class="p">,</span>
|
||||
<span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">montecarlo_trials</span><span class="o">=</span><span class="mi">10000</span><span class="p">):</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="o">=</span> <span class="n">montecarlo_trials</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
|
||||
<span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span>
|
||||
<span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">kde_i</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">n</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rs</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># equiv. to (uniform @ self.reference_classwise_densities)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="c1"># we retain all n*N examples (sampled from a mixture with uniform parameter), and then</span>
|
||||
<span class="c1"># apply importance sampling (IS). In this version we compute D(p_alpha||q) with IS</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
|
||||
|
||||
<span class="n">test_kde</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="n">test_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">test_kde</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
|
||||
|
||||
<span class="c1"># todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'hd'</span><span class="p">:</span>
|
||||
<span class="n">f</span> <span class="o">=</span> <span class="n">f_squared_hellinger</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'only squared HD is currently implemented'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-10</span>
|
||||
<span class="n">qs</span> <span class="o">=</span> <span class="n">test_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
|
||||
<span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">+</span> <span class="n">epsilon</span>
|
||||
<span class="n">iw</span> <span class="o">=</span> <span class="n">qs</span><span class="o">/</span><span class="n">rs</span> <span class="c1">#importance weights</span>
|
||||
<span class="n">p_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
|
||||
<span class="n">fracs</span> <span class="o">=</span> <span class="n">p_class</span><span class="o">/</span><span class="n">qs</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="c1"># ps / qs = (prev @ p_class) / qs = prev @ (p_class / qs) = prev @ fracs</span>
|
||||
<span class="n">ps_div_qs</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">fracs</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">f</span><span class="p">(</span><span class="n">ps_div_qs</span><span class="p">)</span> <span class="o">*</span> <span class="n">iw</span> <span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
|
||||
|
||||
<span class="sd"> The distribution matching optimization problem comes down to solving:</span>
|
||||
|
||||
<span class="sd"> :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
|
||||
<span class="sd"> :math:`\\alpha` defined by</span>
|
||||
|
||||
<span class="sd"> :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
|
||||
|
||||
<span class="sd"> where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
|
||||
<span class="sd"> KDE function that uses the datapoints in X as the kernel centers.</span>
|
||||
|
||||
<span class="sd"> In KDEy-CS, the divergence is taken to be the Cauchy-Schwarz divergence given by:</span>
|
||||
|
||||
<span class="sd"> :math:`\\mathcal{D}_{\\mathrm{CS}}(p||q)=-\\log\\left(\\frac{\\int p(x)q(x)dx}{\\sqrt{\\int p(x)^2dx \\int q(x)^2dx}}\\right)`</span>
|
||||
|
||||
<span class="sd"> The authors showed that this distribution matching admits a closed-form solution</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.gram_matrix_mix_sum">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="c1"># this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))</span>
|
||||
<span class="c1"># to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are </span>
|
||||
<span class="c1"># two "scalar matrices" (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span>
|
||||
<span class="n">h</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span>
|
||||
<span class="n">variance</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">h</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">nD</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">gamma</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">variance</span><span class="p">)</span>
|
||||
<span class="n">norm_factor</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(((</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">**</span><span class="n">nD</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">variance</span><span class="o">**</span><span class="p">(</span><span class="n">nD</span><span class="p">)))</span>
|
||||
<span class="n">gram</span> <span class="o">=</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">gram</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
|
||||
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> \
|
||||
<span class="s1">'label name gaps not allowed in current implementation'</span>
|
||||
|
||||
<span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span>
|
||||
<span class="c1"># (i.e., the weight in its KDE model)</span>
|
||||
<span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">counts</span><span class="p">())</span>
|
||||
|
||||
<span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span>
|
||||
<span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">i</span> <span class="o">></span> <span class="n">j</span><span class="p">:</span>
|
||||
<span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">block</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">P</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">i</span><span class="p">],</span> <span class="n">P</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">j</span><span class="p">]</span> <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="n">j</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">block</span>
|
||||
|
||||
<span class="c1"># keep track of these data structures for the test phase</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span> <span class="o">=</span> <span class="n">P</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ytr</span> <span class="o">=</span> <span class="n">y</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">tr_tr_sums</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span> <span class="o">=</span> <span class="n">counts_inv</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">Ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span>
|
||||
<span class="n">Pte</span> <span class="o">=</span> <span class="n">posteriors</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span>
|
||||
<span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tr_tr_sums</span>
|
||||
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">nD</span> <span class="o">=</span> <span class="n">Pte</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="n">Minv</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">M</span><span class="p">)</span> <span class="c1"># t in the paper</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">Ptr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># becomes a constant that does not affect the optimization, no need to compute it</span>
|
||||
<span class="c1"># partC = 0.5*np.log(self.gram_matrix_mix_sum(Pte) * Kinv * Kinv)</span>
|
||||
|
||||
<span class="c1"># tr_te_sums corresponds to \overline{a}*(1/Li)*(1/M) in the paper (note the constants</span>
|
||||
<span class="c1"># are already aggregated to tr_te_sums, so these multiplications are not carried out</span>
|
||||
<span class="c1"># at each iteration of the optimization phase)</span>
|
||||
<span class="n">tr_te_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="n">tr_te_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">Ptr</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">],</span> <span class="n">Pte</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span>
|
||||
<span class="c1"># called \overline{r} in the paper</span>
|
||||
<span class="n">alpha_ratio</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span>
|
||||
|
||||
<span class="c1"># recal that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span>
|
||||
<span class="n">partA</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_te_sums</span><span class="p">)</span> <span class="o">*</span> <span class="n">Minv</span><span class="p">)</span>
|
||||
<span class="n">partB</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_tr_sums</span> <span class="o">@</span> <span class="n">alpha_ratio</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">partA</span> <span class="o">+</span> <span class="n">partB</span> <span class="c1">#+ partC</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
</pre></div>
|
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|
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<li class="breadcrumb-item active">quapy.method._neural</li>
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<h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
|
||||
<span class="kn">import</span> <span class="nn">random</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">MSELoss</span>
|
||||
<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">relu</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">UPP</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="o">*</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">QuaNetTrainer</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implementation of `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_, a neural network for</span>
|
||||
<span class="sd"> quantification. This implementation uses `PyTorch <https://pytorch.org/>`_ and can take advantage of GPU</span>
|
||||
<span class="sd"> for speeding-up the training phase.</span>
|
||||
|
||||
<span class="sd"> Example:</span>
|
||||
|
||||
<span class="sd"> >>> import quapy as qp</span>
|
||||
<span class="sd"> >>> from quapy.method.meta import QuaNet</span>
|
||||
<span class="sd"> >>> from quapy.classification.neural import NeuralClassifierTrainer, CNNnet</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # use samples of 100 elements</span>
|
||||
<span class="sd"> >>> qp.environ['SAMPLE_SIZE'] = 100</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # load the kindle dataset as text, and convert words to numerical indexes</span>
|
||||
<span class="sd"> >>> dataset = qp.datasets.fetch_reviews('kindle', pickle=True)</span>
|
||||
<span class="sd"> >>> qp.train.preprocessing.index(dataset, min_df=5, inplace=True)</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # the text classifier is a CNN trained by NeuralClassifierTrainer</span>
|
||||
<span class="sd"> >>> cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)</span>
|
||||
<span class="sd"> >>> classifier = NeuralClassifierTrainer(cnn, device='cuda')</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # train QuaNet (QuaNet is an alias to QuaNetTrainer)</span>
|
||||
<span class="sd"> >>> model = QuaNet(classifier, qp.environ['SAMPLE_SIZE'], device='cuda')</span>
|
||||
<span class="sd"> >>> model.fit(dataset.training)</span>
|
||||
<span class="sd"> >>> estim_prevalence = model.quantify(dataset.test.instances)</span>
|
||||
|
||||
<span class="sd"> :param classifier: an object implementing `fit` (i.e., that can be trained on labelled data),</span>
|
||||
<span class="sd"> `predict_proba` (i.e., that can generate posterior probabilities of unlabelled examples) and</span>
|
||||
<span class="sd"> `transform` (i.e., that can generate embedded representations of the unlabelled instances).</span>
|
||||
<span class="sd"> :param sample_size: integer, the sample size; default is None, meaning that the sample size should be</span>
|
||||
<span class="sd"> taken from qp.environ["SAMPLE_SIZE"]</span>
|
||||
<span class="sd"> :param n_epochs: integer, maximum number of training epochs</span>
|
||||
<span class="sd"> :param tr_iter_per_poch: integer, number of training iterations before considering an epoch complete</span>
|
||||
<span class="sd"> :param va_iter_per_poch: integer, number of validation iterations to perform after each epoch</span>
|
||||
<span class="sd"> :param lr: float, the learning rate</span>
|
||||
<span class="sd"> :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cells</span>
|
||||
<span class="sd"> :param lstm_nlayers: integer, number of LSTM layers</span>
|
||||
<span class="sd"> :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the</span>
|
||||
<span class="sd"> quantification embedding</span>
|
||||
<span class="sd"> :param bidirectional: boolean, indicates whether the LSTM is bidirectional or not</span>
|
||||
<span class="sd"> :param qdrop_p: float, dropout probability</span>
|
||||
<span class="sd"> :param patience: integer, number of epochs showing no improvement in the validation set before stopping the</span>
|
||||
<span class="sd"> training phase (early stopping)</span>
|
||||
<span class="sd"> :param checkpointdir: string, a path where to store models' checkpoints</span>
|
||||
<span class="sd"> :param checkpointname: string (optional), the name of the model's checkpoint</span>
|
||||
<span class="sd"> :param device: string, indicate "cpu" or "cuda"</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">classifier</span><span class="p">,</span>
|
||||
<span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">n_epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
|
||||
<span class="n">tr_iter_per_poch</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
|
||||
<span class="n">va_iter_per_poch</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
|
||||
<span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
|
||||
<span class="n">lstm_hidden_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
|
||||
<span class="n">lstm_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">ff_layers</span><span class="o">=</span><span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
|
||||
<span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">qdrop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
|
||||
<span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">checkpointdir</span><span class="o">=</span><span class="s1">'../checkpoint'</span><span class="p">,</span>
|
||||
<span class="n">checkpointname</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">):</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="s1">'transform'</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'the classifier </span><span class="si">{</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> does not seem to be able to produce document embeddings '</span> \
|
||||
<span class="sa">f</span><span class="s1">'since it does not implement the method "transform"'</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="s1">'predict_proba'</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'the classifier </span><span class="si">{</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> does not seem to be able to produce posterior probabilities '</span> \
|
||||
<span class="sa">f</span><span class="s1">'since it does not implement the method "predict_proba"'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span> <span class="o">=</span> <span class="n">n_epochs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tr_iter</span> <span class="o">=</span> <span class="n">tr_iter_per_poch</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">va_iter</span> <span class="o">=</span> <span class="n">va_iter_per_poch</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'lstm_hidden_size'</span><span class="p">:</span> <span class="n">lstm_hidden_size</span><span class="p">,</span>
|
||||
<span class="s1">'lstm_nlayers'</span><span class="p">:</span> <span class="n">lstm_nlayers</span><span class="p">,</span>
|
||||
<span class="s1">'ff_layers'</span><span class="p">:</span> <span class="n">ff_layers</span><span class="p">,</span>
|
||||
<span class="s1">'bidirectional'</span><span class="p">:</span> <span class="n">bidirectional</span><span class="p">,</span>
|
||||
<span class="s1">'qdrop_p'</span><span class="p">:</span> <span class="n">qdrop_p</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
|
||||
<span class="k">if</span> <span class="n">checkpointname</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
|
||||
<span class="n">random_code</span> <span class="o">=</span> <span class="s1">'-'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
|
||||
<span class="n">checkpointname</span> <span class="o">=</span> <span class="s1">'QuaNet-'</span><span class="o">+</span><span class="n">random_code</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span> <span class="o">=</span> <span class="n">checkpointdir</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">checkpointname</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains QuaNet.</span>
|
||||
|
||||
<span class="sd"> :param data: the training data on which to train QuaNet. If `fit_classifier=True`, the data will be split in</span>
|
||||
<span class="sd"> 40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If</span>
|
||||
<span class="sd"> `fit_classifier=False`, the data will be split in 66/34 for training QuaNet and validating it, respectively.</span>
|
||||
<span class="sd"> :param fit_classifier: if True, trains the classifier on a split containing 40% of the data</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">fit_classifier</span><span class="p">:</span>
|
||||
<span class="n">classifier_data</span><span class="p">,</span> <span class="n">unused_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
|
||||
<span class="n">train_data</span><span class="p">,</span> <span class="n">valid_data</span> <span class="o">=</span> <span class="n">unused_data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.66</span><span class="p">)</span> <span class="c1"># 0.66 split of 60% makes 40% and 20%</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">classifier_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">classifier_data</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">train_data</span><span class="p">,</span> <span class="n">valid_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.66</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># estimate the hard and soft stats tpr and fpr of the classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tr_prev</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># compute the posterior probabilities of the instances</span>
|
||||
<span class="n">valid_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">valid_data</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">train_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># turn instances' original representations into embeddings</span>
|
||||
<span class="n">valid_data_embed</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">valid_data</span><span class="o">.</span><span class="n">instances</span><span class="p">),</span> <span class="n">valid_data</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
|
||||
<span class="n">train_data_embed</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">instances</span><span class="p">),</span> <span class="n">train_data</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'cc'</span><span class="p">:</span> <span class="n">CC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
|
||||
<span class="s1">'acc'</span><span class="p">:</span> <span class="n">ACC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">valid_data</span><span class="p">),</span>
|
||||
<span class="s1">'pcc'</span><span class="p">:</span> <span class="n">PCC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
|
||||
<span class="s1">'pacc'</span><span class="p">:</span> <span class="n">PACC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">valid_data</span><span class="p">),</span>
|
||||
<span class="p">}</span>
|
||||
<span class="k">if</span> <span class="n">classifier_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">[</span><span class="s1">'emq'</span><span class="p">]</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">classifier_data</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'tr-loss'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="s1">'va-loss'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="s1">'tr-mae'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="s1">'va-mae'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="n">nQ</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">)</span>
|
||||
<span class="n">nC</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span> <span class="o">=</span> <span class="n">QuaNetModule</span><span class="p">(</span>
|
||||
<span class="n">doc_embedding_size</span><span class="o">=</span><span class="n">train_data_embed</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
|
||||
<span class="n">n_classes</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
|
||||
<span class="n">stats_size</span><span class="o">=</span><span class="n">nQ</span><span class="o">*</span><span class="n">nC</span><span class="p">,</span>
|
||||
<span class="n">order_by</span><span class="o">=</span><span class="mi">0</span> <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr</span><span class="p">)</span>
|
||||
<span class="n">early_stop</span> <span class="o">=</span> <span class="n">EarlyStop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">checkpoint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_epoch</span><span class="p">(</span><span class="n">train_data_embed</span><span class="p">,</span> <span class="n">train_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tr_iter</span><span class="p">,</span> <span class="n">epoch_i</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_epoch</span><span class="p">(</span><span class="n">valid_data_embed</span><span class="p">,</span> <span class="n">valid_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">va_iter</span><span class="p">,</span> <span class="n">epoch_i</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">early_stop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'va-loss'</span><span class="p">],</span> <span class="n">epoch_i</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
|
||||
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">checkpoint</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">STOP</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'training ended by patience exhausted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'for epoch </span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">))</span>
|
||||
<span class="k">break</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_aggregative_estims</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
|
||||
<span class="n">label_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">prevs_estim</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">posteriors</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">)</span> <span class="k">else</span> <span class="n">label_predictions</span>
|
||||
<span class="n">prevs_estim</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">quantifier</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">predictions</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># there is no real need for adding static estims like the TPR or FPR from training since those are constant</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">prevs_estim</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
<span class="n">prevalence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">embeddings</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">):</span>
|
||||
<span class="n">prevalence</span> <span class="o">=</span> <span class="n">prevalence</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
|
||||
<span class="n">prevalence</span> <span class="o">=</span> <span class="n">prevalence</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">prevalence</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
|
||||
<span class="n">mse_loss</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
|
||||
<span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">mae_errors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">sampler</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span>
|
||||
<span class="n">data</span><span class="p">,</span>
|
||||
<span class="n">sample_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span>
|
||||
<span class="n">repeats</span><span class="o">=</span><span class="n">iterations</span><span class="p">,</span>
|
||||
<span class="n">random_state</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">train</span> <span class="k">else</span> <span class="mi">0</span> <span class="c1"># different samples during train, same samples during validation</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">sampler</span><span class="o">.</span><span class="n">total</span><span class="p">())</span>
|
||||
<span class="k">for</span> <span class="n">it</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pbar</span><span class="p">):</span>
|
||||
<span class="n">sample_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="n">sample_posteriors</span> <span class="o">=</span> <span class="n">posteriors</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
|
||||
<span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">sample_posteriors</span><span class="p">)</span>
|
||||
<span class="n">ptrue</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span><span class="n">sample_data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">train</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
|
||||
<span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">sample_data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">sample_posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
|
||||
<span class="n">loss</span> <span class="o">=</span> <span class="n">mse_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
|
||||
<span class="n">mae</span> <span class="o">=</span> <span class="n">mae_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
|
||||
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
<span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">sample_data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">sample_posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
|
||||
<span class="n">loss</span> <span class="o">=</span> <span class="n">mse_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
|
||||
<span class="n">mae</span> <span class="o">=</span> <span class="n">mae_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
|
||||
|
||||
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
|
||||
<span class="n">mae_errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mae</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
|
||||
|
||||
<span class="n">mse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
|
||||
<span class="n">mae</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">mae_errors</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">train</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'tr-loss'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'tr-mae'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mae</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'va-loss'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">'va-mae'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mae</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">train</span><span class="p">:</span>
|
||||
<span class="n">pbar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[QuaNet] '</span>
|
||||
<span class="sa">f</span><span class="s1">'epoch=</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s1"> [it=</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">iterations</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1">'</span>
|
||||
<span class="sa">f</span><span class="s1">'tr-mseloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"tr-loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> tr-maeloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"tr-mae"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="se">\t</span><span class="s1">'</span>
|
||||
<span class="sa">f</span><span class="s1">'val-mseloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"va-loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> val-maeloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">"va-mae"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'patience=</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">patience</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="n">classifier_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
|
||||
<span class="n">classifier_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__'</span><span class="o">+</span><span class="n">k</span><span class="p">:</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">classifier_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
|
||||
<span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">classifier_params</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="n">learner_params</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
|
||||
<span class="k">elif</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'classifier__'</span><span class="p">):</span>
|
||||
<span class="n">learner_params</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'classifier__'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'unknown parameter '</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">learner_params</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
|
||||
<span class="n">quanet_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">quanet_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
||||
<span class="n">learner_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">learner_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
||||
<span class="n">intersection</span> <span class="o">=</span> <span class="n">quanet_keys</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">learner_keys</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">intersection</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the use of parameters </span><span class="si">{</span><span class="n">intersection</span><span class="si">}</span><span class="s1"> is ambiguous sine those can refer to '</span>
|
||||
<span class="sa">f</span><span class="s1">'the parameters of QuaNet or the learner </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">clean_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Removes the checkpoint</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint_dir">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">clean_checkpoint_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Removes anything contained in the checkpoint directory</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">shutil</span>
|
||||
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mae_loss">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.mae_loss">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Torch-like wrapper for the Mean Absolute Error</span>
|
||||
|
||||
<span class="sd"> :param output: predictions</span>
|
||||
<span class="sd"> :param target: ground truth values</span>
|
||||
<span class="sd"> :return: mean absolute error loss</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QuaNetModule">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_ forward pass.</span>
|
||||
<span class="sd"> See :class:`QuaNetTrainer` for training QuaNet.</span>
|
||||
|
||||
<span class="sd"> :param doc_embedding_size: integer, the dimensionality of the document embeddings</span>
|
||||
<span class="sd"> :param n_classes: integer, number of classes</span>
|
||||
<span class="sd"> :param stats_size: integer, number of statistics estimated by simple quantification methods</span>
|
||||
<span class="sd"> :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cell</span>
|
||||
<span class="sd"> :param lstm_nlayers: integer, number of LSTM layers</span>
|
||||
<span class="sd"> :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the</span>
|
||||
<span class="sd"> quantification embedding</span>
|
||||
<span class="sd"> :param bidirectional: boolean, whether or not to use bidirectional LSTM</span>
|
||||
<span class="sd"> :param qdrop_p: float, dropout probability</span>
|
||||
<span class="sd"> :param order_by: integer, class for which the document embeddings are to be sorted</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">doc_embedding_size</span><span class="p">,</span>
|
||||
<span class="n">n_classes</span><span class="p">,</span>
|
||||
<span class="n">stats_size</span><span class="p">,</span>
|
||||
<span class="n">lstm_hidden_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
|
||||
<span class="n">lstm_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">ff_layers</span><span class="o">=</span><span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
|
||||
<span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">qdrop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
|
||||
<span class="n">order_by</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order_by</span> <span class="o">=</span> <span class="n">order_by</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">lstm_hidden_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">=</span> <span class="n">lstm_nlayers</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="o">=</span> <span class="n">bidirectional</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">qdrop_p</span> <span class="o">=</span> <span class="n">qdrop_p</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">doc_embedding_size</span> <span class="o">+</span> <span class="n">n_classes</span><span class="p">,</span> <span class="c1"># +n_classes stands for the posterior probs. (concatenated)</span>
|
||||
<span class="n">lstm_hidden_size</span><span class="p">,</span> <span class="n">lstm_nlayers</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="n">bidirectional</span><span class="p">,</span>
|
||||
<span class="n">dropout</span><span class="o">=</span><span class="n">qdrop_p</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">qdrop_p</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lstm_output_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span>
|
||||
<span class="n">ff_input_size</span> <span class="o">=</span> <span class="n">lstm_output_size</span> <span class="o">+</span> <span class="n">stats_size</span>
|
||||
<span class="n">prev_size</span> <span class="o">=</span> <span class="n">ff_input_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">lin_size</span> <span class="ow">in</span> <span class="n">ff_layers</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">lin_size</span><span class="p">))</span>
|
||||
<span class="n">prev_size</span> <span class="o">=</span> <span class="n">lin_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span> <span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
|
||||
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
|
||||
<span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span> <span class="o">=</span> <span class="n">var_hidden</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">var_cell</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetModule.forward">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule.forward">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
|
||||
<span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span>
|
||||
<span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">statistics</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">statistics</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_by</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_by</span><span class="p">])</span>
|
||||
<span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">doc_embeddings</span><span class="p">[</span><span class="n">order</span><span class="p">]</span>
|
||||
<span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">doc_posteriors</span><span class="p">[</span><span class="n">order</span><span class="p">]</span>
|
||||
|
||||
<span class="n">embeded_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># the entire set represents only one instance in quapy contexts, and so the batch_size=1</span>
|
||||
<span class="c1"># the shape should be (1, number-of-instances, embedding-size + n_classes)</span>
|
||||
<span class="n">embeded_posteriors</span> <span class="o">=</span> <span class="n">embeded_posteriors</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">flatten_parameters</span><span class="p">()</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">rnn_hidden</span><span class="p">,</span><span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="p">(</span><span class="n">embeded_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_hidden</span><span class="p">())</span>
|
||||
<span class="n">rnn_hidden</span> <span class="o">=</span> <span class="n">rnn_hidden</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
|
||||
<span class="n">quant_embedding</span> <span class="o">=</span> <span class="n">rnn_hidden</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">quant_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">quant_embedding</span><span class="p">,</span> <span class="n">statistics</span><span class="p">))</span>
|
||||
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="n">quant_embedding</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">linear</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span><span class="p">:</span>
|
||||
<span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">relu</span><span class="p">(</span><span class="n">linear</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)))</span>
|
||||
|
||||
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">prevalence</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">prevalence</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
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<h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
<span class="sd"> The different variants are based on different heuristics for choosing a decision threshold</span>
|
||||
<span class="sd"> that would allow for more true positives and many more false positives, on the grounds this</span>
|
||||
<span class="sd"> would deliver larger denominators.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the criterion according to which the threshold should be selected.</span>
|
||||
<span class="sd"> This function should return the (float) score to be minimized.</span>
|
||||
|
||||
<span class="sd"> :param tpr: float, true positive rate</span>
|
||||
<span class="sd"> :param fpr: float, false positive rate</span>
|
||||
<span class="sd"> :return: float, a score for the given `tpr` and `fpr`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.discard">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span>
|
||||
|
||||
<span class="sd"> :param tpr: float, true positive rate</span>
|
||||
<span class="sd"> :param fpr: float, false positive rate</span>
|
||||
<span class="sd"> :return: true if the combination is to be discarded, false otherwise</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Seeks for the best `tpr` and `fpr` according to the score obtained at different</span>
|
||||
<span class="sd"> decision thresholds. The scoring function is implemented in function `_condition`.</span>
|
||||
|
||||
<span class="sd"> :param decision_scores: array-like with the classification scores</span>
|
||||
<span class="sd"> :param y: predicted labels for the validation set (or for the training set via `k`-fold cross validation)</span>
|
||||
<span class="sd"> :return: best `tpr` and `fpr` and `threshold` according to `_condition`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">candidate_thresholds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">)</span>
|
||||
|
||||
<span class="n">candidates</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">candidate_threshold</span> <span class="ow">in</span> <span class="n">candidate_thresholds</span><span class="p">:</span>
|
||||
<span class="n">y_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="mi">1</span> <span class="o">*</span> <span class="p">(</span><span class="n">decision_scores</span> <span class="o">>=</span> <span class="n">candidate_threshold</span><span class="p">)]</span>
|
||||
<span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_table</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">)</span>
|
||||
<span class="n">tpr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_tpr</span><span class="p">(</span><span class="n">TP</span><span class="p">,</span> <span class="n">FN</span><span class="p">)</span>
|
||||
<span class="n">fpr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_fpr</span><span class="p">(</span><span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">discard</span><span class="p">(</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">):</span>
|
||||
<span class="n">candidate_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span>
|
||||
<span class="n">candidates</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">candidate_threshold</span><span class="p">])</span>
|
||||
<span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">candidate_score</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="c1"># if no candidate gives rise to a valid combination of tpr and fpr, this method defaults to the standard</span>
|
||||
<span class="c1"># classify & count; this is akin to assign tpr=1, fpr=0, threshold=0</span>
|
||||
<span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
|
||||
<span class="n">candidates</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">threshold</span><span class="p">])</span>
|
||||
<span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">candidates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
|
||||
<span class="n">candidates</span> <span class="o">=</span> <span class="n">candidates</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">scores</span><span class="p">)]</span> <span class="c1"># sort candidates by candidate_score</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">candidates</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
|
||||
<span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span>
|
||||
<span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length > 1</span>
|
||||
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">>=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="p">(</span><span class="n">prevs_estims</span> <span class="o">-</span> <span class="n">fprs</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">tprs</span> <span class="o">-</span> <span class="n">fprs</span><span class="p">)</span>
|
||||
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">prevs_estims</span><span class="p">,</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
|
||||
<span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="n">FP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="n">FN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="n">TN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">TP</span> <span class="o">/</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="mi">0</span>
|
||||
<span class="k">return</span> <span class="n">FP</span> <span class="o">/</span> <span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<span class="c1"># the standard behavior is to keep the best threshold only</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="T50">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks</span>
|
||||
<span class="sd"> for the threshold that makes `tpr` closest to 0.5.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="T50.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MAX">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks</span>
|
||||
<span class="sd"> for the threshold that maximizes `tpr-fpr`.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MAX.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="c1"># MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="X">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks</span>
|
||||
<span class="sd"> for the threshold that yields `tpr=1-fpr`.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="X.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that generates</span>
|
||||
<span class="sd"> class prevalence estimates for all decision thresholds and returns the median of them all.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MS.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="mi">1</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<span class="c1"># keeps all candidates</span>
|
||||
<span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">fprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS2">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
<span class="sd"> `Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that generates</span>
|
||||
<span class="sd"> class prevalence estimates for all decision thresholds and returns the median of for cases in</span>
|
||||
<span class="sd"> which `tpr-fpr>0.25`</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MS2.discard">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span><span class="o">-</span><span class="n">fpr</span><span class="p">)</span> <span class="o"><=</span> <span class="mf">0.25</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
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|
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|
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|
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|
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|
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<p>© Copyright 2024, Alejandro Moreo.</p>
|
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<h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
|
||||
<span class="c1"># Base Quantifier abstract class</span>
|
||||
<span class="c1"># ------------------------------------</span>
|
||||
<div class="viewcode-block" id="BaseQuantifier">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection`, the method :meth:`quantify`, and the :meth:`set_params` and</span>
|
||||
<span class="sd"> :meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains a quantifier.</span>
|
||||
|
||||
<span class="sd"> :param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generate class prevalence estimates for the sample's instances</span>
|
||||
|
||||
<span class="sd"> :param instances: array-like</span>
|
||||
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BinaryQuantifier">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes</span>
|
||||
<span class="sd"> (typically, to be interpreted as one class and its complement).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> works only on problems of binary classification. '</span> \
|
||||
<span class="sa">f</span><span class="s1">'Use the class OneVsAll to enable </span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> work on single-label data.'</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OneVsAll">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">OneVsAll</span><span class="p">:</span>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="newOneVsAll">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">OneVsAllAggregative</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">OneVsAllGeneric</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span>
|
||||
<span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[warning] the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; '</span>
|
||||
<span class="sa">f</span><span class="s1">'you might prefer instantiating </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">OneVsAllAggregative</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">binary_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> expect non-binary data'</span>
|
||||
<span class="k">assert</span> <span class="n">fit_classifier</span> <span class="o">==</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'fit_classifier must be True'</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'threading'</span><span class="p">)(</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">bindata</span><span class="p">)</span></div>
|
||||
|
||||
</pre></div>
|
||||
|
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|
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<h1>Source code for quapy.method.meta</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_neural</span>
|
||||
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
|
||||
<span class="n">_neural</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="n">_neural</span><span class="p">:</span>
|
||||
<span class="n">QuaNet</span> <span class="o">=</span> <span class="n">_neural</span><span class="o">.</span><span class="n">QuaNetTrainer</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">QuaNet</span> <span class="o">=</span> <span class="s2">"QuaNet is not available due to missing torch package"</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MedianEstimator2</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
|
||||
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
|
||||
<span class="sd"> The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,</span>
|
||||
<span class="sd"> i.e., in cases of binary quantification.</span>
|
||||
|
||||
<span class="sd"> :param base_quantifier: the base, binary quantifier</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
|
||||
<span class="sd"> :param n_jobs: number of parllel workes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
|
||||
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
|
||||
<span class="sd"> The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,</span>
|
||||
<span class="sd"> i.e., in cases of binary quantification.</span>
|
||||
|
||||
<span class="sd"> :param base_quantifier: the base, binary quantifier</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
|
||||
<span class="sd"> :param n_jobs: number of parllel workes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">cls_params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">),</span> <span class="n">q_params</span><span class="p">),</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cls_configs</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">models_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_classifier</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">cls_configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_configs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="n">models_preds</span> <span class="o">=</span> <span class="p">[(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_aggregation</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">setup</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">setup</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">models_preds</span><span class="p">,</span> <span class="n">q_configs</span><span class="p">)),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Ensemble">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="n">VALID_POLICIES</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'ave'</span><span class="p">,</span> <span class="s1">'ptr'</span><span class="p">,</span> <span class="s1">'ds'</span><span class="p">}</span> <span class="o">|</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span>
|
||||
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implementation of the Ensemble methods for quantification described by </span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2017 <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_</span>
|
||||
<span class="sd"> and</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
<span class="sd"> The policies implemented include:</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> - Average (`policy='ave'`): computes class prevalence estimates as the average of the estimates </span>
|
||||
<span class="sd"> returned by the base quantifiers.</span>
|
||||
<span class="sd"> - Training Prevalence (`policy='ptr'`): applies a dynamic selection to the ensemble’s members by retaining only </span>
|
||||
<span class="sd"> those members such that the class prevalence values in the samples they use as training set are closest to </span>
|
||||
<span class="sd"> preliminary class prevalence estimates computed as the average of the estimates of all the members. The final </span>
|
||||
<span class="sd"> estimate is recomputed by considering only the selected members.</span>
|
||||
<span class="sd"> - Distribution Similarity (`policy='ds'`): performs a dynamic selection of base members by retaining</span>
|
||||
<span class="sd"> the members trained on samples whose distribution of posterior probabilities is closest, in terms of the</span>
|
||||
<span class="sd"> Hellinger Distance, to the distribution of posterior probabilities in the test sample</span>
|
||||
<span class="sd"> - Accuracy (`policy='<valid error name>'`): performs a static selection of the ensemble members by</span>
|
||||
<span class="sd"> retaining those that minimize a quantification error measure, which is passed as an argument.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Example:</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> >>> model = Ensemble(quantifier=ACC(LogisticRegression()), size=30, policy='ave', n_jobs=-1)</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> :param quantifier: base quantification member of the ensemble </span>
|
||||
<span class="sd"> :param size: number of members</span>
|
||||
<span class="sd"> :param red_size: number of members to retain after selection (depending on the policy)</span>
|
||||
<span class="sd"> :param min_pos: minimum number of positive instances to consider a sample as valid </span>
|
||||
<span class="sd"> :param policy: the selection policy; available policies include: `ave` (default), `ptr`, `ds`, and accuracy </span>
|
||||
<span class="sd"> (which is instantiated via a valid error name, e.g., `mae`)</span>
|
||||
<span class="sd"> :param max_sample_size: maximum number of instances to consider in the samples (set to None </span>
|
||||
<span class="sd"> to indicate no limit, default)</span>
|
||||
<span class="sd"> :param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out</span>
|
||||
<span class="sd"> validation split, or a :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers (default 1)</span>
|
||||
<span class="sd"> :param verbose: set to True (default is False) to get some information in standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
|
||||
<span class="n">red_size</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||||
<span class="n">min_pos</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
|
||||
<span class="n">policy</span><span class="o">=</span><span class="s1">'ave'</span><span class="p">,</span>
|
||||
<span class="n">max_sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">val_split</span><span class="p">:</span><span class="n">Union</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">policy</span> <span class="ow">in</span> <span class="n">Ensemble</span><span class="o">.</span><span class="n">VALID_POLICIES</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unknown policy=</span><span class="si">{</span><span class="n">policy</span><span class="si">}</span><span class="s1">; valid are </span><span class="si">{</span><span class="n">Ensemble</span><span class="o">.</span><span class="n">VALID_POLICIES</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">assert</span> <span class="n">max_sample_size</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_sample_size</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> \
|
||||
<span class="s1">'wrong value for max_sample_size; set it to a positive number or None'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_pos</span> <span class="o">=</span> <span class="n">min_pos</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">red_size</span> <span class="o">=</span> <span class="n">red_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">=</span> <span class="n">policy</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="o">=</span> <span class="n">max_sample_size</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[Ensemble]'</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">val_split</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ds'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'ds policy is only defined for binary quantification, but this dataset is not binary'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">val_split</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
|
||||
|
||||
<span class="c1"># randomly chooses the prevalences for each member of the ensemble (preventing classes with less than</span>
|
||||
<span class="c1"># min_pos positive examples)</span>
|
||||
<span class="n">sample_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="p">[</span><span class="n">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">min_pos</span> <span class="o">/</span> <span class="n">sample_size</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ds'</span><span class="p">:</span>
|
||||
<span class="c1"># precompute the training posterior probabilities</span>
|
||||
<span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy_get_posteriors</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">is_static_policy</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="p">)</span>
|
||||
|
||||
<span class="n">args</span> <span class="o">=</span> <span class="p">(</span>
|
||||
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">is_static_policy</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">prevs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="n">_delayed_new_instance</span><span class="p">,</span>
|
||||
<span class="n">tqdm</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">'fitting ensamble'</span><span class="p">,</span> <span class="n">total</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="k">else</span> <span class="n">args</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># static selection policy (the name of a quantification-oriented error function to minimize)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_accuracy_policy</span><span class="p">(</span><span class="n">error_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">policy</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="s1">'Fit [Done]'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ptr'</span><span class="p">:</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ptr_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ds'</span><span class="p">:</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
|
||||
<span class="sd"> with the abstract class).</span>
|
||||
<span class="sd"> Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or</span>
|
||||
<span class="sd"> `Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for</span>
|
||||
<span class="sd"> classification (not recommended).</span>
|
||||
|
||||
<span class="sd"> :param parameters: dictionary</span>
|
||||
<span class="sd"> :return: raises an Exception</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> should not be used within GridSearchQ; '</span>
|
||||
<span class="sa">f</span><span class="s1">'instead, use Ensemble(GridSearchQ(q),...), with q a Quantifier (recommended), '</span>
|
||||
<span class="sa">f</span><span class="s1">'or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a classifier '</span>
|
||||
<span class="sa">f</span><span class="s1">'l optimized for classification (not recommended).'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
|
||||
<span class="sd"> with the abstract class).</span>
|
||||
<span class="sd"> Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or</span>
|
||||
<span class="sd"> `Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for</span>
|
||||
<span class="sd"> classification (not recommended).</span>
|
||||
|
||||
<span class="sd"> :param deep: for compatibility with scikit-learn</span>
|
||||
<span class="sd"> :return: raises an Exception</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).</span>
|
||||
<span class="sd"> For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of</span>
|
||||
<span class="sd"> the samples used for training the rest of the models in the ensemble.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_on_samples</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_name</span><span class="p">)</span>
|
||||
<span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">):</span>
|
||||
<span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">evaluate_on_samples</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">tests</span><span class="p">[:</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">tests</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">error</span><span class="p">))</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">_select_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Selects the predictions made by models that have been trained on samples with a prevalence that is most similar</span>
|
||||
<span class="sd"> to a first approximation of the test prevalence as made by all models in the ensemble.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">test_prev_estim</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">tr_prevs</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
|
||||
<span class="n">ptr_differences</span> <span class="o">=</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">ptr_i</span><span class="p">,</span> <span class="n">test_prev_estim</span><span class="p">)</span> <span class="k">for</span> <span class="n">ptr_i</span> <span class="ow">in</span> <span class="n">tr_prevs</span><span class="p">]</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ptr_differences</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> In the original article, there are some aspects regarding this method that are not mentioned. The paper says</span>
|
||||
<span class="sd"> that the distribution of posterior probabilities from training and test examples is compared by means of the</span>
|
||||
<span class="sd"> Hellinger Distance. However, how these posterior probabilities are generated is not specified. In the article,</span>
|
||||
<span class="sd"> a Logistic Regressor (LR) is used as the classifier device and that could be used for this purpose. However, in</span>
|
||||
<span class="sd"> general, a Quantifier is not necessarily an instance of Aggreggative Probabilistic Quantifiers, and so, that the</span>
|
||||
<span class="sd"> quantifier builds on top of a probabilistic classifier cannot be given for granted. Additionally, it would not</span>
|
||||
<span class="sd"> be correct to generate the posterior probabilities for training instances that have concurred in training the</span>
|
||||
<span class="sd"> classifier that generates them.</span>
|
||||
|
||||
<span class="sd"> This function thus generates the posterior probabilities for all training documents in a cross-validation way,</span>
|
||||
<span class="sd"> using LR with hyperparameters that have previously been optimized via grid search in 5FCV.</span>
|
||||
|
||||
<span class="sd"> :param data: a LabelledCollection</span>
|
||||
<span class="sd"> :return: (P,f,) where P is an ndarray containing the posterior probabilities of the training data, generated via</span>
|
||||
<span class="sd"> cross-validation and using an optimized LR, and the function to be used in order to generate posterior</span>
|
||||
<span class="sd"> probabilities for test instances.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<span class="n">lr_base</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">class_weight</span><span class="o">=</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
|
||||
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">)}</span>
|
||||
<span class="n">optim</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">lr_base</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">cross_val_predict</span><span class="p">(</span><span class="n">optim</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'predict_proba'</span><span class="p">)</span>
|
||||
<span class="n">posteriors_generator</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">best_estimator_</span><span class="o">.</span><span class="n">predict_proba</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">posteriors_generator</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
|
||||
<span class="n">test_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
|
||||
<span class="n">test_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">test_posteriors</span><span class="p">)</span>
|
||||
<span class="n">tr_distributions</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">HellingerDistance</span><span class="p">(</span><span class="n">tr_dist_i</span><span class="p">,</span> <span class="n">test_distribution</span><span class="p">)</span> <span class="k">for</span> <span class="n">tr_dist_i</span> <span class="ow">in</span> <span class="n">tr_distributions</span><span class="p">]</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates that the quantifier is not aggregative.</span>
|
||||
|
||||
<span class="sd"> :return: False</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates that the quantifier is not probabilistic.</span>
|
||||
|
||||
<span class="sd"> :return: False</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="kc">False</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_probability_distribution">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets a histogram out of the posterior probabilities (only for the binary case).</span>
|
||||
|
||||
<span class="sd"> :param posterior_probabilities: array-like of shape `(n_instances, 2,)`</span>
|
||||
<span class="sd"> :param bins: integer</span>
|
||||
<span class="sd"> :return: `np.ndarray` with the relative frequencies for each bin (for the positive class only)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">posterior_probabilities</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'the posterior probabilities do not seem to be for a binary problem'</span>
|
||||
<span class="n">posterior_probabilities</span> <span class="o">=</span> <span class="n">posterior_probabilities</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="c1"># take the positive posteriors only</span>
|
||||
<span class="n">distribution</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">distribution</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[:</span><span class="n">k</span><span class="p">]]</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">keep_samples</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">fit-start for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">, sample_size=</span><span class="si">{</span><span class="n">sample_size</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val_split</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">val_split</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'val_split should be in (0,1)'</span>
|
||||
<span class="n">data</span><span class="p">,</span> <span class="n">val_split</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample_index</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prev</span><span class="p">)</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">sample_index</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tr_prevalence</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">tr_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">posteriors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">])</span> <span class="k">if</span> <span class="p">(</span><span class="n">posteriors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">\--fit-ended for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tr_prevalence</span><span class="p">,</span> <span class="n">tr_distribution</span><span class="p">,</span> <span class="n">sample</span> <span class="k">if</span> <span class="n">keep_samples</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">quantifier</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions</span>
|
||||
<span class="sd"> are >= min_class_prev (for min_val>0, this makes the sampling not truly uniform)</span>
|
||||
|
||||
<span class="sd"> :param ndim: number of dimensions of the simplex</span>
|
||||
<span class="sd"> :param min_val: minimum class prevalence allowed. If less than 1/ndim a ValueError will be throw since</span>
|
||||
<span class="sd"> there is no possible solution.</span>
|
||||
<span class="sd"> :return: a sample from the ndim-dimensional simplex that is uniform in S(ndim)-R where S(ndim) is the simplex</span>
|
||||
<span class="sd"> and R is the simplex subset containing dimensions lower than min_val</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">min_val</span> <span class="o">>=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">ndim</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'no sample can be draw from the </span><span class="si">{</span><span class="n">ndim</span><span class="si">}</span><span class="s1">-dimensional simplex so that '</span>
|
||||
<span class="sa">f</span><span class="s1">'all its values are >=</span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> (try with a larger value for min_pos)'</span><span class="p">)</span>
|
||||
<span class="n">trials</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
|
||||
<span class="n">u</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">uniform_simplex_sampling</span><span class="p">(</span><span class="n">ndim</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">u</span> <span class="o">>=</span> <span class="n">min_val</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">u</span>
|
||||
<span class="n">trials</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">trials</span> <span class="o">>=</span> <span class="n">max_trials</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'it looks like finding a random simplex with all its dimensions being'</span>
|
||||
<span class="sa">f</span><span class="s1">'>= </span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> is unlikely (it failed after </span><span class="si">{</span><span class="n">max_trials</span><span class="si">}</span><span class="s1"> trials)'</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">optim</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">optim</span> <span class="o">==</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">f1e</span><span class="p">:</span>
|
||||
<span class="n">scoring</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">f1_score</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">optim</span> <span class="o">==</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">acce</span><span class="p">:</span>
|
||||
<span class="n">scoring</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span>
|
||||
<span class="n">classifier</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">)</span>
|
||||
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span><span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">),</span>
|
||||
<span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="n">param_model_sel</span><span class="p">,</span>
|
||||
<span class="n">error</span><span class="o">=</span><span class="n">optim</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span> <span class="ow">or</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">error</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">'</span>
|
||||
<span class="sa">f</span><span class="s1">'the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ERROR_NAMES</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ensembleFactory">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model</span>
|
||||
<span class="sd"> selection for quantification) for a given evaluation metric using :class:`quapy.model_selection.GridSearchQ`.</span>
|
||||
<span class="sd"> If the evaluation metric is classification-oriented</span>
|
||||
<span class="sd"> (instead of quantification-oriented), then the optimization will be carried out via sklearn's</span>
|
||||
<span class="sd"> `GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>`_.</span>
|
||||
|
||||
<span class="sd"> Example to instantiate an :class:`Ensemble` based on :class:`quapy.method.aggregative.PACC`</span>
|
||||
<span class="sd"> in which the base members are optimized for :meth:`quapy.error.mae` via</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`. The ensemble follows the policy `Accuracy` based</span>
|
||||
<span class="sd"> on :meth:`quapy.error.mae` (the same measure being optimized),</span>
|
||||
<span class="sd"> meaning that a static selection of members of the ensemble is made based on their performance</span>
|
||||
<span class="sd"> in terms of this error.</span>
|
||||
|
||||
<span class="sd"> >>> param_grid = {</span>
|
||||
<span class="sd"> >>> 'C': np.logspace(-3,3,7),</span>
|
||||
<span class="sd"> >>> 'class_weight': ['balanced', None]</span>
|
||||
<span class="sd"> >>> }</span>
|
||||
<span class="sd"> >>> param_mod_sel = {</span>
|
||||
<span class="sd"> >>> 'sample_size': 500,</span>
|
||||
<span class="sd"> >>> 'protocol': 'app'</span>
|
||||
<span class="sd"> >>> }</span>
|
||||
<span class="sd"> >>> common={</span>
|
||||
<span class="sd"> >>> 'max_sample_size': 1000,</span>
|
||||
<span class="sd"> >>> 'n_jobs': -1,</span>
|
||||
<span class="sd"> >>> 'param_grid': param_grid,</span>
|
||||
<span class="sd"> >>> 'param_mod_sel': param_mod_sel,</span>
|
||||
<span class="sd"> >>> }</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> ensembleFactory(LogisticRegression(), PACC, optim='mae', policy='mae', **common)</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param base_quantifier_class: a class of quantifiers</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">param_grid</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'param_grid is None but optim was requested.'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">param_model_sel</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'param_model_sel is None but optim was requested.'</span><span class="p">)</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">_check_error</span><span class="p">(</span><span class="n">optim</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ECC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
||||
<span class="sd"> Equivalent to:</span>
|
||||
|
||||
<span class="sd"> >>> ensembleFactory(classifier, CC, param_grid, optim, param_mod_sel, **kwargs)</span>
|
||||
|
||||
<span class="sd"> See :meth:`ensembleFactory` for further details.</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">CC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EACC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
||||
<span class="sd"> Equivalent to:</span>
|
||||
|
||||
<span class="sd"> >>> ensembleFactory(classifier, ACC, param_grid, optim, param_mod_sel, **kwargs)</span>
|
||||
|
||||
<span class="sd"> See :meth:`ensembleFactory` for further details.</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EPACC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.</span>
|
||||
|
||||
<span class="sd"> Equivalent to:</span>
|
||||
|
||||
<span class="sd"> >>> ensembleFactory(classifier, PACC, param_grid, optim, param_mod_sel, **kwargs)</span>
|
||||
|
||||
<span class="sd"> See :meth:`ensembleFactory` for further details.</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EHDy">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
||||
<span class="sd"> Equivalent to:</span>
|
||||
|
||||
<span class="sd"> >>> ensembleFactory(classifier, HDy, param_grid, optim, param_mod_sel, **kwargs)</span>
|
||||
|
||||
<span class="sd"> See :meth:`ensembleFactory` for further details.</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EEMQ">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.</span>
|
||||
|
||||
<span class="sd"> Equivalent to:</span>
|
||||
|
||||
<span class="sd"> >>> ensembleFactory(classifier, EMQ, param_grid, optim, param_mod_sel, **kwargs)</span>
|
||||
|
||||
<span class="sd"> See :meth:`ensembleFactory` for further details.</span>
|
||||
|
||||
<span class="sd"> :param classifier: sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with the grid of parameters to optimize for</span>
|
||||
<span class="sd"> :param optim: a valid quantification or classification error, or a string name of it</span>
|
||||
<span class="sd"> :param param_model_sel: a dictionary containing any keyworded argument to pass to</span>
|
||||
<span class="sd"> :class:`quapy.model_selection.GridSearchQ`</span>
|
||||
<span class="sd"> :param kwargs: kwargs for the class :class:`Ensemble`</span>
|
||||
<span class="sd"> :return: an instance of :class:`Ensemble`</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
</pre></div>
|
||||
|
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<li class="breadcrumb-item active">quapy.method.non_aggregative</li>
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<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">get_divergence</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior</span>
|
||||
<span class="sd"> probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).</span>
|
||||
<span class="sd"> The estimation of class prevalence values for any test sample is always (i.e., irrespective of the test sample</span>
|
||||
<span class="sd"> itself) the class prevalence seen during training. This method is considered to be a lower-bound quantifier that</span>
|
||||
<span class="sd"> any quantification method should beat.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the training prevalence and stores it.</span>
|
||||
|
||||
<span class="sd"> :param data: the training sample</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
|
||||
|
||||
<span class="sd"> :param instances: array-like (ignored)</span>
|
||||
<span class="sd"> :return: the class prevalence seen during training</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DMx">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.</span>
|
||||
<span class="sd"> This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</span>
|
||||
|
||||
<span class="sd"> :param nbins: number of bins used to discretize the distributions (default 8)</span>
|
||||
<span class="sd"> :param divergence: a string representing a divergence measure (currently, "HD" and "topsoe" are implemented)</span>
|
||||
<span class="sd"> or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger</span>
|
||||
<span class="sd"> Distance)</span>
|
||||
<span class="sd"> :param cdf: whether to use CDF instead of PDF (default False)</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">'HD'</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">'optim_minimize'</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="n">cdf</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
|
||||
<div class="viewcode-block" id="DMx.HDx">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.HDx">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> `Hellinger Distance x <https://www.sciencedirect.com/science/article/pii/S0020025512004069>`_ (HDx).</span>
|
||||
<span class="sd"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
|
||||
<span class="sd"> minimizing the average divergence (in terms of the Hellinger Distance) across the feature-specific normalized</span>
|
||||
<span class="sd"> histograms of two representations, one for the unlabelled examples, and another generated from the training</span>
|
||||
<span class="sd"> examples as a mixture model of the class-specific representations. The parameters of the mixture thus represent</span>
|
||||
<span class="sd"> the estimates of the class prevalence values.</span>
|
||||
|
||||
<span class="sd"> The method computes all matchings for nbins in [10, 20, ..., 110] and reports the mean of the median.</span>
|
||||
<span class="sd"> The best prevalence is searched via linear search, from 0 to 1 stepping by 0.01.</span>
|
||||
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span>
|
||||
<span class="sd"> González-Castro, Alaiz-Rodríguez, Alegre (2013)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span>
|
||||
|
||||
<span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">'HD'</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">'linear_search'</span><span class="p">)</span>
|
||||
<span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'nbins'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span>
|
||||
<span class="n">hdx</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">dmx</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">hdx</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span>
|
||||
<span class="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
|
||||
<span class="n">feat_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span><span class="p">[</span><span class="n">feat_idx</span><span class="p">]</span>
|
||||
<span class="n">hist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nbins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">feat_range</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">norm_hist</span> <span class="o">=</span> <span class="n">hist</span> <span class="o">/</span> <span class="n">hist</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="n">histograms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm_hist</span><span class="p">)</span>
|
||||
<span class="n">distributions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">histograms</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">:</span>
|
||||
<span class="n">distributions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">distributions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">distributions</span>
|
||||
|
||||
<div class="viewcode-block" id="DMx.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates the validation distributions out of the training data (covariates).</span>
|
||||
<span class="sd"> The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span>
|
||||
<span class="sd"> the number of features, and `nbins` the number of bins.</span>
|
||||
<span class="sd"> In particular, let `V` be the validation distributions; then `di=V[i]` are the distributions obtained from</span>
|
||||
<span class="sd"> training data labelled with class `i`; while `dij = di[j]` is the discrete distribution for feature j in</span>
|
||||
<span class="sd"> training data labelled with class `i`, and `dij[k]` is the fraction of instances with a value in the `k`-th bin.</span>
|
||||
|
||||
<span class="sd"> :param data: the training set</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Xy</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)]</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DMx.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
|
||||
<span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
|
||||
<span class="sd"> The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)</span>
|
||||
<span class="sd"> between all feature-specific discrete distributions.</span>
|
||||
|
||||
<span class="sd"> :param instances: instances in the sample</span>
|
||||
<span class="sd"> :return: a vector of class prevalence estimates</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span>
|
||||
|
||||
<span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">divs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ncols</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>
|
||||
<span class="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="n">col_idx</span><span class="p">]</span>
|
||||
<span class="n">feat_ranges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">feature</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">feature</span><span class="p">)))</span>
|
||||
<span class="k">return</span> <span class="n">feat_ranges</span>
|
||||
|
||||
|
||||
<span class="c1">#---------------------------------------------------------------</span>
|
||||
<span class="c1"># aliases</span>
|
||||
<span class="c1">#---------------------------------------------------------------</span>
|
||||
|
||||
<span class="n">DistributionMatchingX</span> <span class="o">=</span> <span class="n">DMx</span>
|
||||
</pre></div>
|
||||
|
||||
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|
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<h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">import</span> <span class="nn">signal</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
|
||||
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">clone</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">evaluation</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">timeout</span>
|
||||
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Status">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
<span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span>
|
||||
<span class="n">ERROR</span> <span class="o">=</span> <span class="mi">4</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">ConfigStatus</span><span class="p">:</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="n">status</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="sa">f</span><span class="s1">':params:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s1"> :status:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s1"> '</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus.success">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus.failed">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Grid Search optimization targeting a quantification-oriented metric.</span>
|
||||
|
||||
<span class="sd"> Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span>
|
||||
<span class="sd"> protocol for quantification.</span>
|
||||
|
||||
<span class="sd"> :param model: the quantifier to optimize</span>
|
||||
<span class="sd"> :type model: BaseQuantifier</span>
|
||||
<span class="sd"> :param param_grid: a dictionary with keys the parameter names and values the list of values to explore</span>
|
||||
<span class="sd"> :param protocol: a sample generation protocol, an instance of :class:`quapy.protocol.AbstractProtocol`</span>
|
||||
<span class="sd"> :param error: an error function (callable) or a string indicating the name of an error function (valid ones</span>
|
||||
<span class="sd"> are those in :class:`quapy.error.QUANTIFICATION_ERROR`</span>
|
||||
<span class="sd"> :param refit: whether to refit the model on the whole labelled collection (training+validation) with</span>
|
||||
<span class="sd"> the best chosen hyperparameter combination. Ignored if protocol='gen'</span>
|
||||
<span class="sd"> :param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.</span>
|
||||
<span class="sd"> Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up</span>
|
||||
<span class="sd"> being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.</span>
|
||||
<span class="sd"> :param raise_errors: boolean, if True then raises an exception when a param combination yields any error, if</span>
|
||||
<span class="sd"> otherwise is False (default), then the combination is marked with an error status, but the process goes on.</span>
|
||||
<span class="sd"> However, if no configuration yields a valid model, then a ValueError exception will be raised.</span>
|
||||
<span class="sd"> :param verbose: set to True to get information through the stdout</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">error</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Callable</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">,</span>
|
||||
<span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">raise_errors</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">protocol</span> <span class="o">=</span> <span class="n">protocol</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">refit</span> <span class="o">=</span> <span class="n">refit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">timeout</span> <span class="o">=</span> <span class="n">timeout</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span> <span class="o">=</span> <span class="n">raise_errors</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__check_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">'unknown protocol'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="s1">'__call__'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">'</span>
|
||||
<span class="sa">f</span><span class="s1">'the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">predictions</span>
|
||||
|
||||
<span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[classifier fit] hyperparams=</span><span class="si">{</span><span class="n">cls_params</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cls_params</span><span class="p">,</span> <span class="o">**</span><span class="n">q_params</span><span class="p">}</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">score</span>
|
||||
|
||||
<span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">q_params</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">score</span>
|
||||
|
||||
<span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Decides whether to break down the fit phase in two (classifier-fit followed by aggregation-fit).</span>
|
||||
<span class="sd"> In order to do so, some conditions should be met: a) the quantifier is of type aggregative,</span>
|
||||
<span class="sd"> b) the set of hyperparameters can be split into two disjoint non-empty groups.</span>
|
||||
|
||||
<span class="sd"> :return: True if the conditions are met, False otherwise</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cls_configs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">q_configs</span><span class="p">)</span><span class="o">==</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="k">return</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span>
|
||||
<span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># train all classifiers and get the predictions</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span>
|
||||
<span class="n">cls_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_classifier</span><span class="p">,</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="c1"># filter out classifier configurations that yielded any error</span>
|
||||
<span class="n">success_outs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">),</span> <span class="n">cls_config</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">cls_outs</span><span class="p">,</span> <span class="n">cls_configs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
|
||||
<span class="n">success_outs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">took</span><span class="p">,</span> <span class="n">cls_config</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">success_outs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'No valid configuration found for the classifier!'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># explore the quantifier-specific hyperparameters for each valid training configuration</span>
|
||||
<span class="n">aggr_configs</span> <span class="o">=</span> <span class="p">[(</span><span class="o">*</span><span class="n">out</span><span class="p">,</span> <span class="n">q_config</span><span class="p">)</span> <span class="k">for</span> <span class="n">out</span><span class="p">,</span> <span class="n">q_config</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">success_outs</span><span class="p">,</span> <span class="n">q_configs</span><span class="p">)]</span>
|
||||
<span class="n">aggr_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_aggregation</span><span class="p">,</span>
|
||||
<span class="n">aggr_configs</span><span class="p">,</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">aggr_outs</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_nonaggr_model</span><span class="p">,</span>
|
||||
<span class="n">configs</span><span class="p">,</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">scores</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'hyperparams=[</span><span class="si">{</span><span class="n">params</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1"> got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error=</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.fit">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">""" Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span>
|
||||
<span class="sd"> the error metric.</span>
|
||||
|
||||
<span class="sd"> :param training: the training set on which to optimize the hyperparameters</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">RuntimeWarning</span><span class="p">(</span>
|
||||
<span class="sa">f</span><span class="s1">'"refit" was requested, but the protocol does not implement '</span>
|
||||
<span class="sa">f</span><span class="s1">'the </span><span class="si">{</span><span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> interface'</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'starting model selection with n_jobs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_break_down_fit</span><span class="p">():</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">score</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="n">score</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span> <span class="o">=</span> <span class="n">params</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span> <span class="o">=</span> <span class="n">model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">score</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">status</span><span class="o">.</span><span class="n">status</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'no combination of hyperparameters seemed to work'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'optimization finished: best params </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1"> (score=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">) '</span>
|
||||
<span class="sa">f</span><span class="s1">'[took </span><span class="si">{</span><span class="n">tend</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">no_errors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">no_errors</span><span class="o">></span><span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'warning: </span><span class="si">{</span><span class="n">no_errors</span><span class="si">}</span><span class="s1"> errors found'</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">err</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'refitting on the whole development set'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span>
|
||||
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">refit_time_</span> <span class="o">=</span> <span class="n">tend</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># already checked</span>
|
||||
<span class="k">raise</span> <span class="ne">RuntimeWarning</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the model cannot be refit on the whole dataset'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.quantify">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.quantify">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
|
||||
|
||||
<span class="sd"> :param instances: sample contanining the instances</span>
|
||||
<span class="sd"> :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found</span>
|
||||
<span class="sd"> by the model selection process.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'best_model_'</span><span class="p">),</span> <span class="s1">'quantify called before fit'</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.set_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Sets the hyper-parameters to explore.</span>
|
||||
|
||||
<span class="sd"> :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">parameters</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.get_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Returns the dictionary of hyper-parameters to explore (`param_grid`)</span>
|
||||
|
||||
<span class="sd"> :param deep: Unused</span>
|
||||
<span class="sd"> :return: the dictionary `param_grid`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.best_model">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination</span>
|
||||
<span class="sd"> of hyper-parameters that minimized the error function.</span>
|
||||
|
||||
<span class="sd"> :return: a trained quantifier</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'best_model_'</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'best_model called before fit'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Endorses one job with two returned values: the status, and the time of execution</span>
|
||||
|
||||
<span class="sd"> :param func: the function to be called</span>
|
||||
<span class="sd"> :param params: parameters of the function</span>
|
||||
<span class="sd"> :return: `tuple(out, status, time)` where `out` is the function output,</span>
|
||||
<span class="sd"> `status` is an enum value from `Status`, and `time` is the time it</span>
|
||||
<span class="sd"> took to complete the call</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="n">exception</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">):</span>
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">status</span> <span class="o">=</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span><span class="p">)</span>
|
||||
|
||||
<span class="k">except</span> <span class="ne">TimeoutError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">TIMEOUT</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
|
||||
|
||||
<span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">INVALID</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
|
||||
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">ERROR</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
|
||||
|
||||
<span class="n">took</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
|
||||
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="cross_val_predict">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Akin to `scikit-learn's cross_val_predict <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html>`_</span>
|
||||
<span class="sd"> but for quantification.</span>
|
||||
|
||||
<span class="sd"> :param quantifier: a quantifier issuing class prevalence values</span>
|
||||
<span class="sd"> :param data: a labelled collection</span>
|
||||
<span class="sd"> :param nfolds: number of folds for k-fold cross validation generation</span>
|
||||
<span class="sd"> :param random_state: random seed for reproducibility</span>
|
||||
<span class="sd"> :return: a vector of class prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">total_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
<span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">rel_size</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">total_prev</span> <span class="o">+=</span> <span class="n">fold_prev</span><span class="o">*</span><span class="n">rel_size</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">total_prev</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="expand_grid">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Expands a param_grid dictionary as a list of configurations.</span>
|
||||
<span class="sd"> Example:</span>
|
||||
|
||||
<span class="sd"> >>> combinations = expand_grid({'A': [1, 10, 100], 'B': [True, False]})</span>
|
||||
<span class="sd"> >>> print(combinations)</span>
|
||||
<span class="sd"> >>> [{'A': 1, 'B': True}, {'A': 1, 'B': False}, {'A': 10, 'B': True}, {'A': 10, 'B': False}, {'A': 100, 'B': True}, {'A': 100, 'B': False}]</span>
|
||||
|
||||
<span class="sd"> :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range</span>
|
||||
<span class="sd"> to explore for that hyper-parameter</span>
|
||||
<span class="sd"> :return: a list of configurations, i.e., combinations of hyper-parameter assignments in the grid.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">params_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_grid</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
||||
<span class="n">params_values</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_grid</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="p">[{</span><span class="n">k</span><span class="p">:</span> <span class="n">combs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params_keys</span><span class="p">)}</span> <span class="k">for</span> <span class="n">combs</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">params_values</span><span class="p">)]</span>
|
||||
<span class="k">return</span> <span class="n">configs</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="group_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific</span>
|
||||
<span class="sd"> hyper-parameters, and another for que quantifier-specific hyper-parameters</span>
|
||||
|
||||
<span class="sd"> :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range</span>
|
||||
<span class="sd"> to explore for that hyper-parameter</span>
|
||||
<span class="sd"> :return: two expanded grids of configurations, one for the classifier, another for the quantifier</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">classifier_params</span><span class="p">,</span> <span class="n">quantifier_params</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">param_grid</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'classifier__'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'val_split'</span><span class="p">:</span>
|
||||
<span class="n">classifier_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">quantifier_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
|
||||
|
||||
<span class="n">classifier_configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="n">classifier_params</span><span class="p">)</span>
|
||||
<span class="n">quantifier_configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="n">quantifier_params</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">classifier_configs</span><span class="p">,</span> <span class="n">quantifier_configs</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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<div itemprop="articleBody">
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<h1>Source code for quapy.plot</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib.cm</span> <span class="kn">import</span> <span class="n">get_cmap</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind_from_stats</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">ScalarFormatter</span>
|
||||
<span class="kn">import</span> <span class="nn">math</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.dpi'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'font.size'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">18</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binary_diagonal">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">train_prev</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span>
|
||||
<span class="sd"> values (along the x-axis). The optimal quantifier is described by the diagonal (0,0)-(1,1) of the plot (hence the</span>
|
||||
<span class="sd"> name). It is convenient for binary quantification problems, though it can be used for multiclass problems by</span>
|
||||
<span class="sd"> indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots</span>
|
||||
<span class="sd"> like the :meth:`error_by_drift` might be preferable though).</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient</span>
|
||||
<span class="sd"> for cases in which many methods are compared, or when the standard deviations are high -- default True)</span>
|
||||
<span class="sd"> :param legend: whether or not to display the leyend (default True)</span>
|
||||
<span class="sd"> :param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted</span>
|
||||
<span class="sd"> in the plot. This is convenient when all the experiments have been conducted in the same dataset.</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">'equal'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s1">'--k'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'ideal'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">table</span> <span class="o">=</span> <span class="p">{</span><span class="n">method_name</span><span class="p">:[</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">]</span> <span class="k">for</span> <span class="n">method_name</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="n">order</span><span class="p">}</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="p">[(</span><span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">table</span><span class="p">[</span><span class="n">method_name</span><span class="p">])</span> <span class="k">for</span> <span class="n">method_name</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">]</span>
|
||||
|
||||
<span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">NUM_COLORS</span><span class="o">></span><span class="mi">10</span><span class="p">:</span>
|
||||
<span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'tab20'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span>
|
||||
<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
|
||||
<span class="n">true_prev</span> <span class="o">=</span> <span class="n">true_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
<span class="n">estim_prev</span> <span class="o">=</span> <span class="n">estim_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
|
||||
<span class="n">x_ticks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">true_prev</span><span class="p">)</span>
|
||||
<span class="n">x_ticks</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="n">y_ave</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">estim_prev</span><span class="p">[</span><span class="n">true_prev</span> <span class="o">==</span> <span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">x_ticks</span><span class="p">])</span>
|
||||
<span class="n">y_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">estim_prev</span><span class="p">[</span><span class="n">true_prev</span> <span class="o">==</span> <span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">x_ticks</span><span class="p">])</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">x_ticks</span><span class="p">,</span> <span class="n">y_ave</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">method</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">show_std</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x_ticks</span><span class="p">,</span> <span class="n">y_ave</span> <span class="o">-</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">y_ave</span> <span class="o">+</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">train_prev</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">train_prev</span> <span class="o">=</span> <span class="n">train_prev</span><span class="p">[</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">train_prev</span><span class="p">,</span> <span class="n">train_prev</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'c'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'tr-prev'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">'true prevalence'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">'estimated prevalence'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
|
||||
<span class="c1"># box = ax.get_position()</span>
|
||||
<span class="c1"># ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])</span>
|
||||
<span class="c1"># ax.legend(loc='lower center',</span>
|
||||
<span class="c1"># bbox_to_anchor=(1, -0.5),</span>
|
||||
<span class="c1"># ncol=(len(method_names)+1)//2)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binary_bias_global">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)</span>
|
||||
<span class="sd"> for each quantification method with respect to a given positive class.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
|
||||
|
||||
<span class="n">data</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">true_prev</span> <span class="o">=</span> <span class="n">true_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
<span class="n">estim_prev</span> <span class="o">=</span> <span class="n">estim_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">estim_prev</span><span class="o">-</span><span class="n">true_prev</span><span class="p">)</span>
|
||||
<span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">patch_artist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="s1">'error bias'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binary_bias_bins">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
|
||||
<span class="n">vertical_xticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span>
|
||||
<span class="sd"> for different bins of (true) prevalence of the positive classs, for each quantification method.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param nbins: number of bins</span>
|
||||
<span class="sd"> :param colormap: the matplotlib colormap to use (default cm.tab10)</span>
|
||||
<span class="sd"> :param vertical_xticks: whether or not to add secondary grid (default is False)</span>
|
||||
<span class="sd"> :param legend: whether or not to display the legend (default is True)</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nbins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">binwidth</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">nbins</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">true_prev</span> <span class="o">=</span> <span class="n">true_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
<span class="n">estim_prev</span> <span class="o">=</span> <span class="n">estim_prev</span><span class="p">[:,</span><span class="n">pos_class</span><span class="p">]</span>
|
||||
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">digitize</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">)):</span>
|
||||
<span class="n">selected</span> <span class="o">=</span> <span class="n">inds</span><span class="o">==</span><span class="n">ind</span>
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">estim_prev</span><span class="p">[</span><span class="n">selected</span><span class="p">]</span> <span class="o">-</span> <span class="n">true_prev</span><span class="p">[</span><span class="n">selected</span><span class="p">])</span>
|
||||
|
||||
<span class="n">nmethods</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)</span>
|
||||
<span class="n">boxwidth</span> <span class="o">=</span> <span class="n">binwidth</span><span class="o">/</span><span class="p">(</span><span class="n">nmethods</span><span class="o">+</span><span class="mi">4</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="nb">bin</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">boxdata</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">method_names</span><span class="p">]</span>
|
||||
<span class="n">positions</span> <span class="o">=</span> <span class="p">[</span><span class="nb">bin</span><span class="o">+</span><span class="p">(</span><span class="n">i</span><span class="o">*</span><span class="n">boxwidth</span><span class="p">)</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">boxwidth</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">method_names</span><span class="p">)]</span>
|
||||
<span class="n">box</span> <span class="o">=</span> <span class="n">boxplot</span><span class="p">(</span><span class="n">boxdata</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">positions</span><span class="o">=</span><span class="n">positions</span><span class="p">,</span> <span class="n">widths</span> <span class="o">=</span> <span class="n">boxwidth</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="s1">'+'</span><span class="p">,</span> <span class="n">patch_artist</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">boxid</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)):</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="n">colormap</span><span class="o">.</span><span class="n">colors</span><span class="p">[</span><span class="n">boxid</span><span class="o">%</span><span class="nb">len</span><span class="p">(</span><span class="n">colormap</span><span class="o">.</span><span class="n">colors</span><span class="p">)]</span>
|
||||
<span class="n">setp</span><span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="s1">'fliers'</span><span class="p">][</span><span class="n">boxid</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'+'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">3.</span><span class="p">,</span> <span class="n">markeredgecolor</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="n">setp</span><span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="s1">'boxes'</span><span class="p">][</span><span class="n">boxid</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="n">setp</span><span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="s1">'medians'</span><span class="p">][</span><span class="n">boxid</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">major_xticks_positions</span><span class="p">,</span> <span class="n">minor_xticks_positions</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="n">major_xticks_labels</span><span class="p">,</span> <span class="n">minor_xticks_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">b</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">major_xticks_positions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
|
||||
<span class="n">minor_xticks_positions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">b</span> <span class="o">+</span> <span class="n">binwidth</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">major_xticks_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
|
||||
<span class="n">minor_xticks_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[</span><span class="si">{</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">major_xticks_positions</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">minor_xticks_positions</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">major_xticks_labels</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">minor_xticks_labels</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">'vertical'</span> <span class="k">if</span> <span class="n">vertical_xticks</span> <span class="k">else</span> <span class="s1">'horizontal'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">vertical_xticks</span><span class="p">:</span>
|
||||
<span class="c1"># Pad margins so that markers don't get clipped by the axes</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">margins</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)</span>
|
||||
<span class="c1"># Tweak spacing to prevent clipping of tick-labels</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mf">0.15</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
|
||||
<span class="c1"># adds the legend to the list hs, initialized with the "ideal" quantifier (one that has 0 bias across all bins. i.e.</span>
|
||||
<span class="c1"># a line from (0,0) to (1,0). The other elements are simply labelled dot-plots that are to be removed (setting</span>
|
||||
<span class="c1"># set_visible to False for all but the first element) after the legend has been placed</span>
|
||||
<span class="n">hs</span><span class="o">=</span><span class="p">[</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="s1">'-k'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span>
|
||||
<span class="k">for</span> <span class="n">colorid</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)):</span>
|
||||
<span class="n">color</span><span class="o">=</span><span class="n">colormap</span><span class="o">.</span><span class="n">colors</span><span class="p">[</span><span class="n">colorid</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">colormap</span><span class="o">.</span><span class="n">colors</span><span class="p">)]</span>
|
||||
<span class="n">h</span><span class="p">,</span> <span class="o">=</span> <span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="s1">'-s'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span><span class="n">mec</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
|
||||
<span class="n">hs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
|
||||
<span class="n">box</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">box</span><span class="o">.</span><span class="n">x0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">y0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">width</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">height</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">hs</span><span class="p">,</span> <span class="p">[</span><span class="s1">'ideal'</span><span class="p">]</span><span class="o">+</span><span class="n">method_names</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
|
||||
<span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">hs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]</span>
|
||||
|
||||
<span class="c1"># x-axis and y-axis labels and limits</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">'prevalence'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">'error bias'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="error_by_drift">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">error_name</span><span class="o">=</span><span class="s1">'ae'</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">show_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">show_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">logscale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Quantification error as a function of distribution shift'</span><span class="p">,</span>
|
||||
<span class="n">vlines</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Plots the error (along the x-axis, as measured in terms of `error_name`) as a function of the train-test shift</span>
|
||||
<span class="sd"> (along the y-axis, as measured in terms of :meth:`quapy.error.ae`). This plot is useful especially for multiclass</span>
|
||||
<span class="sd"> problems, in which "diagonal plots" may be cumbersone, and in order to gain understanding about how methods</span>
|
||||
<span class="sd"> fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the</span>
|
||||
<span class="sd"> high-shift regime).</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
|
||||
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
|
||||
<span class="sd"> :param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is "ae")</span>
|
||||
<span class="sd"> :param show_std: whether or not to show standard deviations as color bands (default is False)</span>
|
||||
<span class="sd"> :param show_density: whether or not to display the distribution of experiments for each bin (default is True)</span>
|
||||
<span class="sd"> :param show_density: whether or not to display the legend of the chart (default is True)</span>
|
||||
<span class="sd"> :param logscale: whether or not to log-scale the y-error measure (default is False)</span>
|
||||
<span class="sd"> :param title: title of the plot (default is "Quantification error as a function of distribution shift")</span>
|
||||
<span class="sd"> :param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space</span>
|
||||
<span class="sd"> using vertical dotted lines.</span>
|
||||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
|
||||
|
||||
<span class="n">x_error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ae</span>
|
||||
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">error_name</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same</span>
|
||||
<span class="c1"># order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to</span>
|
||||
<span class="c1"># x_error function) and 'y' is the estim-test shift (computed as according to y_error)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="n">method_names</span>
|
||||
|
||||
<span class="n">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">method_order</span><span class="p">))</span>
|
||||
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">binwidth</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">n_bins</span>
|
||||
<span class="n">min_x</span><span class="p">,</span> <span class="n">max_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">,</span> <span class="n">max_y</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="n">npoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">:</span>
|
||||
<span class="n">tr_test_drifts</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'x'</span><span class="p">]</span>
|
||||
<span class="n">method_drifts</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'y'</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">logscale</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">ScalarFormatter</span><span class="p">())</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">get_major_formatter</span><span class="p">()</span><span class="o">.</span><span class="n">set_scientific</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">minorticks_off</span><span class="p">()</span>
|
||||
|
||||
<span class="n">inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">digitize</span><span class="p">(</span><span class="n">tr_test_drifts</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">ystds</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">p</span><span class="p">,</span><span class="n">ind</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">))):</span>
|
||||
<span class="n">selected</span> <span class="o">=</span> <span class="n">inds</span><span class="o">==</span><span class="n">ind</span>
|
||||
<span class="k">if</span> <span class="n">selected</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">xs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ind</span><span class="o">*</span><span class="n">binwidth</span><span class="o">-</span><span class="n">binwidth</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">method_drifts</span><span class="p">[</span><span class="n">selected</span><span class="p">]))</span>
|
||||
<span class="n">ystds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">method_drifts</span><span class="p">[</span><span class="n">selected</span><span class="p">]))</span>
|
||||
<span class="n">npoints</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">method_drifts</span><span class="p">[</span><span class="n">selected</span><span class="p">])</span>
|
||||
|
||||
<span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span>
|
||||
<span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
|
||||
<span class="n">ystds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ystds</span><span class="p">)</span>
|
||||
|
||||
<span class="n">min_x_method</span><span class="p">,</span> <span class="n">max_x_method</span><span class="p">,</span> <span class="n">min_y_method</span><span class="p">,</span> <span class="n">max_y_method</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xs</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">ys</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">ys</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
|
||||
<span class="n">min_x</span> <span class="o">=</span> <span class="n">min_x_method</span> <span class="k">if</span> <span class="n">min_x</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">min_x_method</span> <span class="o"><</span> <span class="n">min_x</span> <span class="k">else</span> <span class="n">min_x</span>
|
||||
<span class="n">max_x</span> <span class="o">=</span> <span class="n">max_x_method</span> <span class="k">if</span> <span class="n">max_x</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_x_method</span> <span class="o">></span> <span class="n">max_x</span> <span class="k">else</span> <span class="n">max_x</span>
|
||||
<span class="n">max_y</span> <span class="o">=</span> <span class="n">max_y_method</span> <span class="k">if</span> <span class="n">max_y</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_y_method</span> <span class="o">></span> <span class="n">max_y</span> <span class="k">else</span> <span class="n">max_y</span>
|
||||
<span class="n">min_y</span> <span class="o">=</span> <span class="n">min_y_method</span> <span class="k">if</span> <span class="n">min_y</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">min_y_method</span> <span class="o"><</span> <span class="n">min_y</span> <span class="k">else</span> <span class="n">min_y</span>
|
||||
<span class="n">max_y</span> <span class="o">=</span> <span class="n">max_y_method</span> <span class="k">if</span> <span class="n">max_y</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_y_method</span> <span class="o">></span> <span class="n">max_y</span> <span class="k">else</span> <span class="n">max_y</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">method</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">show_std</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="o">-</span><span class="n">ystds</span><span class="p">,</span> <span class="n">ys</span><span class="o">+</span><span class="n">ystds</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">show_density</span><span class="p">:</span>
|
||||
<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
|
||||
<span class="n">densities</span> <span class="o">=</span> <span class="n">npoints</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">npoints</span><span class="p">)</span>
|
||||
<span class="n">ax2</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="n">ind</span> <span class="o">*</span> <span class="n">binwidth</span><span class="o">-</span><span class="n">binwidth</span><span class="o">/</span><span class="mi">2</span> <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">))],</span>
|
||||
<span class="n">densities</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">binwidth</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'density'</span><span class="p">)</span>
|
||||
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">max</span><span class="p">(</span><span class="n">densities</span><span class="p">))</span>
|
||||
<span class="n">ax2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'g'</span><span class="p">)</span>
|
||||
<span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Distribution shift between training set and test sample'</span><span class="p">,</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">error_name</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s1"> (true distribution, predicted distribution)'</span><span class="p">,</span>
|
||||
<span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
<span class="n">box</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">box</span><span class="o">.</span><span class="n">x0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">y0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">width</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">height</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="n">vlines</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">vline</span> <span class="ow">in</span> <span class="n">vlines</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">vline</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">max_x</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">logscale</span><span class="p">:</span>
|
||||
<span class="c1">#nice scale for the logaritmic axis</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span> <span class="o">**</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_y</span><span class="p">)))</span>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="n">show_legend</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'lower center'</span><span class="p">,</span>
|
||||
<span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
|
||||
<span class="n">ncol</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="brokenbar_supremacy_by_drift">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">binning</span><span class="o">=</span><span class="s1">'isomerous'</span><span class="p">,</span>
|
||||
<span class="n">x_error</span><span class="o">=</span><span class="s1">'ae'</span><span class="p">,</span> <span class="n">y_error</span><span class="o">=</span><span class="s1">'ae'</span><span class="p">,</span> <span class="n">ttest_alpha</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">tail_density_threshold</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span>
|
||||
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Displays (only) the top performing methods for different regions of the train-test shift in form of a broken</span>
|
||||
<span class="sd"> bar chart, in which each method has bars only for those regions in which either one of the following conditions</span>
|
||||
<span class="sd"> hold: (i) it is the best method (in average) for the bin, or (ii) it is not statistically significantly different</span>
|
||||
<span class="sd"> (in average) as according to a two-sided t-test on independent samples at confidence `ttest_alpha`.</span>
|
||||
<span class="sd"> The binning can be made "isometric" (same size), or "isomerous" (same number of experiments -- default). A second</span>
|
||||
<span class="sd"> plot is displayed on top, that displays the distribution of experiments for each bin (when binning="isometric") or</span>
|
||||
<span class="sd"> the percentiles points of the distribution (when binning="isomerous").</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
|
||||
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
|
||||
<span class="sd"> :param binning: type of binning, either "isomerous" (default) or "isometric"</span>
|
||||
<span class="sd"> :param x_error: a string representing the name of an error function (as defined in `quapy.error`) to be used for</span>
|
||||
<span class="sd"> measuring the amount of train-test shift (default is "ae")</span>
|
||||
<span class="sd"> :param y_error: a string representing the name of an error function (as defined in `quapy.error`) to be used for</span>
|
||||
<span class="sd"> measuring the amount of error in the prevalence estimations (default is "ae")</span>
|
||||
<span class="sd"> :param ttest_alpha: the confidence interval above which a p-value (two-sided t-test on independent samples) is</span>
|
||||
<span class="sd"> to be considered as an indicator that the two means are not statistically significantly different. Default is</span>
|
||||
<span class="sd"> 0.005, meaning that a `p-value > 0.005` indicates the two methods involved are to be considered similar</span>
|
||||
<span class="sd"> :param tail_density_threshold: sets a threshold on the density of experiments (over the total number of experiments)</span>
|
||||
<span class="sd"> below which a bin in the tail (i.e., the right-most ones) will be discarded. This is in order to avoid some</span>
|
||||
<span class="sd"> bins to be shown for train-test outliers.</span>
|
||||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">binning</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'isomerous'</span><span class="p">,</span> <span class="s1">'isometric'</span><span class="p">],</span> <span class="s1">'unknown binning type; valid types are "isomerous" and "isometric"'</span>
|
||||
|
||||
<span class="n">x_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">x_error</span><span class="p">)</span>
|
||||
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same</span>
|
||||
<span class="c1"># order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to</span>
|
||||
<span class="c1"># x_error function) and 'y' is the estim-test shift (computed as according to y_error)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="n">method_names</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">binning</span> <span class="o">==</span> <span class="s1">'isomerous'</span><span class="p">:</span>
|
||||
<span class="c1"># take bins containing the same amount of examples</span>
|
||||
<span class="n">tr_test_drifts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">'x'</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">])</span>
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">tr_test_drifts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># take equidistant bins</span>
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.001</span>
|
||||
<span class="n">bins</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.001</span>
|
||||
|
||||
<span class="c1"># we use this to keep track of how many datapoits contribute to each bin</span>
|
||||
<span class="n">inds_histogram_global</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_bins</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="n">n_methods</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">method_order</span><span class="p">)</span>
|
||||
<span class="n">buckets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n_methods</span><span class="p">,</span> <span class="n">n_bins</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">method_order</span><span class="p">):</span>
|
||||
<span class="n">tr_test_drifts</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'x'</span><span class="p">]</span>
|
||||
<span class="n">method_drifts</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'y'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">digitize</span><span class="p">(</span><span class="n">tr_test_drifts</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">inds_histogram_global</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">tr_test_drifts</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">)):</span>
|
||||
<span class="n">selected</span> <span class="o">=</span> <span class="n">inds</span> <span class="o">==</span> <span class="n">j</span>
|
||||
<span class="k">if</span> <span class="n">selected</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">method_drifts</span><span class="p">[</span><span class="n">selected</span><span class="p">])</span>
|
||||
<span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">method_drifts</span><span class="p">[</span><span class="n">selected</span><span class="p">])</span>
|
||||
<span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">selected</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># cancel last buckets with low density</span>
|
||||
<span class="n">histogram</span> <span class="o">=</span> <span class="n">inds_histogram_global</span> <span class="o">/</span> <span class="n">inds_histogram_global</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">tail</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">histogram</span><span class="p">))):</span>
|
||||
<span class="k">if</span> <span class="n">histogram</span><span class="p">[</span><span class="n">tail</span><span class="p">]</span> <span class="o"><</span> <span class="n">tail_density_threshold</span><span class="p">:</span>
|
||||
<span class="n">buckets</span><span class="p">[:,</span><span class="n">tail</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">break</span>
|
||||
|
||||
<span class="n">salient_methods</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
|
||||
<span class="n">best_methods</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">bucket</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">buckets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">nc</span> <span class="o">=</span> <span class="n">buckets</span><span class="p">[:,</span> <span class="n">bucket</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">nc</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">best_methods</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
|
||||
<span class="k">continue</span>
|
||||
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">buckets</span><span class="p">[:,</span> <span class="n">bucket</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">rank1</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">best_bucket_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">method_order</span><span class="p">[</span><span class="n">rank1</span><span class="p">]]</span>
|
||||
<span class="n">best_mean</span><span class="p">,</span> <span class="n">best_std</span><span class="p">,</span> <span class="n">best_nc</span> <span class="o">=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">rank1</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="p">:]</span>
|
||||
<span class="k">for</span> <span class="n">method_index</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
|
||||
<span class="n">method_mean</span><span class="p">,</span> <span class="n">method_std</span><span class="p">,</span> <span class="n">method_nc</span> <span class="o">=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">method_index</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="p">:]</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">ttest_ind_from_stats</span><span class="p">(</span><span class="n">best_mean</span><span class="p">,</span> <span class="n">best_std</span><span class="p">,</span> <span class="n">best_nc</span><span class="p">,</span> <span class="n">method_mean</span><span class="p">,</span> <span class="n">method_std</span><span class="p">,</span> <span class="n">method_nc</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">pval</span> <span class="o">></span> <span class="n">ttest_alpha</span><span class="p">:</span>
|
||||
<span class="n">best_bucket_methods</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method_order</span><span class="p">[</span><span class="n">method_index</span><span class="p">])</span>
|
||||
<span class="n">best_methods</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
|
||||
<span class="n">salient_methods</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">binning</span><span class="o">==</span><span class="s1">'isomerous'</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">'height_ratios'</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">salient_methods</span><span class="p">)))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">'height_ratios'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">salient_methods</span><span class="p">)))</span>
|
||||
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">high_from</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">yticks</span><span class="p">,</span> <span class="n">yticks_method_names</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="n">color</span> <span class="o">=</span> <span class="n">get_cmap</span><span class="p">(</span><span class="s1">'Accent'</span><span class="p">)</span><span class="o">.</span><span class="n">colors</span>
|
||||
<span class="n">vlines</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">bar_high</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="n">m</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">method_order</span> <span class="k">if</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">salient_methods</span><span class="p">]:</span>
|
||||
<span class="n">broken_paths</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">path_start</span><span class="p">,</span> <span class="n">path_end</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">best_bucket_methods</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">best_methods</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">best_bucket_methods</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">path_start</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">path_start</span> <span class="o">=</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="n">path_end</span> <span class="o">=</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">path_start</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">path_end</span> <span class="o">+=</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">path_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">broken_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">((</span><span class="n">path_start</span><span class="p">,</span> <span class="n">path_end</span><span class="p">)))</span>
|
||||
<span class="n">path_start</span><span class="p">,</span> <span class="n">path_end</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">path_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">broken_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">((</span><span class="n">path_start</span><span class="p">,</span> <span class="n">path_end</span><span class="p">)))</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">broken_barh</span><span class="p">(</span><span class="n">broken_paths</span><span class="p">,</span> <span class="p">(</span><span class="n">high_from</span><span class="p">,</span> <span class="n">bar_high</span><span class="p">),</span> <span class="n">facecolors</span><span class="o">=</span><span class="n">color</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">yticks_method_names</span><span class="p">)])</span>
|
||||
<span class="n">yticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">high_from</span><span class="o">+</span><span class="n">bar_high</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">high_from</span> <span class="o">+=</span> <span class="n">bar_high</span>
|
||||
<span class="n">yticks_method_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">path_start</span><span class="p">,</span> <span class="n">path_end</span> <span class="ow">in</span> <span class="n">broken_paths</span><span class="p">:</span>
|
||||
<span class="n">vlines</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">path_start</span><span class="p">,</span> <span class="n">path_start</span><span class="o">+</span><span class="n">path_end</span><span class="p">])</span>
|
||||
|
||||
<span class="n">vlines</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">vlines</span><span class="p">)</span>
|
||||
<span class="n">vlines</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">vlines</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vlines</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">v</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">high_from</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">vlines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vlines</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Distribution shift between training set and sample'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">yticks</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">yticks_method_names</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># upper plot (explaining distribution)</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">binning</span> <span class="o">==</span> <span class="s1">'isometric'</span><span class="p">:</span>
|
||||
<span class="c1"># show the density for each region</span>
|
||||
<span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">=</span><span class="mi">0</span>
|
||||
<span class="n">y_pos</span> <span class="o">=</span> <span class="p">[</span><span class="n">b</span><span class="o">+</span><span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">b</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">b</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="n">histogram</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">></span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">bar_width</span> <span class="o">=</span> <span class="p">[</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span> <span class="k">if</span> <span class="n">histogram</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">></span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="p">[</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">histogram</span> <span class="k">if</span> <span class="n">n</span><span class="o">></span><span class="mi">0</span><span class="p">],</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'silver'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'shift</span><span class="se">\n</span><span class="s1">distribution'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">vlines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vlines</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># show the percentiles of the distribution</span>
|
||||
<span class="n">cumsum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">histogram</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])):</span>
|
||||
<span class="n">start</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">broken_barh</span><span class="p">([</span><span class="nb">tuple</span><span class="p">((</span><span class="n">start</span><span class="p">,</span> <span class="n">width</span><span class="p">))],</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">facecolors</span><span class="o">=</span><span class="s1">'whitesmoke'</span> <span class="k">if</span> <span class="n">i</span><span class="o">%</span><span class="mi">2</span><span class="o">==</span><span class="mi">0</span> <span class="k">else</span> <span class="s1">'silver'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">bins</span><span class="p">)</span><span class="o">-</span><span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="mf">0.5</span><span class="p">,</span> <span class="s1">'$P_{'</span><span class="o">+</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">cumsum</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span><span class="si">}</span><span class="s1">'</span><span class="o">+</span><span class="s1">'}$'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">vlines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vlines</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">ndims</span> <span class="o">=</span> <span class="n">true_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">'true'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">)),</span> <span class="s1">'estim'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">))})</span>
|
||||
<span class="n">method_order</span><span class="o">=</span><span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'true'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'true'</span><span class="p">],</span> <span class="n">true_prev</span><span class="p">])</span>
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'estim'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'estim'</span><span class="p">],</span> <span class="n">estim_prev</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">:</span>
|
||||
<span class="n">method_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
|
||||
<span class="n">true_prevs_</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">'true'</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">]</span>
|
||||
<span class="n">estim_prevs_</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">'estim'</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">method_order</span><span class="p">,</span> <span class="n">true_prevs_</span><span class="p">,</span> <span class="n">estim_prevs_</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
|
||||
<span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</span>
|
||||
<span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'tab20'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
|
||||
<span class="c1"># if savepath is specified, then saves the plot in that path; otherwise the plot is shown</span>
|
||||
<span class="k">if</span> <span class="n">savepath</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">create_parent_dir</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
<span class="c1"># plt.tight_layout()</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">savepath</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">'x'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> <span class="s1">'y'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))})</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">test_prevs_i</span><span class="p">,</span> <span class="n">estim_prevs_i</span><span class="p">,</span> <span class="n">tr_prev_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">):</span>
|
||||
<span class="n">tr_prev_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">tr_prev_i</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">repeats</span><span class="o">=</span><span class="n">test_prevs_i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tr_test_drifts</span> <span class="o">=</span> <span class="n">x_error</span><span class="p">(</span><span class="n">test_prevs_i</span><span class="p">,</span> <span class="n">tr_prev_i</span><span class="p">)</span>
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">tr_test_drifts</span><span class="p">])</span>
|
||||
|
||||
<span class="n">method_drifts</span> <span class="o">=</span> <span class="n">y_error</span><span class="p">(</span><span class="n">test_prevs_i</span><span class="p">,</span> <span class="n">estim_prevs_i</span><span class="p">)</span>
|
||||
<span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">method</span><span class="p">][</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">method_drifts</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">method_order</span><span class="p">:</span>
|
||||
<span class="n">method_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>© Copyright 2024, Alejandro Moreo.</p>
|
||||
</div>
|
||||
|
||||
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
|
||||
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
|
||||
provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
||||
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
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<h1>Source code for quapy.protocol</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">ExitStack</span>
|
||||
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">exists</span>
|
||||
<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract parent class for sample generation protocols.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the protocol. Yields one sample at a time along with its prevalence</span>
|
||||
|
||||
<span class="sd"> :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances</span>
|
||||
<span class="sd"> and in which `prev` is an `nd.array` with the class prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractProtocol.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates the total number of samples that the protocol generates.</span>
|
||||
|
||||
<span class="sd"> :return: The number of samples to generate if known, or `None` otherwise.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="kc">None</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IterateProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A very simple protocol which simply iterates over a list of previously generated samples</span>
|
||||
|
||||
<span class="sd"> :param samples: a list of :class:`quapy.data.base.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Yields one sample from the initial list at a time</span>
|
||||
|
||||
<span class="sd"> :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances</span>
|
||||
<span class="sd"> and in which `prev` is an `nd.array` with the class prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="n">sample</span><span class="o">.</span><span class="n">Xp</span>
|
||||
|
||||
<div class="viewcode-block" id="IterateProtocol.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples in this protocol</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,</span>
|
||||
<span class="sd"> via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.</span>
|
||||
<span class="sd"> The protocol abstraction enforces</span>
|
||||
<span class="sd"> the object to be instantiated using a seed, so that the sequence can be fully replicated.</span>
|
||||
<span class="sd"> In order to make this functionality possible, the classes extending this abstraction need to</span>
|
||||
<span class="sd"> implement only two functions, :meth:`samples_parameters` which generates all the parameters</span>
|
||||
<span class="sd"> needed for extracting the samples, and :meth:`sample` that, given some parameters as input,</span>
|
||||
<span class="sd"> deterministically generates a sample.</span>
|
||||
|
||||
<span class="sd"> :param random_state: the seed for allowing to replicate any sequence of samples. Default is 0, meaning that</span>
|
||||
<span class="sd"> the sequence will be consistent every time the protocol is called.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">_random_state</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># means "not set"</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span>
|
||||
|
||||
<span class="nd">@random_state</span><span class="o">.</span><span class="n">setter</span>
|
||||
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function has to return all the necessary parameters to replicate the samples</span>
|
||||
|
||||
<span class="sd"> :return: a list of parameters, each of which serves to deterministically generate a sample</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Extract one sample determined by the given parameters</span>
|
||||
|
||||
<span class="sd"> :param params: all the necessary parameters to generate a sample</span>
|
||||
<span class="sd"> :return: one sample (the same sample has to be generated for the same parameters)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Yields one sample at a time. The type of object returned depends on the `collator` function. The</span>
|
||||
<span class="sd"> default behaviour returns tuples of the form `(sample, prevalence)`.</span>
|
||||
|
||||
<span class="sd"> :return: a tuple `(sample, prevalence)` if return_type='sample_prev', or an instance of</span>
|
||||
<span class="sd"> :class:`qp.data.LabelledCollection` if return_type='labelled_collection'</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The random seed has never been initialized. '</span>
|
||||
<span class="s1">'Set it to None not to impose replicability.'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">():</span>
|
||||
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">params</span><span class="p">))</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The collator prepares the sample to accommodate the desired output format before returning the output.</span>
|
||||
<span class="sd"> This collator simply returns the sample as it is. Classes inheriting from this abstract class can</span>
|
||||
<span class="sd"> implement their custom collators.</span>
|
||||
|
||||
<span class="sd"> :param sample: the sample to be returned</span>
|
||||
<span class="sd"> :param args: additional arguments</span>
|
||||
<span class="sd"> :return: the sample adhering to a desired output format (in this case, the sample is returned as it is)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">sample</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">OnLabelledCollectionProtocol</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">RETURN_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'sample_prev'</span><span class="p">,</span> <span class="s1">'labelled_collection'</span><span class="p">,</span> <span class="s1">'index'</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the labelled collection on which this protocol acts.</span>
|
||||
|
||||
<span class="sd"> :return: an object of type :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a copy of this protocol that acts on a modified version of the original</span>
|
||||
<span class="sd"> :class:`qp.data.LabelledCollection` in which the original instances have been replaced</span>
|
||||
<span class="sd"> with the outputs of a classifier for each instance. (This is convenient for speeding-up</span>
|
||||
<span class="sd"> the evaluation procedures for many samples, by pre-classifying the instances in advance.)</span>
|
||||
|
||||
<span class="sd"> :param pre_classifications: the predictions issued by a classifier, typically an array-like</span>
|
||||
<span class="sd"> with shape `(n_instances,)` when the classifier is a hard one, or with shape</span>
|
||||
<span class="sd"> `(n_instances, n_classes)` when the classifier is a probabilistic one.</span>
|
||||
<span class="sd"> :param in_place: whether or not to apply the modification in-place or in a new copy (default).</span>
|
||||
<span class="sd"> :return: a copy of this protocol</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'error: the pre-classified data has different shape '</span> \
|
||||
<span class="sa">f</span><span class="s1">'(expected </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
<span class="k">if</span> <span class="n">in_place</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">pre_classifications</span>
|
||||
<span class="k">return</span> <span class="bp">self</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">new</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">new</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a collator function, i.e., a function that prepares the yielded data</span>
|
||||
|
||||
<span class="sd"> :param return_type: either 'sample_prev' (default) if the collator is requested to yield tuples of</span>
|
||||
<span class="sd"> `(sample, prevalence)`, or 'labelled_collection' when it is requested to yield instances of</span>
|
||||
<span class="sd"> :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> :return: the collator function (a callable function that takes as input an instance of</span>
|
||||
<span class="sd"> :class:`qp.data.LabelledCollection`)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">return_type</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unknown return type passed as argument; valid ones are </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">if</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">'sample_prev'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span><span class="o">.</span><span class="n">Xp</span>
|
||||
<span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">'labelled_collection'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">APP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implementation of the artificial prevalence protocol (APP).</span>
|
||||
<span class="sd"> The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,</span>
|
||||
<span class="sd"> [0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of</span>
|
||||
<span class="sd"> prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,</span>
|
||||
<span class="sd"> [1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of samples for each valid</span>
|
||||
<span class="sd"> combination of prevalence values is indicated by `repeats`.</span>
|
||||
|
||||
<span class="sd"> :param data: a `LabelledCollection` from which the samples will be drawn</span>
|
||||
<span class="sd"> :param sample_size: integer, number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the</span>
|
||||
<span class="sd"> grid (default is 21)</span>
|
||||
<span class="sd"> :param repeats: number of copies for each valid prevalence vector (default is 10)</span>
|
||||
<span class="sd"> :param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1</span>
|
||||
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
|
||||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> :param sanity_check: int, raises an exception warning the user that the number of examples to be generated exceed</span>
|
||||
<span class="sd"> this number; set to None for skipping this check</span>
|
||||
<span class="sd"> :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or</span>
|
||||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">APP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span> <span class="o">=</span> <span class="n">n_prevalences</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">smooth_limits_epsilon</span> <span class="o">=</span> <span class="n">smooth_limits_epsilon</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">sanity_check</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sanity_check</span><span class="o">></span><span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="n">sanity_check</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'param "sanity_check" must either be None or a positive integer'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sanity_check</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="o">=</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="n">repeats</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">n</span> <span class="o">></span> <span class="n">sanity_check</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
|
||||
<span class="sa">f</span><span class="s2">"Abort: the number of samples that will be generated by </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">) "</span>
|
||||
<span class="sa">f</span><span class="s2">"exceeds the maximum number of allowed samples (</span><span class="si">{</span><span class="n">sanity_check</span><span class="w"> </span><span class="si">= }</span><span class="s2">). Set 'sanity_check' to "</span>
|
||||
<span class="sa">f</span><span class="s2">"None, or to a higher number, for bypassing this check."</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="APP.prevalence_grid">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates vectors of prevalence values from an exhaustive grid of prevalence values. The</span>
|
||||
<span class="sd"> number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,</span>
|
||||
<span class="sd"> `n_prevalences=11` then the prevalence values of the grid are taken from [0, 0.1, 0.2, ..., 0.9, 1]. Only</span>
|
||||
<span class="sd"> valid prevalence distributions are returned, i.e., vectors of prevalence values that sum up to 1. For each</span>
|
||||
<span class="sd"> valid vector of prevalence values, `repeat` copies are returned. The vector of prevalence values can be</span>
|
||||
<span class="sd"> implicit (by setting `return_constrained_dim=False`), meaning that the last dimension (which is constrained</span>
|
||||
<span class="sd"> to 1 - sum of the rest) is not returned (note that, quite obviously, in this case the vector does not sum up to</span>
|
||||
<span class="sd"> 1). Note that this method is deterministic, i.e., there is no random sampling anywhere.</span>
|
||||
|
||||
<span class="sd"> :return: a `np.ndarray` of shape `(n, dimensions)` if `return_constrained_dim=True` or of shape</span>
|
||||
<span class="sd"> `(n, dimensions-1)` if `return_constrained_dim=False`, where `n` is the number of valid combinations found</span>
|
||||
<span class="sd"> in the grid multiplied by `repeat`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">dimensions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_linspace</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">smooth_limits_epsilon</span><span class="p">)</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span> <span class="c1"># handling floating rounding</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">dimensions</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">s</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o"><</span> <span class="p">(</span><span class="mf">1.</span><span class="o">+</span><span class="n">eps</span><span class="p">))]</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevs</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the APP protocol.</span>
|
||||
|
||||
<span class="sd"> :return: a list of indexes that realize the APP sampling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">prevs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence_grid</span><span class="p">():</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">)</span>
|
||||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.sample">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NPP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">NPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing</span>
|
||||
<span class="sd"> samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.</span>
|
||||
|
||||
<span class="sd"> :param data: a `LabelledCollection` from which the samples will be drawn</span>
|
||||
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param repeats: the number of samples to generate. Default is 100.</span>
|
||||
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
|
||||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or</span>
|
||||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">NPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="NPP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the NPP protocol.</span>
|
||||
|
||||
<span class="sd"> :return: a list of indexes that realize the NPP sampling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">):</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NPP.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.sample">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NPP.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="UPP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">UPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,</span>
|
||||
<span class="sd"> relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with</span>
|
||||
<span class="sd"> k the number of classes. This protocol covers the entire range of prevalence values in a</span>
|
||||
<span class="sd"> statistical sense, i.e., unlike APP there is no guarantee that it is covered precisely</span>
|
||||
<span class="sd"> equally for all classes, but it is preferred in cases in which the number of possible</span>
|
||||
<span class="sd"> combinations of the grid values of APP makes this endeavour intractable.</span>
|
||||
|
||||
<span class="sd"> :param data: a `LabelledCollection` from which the samples will be drawn</span>
|
||||
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param repeats: the number of samples to generate. Default is 100.</span>
|
||||
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
|
||||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or</span>
|
||||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">UPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="UPP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the UPP protocol.</span>
|
||||
|
||||
<span class="sd"> :return: a list of indexes that realize the UPP sampling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">prevs</span> <span class="ow">in</span> <span class="n">F</span><span class="o">.</span><span class="n">uniform_simplex_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">):</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">)</span>
|
||||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="UPP.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.sample">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="UPP.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span>
|
||||
|
||||
<span class="sd"> :param domainA: one domain, an object of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> :param domainB: another domain, an object of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param repeats: int, number of samples to draw for every mixture rate</span>
|
||||
<span class="sd"> :param prevalence: the prevalence to preserv along the mixtures. If specified, should be an array containing</span>
|
||||
<span class="sd"> one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence</span>
|
||||
<span class="sd"> will be taken from the domain A (default).</span>
|
||||
<span class="sd"> :param mixture_points: an integer indicating the number of points to take from a linear scale (e.g., 21 will</span>
|
||||
<span class="sd"> generate the mixture points [1, 0.95, 0.9, ..., 0]), or the array of mixture values itself.</span>
|
||||
<span class="sd"> the specific points</span>
|
||||
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
|
||||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">domainA</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
|
||||
<span class="n">domainB</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
|
||||
<span class="n">sample_size</span><span class="p">,</span>
|
||||
<span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">prevalence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span>
|
||||
<span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">DomainMixer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="n">domainA</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">B</span> <span class="o">=</span> <span class="n">domainB</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
|
||||
<span class="k">if</span> <span class="n">prevalence</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span> <span class="o">=</span> <span class="n">domainA</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevalence</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">)</span> <span class="o">==</span> <span class="n">domainA</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'wrong shape for the vector prevalence (expected </span><span class="si">{</span><span class="n">domainA</span><span class="o">.</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">)'</span>
|
||||
<span class="k">assert</span> <span class="n">F</span><span class="o">.</span><span class="n">check_prevalence_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'the prevalence vector is not valid (either it contains values outside [0,1] or does not sum up to 1)'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mixture_points</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">mixture_points</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">mixture_points</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="o"><=</span><span class="mi">1</span><span class="p">)),</span> \
|
||||
<span class="s1">'mixture_model datatype not understood (expected int or a sequence of real values in [0,1])'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the this protocol.</span>
|
||||
|
||||
<span class="sd"> :return: a list of zipped indexes (from A and B) that realize the sampling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">propA</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">):</span>
|
||||
<span class="n">nA</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">*</span> <span class="n">propA</span><span class="p">))</span>
|
||||
<span class="n">nB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="o">-</span><span class="n">nA</span>
|
||||
<span class="n">sampleAidx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="n">nA</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">)</span>
|
||||
<span class="n">sampleBidx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="n">nB</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">)</span>
|
||||
<span class="n">indexesA</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampleAidx</span><span class="p">)</span>
|
||||
<span class="n">indexesB</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampleBidx</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given a pair of indexes of the instances from A and B.</span>
|
||||
|
||||
<span class="sd"> :param indexes: indexes of the instances to select from A and B</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span> <span class="o">=</span> <span class="n">indexes</span>
|
||||
<span class="n">sampleA</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">indexesA</span><span class="p">)</span>
|
||||
<span class="n">sampleB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">indexesB</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">sampleA</span><span class="o">+</span><span class="n">sampleB</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats * mixture_points")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="c1"># aliases</span>
|
||||
|
||||
<span class="n">ArtificialPrevalenceProtocol</span> <span class="o">=</span> <span class="n">APP</span>
|
||||
<span class="n">NaturalPrevalenceProtocol</span> <span class="o">=</span> <span class="n">NPP</span>
|
||||
<span class="n">UniformPrevalenceProtocol</span> <span class="o">=</span> <span class="n">UPP</span>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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|
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|
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|
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||||
<h1>Source code for quapy.tests.test_base</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">pytest</span>
|
||||
|
||||
<div class="viewcode-block" id="test_import">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_base.test_import">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_import</span><span class="p">():</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">__version__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span></div>
|
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<h1>Source code for quapy.tests.test_datasets</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">pytest</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.datasets</span> <span class="kn">import</span> <span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="p">,</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span><span class="p">,</span> \
|
||||
<span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span><span class="p">,</span> <span class="n">UCI_BINARY_DATASETS</span><span class="p">,</span> <span class="n">LEQUA2022_TASKS</span><span class="p">,</span> <span class="n">UCI_MULTICLASS_DATASETS</span><span class="p">,</span>\
|
||||
<span class="n">fetch_reviews</span><span class="p">,</span> <span class="n">fetch_twitter</span><span class="p">,</span> <span class="n">fetch_UCIBinaryDataset</span><span class="p">,</span> <span class="n">fetch_lequa2022</span><span class="p">,</span> <span class="n">fetch_UCIMulticlassLabelledCollection</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_fetch_reviews">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_reviews">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset_name'</span><span class="p">,</span> <span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training set stats'</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test set stats'</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_fetch_twitter">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_twitter">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset_name'</span><span class="p">,</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span> <span class="o">+</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ve</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'semeval'</span> <span class="ow">and</span> <span class="n">ve</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span>
|
||||
<span class="s1">'dataset "semeval" can only be used for model selection.'</span><span class="p">):</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training set stats'</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test set stats'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_fetch_UCIDataset">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_UCIDataset">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset_name'</span><span class="p">,</span> <span class="n">UCI_BINARY_DATASETS</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_fetch_UCIDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">FileNotFoundError</span> <span class="k">as</span> <span class="n">fnfe</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">'pageblocks.5'</span> <span class="ow">and</span> <span class="n">fnfe</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">find</span><span class="p">(</span>
|
||||
<span class="s1">'If this is the first time you attempt to load this dataset'</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The pageblocks.5 dataset requires some hand processing to be usable, skipping this test.'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training set stats'</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test set stats'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_fetch_UCIMultiDataset">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_UCIMultiDataset">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset_name'</span><span class="p">,</span> <span class="n">UCI_MULTICLASS_DATASETS</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_fetch_UCIMultiDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training set stats'</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test set stats'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_fetch_lequa2022">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_lequa2022">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset_name'</span><span class="p">,</span> <span class="n">LEQUA2022_TASKS</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_fetch_lequa2022</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">gen_val</span><span class="p">,</span> <span class="n">gen_test</span> <span class="o">=</span> <span class="n">fetch_lequa2022</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Val:'</span><span class="p">,</span> <span class="n">gen_val</span><span class="o">.</span><span class="n">total</span><span class="p">())</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test:'</span><span class="p">,</span> <span class="n">gen_test</span><span class="o">.</span><span class="n">total</span><span class="p">())</span></div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
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<h1>Source code for quapy.tests.test_evaluation</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.error</span> <span class="kn">import</span> <span class="n">QUANTIFICATION_ERROR_SINGLE</span><span class="p">,</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">,</span> <span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="p">,</span> \
|
||||
<span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">PCC</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EvalTestCase">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">EvalTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="EvalTestCase.test_eval_speedup">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase.test_eval_speedup">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_eval_speedup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'hp'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span>
|
||||
|
||||
<span class="n">protocol</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
|
||||
<span class="n">emq</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">emq</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="s1">'force'</span><span class="p">)</span>
|
||||
<span class="n">tend_optim</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'evaluation (with optimization) took </span><span class="si">{</span><span class="n">tend_optim</span><span class="si">}</span><span class="s1">s [MAE=</span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">]'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">class</span> <span class="nc">NonAggregativeEMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">cls</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">emq</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">emq</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">emq</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span>
|
||||
|
||||
<span class="n">emq</span> <span class="o">=</span> <span class="n">NonAggregativeEMQ</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">emq</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">tend_no_optim</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'evaluation (w/o optimization) took </span><span class="si">{</span><span class="n">tend_no_optim</span><span class="si">}</span><span class="s1">s [MAE=</span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">]'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">tend_no_optim</span><span class="o">></span><span class="p">(</span><span class="n">tend_optim</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EvalTestCase.test_evaluation_output">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase.test_evaluation_output">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_evaluation_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'hp'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span><span class="o">=</span><span class="mi">100</span>
|
||||
|
||||
<span class="n">protocol</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">PCC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
|
||||
<span class="n">single_errors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span><span class="p">)</span>
|
||||
<span class="n">averaged_errors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'m'</span><span class="o">+</span><span class="n">e</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">single_errors</span><span class="p">]</span>
|
||||
<span class="n">single_errors</span> <span class="o">=</span> <span class="n">single_errors</span> <span class="o">+</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">single_errors</span><span class="p">]</span>
|
||||
<span class="n">averaged_errors</span> <span class="o">=</span> <span class="n">averaged_errors</span> <span class="o">+</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">averaged_errors</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">error_metric</span><span class="p">,</span> <span class="n">averaged_error_metric</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">single_errors</span><span class="p">,</span> <span class="n">averaged_errors</span><span class="p">):</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="n">averaged_error_metric</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">score</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>
|
||||
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="n">error_metric</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">score</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
|
||||
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|
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|
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<h1>Source code for quapy.tests.test_hierarchy</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="o">*</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HierarchyTestCase">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">HierarchyTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="HierarchyTestCase.test_aggregative">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_aggregative">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">CC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PCC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">ACC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HierarchyTestCase.test_binary">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_binary">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">HDy</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HierarchyTestCase.test_probabilistic">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_probabilistic">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">CC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">ACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeCrispQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">),</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">PCC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeCrispQuantifier</span><span class="p">),</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
|
||||
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|
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|
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|
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<p>© Copyright 2024, Alejandro Moreo.</p>
|
||||
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|
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<h1>Source code for quapy.tests.test_labelcollection</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">csr_matrix</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelCollectionTestCase">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">LabelCollectionTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="LabelCollectionTestCase.test_split">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase.test_split">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_split</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">tr</span><span class="p">,</span> <span class="n">te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_random</span><span class="p">(</span><span class="mf">0.7</span><span class="p">)</span>
|
||||
<span class="n">check_prev</span> <span class="o">=</span> <span class="n">tr</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span><span class="o">*</span><span class="mf">0.7</span> <span class="o">+</span> <span class="n">te</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span><span class="o">*</span><span class="mf">0.3</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tr</span><span class="p">),</span> <span class="mi">70</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">te</span><span class="p">),</span> <span class="mi">30</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">check_prev</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()),</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tr</span><span class="o">+</span><span class="n">te</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelCollectionTestCase.test_join">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase.test_join">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_join</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
|
||||
<span class="n">data1</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">200</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
||||
<span class="n">data2</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">data3</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">data3</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data1</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data2</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data3</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">all</span><span class="p">(</span><span class="n">combined</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)),</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">data4</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">data3</span><span class="p">,</span> <span class="n">data4</span><span class="p">)</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
|
||||
<span class="n">data5</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data4</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data5</span><span class="p">))</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">data6</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">,</span> <span class="n">data6</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data4</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">data4</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
|
||||
<span class="n">data5</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">data5</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data4</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">data5</span><span class="p">))</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
||||
|
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|
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|
||||
<p>© Copyright 2024, Alejandro Moreo.</p>
|
||||
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|
||||
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<h1>Source code for quapy.tests.test_methods</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">pytest</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">method.aggregative</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BinaryQuantifier</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method</span> <span class="kn">import</span> <span class="n">AGGREGATIVE_METHODS</span><span class="p">,</span> <span class="n">NON_AGGREGATIVE_METHODS</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">Ensemble</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">DMy</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span>
|
||||
|
||||
<span class="c1"># datasets = [pytest.param(qp.datasets.fetch_twitter('hcr', pickle=True), id='hcr'),</span>
|
||||
<span class="c1"># pytest.param(qp.datasets.fetch_UCIDataset('ionosphere'), id='ionosphere')]</span>
|
||||
|
||||
<span class="n">tinydatasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">pytest</span><span class="o">.</span><span class="n">param</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'hcr'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(),</span> <span class="nb">id</span><span class="o">=</span><span class="s1">'tiny_hcr'</span><span class="p">),</span>
|
||||
<span class="n">pytest</span><span class="o">.</span><span class="n">param</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="s1">'ionosphere'</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(),</span> <span class="nb">id</span><span class="o">=</span><span class="s1">'tiny_ionosphere'</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">learners</span> <span class="o">=</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">LinearSVC</span><span class="p">]</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_aggregative_methods">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_aggregative_methods">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset'</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'aggregative_method'</span><span class="p">,</span> <span class="n">AGGREGATIVE_METHODS</span><span class="p">)</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'learner'</span><span class="p">,</span> <span class="n">learners</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_aggregative_methods</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">aggregative_method</span><span class="p">,</span> <span class="n">learner</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">aggregative_method</span><span class="p">(</span><span class="n">learner</span><span class="p">())</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'skipping the test of binary model </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_non_aggregative_methods">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_non_aggregative_methods">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset'</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'non_aggregative_method'</span><span class="p">,</span> <span class="n">NON_AGGREGATIVE_METHODS</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_non_aggregative_methods</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">non_aggregative_method</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">non_aggregative_method</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'skipping the test of binary model </span><span class="si">{</span><span class="n">model</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_ensemble_method">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_ensemble_method">[docs]</a>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'base_method'</span><span class="p">,</span> <span class="p">[</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ACC</span><span class="p">,</span> <span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">PACC</span><span class="p">])</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'learner'</span><span class="p">,</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">])</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'dataset'</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
|
||||
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">'policy'</span><span class="p">,</span> <span class="n">Ensemble</span><span class="o">.</span><span class="n">VALID_POLICIES</span><span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">test_ensemble_method</span><span class="p">(</span><span class="n">base_method</span><span class="p">,</span> <span class="n">learner</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">policy</span><span class="p">):</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">20</span>
|
||||
|
||||
<span class="n">base_quantifier</span><span class="o">=</span><span class="n">base_method</span><span class="p">(</span><span class="n">learner</span><span class="p">())</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span> <span class="ow">and</span> <span class="n">policy</span><span class="o">==</span><span class="s1">'ds'</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'skipping the test of binary policy ds on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">quantifier</span><span class="o">=</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="n">policy</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_quanet_method">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_quanet_method">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_quanet_method</span><span class="p">():</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.classification.neural</span>
|
||||
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'skipping QuaNet test due to missing torch package'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
|
||||
<span class="c1"># load the kindle dataset as text, and convert words to numerical indexes</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">CNNnet</span>
|
||||
<span class="n">cnn</span> <span class="o">=</span> <span class="n">CNNnet</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span>
|
||||
<span class="n">learner</span> <span class="o">=</span> <span class="n">NeuralClassifierTrainer</span><span class="p">(</span><span class="n">cnn</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">QuaNet</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'skipping the test of binary model </span><span class="si">{</span><span class="n">model</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">return</span>
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_str_label_names">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_str_label_names">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_str_label_names</span><span class="p">():</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">CC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="o">*</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()),</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">))</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">int_estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">int_estim_prevalences</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span>
|
||||
|
||||
<span class="n">dataset_str</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="s1">'one'</span> <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">'zero'</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">]),</span>
|
||||
<span class="n">LabelledCollection</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="s1">'one'</span> <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">'zero'</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">]))</span>
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">'wrong indexation'</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">str_estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">str_estim_prevalences</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">int_estim_prevalences</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">str_estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">int_estim_prevalences</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
|
||||
<span class="n">str_estim_prevalences</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">'one'</span><span class="p">)])</span></div>
|
||||
|
||||
|
||||
<span class="c1"># helper</span>
|
||||
<span class="k">def</span> <span class="nf">__fit_test</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
<span class="n">test_samples</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
|
||||
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">prediction</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">test_samples</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">),</span> <span class="n">estim_prevs</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_median_meta">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_median_meta">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_median_meta</span><span class="p">():</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This test compares the performance of the MedianQuantifier with respect to computing the median of the predictions</span>
|
||||
<span class="sd"> of a differently parameterized quantifier. We use the DistributionMatching base quantifier and the median is</span>
|
||||
<span class="sd"> computed across different values of nbins</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
|
||||
<span class="c1"># grid of values</span>
|
||||
<span class="n">nbins_grid</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="s1">'kindle'</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">nbins</span> <span class="ow">in</span> <span class="n">nbins_grid</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">)</span>
|
||||
<span class="n">mae</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
|
||||
<span class="n">prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">estim_prevs</span><span class="p">)</span>
|
||||
<span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mae</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1"> DistributionMatching(nbins=</span><span class="si">{</span><span class="n">nbins</span><span class="si">}</span><span class="s1">) got MAE </span><span class="si">{</span><span class="n">mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span>
|
||||
<span class="n">mae</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'nbins'</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">median_mae</span><span class="p">,</span> <span class="n">prev</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">median_mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">prev</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">median_mae</span> <span class="o"><</span> <span class="n">mae</span><span class="p">,</span> <span class="s1">'the median-based quantifier provided a higher error...'</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_median_meta_modsel">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_median_meta_modsel">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_median_meta_modsel</span><span class="p">():</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This test checks the median-meta quantifier with model selection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="s1">'kindle'</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">nbins_grid</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">]</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'nbins'</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">median_mae</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">median_mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">lr_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)}</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'nbins'</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">lr_params</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">APP</span><span class="p">(</span><span class="n">val</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">optimized_median_ave</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">optimized_median_ave</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">optimized_median_ave</span> <span class="o"><</span> <span class="n">median_mae</span><span class="p">,</span> <span class="s2">"the optimized method yielded worse performance..."</span></div>
|
||||
|
||||
</pre></div>
|
||||
|
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|
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<h1>Source code for quapy.tests.test_modsel</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">PACC</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ModselTestCase">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">ModselTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ModselTestCase.test_modsel">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_modsel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
|
||||
<span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'best params'</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'best score'</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">'classifier__C'</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">'classifier__C'</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ModselTestCase.test_modsel_parallel">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_parallel">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_modsel_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="c1"># test = data.test</span>
|
||||
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
|
||||
<span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'best params'</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'best score'</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">'classifier__C'</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">'classifier__C'</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ModselTestCase.test_modsel_parallel_speedup">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_parallel_speedup">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_modsel_parallel_speedup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">)</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">)}</span>
|
||||
<span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">tend_nooptim</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
|
||||
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">tend_optim</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'parallel training took </span><span class="si">{</span><span class="n">tend_optim</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'sequential training took </span><span class="si">{</span><span class="n">tend_nooptim</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">tend_optim</span> <span class="o"><</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">tend_nooptim</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ModselTestCase.test_modsel_timeout">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_timeout">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_modsel_timeout</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">)</span>
|
||||
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="c1"># test = data.test</span>
|
||||
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'classifier__C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
|
||||
<span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">TimeoutError</span><span class="p">):</span>
|
||||
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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<h1>Source code for quapy.tests.test_protocols</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span><span class="p">,</span> <span class="n">NPP</span><span class="p">,</span> <span class="n">UPP</span><span class="p">,</span> <span class="n">DomainMixer</span><span class="p">,</span> <span class="n">AbstractStochasticSeededProtocol</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mock_labelled_collection">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.mock_labelled_collection">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">mock_labelled_collection</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'-'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">yi</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">yi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y</span><span class="p">)]</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="samples_to_str">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.samples_to_str">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">samples_to_str</span><span class="p">(</span><span class="n">protocol</span><span class="p">):</span>
|
||||
<span class="n">samples_str</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">instances</span><span class="p">,</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">protocol</span><span class="p">():</span>
|
||||
<span class="n">samples_str</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">instances</span><span class="si">}</span><span class="se">\t</span><span class="si">{</span><span class="n">prev</span><span class="si">}</span><span class="se">\n</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="n">samples_str</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">TestProtocols</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_app_sanity_check">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_sanity_check">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_app_sanity_check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">n_prevpoints</span> <span class="o">=</span> <span class="mi">101</span>
|
||||
<span class="n">repeats</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">RuntimeError</span><span class="p">):</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="n">repeats</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
<span class="n">n_combinations</span> <span class="o">=</span> \
|
||||
<span class="n">quapy</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="n">repeats</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="n">n_combinations</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_app_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_app_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span> <span class="c1"># <- random_state is by default set to 0</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_app_not_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_not_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_app_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_app_number">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_number">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_app_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># surprisingly enough, for some n_prevalences the test fails, notwithstanding</span>
|
||||
<span class="c1"># everything is correct. The problem is that in function APP.prevalence_grid()</span>
|
||||
<span class="c1"># there is sometimes one rounding error that gets cumulated and</span>
|
||||
<span class="c1"># surpasses 1.0 (by a very small float value, 0.0000000000002 or sthe like)</span>
|
||||
<span class="c1"># so these tuples are mistakenly removed... I have tried with np.close, and</span>
|
||||
<span class="c1"># other workarounds, but eventually happens that there is some negative probability</span>
|
||||
<span class="c1"># in the sampling function...</span>
|
||||
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">p</span><span class="p">():</span>
|
||||
<span class="n">count</span><span class="o">+=</span><span class="mi">1</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">total</span><span class="p">())</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_npp_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_npp_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_npp_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> <span class="c1"># <- random_state is by default set to 0</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_npp_not_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_npp_not_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_npp_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_kraemer_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_kraemer_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_kraemer_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># <- random_state is by default set to 0</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_kraemer_not_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_kraemer_not_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_kraemer_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_covariate_shift_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_covariate_shift_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_covariate_shift_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">dataA</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">'domA'</span><span class="p">)</span>
|
||||
<span class="n">dataB</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">'domB'</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span> <span class="c1"># <- random_state is by default set to 0</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_covariate_shift_not_replicate">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_covariate_shift_not_replicate">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_covariate_shift_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">dataA</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">'domA'</span><span class="p">)</span>
|
||||
<span class="n">dataB</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">'domB'</span><span class="p">)</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TestProtocols.test_no_seed_init">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_no_seed_init">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_no_seed_init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">class</span> <span class="nc">NoSeedInit</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="c1"># return a matrix containing sampling indexes in the rows</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="mi">10</span><span class="o">*</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="n">NoSeedInit</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># this should raise a ValueError, since the class is said to be AbstractStochasticSeededProtocol but the</span>
|
||||
<span class="c1"># random_seed has never been passed to super(NoSeedInit, self).__init__(random_seed)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">ValueError</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">p</span><span class="p">():</span>
|
||||
<span class="k">pass</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'done'</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
|
||||
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|
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<h1>Source code for quapy.tests.test_replicability</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">unittest</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">strprev</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">PACC</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MyTestCase">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">MyTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MyTestCase.test_prediction_replicability">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_prediction_replicability">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_prediction_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="s1">'yeast'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
|
||||
<span class="n">prev</span> <span class="o">=</span> <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">str_prev1</span> <span class="o">=</span> <span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
|
||||
<span class="n">prev2</span> <span class="o">=</span> <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">str_prev2</span> <span class="o">=</span> <span class="n">strprev</span><span class="p">(</span><span class="n">prev2</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">str_prev1</span><span class="p">,</span> <span class="n">str_prev2</span><span class="p">)</span> <span class="c1"># add assertion here</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MyTestCase.test_samping_replicability">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_samping_replicability">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_samping_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">equal_collections</span><span class="p">(</span><span class="n">c1</span><span class="p">,</span> <span class="n">c2</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">Xtr</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">Xtr</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">ytr</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">value</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
|
||||
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)))</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">instances</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
|
||||
<span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample1_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">sample2_tr</span><span class="p">,</span> <span class="n">sample2_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample2_tr</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_te</span><span class="p">,</span> <span class="n">sample2_te</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample1_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">sample2_tr</span><span class="p">,</span> <span class="n">sample2_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample2_tr</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_te</span><span class="p">,</span> <span class="n">sample2_te</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MyTestCase.test_parallel_replicability">
|
||||
<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_parallel_replicability">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">test_parallel_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIMulticlassDataset</span><span class="p">(</span><span class="s1">'dry-bean'</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
|
||||
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
<span class="n">prev1</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
<span class="n">prev2</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
<span class="n">prev3</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">prev1</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">prev2</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">prev3</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">prev1</span><span class="p">,</span> <span class="n">prev2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">prev2</span><span class="p">,</span> <span class="n">prev3</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
|
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|
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<h1>Source code for quapy.util</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">contextlib</span>
|
||||
<span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span class="kn">import</span> <span class="nn">multiprocessing</span>
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">pickle</span>
|
||||
<span class="kn">import</span> <span class="nn">urllib</span>
|
||||
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
|
||||
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">ExitStack</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
|
||||
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
|
||||
<span class="kn">import</span> <span class="nn">signal</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_tasks</span> <span class="o">/</span> <span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="n">remainder</span> <span class="o">=</span> <span class="n">n_tasks</span> <span class="o">%</span> <span class="n">n_jobs</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="n">job</span> <span class="o">*</span> <span class="n">batch</span><span class="p">,</span> <span class="p">(</span><span class="n">job</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch</span> <span class="o">+</span> <span class="p">(</span><span class="n">remainder</span> <span class="k">if</span> <span class="n">job</span> <span class="o">==</span> <span class="n">n_jobs</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)]</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="map_parallel">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then</span>
|
||||
<span class="sd"> func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function</span>
|
||||
<span class="sd"> that already works with a list of arguments.</span>
|
||||
|
||||
<span class="sd"> :param func: function to be parallelized</span>
|
||||
<span class="sd"> :param args: array-like of arguments to be passed to the function in different parallel calls</span>
|
||||
<span class="sd"> :param n_jobs: the number of workers</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">args</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="n">slices</span> <span class="o">=</span> <span class="n">_get_parallel_slices</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">args</span><span class="p">[</span><span class="n">slice_i</span><span class="p">])</span> <span class="k">for</span> <span class="n">slice_i</span> <span class="ow">in</span> <span class="n">slices</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">results</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="parallel">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'loky'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A wrapper of multiprocessing:</span>
|
||||
|
||||
<span class="sd"> >>> Parallel(n_jobs=n_jobs)(</span>
|
||||
<span class="sd"> >>> delayed(func)(args_i) for args_i in args</span>
|
||||
<span class="sd"> >>> )</span>
|
||||
|
||||
<span class="sd"> that takes the `quapy.environ` variable as input silently.</span>
|
||||
<span class="sd"> Seeds the child processes to ensure reproducibility when n_jobs>1.</span>
|
||||
|
||||
<span class="sd"> :param func: callable</span>
|
||||
<span class="sd"> :param args: args of func</span>
|
||||
<span class="sd"> :param seed: the numeric seed</span>
|
||||
<span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span>
|
||||
<span class="sd"> :param backend: indicates the backend used for handling parallel works</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'N_JOBS'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="c1">#set a context with a temporal seed to ensure results are reproducibles in parallel</span>
|
||||
<span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)(</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func_dec</span><span class="p">)(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">,</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">seed</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">asarray</span><span class="p">:</span>
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">out</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="temp_seed">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a>
|
||||
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span> <span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Can be used in a "with" context to set a temporal seed without modifying the outer numpy's current state. E.g.:</span>
|
||||
|
||||
<span class="sd"> >>> with temp_seed(random_seed):</span>
|
||||
<span class="sd"> >>> pass # do any computation depending on np.random functionality</span>
|
||||
|
||||
<span class="sd"> :param random_state: the seed to set within the "with" context</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span>
|
||||
<span class="c1">#save the seed just in case is needed (for instance for setting the seed to child processes)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'_R_SEED'</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="k">yield</span>
|
||||
<span class="k">finally</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="download_file">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Downloads a file from a url</span>
|
||||
|
||||
<span class="sd"> :param url: the url</span>
|
||||
<span class="sd"> :param archive_filename: destination filename</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
|
||||
<span class="n">total_sz_mb</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%.2f</span><span class="s1"> MB'</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
|
||||
<span class="n">current_sz_mb</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%.2f</span><span class="s1"> MB'</span> <span class="o">%</span> <span class="p">((</span><span class="n">blocknum</span> <span class="o">*</span> <span class="n">bs</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\r</span><span class="s1">downloaded </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">current_sz_mb</span><span class="p">,</span> <span class="n">total_sz_mb</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Downloading </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">url</span><span class="p">)</span>
|
||||
<span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">archive_filename</span><span class="p">,</span> <span class="n">reporthook</span><span class="o">=</span><span class="n">progress</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="download_file_if_not_exists">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Dowloads a function (using :meth:`download_file`) if the file does not exist.</span>
|
||||
|
||||
<span class="sd"> :param url: the url</span>
|
||||
<span class="sd"> :param archive_filename: destination filename</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="k">return</span>
|
||||
<span class="n">create_if_not_exist</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">archive_filename</span><span class="p">))</span>
|
||||
<span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="create_if_not_exist">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span>
|
||||
|
||||
<span class="sd"> >>> path = create_if_not_exist(os.path.join(dir, subdir, anotherdir))</span>
|
||||
|
||||
<span class="sd"> :param path: path to create</span>
|
||||
<span class="sd"> :return: the path itself</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">path</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_quapy_home">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">get_quapy_home</span><span class="p">():</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.</span>
|
||||
<span class="sd"> This directory is `~/quapy_data`</span>
|
||||
|
||||
<span class="sd"> :return: a string representing the path</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">home</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()),</span> <span class="s1">'quapy_data'</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">home</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">home</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="create_parent_dir">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Creates the parent dir (if any) of a given path, if not exists. E.g., for `./path/to/file.txt`, the path `./path/to`</span>
|
||||
<span class="sd"> is created.</span>
|
||||
|
||||
<span class="sd"> :param path: the path</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parentdir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span>
|
||||
<span class="k">if</span> <span class="n">parentdir</span><span class="p">:</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">parentdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="save_text_file">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Saves a text file to disk, given its full path, and creates the parent directory if missing.</span>
|
||||
|
||||
<span class="sd"> :param path: path where to save the path.</span>
|
||||
<span class="sd"> :param text: text to save.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="s1">'wt'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
|
||||
<span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="pickled_resource">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="n">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times</span>
|
||||
<span class="sd"> this function is invoked, it loads the pickled resource. Example:</span>
|
||||
|
||||
<span class="sd"> >>> def some_array(n): # a mock resource created with one parameter (`n`)</span>
|
||||
<span class="sd"> >>> return np.random.rand(n)</span>
|
||||
<span class="sd"> >>> pickled_resource('./my_array.pkl', some_array, 10) # the resource does not exist: it is created by calling some_array(10)</span>
|
||||
<span class="sd"> >>> pickled_resource('./my_array.pkl', some_array, 10) # the resource exists; it is loaded from './my_array.pkl'</span>
|
||||
|
||||
<span class="sd"> :param pickle_path: the path where to save (first time) and load (next times) the resource</span>
|
||||
<span class="sd"> :param generation_func: the function that generates the resource, in case it does not exist in pickle_path</span>
|
||||
<span class="sd"> :param args: any arg that generation_func uses for generating the resources</span>
|
||||
<span class="sd"> :return: the resource</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">pickle_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">instance</span> <span class="o">=</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">),</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">instance</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
|
||||
<span class="s1">'error: sample_size set to None, and cannot be resolved from the environment'</span>
|
||||
<span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sample_size</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> \
|
||||
<span class="s1">'error: sample_size is not a positive integer'</span>
|
||||
<span class="k">return</span> <span class="n">sample_size</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EarlyStop">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">EarlyStop</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A class implementing the early-stopping condition typically used for training neural networks.</span>
|
||||
|
||||
<span class="sd"> >>> earlystop = EarlyStop(patience=2, lower_is_better=True)</span>
|
||||
<span class="sd"> >>> earlystop(0.9, epoch=0)</span>
|
||||
<span class="sd"> >>> earlystop(0.7, epoch=1)</span>
|
||||
<span class="sd"> >>> earlystop.IMPROVED # is True</span>
|
||||
<span class="sd"> >>> earlystop(1.0, epoch=2)</span>
|
||||
<span class="sd"> >>> earlystop.STOP # is False (patience=1)</span>
|
||||
<span class="sd"> >>> earlystop(1.0, epoch=3)</span>
|
||||
<span class="sd"> >>> earlystop.STOP # is True (patience=0)</span>
|
||||
<span class="sd"> >>> earlystop.best_epoch # is 1</span>
|
||||
<span class="sd"> >>> earlystop.best_score # is 0.7</span>
|
||||
|
||||
<span class="sd"> :param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a</span>
|
||||
<span class="sd"> held-out validation split) can be found to be worse than the best one obtained so far, before flagging the</span>
|
||||
<span class="sd"> stopping condition. An instance of this class is `callable`, and is to be used as follows:</span>
|
||||
<span class="sd"> :param lower_is_better: if True (default) the metric is to be minimized.</span>
|
||||
<span class="sd"> :ivar best_score: keeps track of the best value seen so far</span>
|
||||
<span class="sd"> :ivar best_epoch: keeps track of the epoch in which the best score was set</span>
|
||||
<span class="sd"> :ivar STOP: flag (boolean) indicating the stopping condition</span>
|
||||
<span class="sd"> :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span> <span class="o">=</span> <span class="n">patience</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">better</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o"><</span><span class="n">b</span> <span class="k">if</span> <span class="n">lower_is_better</span> <span class="k">else</span> <span class="n">a</span><span class="o">></span><span class="n">b</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_epoch</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Commits the new score found in epoch `epoch`. If the score improves over the best score found so far, then</span>
|
||||
<span class="sd"> the patiente counter gets reset. If otherwise, the patience counter is decreased, and in case it reachs 0,</span>
|
||||
<span class="sd"> the flag STOP becomes True.</span>
|
||||
|
||||
<span class="sd"> :param watch_score: the new score</span>
|
||||
<span class="sd"> :param epoch: the current epoch</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">better</span><span class="p">(</span><span class="n">watch_score</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="o">=</span> <span class="n">watch_score</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_epoch</span> <span class="o">=</span> <span class="n">epoch</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">True</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="timeout">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a>
|
||||
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span> <span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Opens a context that will launch an exception if not closed after a given number of seconds</span>
|
||||
|
||||
<span class="sd"> >>> def func(start_msg, end_msg):</span>
|
||||
<span class="sd"> >>> print(start_msg)</span>
|
||||
<span class="sd"> >>> sleep(2)</span>
|
||||
<span class="sd"> >>> print(end_msg)</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> with timeout(1):</span>
|
||||
<span class="sd"> >>> func('begin function', 'end function')</span>
|
||||
<span class="sd"> >>> Out[]</span>
|
||||
<span class="sd"> >>> begin function</span>
|
||||
<span class="sd"> >>> TimeoutError</span>
|
||||
|
||||
|
||||
<span class="sd"> :param seconds: number of seconds, set to <=0 to ignore the timer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">seconds</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">def</span> <span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">TimeoutError</span><span class="p">()</span>
|
||||
|
||||
<span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGALRM</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span>
|
||||
<span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="n">seconds</span><span class="p">)</span>
|
||||
|
||||
<span class="k">yield</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">seconds</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>© Copyright 2024, Alejandro Moreo.</p>
|
||||
</div>
|
||||
|
||||
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
|
||||
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
|
||||
provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
||||
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</div>
|
||||
<script>
|
||||
jQuery(function () {
|
||||
SphinxRtdTheme.Navigation.enable(true);
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
API
|
||||
===
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
|
||||
quapy
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
quapy
|
||||
=====
|
||||
|
||||
.. automodule:: quapy
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
quapy.benchmarking package
|
||||
==========================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.benchmarking.typical module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: quapy.benchmarking.typical
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.benchmarking
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,123 +0,0 @@
|
|||
/* Compatability shim for jQuery and underscores.js.
|
||||
*
|
||||
* Copyright Sphinx contributors
|
||||
* Released under the two clause BSD licence
|
||||
*/
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*
|
||||
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
|
||||
*/
|
||||
jQuery.urldecode = function(x) {
|
||||
if (!x) {
|
||||
return x
|
||||
}
|
||||
return decodeURIComponent(x.replace(/\+/g, ' '));
|
||||
};
|
||||
|
||||
/**
|
||||
* small helper function to urlencode strings
|
||||
*/
|
||||
jQuery.urlencode = encodeURIComponent;
|
||||
|
||||
/**
|
||||
* This function returns the parsed url parameters of the
|
||||
* current request. Multiple values per key are supported,
|
||||
* it will always return arrays of strings for the value parts.
|
||||
*/
|
||||
jQuery.getQueryParameters = function(s) {
|
||||
if (typeof s === 'undefined')
|
||||
s = document.location.search;
|
||||
var parts = s.substr(s.indexOf('?') + 1).split('&');
|
||||
var result = {};
|
||||
for (var i = 0; i < parts.length; i++) {
|
||||
var tmp = parts[i].split('=', 2);
|
||||
var key = jQuery.urldecode(tmp[0]);
|
||||
var value = jQuery.urldecode(tmp[1]);
|
||||
if (key in result)
|
||||
result[key].push(value);
|
||||
else
|
||||
result[key] = [value];
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/**
|
||||
* highlight a given string on a jquery object by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
jQuery.fn.highlightText = function(text, className) {
|
||||
function highlight(node, addItems) {
|
||||
if (node.nodeType === 3) {
|
||||
var val = node.nodeValue;
|
||||
var pos = val.toLowerCase().indexOf(text);
|
||||
if (pos >= 0 &&
|
||||
!jQuery(node.parentNode).hasClass(className) &&
|
||||
!jQuery(node.parentNode).hasClass("nohighlight")) {
|
||||
var span;
|
||||
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
|
||||
if (isInSVG) {
|
||||
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
|
||||
} else {
|
||||
span = document.createElement("span");
|
||||
span.className = className;
|
||||
}
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
if (isInSVG) {
|
||||
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
|
||||
var bbox = node.parentElement.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
rect.y.baseVal.value = bbox.y;
|
||||
rect.width.baseVal.value = bbox.width;
|
||||
rect.height.baseVal.value = bbox.height;
|
||||
rect.setAttribute('class', className);
|
||||
addItems.push({
|
||||
"parent": node.parentNode,
|
||||
"target": rect});
|
||||
}
|
||||
}
|
||||
}
|
||||
else if (!jQuery(node).is("button, select, textarea")) {
|
||||
jQuery.each(node.childNodes, function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
}
|
||||
}
|
||||
var addItems = [];
|
||||
var result = this.each(function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
for (var i = 0; i < addItems.length; ++i) {
|
||||
jQuery(addItems[i].parent).before(addItems[i].target);
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/*
|
||||
* backward compatibility for jQuery.browser
|
||||
* This will be supported until firefox bug is fixed.
|
||||
*/
|
||||
if (!jQuery.browser) {
|
||||
jQuery.uaMatch = function(ua) {
|
||||
ua = ua.toLowerCase();
|
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(msie) ([\w.]+)/.exec(ua) ||
|
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
version: match[ 2 ] || "0"
|
||||
};
|
||||
};
|
||||
jQuery.browser = {};
|
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
||||
}
|
||||
|
|
@ -4,7 +4,7 @@
|
|||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
|
@ -237,10 +237,6 @@ a.headerlink {
|
|||
visibility: hidden;
|
||||
}
|
||||
|
||||
a:visited {
|
||||
color: #551A8B;
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
|
|
@ -328,7 +324,6 @@ aside.sidebar {
|
|||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
nav.contents,
|
||||
aside.topic,
|
||||
div.admonition, div.topic, blockquote {
|
||||
|
|
@ -336,7 +331,6 @@ div.admonition, div.topic, blockquote {
|
|||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
nav.contents,
|
||||
aside.topic,
|
||||
div.topic {
|
||||
|
|
@ -612,7 +606,6 @@ ol.simple p,
|
|||
ul.simple p {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
aside.footnote > span,
|
||||
div.citation > span {
|
||||
float: left;
|
||||
|
|
@ -674,16 +667,6 @@ dd {
|
|||
margin-left: 30px;
|
||||
}
|
||||
|
||||
.sig dd {
|
||||
margin-top: 0px;
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
.sig dl {
|
||||
margin-top: 0px;
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
dl > dd:last-child,
|
||||
dl > dd:last-child > :last-child {
|
||||
margin-bottom: 0;
|
||||
|
|
@ -752,14 +735,6 @@ abbr, acronym {
|
|||
cursor: help;
|
||||
}
|
||||
|
||||
.translated {
|
||||
background-color: rgba(207, 255, 207, 0.2)
|
||||
}
|
||||
|
||||
.untranslated {
|
||||
background-color: rgba(255, 207, 207, 0.2)
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 107 B |
|
|
@ -1 +0,0 @@
|
|||
.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
|
||||
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|
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|
|
@ -4,7 +4,7 @@
|
|||
*
|
||||
* Base JavaScript utilities for all Sphinx HTML documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
const DOCUMENTATION_OPTIONS = {
|
||||
VERSION: '0.1.8',
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '0.1.9',
|
||||
LANGUAGE: 'en',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
|
|
|
|||
|
|
@ -1 +0,0 @@
|
|||
!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
/**
|
||||
* @preserve HTML5 Shiv 3.7.3-pre | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
|
||||
*/
|
||||
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|
||||
|
|
@ -1,4 +0,0 @@
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|||
/**
|
||||
* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
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||||
*/
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||||
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|
||||
File diff suppressed because one or more lines are too long
|
|
@ -5,7 +5,7 @@
|
|||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 120 B |
|
|
@ -4,7 +4,7 @@
|
|||
*
|
||||
* Sphinx JavaScript utilities for the full-text search.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
|
@ -57,12 +57,12 @@ const _removeChildren = (element) => {
|
|||
const _escapeRegExp = (string) =>
|
||||
string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
|
||||
|
||||
const _displayItem = (item, searchTerms, highlightTerms) => {
|
||||
const _displayItem = (item, searchTerms) => {
|
||||
const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
|
||||
const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
|
||||
const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
|
||||
const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
|
||||
const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
|
||||
const contentRoot = document.documentElement.dataset.content_root;
|
||||
|
||||
const [docName, title, anchor, descr, score, _filename] = item;
|
||||
|
||||
|
|
@ -75,24 +75,20 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
|
|||
if (dirname.match(/\/index\/$/))
|
||||
dirname = dirname.substring(0, dirname.length - 6);
|
||||
else if (dirname === "index/") dirname = "";
|
||||
requestUrl = contentRoot + dirname;
|
||||
requestUrl = docUrlRoot + dirname;
|
||||
linkUrl = requestUrl;
|
||||
} else {
|
||||
// normal html builders
|
||||
requestUrl = contentRoot + docName + docFileSuffix;
|
||||
requestUrl = docUrlRoot + docName + docFileSuffix;
|
||||
linkUrl = docName + docLinkSuffix;
|
||||
}
|
||||
let linkEl = listItem.appendChild(document.createElement("a"));
|
||||
linkEl.href = linkUrl + anchor;
|
||||
linkEl.dataset.score = score;
|
||||
linkEl.innerHTML = title;
|
||||
if (descr) {
|
||||
if (descr)
|
||||
listItem.appendChild(document.createElement("span")).innerHTML =
|
||||
" (" + descr + ")";
|
||||
// highlight search terms in the description
|
||||
if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
|
||||
highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
|
||||
}
|
||||
else if (showSearchSummary)
|
||||
fetch(requestUrl)
|
||||
.then((responseData) => responseData.text())
|
||||
|
|
@ -101,9 +97,6 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
|
|||
listItem.appendChild(
|
||||
Search.makeSearchSummary(data, searchTerms)
|
||||
);
|
||||
// highlight search terms in the summary
|
||||
if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
|
||||
highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
|
||||
});
|
||||
Search.output.appendChild(listItem);
|
||||
};
|
||||
|
|
@ -122,15 +115,14 @@ const _finishSearch = (resultCount) => {
|
|||
const _displayNextItem = (
|
||||
results,
|
||||
resultCount,
|
||||
searchTerms,
|
||||
highlightTerms,
|
||||
searchTerms
|
||||
) => {
|
||||
// results left, load the summary and display it
|
||||
// this is intended to be dynamic (don't sub resultsCount)
|
||||
if (results.length) {
|
||||
_displayItem(results.pop(), searchTerms, highlightTerms);
|
||||
_displayItem(results.pop(), searchTerms);
|
||||
setTimeout(
|
||||
() => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
|
||||
() => _displayNextItem(results, resultCount, searchTerms),
|
||||
5
|
||||
);
|
||||
}
|
||||
|
|
@ -164,7 +156,7 @@ const Search = {
|
|||
const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
|
||||
htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
|
||||
const docContent = htmlElement.querySelector('[role="main"]');
|
||||
if (docContent) return docContent.textContent;
|
||||
if (docContent !== undefined) return docContent.textContent;
|
||||
console.warn(
|
||||
"Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
|
||||
);
|
||||
|
|
@ -288,9 +280,9 @@ const Search = {
|
|||
let results = [];
|
||||
_removeChildren(document.getElementById("search-progress"));
|
||||
|
||||
const queryLower = query.toLowerCase().trim();
|
||||
const queryLower = query.toLowerCase();
|
||||
for (const [title, foundTitles] of Object.entries(allTitles)) {
|
||||
if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
|
||||
if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
|
||||
for (const [file, id] of foundTitles) {
|
||||
let score = Math.round(100 * queryLower.length / title.length)
|
||||
results.push([
|
||||
|
|
@ -368,7 +360,7 @@ const Search = {
|
|||
// console.info("search results:", Search.lastresults);
|
||||
|
||||
// print the results
|
||||
_displayNextItem(results, results.length, searchTerms, highlightTerms);
|
||||
_displayNextItem(results, results.length, searchTerms);
|
||||
},
|
||||
|
||||
/**
|
||||
|
|
|
|||
|
|
@ -1,154 +0,0 @@
|
|||
/* Highlighting utilities for Sphinx HTML documentation. */
|
||||
"use strict";
|
||||
|
||||
const SPHINX_HIGHLIGHT_ENABLED = true
|
||||
|
||||
/**
|
||||
* highlight a given string on a node by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
const _highlight = (node, addItems, text, className) => {
|
||||
if (node.nodeType === Node.TEXT_NODE) {
|
||||
const val = node.nodeValue;
|
||||
const parent = node.parentNode;
|
||||
const pos = val.toLowerCase().indexOf(text);
|
||||
if (
|
||||
pos >= 0 &&
|
||||
!parent.classList.contains(className) &&
|
||||
!parent.classList.contains("nohighlight")
|
||||
) {
|
||||
let span;
|
||||
|
||||
const closestNode = parent.closest("body, svg, foreignObject");
|
||||
const isInSVG = closestNode && closestNode.matches("svg");
|
||||
if (isInSVG) {
|
||||
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
|
||||
} else {
|
||||
span = document.createElement("span");
|
||||
span.classList.add(className);
|
||||
}
|
||||
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
const rest = document.createTextNode(val.substr(pos + text.length));
|
||||
parent.insertBefore(
|
||||
span,
|
||||
parent.insertBefore(
|
||||
rest,
|
||||
node.nextSibling
|
||||
)
|
||||
);
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
/* There may be more occurrences of search term in this node. So call this
|
||||
* function recursively on the remaining fragment.
|
||||
*/
|
||||
_highlight(rest, addItems, text, className);
|
||||
|
||||
if (isInSVG) {
|
||||
const rect = document.createElementNS(
|
||||
"http://www.w3.org/2000/svg",
|
||||
"rect"
|
||||
);
|
||||
const bbox = parent.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
rect.y.baseVal.value = bbox.y;
|
||||
rect.width.baseVal.value = bbox.width;
|
||||
rect.height.baseVal.value = bbox.height;
|
||||
rect.setAttribute("class", className);
|
||||
addItems.push({ parent: parent, target: rect });
|
||||
}
|
||||
}
|
||||
} else if (node.matches && !node.matches("button, select, textarea")) {
|
||||
node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
|
||||
}
|
||||
};
|
||||
const _highlightText = (thisNode, text, className) => {
|
||||
let addItems = [];
|
||||
_highlight(thisNode, addItems, text, className);
|
||||
addItems.forEach((obj) =>
|
||||
obj.parent.insertAdjacentElement("beforebegin", obj.target)
|
||||
);
|
||||
};
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
const SphinxHighlight = {
|
||||
|
||||
/**
|
||||
* highlight the search words provided in localstorage in the text
|
||||
*/
|
||||
highlightSearchWords: () => {
|
||||
if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
|
||||
|
||||
// get and clear terms from localstorage
|
||||
const url = new URL(window.location);
|
||||
const highlight =
|
||||
localStorage.getItem("sphinx_highlight_terms")
|
||||
|| url.searchParams.get("highlight")
|
||||
|| "";
|
||||
localStorage.removeItem("sphinx_highlight_terms")
|
||||
url.searchParams.delete("highlight");
|
||||
window.history.replaceState({}, "", url);
|
||||
|
||||
// get individual terms from highlight string
|
||||
const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
|
||||
if (terms.length === 0) return; // nothing to do
|
||||
|
||||
// There should never be more than one element matching "div.body"
|
||||
const divBody = document.querySelectorAll("div.body");
|
||||
const body = divBody.length ? divBody[0] : document.querySelector("body");
|
||||
window.setTimeout(() => {
|
||||
terms.forEach((term) => _highlightText(body, term, "highlighted"));
|
||||
}, 10);
|
||||
|
||||
const searchBox = document.getElementById("searchbox");
|
||||
if (searchBox === null) return;
|
||||
searchBox.appendChild(
|
||||
document
|
||||
.createRange()
|
||||
.createContextualFragment(
|
||||
'<p class="highlight-link">' +
|
||||
'<a href="javascript:SphinxHighlight.hideSearchWords()">' +
|
||||
_("Hide Search Matches") +
|
||||
"</a></p>"
|
||||
)
|
||||
);
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to hide the search marks again
|
||||
*/
|
||||
hideSearchWords: () => {
|
||||
document
|
||||
.querySelectorAll("#searchbox .highlight-link")
|
||||
.forEach((el) => el.remove());
|
||||
document
|
||||
.querySelectorAll("span.highlighted")
|
||||
.forEach((el) => el.classList.remove("highlighted"));
|
||||
localStorage.removeItem("sphinx_highlight_terms")
|
||||
},
|
||||
|
||||
initEscapeListener: () => {
|
||||
// only install a listener if it is really needed
|
||||
if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
|
||||
|
||||
document.addEventListener("keydown", (event) => {
|
||||
// bail for input elements
|
||||
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
|
||||
// bail with special keys
|
||||
if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
|
||||
if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
|
||||
SphinxHighlight.hideSearchWords();
|
||||
event.preventDefault();
|
||||
}
|
||||
});
|
||||
},
|
||||
};
|
||||
|
||||
_ready(() => {
|
||||
/* Do not call highlightSearchWords() when we are on the search page.
|
||||
* It will highlight words from the *previous* search query.
|
||||
*/
|
||||
if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
|
||||
SphinxHighlight.initEscapeListener();
|
||||
});
|
||||
|
|
@ -1,354 +0,0 @@
|
|||
/*
|
||||
* sphinxdoc.css_t
|
||||
* ~~~~~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- sphinxdoc theme. Originally created by
|
||||
* Armin Ronacher for Werkzeug.
|
||||
*
|
||||
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<section id="module-quapy">
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<span id="quapy"></span><h1>quapy<a class="headerlink" href="#module-quapy" title="Link to this heading"></a></h1>
|
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<p>QuaPy module for quantification</p>
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@ -1,22 +1,23 @@
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<!DOCTYPE html>
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<html class="writer-html5" lang="en" data-content_root="./">
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<html class="writer-html5" lang="en">
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<link rel="index" title="Index" href="#" />
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<link rel="search" title="Search" href="search.html" />
|
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|
|
@ -115,8 +116,6 @@
|
|||
<li><a href="quapy.html#quapy.error.acce">acce() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">add_word() (quapy.data.preprocessing.IndexTransformer method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.adjusted_quantification">adjusted_quantification() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AdjustedClassifyAndCount">AdjustedClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
|
|
@ -136,6 +135,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.aggregate">(quapy.method.aggregative.ACC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">(quapy.method.aggregative.AggregativeQuantifier method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregate">(quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregate">(quapy.method.aggregative.CC method)</a>
|
||||
</li>
|
||||
|
|
@ -174,6 +175,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.aggregation_fit">(quapy.method.aggregative.ACC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit">(quapy.method.aggregative.AggregativeQuantifier method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregation_fit">(quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregation_fit">(quapy.method.aggregative.CC method)</a>
|
||||
</li>
|
||||
|
|
@ -221,6 +224,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method._kdey.KDEBase.BANDWIDTH_METHOD">BANDWIDTH_METHOD (quapy.method._kdey.KDEBase attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier">BaseQuantifier (class in quapy.method.base)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC">BayesianCC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.BCTSCalibration">BCTSCalibration (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
|
|
@ -284,11 +289,13 @@
|
|||
</ul></li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ClassifyAndCount">ClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">clean_checkpoint() (quapy.method._neural.QuaNetTrainer method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">clean_checkpoint_dir() (quapy.method._neural.QuaNetTrainer method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.functional.clip">clip() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet">CNNnet (class in quapy.classification.neural)</a>
|
||||
</li>
|
||||
|
|
@ -306,9 +313,13 @@
|
|||
<li><a href="quapy.method.html#quapy.method._threshold_optim.X.condition">(quapy.method._threshold_optim.X method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.html#quapy.functional.condsoftmax">condsoftmax() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.ConfigStatus">ConfigStatus (class in quapy.model_selection)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.counts">counts() (quapy.data.base.LabelledCollection method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.counts_from_labels">counts_from_labels() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.create_if_not_exist">create_if_not_exist() (in module quapy.util)</a>
|
||||
</li>
|
||||
|
|
@ -472,6 +483,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.non_aggregative.DMx.fit">(quapy.method.non_aggregative.DMx method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">(quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.fit">(quapy.model_selection.GridSearchQ method)</a>
|
||||
</li>
|
||||
|
|
@ -505,6 +518,8 @@
|
|||
<table style="width: 100%" class="indextable genindextable"><tr>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">get_collator() (quapy.protocol.OnLabelledCollectionProtocol class method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples">get_conditional_probability_samples() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.get_divergence">get_divergence() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -542,6 +557,8 @@
|
|||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_prevalence_samples">get_prevalence_samples() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.meta.get_probability_distribution">get_probability_distribution() (in module quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.get_quapy_home">get_quapy_home() (in module quapy.util)</a>
|
||||
|
|
@ -628,18 +645,20 @@
|
|||
<h2 id="L">L</h2>
|
||||
<table style="width: 100%" class="indextable genindextable"><tr>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.html#quapy.functional.l1_norm">l1_norm() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection">LabelledCollection (class in quapy.data.base)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.linear_search">linear_search() (in module quapy.functional)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.load">(quapy.data.base.LabelledCollection class method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">LowRankLogisticRegression (class in quapy.classification.methods)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet">LSTMnet (class in quapy.classification.neural)</a>
|
||||
|
|
@ -673,6 +692,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.meta.MedianEstimator">MedianEstimator (class in quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.meta.MedianEstimator2">MedianEstimator2 (class in quapy.method.meta)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.METHODS">METHODS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.error.mkld">mkld() (in module quapy.error)</a>
|
||||
</li>
|
||||
|
|
@ -686,7 +707,7 @@
|
|||
module
|
||||
|
||||
<ul>
|
||||
<li><a href="generated/quapy.html#module-quapy">quapy</a>, <a href="quapy.html#module-quapy">[1]</a>
|
||||
<li><a href="quapy.html#module-quapy">quapy</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#module-quapy.classification">quapy.classification</a>
|
||||
</li>
|
||||
|
|
@ -772,6 +793,8 @@
|
|||
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">NeuralClassifierTrainer (class in quapy.classification.neural)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.newELM">newELM() (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.newInvariantRatioEstimation">newInvariantRatioEstimation() (quapy.method.aggregative.ACC class method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.base.newOneVsAll">newOneVsAll() (in module quapy.method.base)</a>
|
||||
</li>
|
||||
|
|
@ -786,6 +809,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.newSVMRAE">newSVMRAE() (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.error.nkld">nkld() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.NORMALIZATIONS">NORMALIZATIONS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.normalize_prevalence">normalize_prevalence() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -830,6 +855,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.PACC">PACC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.parallel">parallel() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.parallel_unpack">parallel_unpack() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.PCC">PCC (class in quapy.method.aggregative)</a>
|
||||
</li>
|
||||
|
|
@ -880,6 +907,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">ProbabilisticAdjustedClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount">ProbabilisticClassifyAndCount (in module quapy.method.aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.projection_simplex_sort">projection_simplex_sort() (in module quapy.functional)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
</tr></table>
|
||||
|
|
@ -911,6 +940,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method.non_aggregative.DMx.quantify">(quapy.method.non_aggregative.DMx method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.quantify">(quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.quantify">(quapy.model_selection.GridSearchQ method)</a>
|
||||
</li>
|
||||
|
|
@ -919,7 +950,7 @@
|
|||
quapy
|
||||
|
||||
<ul>
|
||||
<li><a href="generated/quapy.html#module-quapy">module</a>, <a href="quapy.html#module-quapy">[1]</a>
|
||||
<li><a href="quapy.html#module-quapy">module</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li>
|
||||
|
|
@ -1108,15 +1139,17 @@
|
|||
<li><a href="quapy.html#quapy.error.rae">rae() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state">random_state (quapy.protocol.AbstractStochasticSeededProtocol property)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe">ReadMe (class in quapy.method.non_aggregative)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">RecalibratedProbabilisticClassifier (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">RecalibratedProbabilisticClassifierBase (class in quapy.classification.calibration)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.reduce">reduce() (quapy.data.base.Dataset method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.reduce">reduce() (quapy.data.base.Dataset method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.reduce_columns">reduce_columns() (in module quapy.data.preprocessing)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.reader.reindex_labels">reindex_labels() (in module quapy.data.reader)</a>
|
||||
|
|
@ -1145,6 +1178,8 @@
|
|||
<li><a href="quapy.html#quapy.protocol.UPP.sample">(quapy.protocol.UPP method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.sample_from_posterior">sample_from_posterior() (quapy.method.aggregative.BayesianCC method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">samples_parameters() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
|
||||
|
||||
<ul>
|
||||
|
|
@ -1193,7 +1228,13 @@
|
|||
</li>
|
||||
<li><a href="quapy.html#quapy.error.smooth">smooth() (in module quapy.error)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment">solve_adjustment() (quapy.method.aggregative.ACC class method)</a>
|
||||
<li><a href="quapy.html#quapy.functional.softmax">softmax() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.solve_adjustment">solve_adjustment() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.solve_adjustment_binary">solve_adjustment_binary() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.SOLVERS">SOLVERS (quapy.method.aggregative.ACC attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_random">split_random() (quapy.data.base.LabelledCollection method)</a>
|
||||
</li>
|
||||
|
|
@ -1210,6 +1251,8 @@
|
|||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.html#quapy.model_selection.Status">Status (class in quapy.model_selection)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method.non_aggregative.ReadMe.std_constrained_linear_ls">std_constrained_linear_ls() (quapy.method.non_aggregative.ReadMe method)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.strprev">strprev() (in module quapy.functional)</a>
|
||||
</li>
|
||||
|
|
@ -1228,6 +1271,8 @@
|
|||
<li><a href="quapy.method.html#quapy.method._threshold_optim.T50">T50 (class in quapy.method._threshold_optim)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.util.temp_seed">temp_seed() (in module quapy.util)</a>
|
||||
</li>
|
||||
<li><a href="quapy.html#quapy.functional.ternary_search">ternary_search() (in module quapy.functional)</a>
|
||||
</li>
|
||||
<li><a href="quapy.data.html#quapy.data.preprocessing.text2tfidf">text2tfidf() (in module quapy.data.preprocessing)</a>
|
||||
</li>
|
||||
|
|
@ -1261,6 +1306,16 @@
|
|||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="quapy.data.html#quapy.data.base.Dataset.train_test">train_test (quapy.data.base.Dataset property)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet.training">training (quapy.classification.neural.CNNnet attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet.training">(quapy.classification.neural.LSTMnet attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.training">(quapy.classification.neural.TextClassifierNet attribute)</a>
|
||||
</li>
|
||||
<li><a href="quapy.method.html#quapy.method._neural.QuaNetModule.training">(quapy.method._neural.QuaNetModule attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">transform() (quapy.classification.methods.LowRankLogisticRegression method)</a>
|
||||
|
||||
<ul>
|
||||
|
|
|
|||
|
|
@ -1,24 +1,24 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Welcome to QuaPy’s documentation! — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>Welcome to QuaPy’s documentation! — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
|
|
@ -73,21 +73,21 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<section id="welcome-to-quapy-s-documentation">
|
||||
<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Link to this heading"></a></h1>
|
||||
<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this heading"></a></h1>
|
||||
<p>QuaPy is a Python-based open-source framework for quantification.</p>
|
||||
<p>This document contains the API of the modules included in QuaPy.</p>
|
||||
<section id="installation">
|
||||
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h2>
|
||||
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
|
||||
<p><cite>pip install quapy</cite></p>
|
||||
</section>
|
||||
<section id="github">
|
||||
<h2>GitHub<a class="headerlink" href="#github" title="Link to this heading"></a></h2>
|
||||
<h2>GitHub<a class="headerlink" href="#github" title="Permalink to this heading"></a></h2>
|
||||
<p>QuaPy is hosted in GitHub at <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy">https://github.com/HLT-ISTI/QuaPy</a></p>
|
||||
<div class="toctree-wrapper compound">
|
||||
</div>
|
||||
</section>
|
||||
<section id="contents">
|
||||
<h2>Contents<a class="headerlink" href="#contents" title="Link to this heading"></a></h2>
|
||||
<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this heading"></a></h2>
|
||||
<div class="toctree-wrapper compound">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a><ul>
|
||||
|
|
@ -128,12 +128,14 @@
|
|||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet"><code class="docutils literal notranslate"><span class="pre">CNNnet</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding"><code class="docutils literal notranslate"><span class="pre">CNNnet.document_embedding()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.get_params"><code class="docutils literal notranslate"><span class="pre">CNNnet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.training"><code class="docutils literal notranslate"><span class="pre">CNNnet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.CNNnet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">CNNnet.vocabulary_size</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet"><code class="docutils literal notranslate"><span class="pre">LSTMnet</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding"><code class="docutils literal notranslate"><span class="pre">LSTMnet.document_embedding()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.get_params"><code class="docutils literal notranslate"><span class="pre">LSTMnet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.training"><code class="docutils literal notranslate"><span class="pre">LSTMnet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.LSTMnet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">LSTMnet.vocabulary_size</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
|
|
@ -154,6 +156,7 @@
|
|||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.forward()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.get_params()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.predict_proba()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.training"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.training</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.vocabulary_size"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.vocabulary_size</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform"><code class="docutils literal notranslate"><span class="pre">TextClassifierNet.xavier_uniform()</span></code></a></li>
|
||||
</ul>
|
||||
|
|
@ -260,10 +263,13 @@
|
|||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a><ul>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC"><code class="docutils literal notranslate"><span class="pre">ACC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.METHODS"><code class="docutils literal notranslate"><span class="pre">ACC.METHODS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.NORMALIZATIONS"><code class="docutils literal notranslate"><span class="pre">ACC.NORMALIZATIONS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.SOLVERS"><code class="docutils literal notranslate"><span class="pre">ACC.SOLVERS</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.aggregate"><code class="docutils literal notranslate"><span class="pre">ACC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">ACC.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.getPteCondEstim"><code class="docutils literal notranslate"><span class="pre">ACC.getPteCondEstim()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">ACC.solve_adjustment()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.ACC.newInvariantRatioEstimation"><code class="docutils literal notranslate"><span class="pre">ACC.newInvariantRatioEstimation()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AdjustedClassifyAndCount"><code class="docutils literal notranslate"><span class="pre">AdjustedClassifyAndCount</span></code></a></li>
|
||||
|
|
@ -289,6 +295,14 @@
|
|||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.AggregativeSoftQuantifier"><code class="docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC"><code class="docutils literal notranslate"><span class="pre">BayesianCC</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregate"><code class="docutils literal notranslate"><span class="pre">BayesianCC.aggregate()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.aggregation_fit"><code class="docutils literal notranslate"><span class="pre">BayesianCC.aggregation_fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples"><code class="docutils literal notranslate"><span class="pre">BayesianCC.get_conditional_probability_samples()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_prevalence_samples"><code class="docutils literal notranslate"><span class="pre">BayesianCC.get_prevalence_samples()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BayesianCC.sample_from_posterior"><code class="docutils literal notranslate"><span class="pre">BayesianCC.sample_from_posterior()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.fit"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.neg_label"><code class="docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier.neg_label</span></code></a></li>
|
||||
|
|
@ -385,6 +399,7 @@
|
|||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule"><code class="docutils literal notranslate"><span class="pre">QuaNetModule</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.device"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.device</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.forward"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.forward()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetModule.training"><code class="docutils literal notranslate"><span class="pre">QuaNetModule.training</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method._neural.QuaNetTrainer"><code class="docutils literal notranslate"><span class="pre">QuaNetTrainer</span></code></a><ul>
|
||||
|
|
@ -494,6 +509,12 @@
|
|||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify"><code class="docutils literal notranslate"><span class="pre">MaximumLikelihoodPrevalenceEstimation.quantify()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe"><code class="docutils literal notranslate"><span class="pre">ReadMe</span></code></a><ul>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.fit"><code class="docutils literal notranslate"><span class="pre">ReadMe.fit()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.quantify"><code class="docutils literal notranslate"><span class="pre">ReadMe.quantify()</span></code></a></li>
|
||||
<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.non_aggregative.ReadMe.std_constrained_linear_ls"><code class="docutils literal notranslate"><span class="pre">ReadMe.std_constrained_linear_ls()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
|
||||
|
|
@ -543,12 +564,15 @@
|
|||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional module</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.HellingerDistance"><code class="docutils literal notranslate"><span class="pre">HellingerDistance()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.TopsoeDistance"><code class="docutils literal notranslate"><span class="pre">TopsoeDistance()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.adjusted_quantification"><code class="docutils literal notranslate"><span class="pre">adjusted_quantification()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.argmin_prevalence"><code class="docutils literal notranslate"><span class="pre">argmin_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.as_binary_prevalence"><code class="docutils literal notranslate"><span class="pre">as_binary_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.check_prevalence_vector"><code class="docutils literal notranslate"><span class="pre">check_prevalence_vector()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.clip"><code class="docutils literal notranslate"><span class="pre">clip()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.condsoftmax"><code class="docutils literal notranslate"><span class="pre">condsoftmax()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.counts_from_labels"><code class="docutils literal notranslate"><span class="pre">counts_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.get_divergence"><code class="docutils literal notranslate"><span class="pre">get_divergence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.get_nprevpoints_approximation"><code class="docutils literal notranslate"><span class="pre">get_nprevpoints_approximation()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.l1_norm"><code class="docutils literal notranslate"><span class="pre">l1_norm()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.linear_search"><code class="docutils literal notranslate"><span class="pre">linear_search()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.normalize_prevalence"><code class="docutils literal notranslate"><span class="pre">normalize_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.num_prevalence_combinations"><code class="docutils literal notranslate"><span class="pre">num_prevalence_combinations()</span></code></a></li>
|
||||
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|
|||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_labels"><code class="docutils literal notranslate"><span class="pre">prevalence_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_probabilities"><code class="docutils literal notranslate"><span class="pre">prevalence_from_probabilities()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_linspace"><code class="docutils literal notranslate"><span class="pre">prevalence_linspace()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.projection_simplex_sort"><code class="docutils literal notranslate"><span class="pre">projection_simplex_sort()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.softmax"><code class="docutils literal notranslate"><span class="pre">softmax()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">solve_adjustment()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
|
||||
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|
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|
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|
|||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.pickled_resource"><code class="docutils literal notranslate"><span class="pre">pickled_resource()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.save_text_file"><code class="docutils literal notranslate"><span class="pre">save_text_file()</span></code></a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.temp_seed"><code class="docutils literal notranslate"><span class="pre">temp_seed()</span></code></a></li>
|
||||
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|
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</section>
|
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</section>
|
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<section id="indices-and-tables">
|
||||
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading"></a></h1>
|
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading"></a></h1>
|
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|
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<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
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<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
|
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@ -1,24 +1,24 @@
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<title>quapy — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
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@ -77,7 +77,7 @@
|
|||
<div itemprop="articleBody">
|
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|
||||
<section id="quapy">
|
||||
<h1>quapy<a class="headerlink" href="#quapy" title="Link to this heading"></a></h1>
|
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<h1>quapy<a class="headerlink" href="#quapy" title="Permalink to this heading"></a></h1>
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<div class="toctree-wrapper compound">
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@ -153,12 +153,15 @@
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional module</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.HellingerDistance"><code class="docutils literal notranslate"><span class="pre">HellingerDistance()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.TopsoeDistance"><code class="docutils literal notranslate"><span class="pre">TopsoeDistance()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.adjusted_quantification"><code class="docutils literal notranslate"><span class="pre">adjusted_quantification()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.argmin_prevalence"><code class="docutils literal notranslate"><span class="pre">argmin_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.as_binary_prevalence"><code class="docutils literal notranslate"><span class="pre">as_binary_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.check_prevalence_vector"><code class="docutils literal notranslate"><span class="pre">check_prevalence_vector()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.clip"><code class="docutils literal notranslate"><span class="pre">clip()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.condsoftmax"><code class="docutils literal notranslate"><span class="pre">condsoftmax()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.counts_from_labels"><code class="docutils literal notranslate"><span class="pre">counts_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.get_divergence"><code class="docutils literal notranslate"><span class="pre">get_divergence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.get_nprevpoints_approximation"><code class="docutils literal notranslate"><span class="pre">get_nprevpoints_approximation()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.l1_norm"><code class="docutils literal notranslate"><span class="pre">l1_norm()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.linear_search"><code class="docutils literal notranslate"><span class="pre">linear_search()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.normalize_prevalence"><code class="docutils literal notranslate"><span class="pre">normalize_prevalence()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.num_prevalence_combinations"><code class="docutils literal notranslate"><span class="pre">num_prevalence_combinations()</span></code></a></li>
|
||||
|
|
@ -166,7 +169,12 @@
|
|||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_labels"><code class="docutils literal notranslate"><span class="pre">prevalence_from_labels()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_from_probabilities"><code class="docutils literal notranslate"><span class="pre">prevalence_from_probabilities()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.prevalence_linspace"><code class="docutils literal notranslate"><span class="pre">prevalence_linspace()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.projection_simplex_sort"><code class="docutils literal notranslate"><span class="pre">projection_simplex_sort()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.softmax"><code class="docutils literal notranslate"><span class="pre">softmax()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment"><code class="docutils literal notranslate"><span class="pre">solve_adjustment()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
|
||||
</ul>
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|
|||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.pickled_resource"><code class="docutils literal notranslate"><span class="pre">pickled_resource()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.save_text_file"><code class="docutils literal notranslate"><span class="pre">save_text_file()</span></code></a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.temp_seed"><code class="docutils literal notranslate"><span class="pre">temp_seed()</span></code></a></li>
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<h1>quapy.benchmarking package<a class="headerlink" href="#quapy-benchmarking-package" title="Link to this heading"></a></h1>
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<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
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<section id="module-quapy.benchmarking.typical">
|
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<span id="quapy-benchmarking-typical-module"></span><h2>quapy.benchmarking.typical module<a class="headerlink" href="#module-quapy.benchmarking.typical" title="Link to this heading"></a></h2>
|
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<dl class="py function">
|
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<dt class="sig sig-object py" id="quapy.benchmarking.typical.wrap_cls_params">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.benchmarking.typical.</span></span><span class="sig-name descname"><span class="pre">wrap_cls_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.benchmarking.typical.wrap_cls_params" title="Link to this definition"></a></dt>
|
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<dd></dd></dl>
|
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|
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<section id="module-quapy.benchmarking">
|
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.benchmarking" title="Link to this heading"></a></h2>
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<section id="quapy-classification-package">
|
||||
<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Link to this heading"></a></h1>
|
||||
<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this heading"></a></h1>
|
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<section id="submodules">
|
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<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
|
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
|
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</section>
|
||||
<section id="module-quapy.classification.calibration">
|
||||
<span id="quapy-classification-calibration-module"></span><h2>quapy.classification.calibration module<a class="headerlink" href="#module-quapy.classification.calibration" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-classification-calibration-module"></span><h2>quapy.classification.calibration module<a class="headerlink" href="#module-quapy.classification.calibration" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.BCTSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">BCTSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#BCTSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.BCTSCalibration" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">BCTSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#BCTSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.BCTSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
|
|
@ -124,7 +125,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.NBVSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">NBVSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#NBVSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.NBVSCalibration" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">NBVSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#NBVSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.NBVSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the No-Bias Vector Scaling (NBVS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
|
|
@ -145,7 +146,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifier">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifier</span></span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifier</span></span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>Abstract class for (re)calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration
|
||||
|
|
@ -154,7 +155,7 @@ is hard-to-beat at label shift adaptation. In International Conference on Machin
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifierBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifierBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="quapy.classification.calibration.RecalibratedProbabilisticClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifier</span></code></a></p>
|
||||
<p>Applies a (re)calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>.</p>
|
||||
|
|
@ -174,7 +175,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the classes on which the classifier has been trained on</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -185,7 +186,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration for the probabilistic classifier.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -202,7 +203,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">
|
||||
<span class="sig-name descname"><span class="pre">fit_cv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_cv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit_cv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_cv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all
|
||||
training instances via cross-validation, and then retrains the classifier on all training instances.
|
||||
The posterior probabilities thus generated are used for calibrating the outputs of the classifier.</p>
|
||||
|
|
@ -221,7 +222,7 @@ The posterior probabilities thus generated are used for calibrating the outputs
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">
|
||||
<span class="sig-name descname"><span class="pre">fit_tr_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_tr_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit_tr_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.fit_tr_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a
|
||||
training and a validation set, and then uses the training samples to learn classifier which is then used
|
||||
to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate
|
||||
|
|
@ -241,7 +242,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts class labels for the data instances in <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -255,7 +256,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#RecalibratedProbabilisticClassifierBase.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generates posterior probabilities for the data instances in <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -271,7 +272,7 @@ the classifier. The classifier is not retrained on the whole dataset.</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.TSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">TSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#TSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.TSCalibration" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">TSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#TSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.TSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Temperature Scaling (TS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
|
|
@ -292,7 +293,7 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.calibration.VSCalibration">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">VSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#VSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.VSCalibration" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">VSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/calibration.html#VSCalibration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.calibration.VSCalibration" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
|
||||
<p>Applies the Vector Scaling (VS) calibration method from <cite>abstention.calibration</cite>, as defined in
|
||||
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
|
||||
|
|
@ -313,10 +314,10 @@ training set afterwards. Default value is 5.</p></li>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.classification.methods">
|
||||
<span id="quapy-classification-methods-module"></span><h2>quapy.classification.methods module<a class="headerlink" href="#module-quapy.classification.methods" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-classification-methods-module"></span><h2>quapy.classification.methods module<a class="headerlink" href="#module-quapy.classification.methods" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.methods.</span></span><span class="sig-name descname"><span class="pre">LowRankLogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.methods.</span></span><span class="sig-name descname"><span class="pre">LowRankLogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code></p>
|
||||
<p>An example of a classification method (i.e., an object that implements <cite>fit</cite>, <cite>predict</cite>, and <cite>predict_proba</cite>)
|
||||
that also generates embedded inputs (i.e., that implements <cite>transform</cite>), as those required for
|
||||
|
|
@ -335,7 +336,7 @@ while classification is performed using <code class="xref py py-class docutils l
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.fit" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fit the model according to the given training data. The fit consists of
|
||||
fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -353,7 +354,7 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.get_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -364,7 +365,7 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -379,7 +380,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict_proba" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -393,7 +394,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.set_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.set_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Set the parameters of this estimator.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -406,7 +407,7 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.transform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/methods.html#LowRankLogisticRegression.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the low-rank approximation of <cite>X</cite> with <cite>n_components</cite> dimensions, or <cite>X</cite> unaltered if
|
||||
<cite>n_components</cite> >= <cite>X.shape[1]</cite>.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -423,10 +424,10 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.classification.neural">
|
||||
<span id="quapy-classification-neural-module"></span><h2>quapy.classification.neural module<a class="headerlink" href="#module-quapy.classification.neural" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-classification-neural-module"></span><h2>quapy.classification.neural module<a class="headerlink" href="#module-quapy.classification.neural" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">CNNnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[3,</span> <span class="pre">5,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">CNNnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[3,</span> <span class="pre">5,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">TextClassifierNet</span></code></a></p>
|
||||
<p>An implementation of <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a> based on
|
||||
Convolutional Neural Networks.</p>
|
||||
|
|
@ -448,7 +449,7 @@ consecutive tokens that each kernel covers</p></li>
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.document_embedding" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -466,7 +467,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.get_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#CNNnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.CNNnet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -475,9 +476,14 @@ dimensionality of the embedding</p>
|
|||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.CNNnet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.CNNnet.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.CNNnet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -490,7 +496,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">LSTMnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_class_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">LSTMnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_class_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">TextClassifierNet</span></code></a></p>
|
||||
<p>An implementation of <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a> based on
|
||||
Long Short Term Memory networks.</p>
|
||||
|
|
@ -509,7 +515,7 @@ Long Short Term Memory networks.</p>
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.document_embedding" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -527,7 +533,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.get_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#LSTMnet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.LSTMnet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -536,9 +542,14 @@ dimensionality of the embedding</p>
|
|||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.LSTMnet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -551,7 +562,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">NeuralClassifierTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">TextClassifierNet</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointpath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint/classifier_net.dat'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">NeuralClassifierTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">TextClassifierNet</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointpath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint/classifier_net.dat'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>Trains a neural network for text classification.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -574,7 +585,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.device" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.device" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the device in which the network is allocated</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -585,7 +596,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.fit" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the model according to the given training data.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -603,7 +614,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.get_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -614,7 +625,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -629,7 +640,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -643,7 +654,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">
|
||||
<span class="sig-name descname"><span class="pre">reset_net_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.reset_net_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">reset_net_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.reset_net_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reinitialize the network parameters</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -657,7 +668,7 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.set_params" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.set_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Set the parameters of this trainer and the learner it is training.
|
||||
In this current version, parameter names for the trainer and learner should
|
||||
be disjoint.</p>
|
||||
|
|
@ -670,7 +681,7 @@ be disjoint.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.transform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#NeuralClassifierTrainer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the embeddings of the instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -687,12 +698,12 @@ where <cite>embed_size</cite> is defined by the classification network</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TextClassifierNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TextClassifierNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
|
||||
<p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.dimensions">
|
||||
<span class="sig-name descname"><span class="pre">dimensions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.dimensions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.dimensions" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">dimensions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.dimensions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.dimensions" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the number of dimensions of the embedding space</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -703,7 +714,7 @@ where <cite>embed_size</cite> is defined by the classification network</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding">
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.document_embedding" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.document_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.document_embedding" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Embeds documents (i.e., performs the forward pass up to the
|
||||
next-to-last layer).</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -721,7 +732,7 @@ dimensionality of the embedding</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.forward">
|
||||
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.forward" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.forward" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Performs the forward pass.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -737,7 +748,7 @@ for each of the instances and classes</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params">
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.get_params" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.get_params" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Get hyper-parameters for this estimator</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -748,7 +759,7 @@ for each of the instances and classes</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.predict_proba" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.predict_proba" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts posterior probabilities for the instances in <cite>x</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -762,9 +773,14 @@ is length of the pad in the batch</p>
|
|||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.training">
|
||||
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.training" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -775,7 +791,7 @@ is length of the pad in the batch</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.xavier_uniform">
|
||||
<span class="sig-name descname"><span class="pre">xavier_uniform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.xavier_uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.xavier_uniform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">xavier_uniform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TextClassifierNet.xavier_uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.xavier_uniform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Performs Xavier initialization of the network parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
|
|
@ -783,7 +799,7 @@ is length of the pad in the batch</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
|
||||
<p>Transforms labelled instances into a Torch’s <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -796,7 +812,7 @@ is length of the pad in the batch</p>
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset.asDataloader">
|
||||
<span class="sig-name descname"><span class="pre">asDataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset.asDataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset.asDataloader" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">asDataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/neural.html#TorchDataset.asDataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.neural.TorchDataset.asDataloader" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Converts the labelled collection into a Torch DataLoader with dynamic padding for
|
||||
the batch</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -820,10 +836,10 @@ applied, meaning that if the longest document in the batch is shorter than
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.classification.svmperf">
|
||||
<span id="quapy-classification-svmperf-module"></span><h2>quapy.classification.svmperf module<a class="headerlink" href="#module-quapy.classification.svmperf" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-classification-svmperf-module"></span><h2>quapy.classification.svmperf module<a class="headerlink" href="#module-quapy.classification.svmperf" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.svmperf.</span></span><span class="sig-name descname"><span class="pre">SVMperf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">host_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.svmperf.</span></span><span class="sig-name descname"><span class="pre">SVMperf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">host_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></p>
|
||||
<p>A wrapper for the <a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">SVM-perf package</a> by Thorsten Joachims.
|
||||
When using losses for quantification, the source code has to be patched. See
|
||||
|
|
@ -848,7 +864,7 @@ for further details.</p>
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.decision_function">
|
||||
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.decision_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.decision_function" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.decision_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.decision_function" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Evaluate the decision function for the samples in <cite>X</cite>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -865,7 +881,7 @@ for further details.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.fit" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Trains the SVM for the multivariate performance loss</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -882,7 +898,7 @@ for further details.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.predict" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/classification/svmperf.html#SVMperf.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.predict" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Predicts labels for the instances <cite>X</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -897,14 +913,14 @@ instances in <cite>X</cite></p>
|
|||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses">
|
||||
<span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'01':</span> <span class="pre">0,</span> <span class="pre">'f1':</span> <span class="pre">1,</span> <span class="pre">'kld':</span> <span class="pre">12,</span> <span class="pre">'mae':</span> <span class="pre">26,</span> <span class="pre">'mrae':</span> <span class="pre">27,</span> <span class="pre">'nkld':</span> <span class="pre">13,</span> <span class="pre">'q':</span> <span class="pre">22,</span> <span class="pre">'qacc':</span> <span class="pre">23,</span> <span class="pre">'qf1':</span> <span class="pre">24,</span> <span class="pre">'qgm':</span> <span class="pre">25}</span></em><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.valid_losses" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'01':</span> <span class="pre">0,</span> <span class="pre">'f1':</span> <span class="pre">1,</span> <span class="pre">'kld':</span> <span class="pre">12,</span> <span class="pre">'mae':</span> <span class="pre">26,</span> <span class="pre">'mrae':</span> <span class="pre">27,</span> <span class="pre">'nkld':</span> <span class="pre">13,</span> <span class="pre">'q':</span> <span class="pre">22,</span> <span class="pre">'qacc':</span> <span class="pre">23,</span> <span class="pre">'qf1':</span> <span class="pre">24,</span> <span class="pre">'qgm':</span> <span class="pre">25}</span></em><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.valid_losses" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.classification">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Link to this heading"></a></h2>
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,23 +1,24 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.data package — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>quapy.data package — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
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|
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|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
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<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
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<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
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<script src="_static/jquery.js"></script>
|
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<script src="_static/underscore.js"></script>
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<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
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<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
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<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
|
|
@ -95,15 +96,15 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<section id="quapy-data-package">
|
||||
<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Link to this heading"></a></h1>
|
||||
<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Permalink to this heading"></a></h1>
|
||||
<section id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
<section id="module-quapy.data.base">
|
||||
<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>Abstraction of training and test <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> objects.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -118,7 +119,7 @@
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.SplitStratified">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SplitStratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.SplitStratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.SplitStratified" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SplitStratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.SplitStratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.SplitStratified" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generates a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> from a stratified split of a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> instance.
|
||||
See <a class="reference internal" href="#quapy.data.base.LabelledCollection.split_stratified" title="quapy.data.base.LabelledCollection.split_stratified"><code class="xref py py-meth docutils literal notranslate"><span class="pre">LabelledCollection.split_stratified()</span></code></a></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -136,7 +137,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.binary">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.Dataset.binary" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.Dataset.binary" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns True if the training collection is labelled according to two classes</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -147,7 +148,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.classes_">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.data.base.Dataset.classes_" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.data.base.Dataset.classes_" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The classes according to which the training collection is labelled</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -158,7 +159,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.kFCV">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.kFCV" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.kFCV" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around
|
||||
<a class="reference internal" href="#quapy.data.base.LabelledCollection.kFCV" title="quapy.data.base.LabelledCollection.kFCV"><code class="xref py py-meth docutils literal notranslate"><span class="pre">LabelledCollection.kFCV()</span></code></a> that returns <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> instances made of training and test folds.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -177,7 +178,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.spli
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.load">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.load" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.load" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a training and a test labelled set of data and convert it into a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> instance.
|
||||
The function in charge of reading the instances must be specified. This function can be a custom one, or any of
|
||||
the reading functions defined in <a class="reference internal" href="#module-quapy.data.reader" title="quapy.data.reader"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.data.reader</span></code></a> module.</p>
|
||||
|
|
@ -201,7 +202,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.n_classes">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.Dataset.n_classes" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.Dataset.n_classes" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The number of classes according to which the training collection is labelled</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -212,7 +213,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.reduce">
|
||||
<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -229,7 +230,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.stats" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.stats" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
|
|
@ -252,7 +253,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.train_test">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">train_test</span></span><a class="headerlink" href="#quapy.data.base.Dataset.train_test" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">train_test</span></span><a class="headerlink" href="#quapy.data.base.Dataset.train_test" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Alias to <cite>self.training</cite> and <cite>self.test</cite></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -266,7 +267,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.data.base.Dataset.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.data.base.Dataset.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -279,7 +280,7 @@ the collection), <cite>prevs</cite> (the prevalence values for each class)</p>
|
|||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">LabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">LabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>A LabelledCollection is a set of objects each with a label attached to each of them.
|
||||
This class implements several sampling routines and other utilities.</p>
|
||||
|
|
@ -296,7 +297,7 @@ from the labels. The classes must be indicated in cases in which some of the lab
|
|||
</dl>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.X">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">X</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.X" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">X</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.X" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.instances</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -307,7 +308,7 @@ from the labels. The classes must be indicated in cases in which some of the lab
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.Xp">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xp</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xp" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xp</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xp" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from
|
||||
a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> object.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -319,7 +320,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.Xy">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xy</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xy" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Xy</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xy" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the instances and labels. This is useful when working with <cite>sklearn</cite> estimators, e.g.:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">my_collection</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
|
@ -333,7 +334,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.binary">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.binary" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.binary" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns True if the number of classes is 2</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -344,7 +345,7 @@ a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.counts">
|
||||
<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.counts" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.counts" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the number of instances for each of the classes in the codeframe.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -356,7 +357,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.join">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.join" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.join" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns a new <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> as the union of the collections given in input.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -370,7 +371,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.kFCV">
|
||||
<span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.kFCV" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.kFCV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.kFCV" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Generator of stratified folds to be used in k-fold cross validation.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -388,7 +389,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.load">
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.load" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">loader_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.load" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a labelled set of data and convert it into a <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> instance. The function in charge
|
||||
of reading the instances must be specified. This function can be a custom one, or any of the reading functions
|
||||
defined in <a class="reference internal" href="#module-quapy.data.reader" title="quapy.data.reader"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.data.reader</span></code></a> module.</p>
|
||||
|
|
@ -411,7 +412,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.n_classes">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.n_classes" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.n_classes" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>The number of classes</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -422,7 +423,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.p">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.p" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.p" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.prevalence()</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -433,7 +434,7 @@ these arguments are used to call <cite>loader_func(path, **loader_kwargs)</cite>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.prevalence">
|
||||
<span class="sig-name descname"><span class="pre">prevalence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.prevalence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.prevalence" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">prevalence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.prevalence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.prevalence" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns the prevalence, or relative frequency, of the classes in the codeframe.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -445,7 +446,7 @@ as listed by <cite>self.classes_</cite></p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling">
|
||||
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Return a random sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size and desired prevalence
|
||||
values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
|
||||
the actual prevalence of the class, or with replacement otherwise.</p>
|
||||
|
|
@ -469,7 +470,7 @@ prevalence == <cite>prevs</cite> if the exact prevalence values can be met as pr
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_from_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_from_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_from_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_from_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling_from_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_from_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_from_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> whose elements are sampled from this collection using the
|
||||
index.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -484,7 +485,7 @@ index.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the
|
||||
prevalence values are not specified, then returns the index of a uniform sampling.
|
||||
For each class, the sampling is drawn with replacement if the requested prevalence is larger than
|
||||
|
|
@ -508,7 +509,7 @@ it is constrained. E.g., for binary collections, only the prevalence <cite>p</ci
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.split_random">
|
||||
<span class="sig-name descname"><span class="pre">split_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.split_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_random" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">split_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.split_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_random" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns two instances of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> split randomly from this collection, at desired
|
||||
proportion.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -529,7 +530,7 @@ second one with <cite>1-train_prop</cite> elements</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.split_stratified">
|
||||
<span class="sig-name descname"><span class="pre">split_stratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.split_stratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_stratified" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">split_stratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.split_stratified"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_stratified" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns two instances of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a> split with stratification from this collection, at desired
|
||||
proportion.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -550,7 +551,7 @@ second one with <cite>1-train_prop</cite> elements</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.stats" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.stats" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
|
||||
|
|
@ -572,7 +573,7 @@ values for each class)</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns a uniform sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size. The sampling is drawn
|
||||
with replacement if the requested size is greater than the number of instances, or without replacement
|
||||
otherwise.</p>
|
||||
|
|
@ -591,7 +592,7 @@ otherwise.</p>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
|
||||
with replacement if the requested size is greater than the number of instances, or without replacement
|
||||
otherwise.</p>
|
||||
|
|
@ -610,7 +611,7 @@ otherwise.</p>
|
|||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.y">
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.y" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.y" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>An alias to self.labels</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -623,10 +624,10 @@ otherwise.</p>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data.datasets">
|
||||
<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_IFCB">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads the IFCB dataset for quantification from <a class="reference external" href="https://zenodo.org/records/10036244">Zenodo</a> (for more
|
||||
information on this dataset, please follow the zenodo link).
|
||||
This dataset is based on the data available publicly at
|
||||
|
|
@ -658,7 +659,7 @@ i.e., a sampling protocol that returns a series of samples labelled by prevalenc
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIBinaryDataset">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryDataset" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI dataset as an instance of <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>, as used in
|
||||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
|
|
@ -688,7 +689,7 @@ The list of valid dataset names can be accessed in <cite>quapy.data.datasets.UCI
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIBinaryLabelledCollection">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryLabelledCollection" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIBinaryLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIBinaryLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIBinaryLabelledCollection" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI collection as an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, as used in
|
||||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
|
|
@ -725,7 +726,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassDataset">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI multiclass dataset as an instance of <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>.</p>
|
||||
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
|
||||
- It has more than 1000 instances
|
||||
|
|
@ -758,7 +759,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a UCI multiclass collection as an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>.</p>
|
||||
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
|
||||
- It has more than 1000 instances
|
||||
|
|
@ -791,7 +792,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_lequa2022">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lequa2022</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_lequa2022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_lequa2022" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lequa2022</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_lequa2022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_lequa2022" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads the official datasets provided for the <a class="reference external" href="https://lequa2022.github.io/index">LeQua</a> competition.
|
||||
In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
|
||||
problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead.
|
||||
|
|
@ -822,7 +823,7 @@ that return a series of samples stored in a directory which are labelled by prev
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_reviews">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_reviews</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tfidf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_reviews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_reviews" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_reviews</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tfidf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_reviews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_reviews" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a Reviews dataset as a Dataset instance, as used in
|
||||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">Esuli, A., Moreo, A., and Sebastiani, F. “A recurrent neural network for sentiment quantification.”
|
||||
Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.</a>.
|
||||
|
|
@ -848,7 +849,7 @@ faster subsequent invokations</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_twitter">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_twitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_twitter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_twitter" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_twitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_twitter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_twitter" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Loads a Twitter dataset as a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> instance, as used in:
|
||||
<a class="reference external" href="https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf">Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
|
||||
Social Network Analysis and Mining6(19), 1–22 (2016)</a>
|
||||
|
|
@ -879,15 +880,15 @@ faster subsequent invokations</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.warn">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#warn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.warn" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#warn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.warn" title="Permalink to this definition"></a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.data.preprocessing">
|
||||
<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">IndexTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">IndexTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>This class implements a sklearn’s-style transformer that indexes text as numerical ids for the tokens it
|
||||
contains, and that would be generated by sklearn’s
|
||||
|
|
@ -901,7 +902,7 @@ contains, and that would be generated by sklearn’s
|
|||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.add_word">
|
||||
<span class="sig-name descname"><span class="pre">add_word</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">word</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nogaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.add_word"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.add_word" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">add_word</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">word</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nogaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.add_word"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.add_word" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.
|
||||
Useful to define special tokens for codifying unknown words, or padding tokens.</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -922,7 +923,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -936,7 +937,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.fit_transform">
|
||||
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit_transform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit_transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Fits the transform on <cite>X</cite> and transforms it.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -953,7 +954,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.transform" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.transform" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Transforms the strings in <cite>X</cite> as lists of numerical ids</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
|
|
@ -970,7 +971,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.vocabulary_size">
|
||||
<span class="sig-name descname"><span class="pre">vocabulary_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.vocabulary_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.vocabulary_size" title="Link to this definition"></a></dt>
|
||||
<span class="sig-name descname"><span class="pre">vocabulary_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#IndexTransformer.vocabulary_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.vocabulary_size" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Gets the length of the vocabulary according to which the document tokens have been indexed</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||||
|
|
@ -983,7 +984,7 @@ precedent ids stored so far</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.index">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.index" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.index" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Indexes the tokens of a textual <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of string documents.
|
||||
To index a document means to replace each different token by a unique numerical index.
|
||||
Rare words (i.e., words occurring less than <cite>min_df</cite> times) are replaced by a special token <cite>UNK</cite></p>
|
||||
|
|
@ -1007,7 +1008,7 @@ are lists of str</p></li>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.reduce_columns">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">reduce_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#reduce_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.reduce_columns" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">reduce_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#reduce_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.reduce_columns" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reduces the dimensionality of the instances, represented as a <cite>csr_matrix</cite> (or any subtype of
|
||||
<cite>scipy.sparse.spmatrix</cite>), of training and test documents by removing the columns of words which are not present
|
||||
in at least <cite>min_df</cite> instances in the training set</p>
|
||||
|
|
@ -1030,7 +1031,7 @@ in the training set have been removed</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.standardize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#standardize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.standardize" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#standardize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.standardize" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Standardizes the real-valued columns of a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>.
|
||||
Standardization, aka z-scoring, of a variable <cite>X</cite> comes down to subtracting the average and normalizing by the
|
||||
standard deviation.</p>
|
||||
|
|
@ -1050,7 +1051,7 @@ standard deviation.</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.text2tfidf">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">text2tfidf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#text2tfidf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.text2tfidf" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">text2tfidf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/preprocessing.html#text2tfidf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.preprocessing.text2tfidf" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Transforms a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of textual instances into a <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a> of
|
||||
tfidf weighted sparse vectors</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1074,10 +1075,10 @@ current Dataset (if inplace=True) where the instances are stored in a <cite>csr_
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data.reader">
|
||||
<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Link to this heading"></a></h2>
|
||||
<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Permalink to this heading"></a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.binarize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#binarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.binarize" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#binarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.binarize" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Binarizes a categorical array-like collection of labels towards the positive class <cite>pos_class</cite>. E.g.,:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">binarize</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="gp">>>> </span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
|
||||
|
|
@ -1099,7 +1100,7 @@ current Dataset (if inplace=True) where the instances are stored in a <cite>csr_
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_csv">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_csv" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_csv" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a csv file in which columns are separated by ‘,’.
|
||||
File format <label>,<feat1>,<feat2>,…,<featn></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1117,7 +1118,7 @@ File format <label>,<feat1>,<feat2>,…,<featn></p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_sparse">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_sparse" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_sparse" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a labelled collection of real-valued instances expressed in sparse format
|
||||
File format <-1 or 0 or 1>[s col(int):val(float)]</p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1132,7 +1133,7 @@ File format <-1 or 0 or 1>[s col(int):val(float)]</p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_text">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class2int</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_text" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class2int</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#from_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.from_text" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Reads a labelled colletion of documents.
|
||||
File fomart <0 or 1> <document></p>
|
||||
<dl class="field-list simple">
|
||||
|
|
@ -1151,7 +1152,7 @@ File fomart <0 or 1> <document></p>
|
|||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.reindex_labels">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">reindex_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#reindex_labels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.reindex_labels" title="Link to this definition"></a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">reindex_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/reader.html#reindex_labels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.reader.reindex_labels" title="Permalink to this definition"></a></dt>
|
||||
<dd><p>Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
|
||||
E.g.:</p>
|
||||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">reindex_labels</span><span class="p">([</span><span class="s1">'B'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span>
|
||||
|
|
@ -1170,7 +1171,7 @@ E.g.:</p>
|
|||
|
||||
</section>
|
||||
<section id="module-quapy.data">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Link to this heading"></a></h2>
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Permalink to this heading"></a></h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
|
|
@ -1,11 +1,11 @@
|
|||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<html class="writer-html5" lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Search — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<title>Search — QuaPy: A Python-based open-source framework for quantification 0.1.9 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
|
||||
|
||||
|
||||
|
||||
|
|
@ -13,11 +13,12 @@
|
|||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/sphinx_highlight.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<script src="_static/searchtools.js"></script>
|
||||
<script src="_static/language_data.js"></script>
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -1,10 +0,0 @@
|
|||
Para meter los módulos dentro de doc hay que hacer un
|
||||
|
||||
sphinx-apidoc -o docs/source/ quapy/ -P
|
||||
|
||||
Eso importa todo lo que haya en quapy/ (incluidos los ficheros _ gracias a -P) en source y crea un rst para cada uno.
|
||||
|
||||
Parece que lo del -P no funciona. Hay que meterlos a mano en quapy.method.rst
|
||||
|
||||
Luego, simplemente
|
||||
make html
|
||||
|
|
@ -0,0 +1,194 @@
|
|||
"""
|
||||
.. author:: Paweł Czyż
|
||||
|
||||
This example shows how to use Bayesian quantification (https://arxiv.org/abs/2302.09159),
|
||||
which is suitable for low-data situations and when the uncertainty of the prevalence estimate is of interest.
|
||||
|
||||
For this, we will need to install extra dependencies:
|
||||
|
||||
```
|
||||
$ pip install quapy[bayesian]
|
||||
```
|
||||
|
||||
Running the script via:
|
||||
|
||||
```
|
||||
$ python examples/bayesian_quantification.py
|
||||
```
|
||||
|
||||
will produce a plot `bayesian_quantification.pdf`.
|
||||
|
||||
Due to a low sample size and the fact that classes 2 and 3 are hard to distinguish,
|
||||
it is hard to estimate the proportions accurately, what is visible by looking at the posterior samples,
|
||||
showing large uncertainty.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import quapy as qp
|
||||
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
|
||||
from quapy.method.aggregative import BayesianCC, ACC, PACC
|
||||
from quapy.data import LabelledCollection, Dataset
|
||||
|
||||
|
||||
FIGURE_PATH = "bayesian_quantification.pdf"
|
||||
|
||||
|
||||
def simulate_data(rng) -> Dataset:
|
||||
"""Generates a simulated data set with three classes."""
|
||||
|
||||
# Number of examples of each class in both data sets
|
||||
n_train = [400, 400, 400]
|
||||
n_test = [40, 25, 15]
|
||||
|
||||
# Mean vectors and shared covariance of P(X|Y) distributions
|
||||
mus = [np.zeros(2), np.array([1, 1.5]), np.array([1.5, 1])]
|
||||
cov = np.eye(2)
|
||||
|
||||
def gen_Xy(centers, sizes):
|
||||
X = np.concatenate([rng.multivariate_normal(mu_i, cov, size_i) for mu_i, size_i in zip(centers, sizes)])
|
||||
y = np.concatenate([[i] * n for i, n in enumerate(sizes)])
|
||||
return X, y
|
||||
|
||||
# Generate the features accordingly
|
||||
train = LabelledCollection(*gen_Xy(centers=mus, sizes=n_train))
|
||||
test = LabelledCollection(*gen_Xy(centers=mus, sizes=n_test))
|
||||
|
||||
return Dataset(training=train, test=test)
|
||||
|
||||
|
||||
def plot_simulated_data(axs, data: Dataset) -> None:
|
||||
"""Plots a simulated data set.
|
||||
|
||||
:param axs: a list of three `plt.Axes` objects, on which the samples will be plotted.
|
||||
:param data: the simulated data set.
|
||||
"""
|
||||
train, test = data.train_test
|
||||
xlim = (
|
||||
-0.3 + min(train.X[:, 0].min(), test.X[:, 0].min()),
|
||||
0.3 + max(train.X[:, 0].max(), test.X[:, 0].max())
|
||||
)
|
||||
ylim = (
|
||||
-0.3 + min(train.X[:, 1].min(), test.X[:, 1].min()),
|
||||
0.3 + max(train.X[:, 1].max(), test.X[:, 1].max())
|
||||
)
|
||||
|
||||
for ax in axs:
|
||||
ax.set_xlabel("$X_1$")
|
||||
ax.set_ylabel("$X_2$")
|
||||
ax.set_aspect("equal")
|
||||
ax.set_xlim(*xlim)
|
||||
ax.set_ylim(*ylim)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
|
||||
ax = axs[0]
|
||||
ax.set_title("Training set")
|
||||
for i in range(data.n_classes):
|
||||
ax.scatter(train.X[train.y == i, 0], train.X[train.y == i, 1], c=f"C{i}", s=3, rasterized=True)
|
||||
|
||||
ax = axs[1]
|
||||
ax.set_title("Test set\n(with labels)")
|
||||
for i in range(data.n_classes):
|
||||
ax.scatter(test.X[test.y == i, 0], test.X[test.y == i, 1], c=f"C{i}", s=3, rasterized=True)
|
||||
|
||||
ax = axs[2]
|
||||
ax.set_title("Test set\n(as observed)")
|
||||
ax.scatter(test.X[:, 0], test.X[:, 1], c="C5", s=3, rasterized=True)
|
||||
|
||||
|
||||
def plot_true_proportions(ax: plt.Axes, test_prevalence: np.ndarray) -> None:
|
||||
"""Plots the true proportions."""
|
||||
n_classes = len(test_prevalence)
|
||||
x_ax = np.arange(n_classes)
|
||||
ax.plot(x_ax, test_prevalence, c="black", linewidth=2, label="True")
|
||||
|
||||
ax.set_xlabel("Class")
|
||||
ax.set_ylabel("Prevalence")
|
||||
ax.set_xticks(x_ax, x_ax + 1)
|
||||
ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
|
||||
ax.set_xlim(-0.1, n_classes - 0.9)
|
||||
ax.set_ylim(-0.01, 1.01)
|
||||
|
||||
|
||||
def get_random_forest() -> RandomForestClassifier:
|
||||
"""An auxiliary factory method to generate a random forest."""
|
||||
return RandomForestClassifier(n_estimators=10, random_state=5)
|
||||
|
||||
|
||||
def _get_estimate(estimator_class, training: LabelledCollection, test: np.ndarray) -> None:
|
||||
"""Auxiliary method for running ACC and PACC."""
|
||||
estimator = estimator_class(get_random_forest())
|
||||
estimator.fit(training)
|
||||
return estimator.quantify(test)
|
||||
|
||||
|
||||
def train_and_plot_bayesian_quantification(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
"""Fits Bayesian quantification and plots posterior mean as well as individual samples"""
|
||||
print('training model Bayesian CC...', end='')
|
||||
quantifier = BayesianCC(classifier=get_random_forest())
|
||||
quantifier.fit(training)
|
||||
|
||||
# Obtain mean prediction
|
||||
mean_prediction = quantifier.quantify(test.X)
|
||||
mae = qp.error.mae(test.prevalence(), mean_prediction)
|
||||
x_ax = np.arange(training.n_classes)
|
||||
ax.plot(x_ax, mean_prediction, c="salmon", linewidth=2, linestyle=":", label="Bayesian")
|
||||
|
||||
# Obtain individual samples
|
||||
samples = quantifier.get_prevalence_samples()
|
||||
for sample in samples[::5, :]:
|
||||
ax.plot(x_ax, sample, c="salmon", alpha=0.1, linewidth=0.3, rasterized=True)
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def train_and_plot_acc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
print('training model ACC...', end='')
|
||||
estimate = _get_estimate(ACC, training, test.X)
|
||||
mae = qp.error.mae(test.prevalence(), estimate)
|
||||
ax.plot(np.arange(training.n_classes), estimate, c="darkblue", linewidth=2, linestyle=":", label="ACC")
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def train_and_plot_pacc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
|
||||
print('training model PACC...', end='')
|
||||
estimate = _get_estimate(PACC, training, test.X)
|
||||
mae = qp.error.mae(test.prevalence(), estimate)
|
||||
ax.plot(np.arange(training.n_classes), estimate, c="limegreen", linewidth=2, linestyle=":", label="PACC")
|
||||
print(f'MAE={mae:.4f} [done]')
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# --- Simulate data ---
|
||||
print('generating simulated data')
|
||||
rng = np.random.default_rng(42)
|
||||
data = simulate_data(rng)
|
||||
training, test = data.train_test
|
||||
|
||||
# --- Plot simulated data ---
|
||||
fig, axs = plt.subplots(1, 4, figsize=(13, 3), dpi=300)
|
||||
for ax in axs:
|
||||
ax.spines[['top', 'right']].set_visible(False)
|
||||
plot_simulated_data(axs[:3], data)
|
||||
|
||||
# --- Plot quantification results ---
|
||||
ax = axs[3]
|
||||
plot_true_proportions(ax, test_prevalence=test.prevalence())
|
||||
|
||||
train_and_plot_acc(ax, training=training, test=test)
|
||||
train_and_plot_pacc(ax, training=training, test=test)
|
||||
train_and_plot_bayesian_quantification(ax=ax, training=training, test=test)
|
||||
print('[done]')
|
||||
|
||||
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)
|
||||
|
||||
print(f'saving plot in path {FIGURE_PATH}...', end='')
|
||||
fig.tight_layout()
|
||||
fig.savefig(FIGURE_PATH)
|
||||
print('[done]')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -1,33 +1,79 @@
|
|||
import quapy as qp
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.base import BinaryQuantifier
|
||||
from quapy.method.base import BinaryQuantifier, BaseQuantifier
|
||||
from quapy.model_selection import GridSearchQ
|
||||
from quapy.method.aggregative import AggregativeSoftQuantifier
|
||||
from quapy.protocol import APP
|
||||
import numpy as np
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from time import time
|
||||
|
||||
|
||||
# Define a custom quantifier: for this example, we will consider a new quantification algorithm that uses a
|
||||
# logistic regressor for generating posterior probabilities, and then applies a custom threshold value to the
|
||||
# posteriors. Since the quantifier internally uses a classifier, it is an aggregative quantifier; and since it
|
||||
# relies on posterior probabilities, it is a probabilistic-aggregative quantifier. Note also it has an
|
||||
# internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier
|
||||
# is binary, for simplicity.
|
||||
# relies on posterior probabilities, it is a probabilistic-aggregative quantifier (aka AggregativeSoftQuantifier).
|
||||
# Note also it has an internal hyperparameter (let say, alpha) which is the decision threshold.
|
||||
#
|
||||
# Let's also assume the quantifier is binary, for simplicity. Any quantifier (i.e., any subclass of BaseQuantifier)
|
||||
# is required to implement the "fit" and "quantify" methods. Aggregative quantifiers are special subtypes of base
|
||||
# quantifiers, i.e., are quantifiers that undertake a classification-phase followed by an aggregation-phase. QuaPy
|
||||
# already implements most common functionality, and requires the developer to simply implement the "aggregation_fit"
|
||||
# and the "aggregation" methods.
|
||||
#
|
||||
# We are providing two implementations of the same method to illustrate this characteristic of QuaPy. Let us begin
|
||||
# with the general case, in which we implement a (base) quantifier
|
||||
|
||||
class MyQuantifier(BaseQuantifier):
|
||||
|
||||
class MyQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
|
||||
def __init__(self, classifier, alpha=0.5):
|
||||
self.alpha = alpha
|
||||
# aggregative quantifiers have an internal self.classifier attribute
|
||||
self.classifier = classifier
|
||||
|
||||
def fit(self, data: LabelledCollection, fit_classifier=True):
|
||||
assert fit_classifier, 'this quantifier needs to fit the classifier!'
|
||||
# in general, we would need to implement the method fit(self, data: LabelledCollection, fit_classifier=True,
|
||||
# val_split=None); this would amount to:
|
||||
def fit(self, data: LabelledCollection):
|
||||
assert data.n_classes==2, \
|
||||
'this quantifier is only valid for binary problems [abort]'
|
||||
self.classifier.fit(*data.Xy)
|
||||
return self
|
||||
|
||||
# in general, we would need to implement the method quantify(self, instances) but, since this method is of
|
||||
# type aggregative, we can simply implement the method aggregate, which has the following interface
|
||||
# in general, we would need to implement the method quantify(self, instances); this would amount to:
|
||||
def quantify(self, instances):
|
||||
assert hasattr(self.classifier, 'predict_proba'), \
|
||||
'the underlying classifier is not probabilistic! [abort]'
|
||||
posterior_probabilities = self.classifier.predict_proba(instances)
|
||||
positive_probabilities = posterior_probabilities[:, 1]
|
||||
crisp_decisions = positive_probabilities > self.alpha
|
||||
pos_prev = crisp_decisions.mean()
|
||||
neg_prev = 1 - pos_prev
|
||||
return np.asarray([neg_prev, pos_prev])
|
||||
|
||||
|
||||
# Note that the above implementation contains a lot of boilerplate code. Many parts can be omitted since QuaPy
|
||||
# provides implementations for them. Some of these routines (like, for example, training a classifier and generating
|
||||
# posterior probabilities) are often carried out in a k-fold cross-validation manner. These, along with many other
|
||||
# common routines are already provided by highly-optimized routines in QuaPy. Let's see a much better implementation
|
||||
# of the method, now adhering to the AggregativeSoftQuantifier:
|
||||
|
||||
class MyAggregativeSoftQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
|
||||
def __init__(self, classifier, alpha=0.5):
|
||||
# aggregative quantifiers have an internal attribute called self.classifier
|
||||
self.classifier = classifier
|
||||
self.alpha = alpha
|
||||
|
||||
# since this method is of type aggregative, we can simply implement the method aggregation_fit, which
|
||||
# assumes the classifier has already been fitted properly and the predictions for the training set required
|
||||
# to train the aggregation function have been properly generated (i.e., on a validation split, or using a
|
||||
# k-fold cross validation strategy). What remains ahead is to learn an aggregation function. In our case
|
||||
# this amounts to doing... nothing, since our method was pretty basic. BinaryQuantifier also add some
|
||||
# basic functionality for checking binary consistency.
|
||||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||||
pass
|
||||
|
||||
# since this method is of type aggregative, we can simply implement the method aggregate (i.e., we should
|
||||
# only describe what to do with the classifier predictions --which in this case are posterior probabilities
|
||||
# because we are inheriting from the "Soft" subtype). This comes down to:
|
||||
def aggregate(self, classif_predictions: np.ndarray):
|
||||
# the posterior probabilities have already been generated by the quantify method; we only need to
|
||||
# specify what to do with them
|
||||
|
|
@ -38,31 +84,68 @@ class MyQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
|
|||
return np.asarray([neg_prev, pos_prev])
|
||||
|
||||
|
||||
# a small example using these two implementations of our method
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = 100
|
||||
|
||||
# define an instance of our custom quantifier
|
||||
quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)
|
||||
qp.environ['SAMPLE_SIZE'] = 250
|
||||
|
||||
# load the IMDb dataset
|
||||
train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
|
||||
train, val = train.split_stratified(train_prop=0.75) # let's create a validation set for optimizing hyperparams
|
||||
|
||||
def test_implementation(quantifier):
|
||||
class_name = quantifier.__class__.__name__
|
||||
print(f'\ntesting implementation {class_name}...')
|
||||
# model selection
|
||||
# let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier
|
||||
train, val = train.split_stratified(train_prop=0.75)
|
||||
tinit = time()
|
||||
param_grid = {
|
||||
'alpha': np.linspace(0, 1, 11), # quantifier-dependent hyperparameter
|
||||
'classifier__C': np.logspace(-2, 2, 5) # classifier-dependent hyperparameter
|
||||
}
|
||||
quantifier = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=True).fit(train)
|
||||
gridsearch = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=False).fit(train)
|
||||
t_modsel = time() - tinit
|
||||
print(f'\tmodel selection took {t_modsel:.2f}s', flush=True)
|
||||
|
||||
# evaluation
|
||||
mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae')
|
||||
optimized_model = gridsearch.best_model_
|
||||
mae = qp.evaluation.evaluate(
|
||||
optimized_model,
|
||||
protocol=APP(test, repeats=5000, sanity_check=None), # disable the check, we want to generate many tests!
|
||||
error_metric='mae',
|
||||
verbose=True)
|
||||
|
||||
print(f'MAE = {mae:.4f}')
|
||||
t_eval = time() - t_modsel - tinit
|
||||
print(f'\tevaluation took {t_eval:.2f}s [MAE = {mae:.4f}]')
|
||||
|
||||
# final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
|
||||
# define an instance of our custom quantifier and test it!
|
||||
quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)
|
||||
test_implementation(quantifier)
|
||||
|
||||
# define an instance of our custom quantifier, with the second implementation, and test it!
|
||||
quantifier = MyAggregativeSoftQuantifier(LogisticRegression(), alpha=0.5)
|
||||
test_implementation(quantifier)
|
||||
|
||||
# the output should look like this:
|
||||
"""
|
||||
testing implementation MyQuantifier...
|
||||
model selection took 12.86s
|
||||
predicting: 100%|██████████| 105000/105000 [00:22<00:00, 4626.30it/s]
|
||||
evaluation took 22.75s [MAE = 0.0630]
|
||||
|
||||
testing implementation MyAggregativeSoftQuantifier...
|
||||
model selection took 3.10s
|
||||
speeding up the prediction for the aggregative quantifier, total classifications 25000 instead of 26250000
|
||||
predicting: 100%|██████████| 105000/105000 [00:04<00:00, 22779.62it/s]
|
||||
evaluation took 4.66s [MAE = 0.0630]
|
||||
"""
|
||||
# Note that the first implementation is much slower, both in terms of grid-search optimization and in terms of
|
||||
# evaluation. The reason why is that QuaPy is highly optimized for aggregative quantifiers (by far, the most
|
||||
# popular type of quantification methods), thus significantly speeding up model selection and test routines.
|
||||
# Furthermore, it is simpler to extend an aggregation type since QuaPy implements boilerplate functions for you.
|
||||
|
||||
# Final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
|
||||
# on an hyperparameter alpha for binarizing the posterior probabilities. A much better way for fulfilling this
|
||||
# goal would be to calibrate the classifier (LogisticRegression is already reasonably well calibrated) and then
|
||||
# simply cut at 0.5.
|
||||
|
|
|
|||
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|
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|
Before Width: | Height: | Size: 57 KiB |
|
|
@ -11,7 +11,7 @@ from . import util
|
|||
from . import model_selection
|
||||
from . import classification
|
||||
|
||||
__version__ = '0.1.8'
|
||||
__version__ = '0.1.9'
|
||||
|
||||
environ = {
|
||||
'SAMPLE_SIZE': None,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,358 @@
|
|||
import itertools
|
||||
import os
|
||||
from copy import deepcopy
|
||||
from os.path import join
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Union, Callable
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pickle
|
||||
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
import quapy as qp
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.aggregative import PACC
|
||||
from quapy.protocol import APP, UPP, AbstractProtocol
|
||||
from quapy.model_selection import GridSearchQ
|
||||
from quapy.method.base import BaseQuantifier
|
||||
from result_table.src.table import Table
|
||||
|
||||
|
||||
def makedirs(dir):
|
||||
print('creating ', dir)
|
||||
os.makedirs(dir, exist_ok=True)
|
||||
|
||||
|
||||
@dataclass
|
||||
class MethodDescriptor:
|
||||
id: str
|
||||
name: str
|
||||
instance: BaseQuantifier
|
||||
hyperparams: dict
|
||||
|
||||
|
||||
class Benchmark(ABC):
|
||||
|
||||
ID_SEPARATOR = '__' # used to separate components in a run-ID, cannot be used within the component IDs
|
||||
|
||||
def __init__(self, home_dir, n_jobs=3):
|
||||
self.home_dir = home_dir
|
||||
self.n_jobs = n_jobs
|
||||
assert n_jobs!=-1, ('Setting n_jobs=-1 will probably blow your memory. '
|
||||
'Specify a positive number.')
|
||||
makedirs(home_dir)
|
||||
makedirs(join(home_dir, 'results'))
|
||||
makedirs(join(home_dir, 'params'))
|
||||
makedirs(join(home_dir, 'tables'))
|
||||
makedirs(join(home_dir, 'plots'))
|
||||
|
||||
self.train_prevalence = {}
|
||||
|
||||
def _run_id(self, method: MethodDescriptor, dataset: str):
|
||||
sep = Benchmark.ID_SEPARATOR
|
||||
assert sep not in method.id, \
|
||||
(f'separator {sep} cannot be used in method ID ({method.id}), '
|
||||
f'please change the method ID or redefine {Benchmark.ID_SEPARATOR=}')
|
||||
assert sep not in dataset, \
|
||||
(f'separator {sep} cannot be used in dataset name ({dataset}), '
|
||||
f'please redefine {Benchmark.ID_SEPARATOR=}')
|
||||
return sep.join([method.id, dataset])
|
||||
|
||||
def _result_path(self, method: MethodDescriptor, dataset: str):
|
||||
id = self._run_id(method, dataset)
|
||||
return join(self.home_dir, 'results', id + '.pkl')
|
||||
|
||||
def _params_path(self, method: MethodDescriptor, dataset: str):
|
||||
id = self._run_id(method, dataset)
|
||||
chosen = join(self.home_dir, 'params', id + 'chosen.pkl')
|
||||
scores = join(self.home_dir, 'params', id + 'scores.pkl')
|
||||
return chosen, scores
|
||||
|
||||
def _exist_run(self, method: MethodDescriptor, dataset: str):
|
||||
return os.path.exists(self._result_path(method, dataset))
|
||||
|
||||
def _open_method_dataset_result(self, method: MethodDescriptor, dataset: str):
|
||||
if not self._exist_run(method, dataset):
|
||||
raise ValueError(f'cannot open result for method={method.id} and {dataset=}')
|
||||
|
||||
def check_dataset(self, dataset:str):
|
||||
assert dataset in self.list_datasets(), f'unknown dataset {dataset}'
|
||||
|
||||
@abstractmethod
|
||||
def list_datasets(self)-> List[str]:
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def run_method_dataset(self, method: MethodDescriptor, dataset:str, random_state=0)-> pd.DataFrame:
|
||||
...
|
||||
|
||||
def gen_tables(self, results, metrics=None):
|
||||
if metrics is None:
|
||||
metrics = ['mae', 'mrae', 'mkld', 'mnkld']
|
||||
tables = {}
|
||||
for (method, dataset, result) in results:
|
||||
col_metrics = result.columns.values[2:]
|
||||
for metric in metrics:
|
||||
if metric not in col_metrics:
|
||||
print(f'error; requested {metric=} not found among the columns in the dataframe')
|
||||
continue
|
||||
if metric not in tables:
|
||||
tables[metric] = Table(name=metric)
|
||||
table = tables[metric]
|
||||
table.add(dataset, method.name, result[metric].values)
|
||||
Table.LatexPDF(join(self.home_dir, 'tables', 'results.pdf'), list(tables.values()))
|
||||
|
||||
|
||||
def gen_plots(self, results, metrics=None):
|
||||
import matplotlib.pyplot as plt
|
||||
plt.rcParams.update({'font.size': 11})
|
||||
|
||||
if metrics is None:
|
||||
metrics = ['ae']
|
||||
|
||||
for metric in metrics:
|
||||
method_names, true_prevs, estim_prevs, train_prevs = [], [], [], []
|
||||
skip=False
|
||||
for (method, dataset, result) in results:
|
||||
method_names.append(method.name)
|
||||
true_prevs.append(np.vstack(result['true-prev'].values))
|
||||
estim_prevs.append(np.vstack(result['estim-prev'].values))
|
||||
train_prevs.append(self.get_training_prevalence(dataset))
|
||||
if not skip:
|
||||
path = join(self.home_dir, 'plots', f'err_by_drift_{metric}.pdf')
|
||||
qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, train_prevs, error_name=metric, n_bins=20, savepath=path)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def _show_report(self, method, dataset, report: pd.DataFrame):
|
||||
id = method.id
|
||||
MAE = report['mae'].mean()
|
||||
mae_std = report['mae'].std()
|
||||
MRAE = report['mrae'].mean()
|
||||
mrae_std = report['mrae'].std()
|
||||
print(f'{id}\t{dataset}:\t{MAE=:.4f}+-{mae_std:.4f}\t{MRAE=:.4f}+-{mrae_std:.4f}')
|
||||
|
||||
def run(self,
|
||||
methods: Union[List[MethodDescriptor], MethodDescriptor],
|
||||
datasets:Union[List[str],str]=None,
|
||||
force=False):
|
||||
|
||||
if not isinstance(methods, list):
|
||||
methods = [methods]
|
||||
|
||||
if datasets is None:
|
||||
datasets = self.list_datasets()
|
||||
elif not isinstance(datasets, list):
|
||||
datasets = [datasets]
|
||||
|
||||
results = []
|
||||
pending_job_args = []
|
||||
for method, dataset in itertools.product(methods, datasets):
|
||||
self.check_dataset(dataset)
|
||||
if not force and self._exist_run(method, dataset):
|
||||
result = pd.read_pickle(self._result_path(method, dataset))
|
||||
results.append((method, dataset, result))
|
||||
else:
|
||||
pending_job_args.append((method, dataset))
|
||||
|
||||
if len(pending_job_args)>0:
|
||||
remaining_results = qp.util.parallel_unpack(
|
||||
func=self.run_method_dataset,
|
||||
args=pending_job_args,
|
||||
n_jobs=self.n_jobs,
|
||||
seed=0,
|
||||
asarray=False
|
||||
)
|
||||
results += [
|
||||
(method, dataset, result) for (method, dataset), result in zip(pending_job_args, remaining_results)
|
||||
]
|
||||
|
||||
# print results
|
||||
for method, dataset, result in results:
|
||||
self._show_report(method, dataset, result)
|
||||
|
||||
self.gen_tables(results)
|
||||
self.gen_plots(results)
|
||||
|
||||
@abstractmethod
|
||||
def get_training_prevalence(self, dataset: str):
|
||||
...
|
||||
|
||||
def __add__(self, other: 'Benchmark'):
|
||||
return CombinedBenchmark(self, other, self.n_jobs)
|
||||
|
||||
|
||||
class CombinedBenchmark(Benchmark):
|
||||
|
||||
def __init__(self, benchmark_a:Benchmark, benchmark_b:Benchmark, n_jobs=-1):
|
||||
self.router = {
|
||||
**{dataset: benchmark_a for dataset in benchmark_a.list_datasets()},
|
||||
**{dataset: benchmark_b for dataset in benchmark_b.list_datasets()}
|
||||
}
|
||||
self.datasets = benchmark_a.list_datasets() + benchmark_b.list_datasets()
|
||||
self.n_jobs = n_jobs
|
||||
|
||||
def list_datasets(self) -> List[str]:
|
||||
return self.datasets
|
||||
|
||||
def run_method_dataset(self, method: MethodDescriptor, dataset:str, random_state=0) -> pd.DataFrame:
|
||||
return self.router[dataset].run_method_dataset(method, dataset, random_state)
|
||||
|
||||
def _exist_run(self, method: MethodDescriptor, dataset: str):
|
||||
return self.router[dataset]._exist_run(method, dataset)
|
||||
|
||||
|
||||
class TypicalBenchmark(Benchmark):
|
||||
|
||||
# def __init__(self, home_dir, ):
|
||||
|
||||
@abstractmethod
|
||||
def get_sample_size(self)-> int:
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def get_training(self, dataset:str)-> LabelledCollection:
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def get_trModsel_valprotModsel_trEval_teprotEval(self, dataset:str)->\
|
||||
(LabelledCollection, AbstractProtocol, LabelledCollection, AbstractProtocol):
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def get_target_error_for_modsel(self)-> Union[str, Callable]:
|
||||
...
|
||||
|
||||
def run_method_dataset(self, method: MethodDescriptor, dataset: str, random_state=0) -> pd.DataFrame:
|
||||
print(f'Running method={method.id} in {dataset=}')
|
||||
|
||||
sample_size = self.get_sample_size()
|
||||
qp.environ['SAMPLE_SIZE'] = sample_size
|
||||
|
||||
q = deepcopy(method.instance)
|
||||
optim_for = self.get_target_error_for_modsel()
|
||||
|
||||
with qp.util.temp_seed(random_state):
|
||||
# data split
|
||||
trModSel, valprotModSel, trEval, teprotEval = self.get_trModsel_valprotModsel_trEval_teprotEval(dataset)
|
||||
self.train_prevalence[dataset] = trEval.prevalence()
|
||||
|
||||
# model selection
|
||||
modsel = GridSearchQ(
|
||||
model=q,
|
||||
param_grid=method.hyperparams,
|
||||
protocol=valprotModSel,
|
||||
error=optim_for,
|
||||
refit=False,
|
||||
n_jobs=-1,
|
||||
raise_errors=True,
|
||||
verbose=True
|
||||
).fit(trModSel)
|
||||
|
||||
# fit on the whole training data
|
||||
optimized_model = modsel.best_model_
|
||||
optimized_model.fit(trEval)
|
||||
|
||||
# evaluation
|
||||
report = qp.evaluation.evaluation_report(
|
||||
model=optimized_model,
|
||||
protocol=teprotEval,
|
||||
error_metrics=qp.error.QUANTIFICATION_ERROR_NAMES
|
||||
)
|
||||
|
||||
# data persistence
|
||||
chosen_path, scores_path = self._params_path(method, dataset)
|
||||
pickle.dump(modsel.best_params_, open(chosen_path, 'wb'), pickle.HIGHEST_PROTOCOL)
|
||||
pickle.dump(modsel.param_scores_, open(scores_path, 'wb'), pickle.HIGHEST_PROTOCOL)
|
||||
|
||||
result_path = self._result_path(method, dataset)
|
||||
report.to_pickle(result_path)
|
||||
|
||||
return report
|
||||
|
||||
def get_training_prevalence(self, dataset: str):
|
||||
if not dataset in self.train_prevalence:
|
||||
training = self.get_training(dataset)
|
||||
self.train_prevalence[dataset] = training.prevalence()
|
||||
return self.train_prevalence[dataset]
|
||||
|
||||
|
||||
class UCIBinaryBenchmark(TypicalBenchmark):
|
||||
|
||||
def get_trModsel_valprotModsel_trEval_teprotEval(self, dataset: str) -> \
|
||||
(LabelledCollection, LabelledCollection, LabelledCollection, LabelledCollection):
|
||||
data = qp.datasets.fetch_UCIBinaryDataset(dataset)
|
||||
trEval, teEval = data.train_test
|
||||
trModsel, vaModsel = trEval.split_stratified()
|
||||
valprotModsel = APP(vaModsel, n_prevalences=21, repeats=25)
|
||||
testprotModsel = APP(teEval, n_prevalences=21, repeats=100)
|
||||
return trModsel, valprotModsel, trEval, testprotModsel
|
||||
|
||||
def get_training(self, dataset:str) -> LabelledCollection:
|
||||
return qp.datasets.fetch_UCIBinaryDataset(dataset).training
|
||||
|
||||
def get_sample_size(self) -> int:
|
||||
return 100
|
||||
|
||||
def get_target_error_for_modsel(self) -> Union[str, Callable]:
|
||||
return 'mae'
|
||||
|
||||
def list_datasets(self)->List[str]:
|
||||
ignore = ['acute.a', 'acute.b', 'balance.2']
|
||||
return [d for d in qp.datasets.UCI_BINARY_DATASETS if d not in ignore]
|
||||
|
||||
|
||||
class UCIMultiBenchmark(TypicalBenchmark):
|
||||
|
||||
def list_datasets(self) -> List[str]:
|
||||
return qp.datasets.UCI_MULTICLASS_DATASETS
|
||||
|
||||
def get_trModsel_valprotModsel_trEval_teprotEval(self, dataset: str) -> \
|
||||
(LabelledCollection, LabelledCollection, LabelledCollection, LabelledCollection):
|
||||
data = qp.datasets.fetch_UCIMulticlassDataset(dataset)
|
||||
trEval, teEval = data.train_test
|
||||
trModsel, vaModsel = trEval.split_stratified()
|
||||
valprotModsel = UPP(vaModsel, repeats=250)
|
||||
testprotModsel = UPP(teEval, repeats=1000)
|
||||
return trModsel, valprotModsel, trEval, testprotModsel
|
||||
|
||||
def get_training(self, dataset:str) -> LabelledCollection:
|
||||
return qp.datasets.fetch_UCIMulticlassDataset(dataset).training
|
||||
|
||||
def get_sample_size(self) -> int:
|
||||
return 500
|
||||
|
||||
def get_target_error_for_modsel(self) -> Union[str, Callable]:
|
||||
return 'mae'
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
from quapy.benchmarking.typical import *
|
||||
|
||||
# from quapy.method.aggregative import BayesianCC
|
||||
# bayes = MethodDescriptor(
|
||||
# id='Bayesian',
|
||||
# name='Bayesian(LR)',
|
||||
# instance=BayesianCC(LogisticRegression()),
|
||||
# hyperparams=wrap_cls_params(lr_hyper)
|
||||
# )
|
||||
|
||||
# bench_bin = UCIBinaryBenchmark('../../Benchmarks/UCIbinary')
|
||||
bench_multi = UCIMultiBenchmark('../../Benchmarks/UCIMulti')
|
||||
# bench = bench_bin + bench_multi
|
||||
bench = bench_multi
|
||||
|
||||
bench.run(methods=[cc, pcc, acc, pacc, sld, sld_bcts])
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
import numpy as np
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
|
||||
from quapy.benchmarking._base import MethodDescriptor
|
||||
|
||||
lr_hyper = {'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]}
|
||||
|
||||
wrap_cls_params = lambda params: {'classifier__' + key: val for key, val in params.items()}
|
||||
|
||||
cc = MethodDescriptor(
|
||||
id='CC',
|
||||
name='CC(LR)',
|
||||
instance=CC(LogisticRegression()),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
||||
pcc = MethodDescriptor(
|
||||
id='PCC',
|
||||
name='PCC(LR)',
|
||||
instance=PCC(LogisticRegression()),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
||||
acc = MethodDescriptor(
|
||||
id='ACC',
|
||||
name='ACC(LR)',
|
||||
instance=ACC(LogisticRegression()),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
||||
pacc = MethodDescriptor(
|
||||
id='PACC',
|
||||
name='PACC(LR)',
|
||||
instance=PACC(LogisticRegression()),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
||||
sld = MethodDescriptor(
|
||||
id='SLD',
|
||||
name='SLD',
|
||||
instance=EMQ(LogisticRegression()),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
||||
sld_bcts = MethodDescriptor(
|
||||
id='SLD-BCTS',
|
||||
name='SLD-BCTS',
|
||||
instance=EMQ(LogisticRegression(), recalib='bcts', exact_train_prev=False),
|
||||
hyperparams=wrap_cls_params(lr_hyper)
|
||||
)
|
||||
|
|
@ -1,52 +1,51 @@
|
|||
import itertools
|
||||
import warnings
|
||||
from collections import defaultdict
|
||||
from typing import Union, Callable
|
||||
from typing import Literal, Union, Callable
|
||||
from numpy.typing import ArrayLike
|
||||
|
||||
import scipy
|
||||
import numpy as np
|
||||
|
||||
|
||||
def prevalence_linspace(n_prevalences=21, repeats=1, smooth_limits_epsilon=0.01):
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Counter utils
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def counts_from_labels(labels: ArrayLike, classes: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Produces an array of uniformly separated values of prevalence.
|
||||
By default, produces an array of 21 prevalence values, with
|
||||
step 0.05 and with the limits smoothed, i.e.:
|
||||
[0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]
|
||||
Computes the raw count values from a vector of labels.
|
||||
|
||||
:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
|
||||
:param repeats: number of times each prevalence is to be repeated (defaults to 1)
|
||||
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
|
||||
:return: an array of uniformly separated prevalence values
|
||||
"""
|
||||
p = np.linspace(0., 1., num=n_prevalences, endpoint=True)
|
||||
p[0] += smooth_limits_epsilon
|
||||
p[-1] -= smooth_limits_epsilon
|
||||
if p[0] > p[1]:
|
||||
raise ValueError(f'the smoothing in the limits is greater than the prevalence step')
|
||||
if repeats > 1:
|
||||
p = np.repeat(p, repeats)
|
||||
return p
|
||||
|
||||
|
||||
def prevalence_from_labels(labels, classes):
|
||||
"""
|
||||
Computed the prevalence values from a vector of labels.
|
||||
|
||||
:param labels: array-like of shape `(n_instances)` with the label for each instance
|
||||
:param labels: array-like of shape `(n_instances,)` with the label for each instance
|
||||
:param classes: the class labels. This is needed in order to correctly compute the prevalence vector even when
|
||||
some classes have no examples.
|
||||
:return: an ndarray of shape `(len(classes))` with the class prevalence values
|
||||
:return: ndarray of shape `(len(classes),)` with the raw counts for each class, in the same order
|
||||
as they appear in `classes`
|
||||
"""
|
||||
if labels.ndim != 1:
|
||||
if np.asarray(labels).ndim != 1:
|
||||
raise ValueError(f'param labels does not seem to be a ndarray of label predictions')
|
||||
unique, counts = np.unique(labels, return_counts=True)
|
||||
by_class = defaultdict(lambda:0, dict(zip(unique, counts)))
|
||||
prevalences = np.asarray([by_class[class_] for class_ in classes], dtype=float)
|
||||
prevalences /= prevalences.sum()
|
||||
counts = np.asarray([by_class[class_] for class_ in classes], dtype=int)
|
||||
return counts
|
||||
|
||||
|
||||
def prevalence_from_labels(labels: ArrayLike, classes: ArrayLike):
|
||||
"""
|
||||
Computes the prevalence values from a vector of labels.
|
||||
|
||||
:param labels: array-like of shape `(n_instances,)` with the label for each instance
|
||||
:param classes: the class labels. This is needed in order to correctly compute the prevalence vector even when
|
||||
some classes have no examples.
|
||||
:return: ndarray of shape `(len(classes),)` with the class proportions for each class, in the same order
|
||||
as they appear in `classes`
|
||||
"""
|
||||
counts = counts_from_labels(labels, classes)
|
||||
prevalences = counts.astype(float) / np.sum(counts)
|
||||
return prevalences
|
||||
|
||||
|
||||
def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
||||
def prevalence_from_probabilities(posteriors: ArrayLike, binarize: bool = False):
|
||||
"""
|
||||
Returns a vector of prevalence values from a matrix of posterior probabilities.
|
||||
|
||||
|
|
@ -55,8 +54,9 @@ def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
|||
converting the vectors of posterior probabilities into class indices, by taking the argmax).
|
||||
:return: array of shape `(n_classes,)` containing the prevalence values
|
||||
"""
|
||||
posteriors = np.asarray(posteriors)
|
||||
if posteriors.ndim != 2:
|
||||
raise ValueError(f'param posteriors does not seem to be a ndarray of posteior probabilities')
|
||||
raise ValueError(f'param posteriors does not seem to be a ndarray of posterior probabilities')
|
||||
if binarize:
|
||||
predictions = np.argmax(posteriors, axis=-1)
|
||||
return prevalence_from_labels(predictions, np.arange(posteriors.shape[1]))
|
||||
|
|
@ -66,25 +66,264 @@ def prevalence_from_probabilities(posteriors, binarize: bool = False):
|
|||
return prevalences
|
||||
|
||||
|
||||
def as_binary_prevalence(positive_prevalence: Union[float, np.ndarray], clip_if_necessary=False):
|
||||
def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1) -> int:
|
||||
"""
|
||||
Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally
|
||||
distant prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
The computation comes down to calculating:
|
||||
|
||||
.. math::
|
||||
\\binom{N+C-1}{C-1} \\times r
|
||||
|
||||
where `N` is `n_prevpoints-1`, i.e., the number of probability mass blocks to allocate, `C` is the number of
|
||||
classes, and `r` is `n_repeats`. This solution comes from the
|
||||
`Stars and Bars <https://brilliant.org/wiki/integer-equations-star-and-bars/>`_ problem.
|
||||
|
||||
:param int n_classes: number of classes
|
||||
:param int n_prevpoints: number of prevalence points.
|
||||
:param int n_repeats: number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if `n_classes`=2, `n_prevpoints`=5, `n_repeats`=1,
|
||||
then the number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25],
|
||||
and [1.0,0.0]
|
||||
"""
|
||||
N = n_prevpoints-1
|
||||
C = n_classes
|
||||
r = n_repeats
|
||||
return int(scipy.special.binom(N + C - 1, C - 1) * r)
|
||||
|
||||
|
||||
def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1) -> int:
|
||||
"""
|
||||
Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so
|
||||
that the number of valid prevalence values generated as combinations of prevalence points (points in a
|
||||
`n_classes`-dimensional simplex) do not exceed combinations_budget.
|
||||
|
||||
:param int combinations_budget: maximum number of combinations allowed
|
||||
:param int n_classes: number of classes
|
||||
:param int n_repeats: number of repetitions for each prevalence combination
|
||||
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
|
||||
"""
|
||||
assert n_classes > 0 and n_repeats > 0 and combinations_budget > 0, 'parameters must be positive integers'
|
||||
n_prevpoints = 1
|
||||
while True:
|
||||
combinations = num_prevalence_combinations(n_prevpoints, n_classes, n_repeats)
|
||||
if combinations > combinations_budget:
|
||||
return n_prevpoints-1
|
||||
else:
|
||||
n_prevpoints += 1
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Prevalence vectors
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def as_binary_prevalence(positive_prevalence: Union[float, ArrayLike], clip_if_necessary: bool=False) -> np.ndarray:
|
||||
"""
|
||||
Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two
|
||||
values representing a binary distribution.
|
||||
|
||||
:param positive_prevalence: prevalence for the positive class
|
||||
:param clip_if_necessary: if True, clips the value in [0,1] in order to guarantee the resulting distribution
|
||||
:param positive_prevalence: float or array-like of floats with the prevalence for the positive class
|
||||
:param bool clip_if_necessary: if True, clips the value in [0,1] in order to guarantee the resulting distribution
|
||||
is valid. If False, it then checks that the value is in the valid range, and raises an error if not.
|
||||
:return: np.ndarray of shape `(2,)`
|
||||
"""
|
||||
positive_prevalence = np.asarray(positive_prevalence, float)
|
||||
if clip_if_necessary:
|
||||
positive_prevalence = np.clip(positive_prevalence, 0, 1)
|
||||
else:
|
||||
assert 0 <= positive_prevalence <= 1, 'the value provided is not a valid prevalence for the positive class'
|
||||
assert np.logical_and(0 <= positive_prevalence, positive_prevalence <= 1).all(), \
|
||||
'the value provided is not a valid prevalence for the positive class'
|
||||
return np.asarray([1-positive_prevalence, positive_prevalence]).T
|
||||
|
||||
|
||||
def strprev(prevalences: ArrayLike, prec: int=3) -> str:
|
||||
"""
|
||||
Returns a string representation for a prevalence vector. E.g.,
|
||||
|
||||
def HellingerDistance(P, Q) -> float:
|
||||
>>> strprev([1/3, 2/3], prec=2)
|
||||
>>> '[0.33, 0.67]'
|
||||
|
||||
:param prevalences: array-like of prevalence values
|
||||
:param prec: int, indicates the float precision (number of decimal values to print)
|
||||
:return: string
|
||||
"""
|
||||
return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
|
||||
|
||||
|
||||
def check_prevalence_vector(prevalences: ArrayLike, raise_exception: bool=False, tolerance: float=1e-08, aggr=True):
|
||||
"""
|
||||
Checks that `prevalences` is a valid prevalence vector, i.e., it contains values in [0,1] and
|
||||
the values sum up to 1. In other words, verifies that the `prevalences` vectors lies in the
|
||||
probability simplex.
|
||||
|
||||
:param ArrayLike prevalences: the prevalence vector, or vectors, to check
|
||||
:param bool raise_exception: whether to raise an exception if the vector (or any of the vectors) does
|
||||
not lie in the simplex (default False)
|
||||
:param float tolerance: error tolerance for the check `sum(prevalences) - 1 = 0`
|
||||
:param bool aggr: if True (default) returns one single bool (True if all prevalence vectors are valid,
|
||||
False otherwise), if False returns an array of bool, one for each prevalence vector
|
||||
:return: a single bool True if `prevalences` is a vector of prevalence values that lies on the simplex,
|
||||
or False otherwise; alternatively, if `prevalences` is a matrix of shape `(num_vectors, n_classes,)`
|
||||
then it returns one such bool for each prevalence vector
|
||||
"""
|
||||
prevalences = np.asarray(prevalences)
|
||||
|
||||
all_positive = prevalences>=0
|
||||
if not all_positive.all():
|
||||
if raise_exception:
|
||||
raise ValueError('some prevalence vectors contain negative numbers; '
|
||||
'consider using the qp.functional.normalize_prevalence with '
|
||||
'any method from ["clip", "mapsimplex", "softmax"]')
|
||||
|
||||
all_close_1 = np.isclose(prevalences.sum(axis=-1), 1, atol=tolerance)
|
||||
if not all_close_1.all():
|
||||
if raise_exception:
|
||||
raise ValueError('some prevalence vectors do not sum up to 1; '
|
||||
'consider using the qp.functional.normalize_prevalence with '
|
||||
'any method from ["l1", "clip", "mapsimplex", "softmax"]')
|
||||
|
||||
valid = np.logical_and(all_positive.all(axis=-1), all_close_1)
|
||||
if aggr:
|
||||
return valid.all()
|
||||
else:
|
||||
return valid
|
||||
|
||||
|
||||
def normalize_prevalence(prevalences: ArrayLike, method='l1'):
|
||||
"""
|
||||
Normalizes a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
|
||||
cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in
|
||||
cases in which all values are zero.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:param str method: indicates the normalization method to employ, options are:
|
||||
|
||||
* `l1`: applies L1 normalization (default); a 0 vector is mapped onto the uniform prevalence
|
||||
* `clip`: clip values in [0,1] and then rescales so that the L1 norm is 1
|
||||
* `mapsimplex`: projects vectors onto the probability simplex. This implementation relies on
|
||||
`Mathieu Blondel's projection_simplex_sort <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
* `softmax`: applies softmax to all vectors
|
||||
* `condsoftmax`: applies softmax only to invalid prevalence vectors
|
||||
|
||||
:return: a normalized vector or matrix of prevalence values
|
||||
"""
|
||||
if method in ['none', None]:
|
||||
return prevalences
|
||||
|
||||
prevalences = np.asarray(prevalences, dtype=float)
|
||||
|
||||
if method=='l1':
|
||||
normalized = l1_norm(prevalences)
|
||||
check_prevalence_vector(normalized, raise_exception=True)
|
||||
elif method=='clip':
|
||||
normalized = clip(prevalences) # no need to check afterwards
|
||||
elif method=='mapsimplex':
|
||||
normalized = projection_simplex_sort(prevalences)
|
||||
elif method=='softmax':
|
||||
normalized = softmax(prevalences)
|
||||
elif method=='condsoftmax':
|
||||
normalized = condsoftmax(prevalences)
|
||||
else:
|
||||
raise ValueError(f'unknown {method=}, valid ones are ["l1", "clip", "mapsimplex", "softmax", "condsoftmax"]')
|
||||
|
||||
return normalized
|
||||
|
||||
|
||||
def l1_norm(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies L1 normalization to the `unnormalized_arr` so that it becomes a valid prevalence
|
||||
vector. Zero vectors are mapped onto the uniform distribution. Raises an exception if
|
||||
the resulting vectors are not valid distributions. This may happen when the original
|
||||
prevalence vectors contain negative values. Use the `clip` normalization function
|
||||
instead to avoid this possibility.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
n_classes = prevalences.shape[-1]
|
||||
accum = prevalences.sum(axis=-1, keepdims=True)
|
||||
prevalences = np.true_divide(prevalences, accum, where=accum > 0)
|
||||
allzeros = accum.flatten() == 0
|
||||
if any(allzeros):
|
||||
if prevalences.ndim == 1:
|
||||
prevalences = np.full(shape=n_classes, fill_value=1. / n_classes)
|
||||
else:
|
||||
prevalences[allzeros] = np.full(shape=n_classes, fill_value=1. / n_classes)
|
||||
return prevalences
|
||||
|
||||
|
||||
def clip(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Clips the values in [0,1] and then applies the L1 normalization.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
clipped = np.clip(prevalences, 0, 1)
|
||||
normalized = l1_norm(clipped)
|
||||
return normalized
|
||||
|
||||
|
||||
def projection_simplex_sort(unnormalized_arr: ArrayLike) -> np.ndarray:
|
||||
"""Projects a point onto the probability simplex.
|
||||
|
||||
The code is adapted from Mathieu Blondel's BSD-licensed
|
||||
`implementation <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
|
||||
(see function `projection_simplex_sort` in their repo) which is accompanying the paper
|
||||
|
||||
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
|
||||
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex,
|
||||
ICPR 2014, `URL <http://www.mblondel.org/publications/mblondel-icpr2014.pdf>`_
|
||||
|
||||
:param `unnormalized_arr`: point in n-dimensional space, shape `(n,)`
|
||||
:return: projection of `unnormalized_arr` onto the (n-1)-dimensional probability simplex, shape `(n,)`
|
||||
"""
|
||||
unnormalized_arr = np.asarray(unnormalized_arr)
|
||||
n = len(unnormalized_arr)
|
||||
u = np.sort(unnormalized_arr)[::-1]
|
||||
cssv = np.cumsum(u) - 1.0
|
||||
ind = np.arange(1, n + 1)
|
||||
cond = u - cssv / ind > 0
|
||||
rho = ind[cond][-1]
|
||||
theta = cssv[cond][-1] / float(rho)
|
||||
return np.maximum(unnormalized_arr - theta, 0)
|
||||
|
||||
|
||||
def softmax(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies the softmax function to all vectors even if the original vectors were valid distributions.
|
||||
If you want to leave valid vectors untouched, use condsoftmax instead.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
normalized = scipy.special.softmax(prevalences, axis=-1)
|
||||
return normalized
|
||||
|
||||
|
||||
def condsoftmax(prevalences: ArrayLike) -> np.ndarray:
|
||||
"""
|
||||
Applies the softmax function only to vectors that do not represent valid distributions.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: np.ndarray representing a valid distribution
|
||||
"""
|
||||
invalid_idx = ~ check_prevalence_vector(prevalences, aggr=False, raise_exception=False)
|
||||
if isinstance(invalid_idx, np.bool_) and invalid_idx:
|
||||
# only one vector
|
||||
normalized = scipy.special.softmax(prevalences)
|
||||
else:
|
||||
prevalences = np.copy(prevalences)
|
||||
prevalences[invalid_idx] = scipy.special.softmax(prevalences[invalid_idx], axis=-1)
|
||||
normalized = prevalences
|
||||
return normalized
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Divergences
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def HellingerDistance(P: np.ndarray, Q: np.ndarray) -> float:
|
||||
"""
|
||||
Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.
|
||||
The HD for two discrete distributions of `k` bins is defined as:
|
||||
|
|
@ -99,7 +338,7 @@ def HellingerDistance(P, Q) -> float:
|
|||
return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
|
||||
|
||||
|
||||
def TopsoeDistance(P, Q, epsilon=1e-20):
|
||||
def TopsoeDistance(P: np.ndarray, Q: np.ndarray, epsilon: float=1e-20):
|
||||
"""
|
||||
Topsoe distance between two (discretized) distributions `P` and `Q`.
|
||||
The Topsoe distance for two discrete distributions of `k` bins is defined as:
|
||||
|
|
@ -115,7 +354,130 @@ def TopsoeDistance(P, Q, epsilon=1e-20):
|
|||
return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + Q*np.log((2*Q+epsilon)/(P+Q+epsilon)))
|
||||
|
||||
|
||||
def uniform_prevalence_sampling(n_classes, size=1):
|
||||
def get_divergence(divergence: Union[str, Callable]):
|
||||
"""
|
||||
Guarantees that the divergence received as argument is a function. That is, if this argument is already
|
||||
a callable, then it is returned, if it is instead a string, then tries to instantiate the corresponding
|
||||
divergence from the string name.
|
||||
|
||||
:param divergence: callable or string indicating the name of the divergence function
|
||||
:return: callable
|
||||
"""
|
||||
if isinstance(divergence, str):
|
||||
if divergence=='HD':
|
||||
return HellingerDistance
|
||||
elif divergence=='topsoe':
|
||||
return TopsoeDistance
|
||||
else:
|
||||
raise ValueError(f'unknown divergence {divergence}')
|
||||
elif callable(divergence):
|
||||
return divergence
|
||||
else:
|
||||
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Solvers
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def argmin_prevalence(loss: Callable,
|
||||
n_classes: int,
|
||||
method: Literal["optim_minimize", "linear_search", "ternary_search"]='optim_minimize'):
|
||||
"""
|
||||
Searches for the prevalence vector that minimizes a loss function.
|
||||
|
||||
:param loss: callable, the function to minimize
|
||||
:param n_classes: int, number of classes
|
||||
:param method: string indicating the search strategy. Possible values are::
|
||||
'optim_minimize': uses scipy.optim
|
||||
'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1]
|
||||
'ternary_search': implements the ternary search (not yet implemented)
|
||||
:return: np.ndarray, a prevalence vector
|
||||
"""
|
||||
if method == 'optim_minimize':
|
||||
return optim_minimize(loss, n_classes)
|
||||
elif method == 'linear_search':
|
||||
return linear_search(loss, n_classes)
|
||||
elif method == 'ternary_search':
|
||||
ternary_search(loss, n_classes)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
def optim_minimize(loss: Callable, n_classes: int):
|
||||
"""
|
||||
Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
|
||||
that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
|
||||
SLSQP routine.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
from scipy import optimize
|
||||
|
||||
# the initial point is set as the uniform distribution
|
||||
uniform_distribution = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
|
||||
# solutions are bounded to those contained in the unit-simplex
|
||||
bounds = tuple((0, 1) for _ in range(n_classes)) # values in [0,1]
|
||||
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
|
||||
r = optimize.minimize(loss, x0=uniform_distribution, method='SLSQP', bounds=bounds, constraints=constraints)
|
||||
return r.x
|
||||
|
||||
|
||||
def linear_search(loss: Callable, n_classes: int):
|
||||
"""
|
||||
Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring
|
||||
the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the
|
||||
early methods in quantification literature used it, e.g., HDy). A most powerful alternative is `optim_minimize`.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
assert n_classes==2, 'linear search is only available for binary problems'
|
||||
|
||||
prev_selected, min_score = None, None
|
||||
for prev in prevalence_linspace(grid_points=100, repeats=1, smooth_limits_epsilon=0.0):
|
||||
score = loss(np.asarray([1 - prev, prev]))
|
||||
if min_score is None or score < min_score:
|
||||
prev_selected, min_score = prev, score
|
||||
|
||||
return np.asarray([1 - prev_selected, prev_selected])
|
||||
|
||||
|
||||
def ternary_search(loss: Callable, n_classes: int):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Sampling utils
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
def prevalence_linspace(grid_points:int=21, repeats:int=1, smooth_limits_epsilon:float=0.01) -> np.ndarray:
|
||||
"""
|
||||
Produces an array of uniformly separated values of prevalence.
|
||||
By default, produces an array of 21 prevalence values, with
|
||||
step 0.05 and with the limits smoothed, i.e.:
|
||||
[0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]
|
||||
|
||||
:param grid_points: the number of prevalence values to sample from the [0,1] interval (default 21)
|
||||
:param repeats: number of times each prevalence is to be repeated (defaults to 1)
|
||||
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
|
||||
:return: an array of uniformly separated prevalence values
|
||||
"""
|
||||
p = np.linspace(0., 1., num=grid_points, endpoint=True)
|
||||
p[0] += smooth_limits_epsilon
|
||||
p[-1] -= smooth_limits_epsilon
|
||||
if p[0] > p[1]:
|
||||
raise ValueError(f'the smoothing in the limits is greater than the prevalence step')
|
||||
if repeats > 1:
|
||||
p = np.repeat(p, repeats)
|
||||
return p
|
||||
|
||||
|
||||
def uniform_prevalence_sampling(n_classes: int, size: int=1) -> np.ndarray:
|
||||
"""
|
||||
Implements the `Kraemer algorithm <http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf>`_
|
||||
for sampling uniformly at random from the unit simplex. This implementation is adapted from this
|
||||
|
|
@ -144,21 +506,11 @@ def uniform_prevalence_sampling(n_classes, size=1):
|
|||
uniform_simplex_sampling = uniform_prevalence_sampling
|
||||
|
||||
|
||||
def strprev(prevalences, prec=3):
|
||||
"""
|
||||
Returns a string representation for a prevalence vector. E.g.,
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Adjustment
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
>>> strprev([1/3, 2/3], prec=2)
|
||||
>>> '[0.33, 0.67]'
|
||||
|
||||
:param prevalences: a vector of prevalence values
|
||||
:param prec: float precision
|
||||
:return: string
|
||||
"""
|
||||
return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
|
||||
|
||||
|
||||
def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
||||
def solve_adjustment_binary(prevalence_estim: ArrayLike, tpr: float, fpr: float, clip: bool=True):
|
||||
"""
|
||||
Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the
|
||||
positive class `p` comes down to computing:
|
||||
|
|
@ -166,10 +518,10 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
|||
.. math::
|
||||
ACC(p) = \\frac{ p - fpr }{ tpr - fpr }
|
||||
|
||||
:param prevalence_estim: float, the estimated value for the positive class
|
||||
:param tpr: float, the true positive rate of the classifier
|
||||
:param fpr: float, the false positive rate of the classifier
|
||||
:param clip: set to True (default) to clip values that might exceed the range [0,1]
|
||||
:param float prevalence_estim: the estimated value for the positive class (`p` in the formula)
|
||||
:param float tpr: the true positive rate of the classifier
|
||||
:param float fpr: the false positive rate of the classifier
|
||||
:param bool clip: set to True (default) to clip values that might exceed the range [0,1]
|
||||
:return: float, the adjusted count
|
||||
"""
|
||||
|
||||
|
|
@ -182,184 +534,75 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
|
|||
return adjusted
|
||||
|
||||
|
||||
def normalize_prevalence(prevalences):
|
||||
def solve_adjustment(
|
||||
class_conditional_rates: np.ndarray,
|
||||
unadjusted_counts: np.ndarray,
|
||||
method: Literal["inversion", "invariant-ratio"],
|
||||
solver: Literal["exact", "minimize", "exact-raise", "exact-cc"]) -> np.ndarray:
|
||||
"""
|
||||
Normalize a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
|
||||
cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in
|
||||
cases in which all values are zero.
|
||||
Function that tries to solve for :math:`p` the equation :math:`q = M p`, where :math:`q` is the vector of
|
||||
`unadjusted counts` (as estimated, e.g., via classify and count) with :math:`q_i` an estimate of
|
||||
:math:`P(\hat{Y}=y_i)`, and where :math:`M` is the matrix of `class-conditional rates` with :math:`M_{ij}` an
|
||||
estimate of :math:`P(\hat{Y}=y_i|Y=y_j)`.
|
||||
|
||||
:param prevalences: array-like of shape `(n_classes,)` or of shape `(n_samples, n_classes,)` with prevalence values
|
||||
:return: a normalized vector or matrix of prevalence values
|
||||
:param class_conditional_rates: array of shape `(n_classes, n_classes,)` with entry `(i,j)` being the estimate
|
||||
of :math:`P(\hat{Y}=y_i|Y=y_j)`, that is, the probability that an instance that belongs to class :math:`y_j`
|
||||
ends up being classified as belonging to class :math:`y_i`
|
||||
|
||||
:param unadjusted_counts: array of shape `(n_classes,)` containing the unadjusted prevalence values (e.g., as
|
||||
estimated by CC or PCC)
|
||||
|
||||
:param str method: indicates the adjustment method to be used. Valid options are:
|
||||
|
||||
* `inversion`: tries to solve the equation :math:`q = M p` as :math:`p = M^{-1} q` where
|
||||
:math:`M^{-1}` is the matrix inversion of :math:`M`. This inversion may not exist in
|
||||
degenerated cases.
|
||||
* `invariant-ratio`: invariant ratio estimator of `Vaz et al. 2018 <https://jmlr.org/papers/v20/18-456.html>`_,
|
||||
which replaces the last equation in :math:`M` with the normalization condition (i.e., that the sum of
|
||||
all prevalence values must equal 1).
|
||||
|
||||
:param str solver: the method to use for solving the system of linear equations. Valid options are:
|
||||
|
||||
* `exact-raise`: tries to solve the system using matrix inversion. Raises an error if the matrix has rank
|
||||
strictly lower than `n_classes`.
|
||||
* `exact-cc`: if the matrix is not full rank, returns :math:`q` (i.e., the unadjusted counts) as the estimates
|
||||
* `exact`: deprecated, defaults to 'exact-cc' (will be removed in future versions)
|
||||
* `minimize`: minimizes a loss, so the solution always exists
|
||||
"""
|
||||
prevalences = np.asarray(prevalences)
|
||||
n_classes = prevalences.shape[-1]
|
||||
accum = prevalences.sum(axis=-1, keepdims=True)
|
||||
prevalences = np.true_divide(prevalences, accum, where=accum>0)
|
||||
allzeros = accum.flatten()==0
|
||||
if any(allzeros):
|
||||
if prevalences.ndim == 1:
|
||||
prevalences = np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
if solver == "exact":
|
||||
warnings.warn(
|
||||
"The 'exact' solver is deprecated. Use 'exact-raise' or 'exact-cc'", DeprecationWarning, stacklevel=2)
|
||||
solver = "exact-cc"
|
||||
|
||||
A = np.asarray(class_conditional_rates, dtype=float)
|
||||
B = np.asarray(unadjusted_counts, dtype=float)
|
||||
|
||||
if method == "inversion":
|
||||
pass # We leave A and B unchanged
|
||||
elif method == "invariant-ratio":
|
||||
# Change the last equation to replace it with the normalization condition
|
||||
A[-1, :] = 1.0
|
||||
B[-1] = 1.0
|
||||
else:
|
||||
prevalences[accum.flatten()==0] = np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
return prevalences
|
||||
raise ValueError(f"unknown {method=}")
|
||||
|
||||
|
||||
def __num_prevalence_combinations_depr(n_prevpoints:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Computes the number of prevalence combinations in the n_classes-dimensional simplex if `nprevpoints` equally distant
|
||||
prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_prevpoints: integer, number of prevalence points.
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
|
||||
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
|
||||
"""
|
||||
__cache={}
|
||||
def __f(nc,np):
|
||||
if (nc,np) in __cache: # cached result
|
||||
return __cache[(nc,np)]
|
||||
if nc==1: # stop condition
|
||||
return 1
|
||||
else: # recursive call
|
||||
x = sum([__f(nc-1, np-i) for i in range(np)])
|
||||
__cache[(nc,np)] = x
|
||||
return x
|
||||
return __f(n_classes, n_prevpoints) * n_repeats
|
||||
|
||||
|
||||
def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally
|
||||
distant prevalence values are generated and `n_repeats` repetitions are requested.
|
||||
The computation comes down to calculating:
|
||||
|
||||
.. math::
|
||||
\\binom{N+C-1}{C-1} \\times r
|
||||
|
||||
where `N` is `n_prevpoints-1`, i.e., the number of probability mass blocks to allocate, `C` is the number of
|
||||
classes, and `r` is `n_repeats`. This solution comes from the
|
||||
`Stars and Bars <https://brilliant.org/wiki/integer-equations-star-and-bars/>`_ problem.
|
||||
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_prevpoints: integer, number of prevalence points.
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
|
||||
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
|
||||
"""
|
||||
N = n_prevpoints-1
|
||||
C = n_classes
|
||||
r = n_repeats
|
||||
return int(scipy.special.binom(N + C - 1, C - 1) * r)
|
||||
|
||||
|
||||
def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1):
|
||||
"""
|
||||
Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so
|
||||
that the number of valid prevalence values generated as combinations of prevalence points (points in a
|
||||
`n_classes`-dimensional simplex) do not exceed combinations_budget.
|
||||
|
||||
:param combinations_budget: integer, maximum number of combinations allowed
|
||||
:param n_classes: integer, number of classes
|
||||
:param n_repeats: integer, number of repetitions for each prevalence combination
|
||||
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
|
||||
"""
|
||||
assert n_classes > 0 and n_repeats > 0 and combinations_budget > 0, 'parameters must be positive integers'
|
||||
n_prevpoints = 1
|
||||
while True:
|
||||
combinations = num_prevalence_combinations(n_prevpoints, n_classes, n_repeats)
|
||||
if combinations > combinations_budget:
|
||||
return n_prevpoints-1
|
||||
if solver == "minimize":
|
||||
def loss(prev):
|
||||
return np.linalg.norm(A @ prev - B)
|
||||
return optim_minimize(loss, n_classes=A.shape[0])
|
||||
elif solver in ["exact-raise", "exact-cc"]:
|
||||
# Solvers based on matrix inversion, so we use try/except block
|
||||
try:
|
||||
return np.linalg.solve(A, B)
|
||||
except np.linalg.LinAlgError:
|
||||
# The matrix is not invertible.
|
||||
# Depending on the solver, we either raise an error
|
||||
# or return the classifier predictions without adjustment
|
||||
if solver == "exact-raise":
|
||||
raise
|
||||
elif solver == "exact-cc":
|
||||
return unadjusted_counts
|
||||
else:
|
||||
n_prevpoints += 1
|
||||
|
||||
|
||||
def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08):
|
||||
"""
|
||||
Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1.
|
||||
|
||||
:param p: the prevalence vector to check
|
||||
:return: True if `p` is valid, False otherwise
|
||||
"""
|
||||
p = np.asarray(p)
|
||||
if not all(p>=0):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector contains negative numbers')
|
||||
return False
|
||||
if not all(p<=1):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector contains values >1')
|
||||
return False
|
||||
if not np.isclose(p.sum(), 1, atol=toleranze):
|
||||
if raise_exception:
|
||||
raise ValueError('the prevalence vector does not sum up to 1')
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_divergence(divergence: Union[str, Callable]):
|
||||
if isinstance(divergence, str):
|
||||
if divergence=='HD':
|
||||
return HellingerDistance
|
||||
elif divergence=='topsoe':
|
||||
return TopsoeDistance
|
||||
raise ValueError(f"Solver {solver} not known.")
|
||||
else:
|
||||
raise ValueError(f'unknown divergence {divergence}')
|
||||
elif callable(divergence):
|
||||
return divergence
|
||||
else:
|
||||
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
|
||||
|
||||
|
||||
def argmin_prevalence(loss, n_classes, method='optim_minimize'):
|
||||
if method == 'optim_minimize':
|
||||
return optim_minimize(loss, n_classes)
|
||||
elif method == 'linear_search':
|
||||
return linear_search(loss, n_classes)
|
||||
elif method == 'ternary_search':
|
||||
raise NotImplementedError()
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
def optim_minimize(loss, n_classes):
|
||||
"""
|
||||
Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
|
||||
that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
|
||||
SLSQP routine.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
from scipy import optimize
|
||||
|
||||
# the initial point is set as the uniform distribution
|
||||
uniform_distribution = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
|
||||
# solutions are bounded to those contained in the unit-simplex
|
||||
bounds = tuple((0, 1) for _ in range(n_classes)) # values in [0,1]
|
||||
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
|
||||
r = optimize.minimize(loss, x0=uniform_distribution, method='SLSQP', bounds=bounds, constraints=constraints)
|
||||
return r.x
|
||||
|
||||
|
||||
def linear_search(loss, n_classes):
|
||||
"""
|
||||
Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring
|
||||
the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the
|
||||
early methods in quantification literature used it, e.g., HDy). A most powerful alternative is `optim_minimize`.
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
assert n_classes==2, 'linear search is only available for binary problems'
|
||||
|
||||
prev_selected, min_score = None, None
|
||||
for prev in prevalence_linspace(n_prevalences=100, repeats=1, smooth_limits_epsilon=0.0):
|
||||
score = loss(np.asarray([1 - prev, prev]))
|
||||
if min_score is None or score < min_score:
|
||||
prev_selected, min_score = prev, score
|
||||
|
||||
return np.asarray([1 - prev_selected, prev_selected])
|
||||
raise ValueError(f'unknown {solver=}')
|
||||
|
|
|
|||
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Reference in New Issue