Compare commits
18 Commits
master
...
ediscovery
|
|
@ -1,108 +0,0 @@
|
|||
name: CI
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
- devel
|
||||
tags:
|
||||
- "[0-9]+.[0-9]+.[0-9]+"
|
||||
|
||||
jobs:
|
||||
|
||||
# take out unit tests
|
||||
test:
|
||||
name: Unit tests (Python ${{ matrix.python-version }})
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
- "3.11"
|
||||
env:
|
||||
QUAPY_TESTS_OMIT_LARGE_DATASETS: True
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up Python ${{ matrix.python-version }}
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip setuptools wheel
|
||||
python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@main"
|
||||
python -m pip install -e .[bayes,tests]
|
||||
- name: Test with unittest
|
||||
run: python -m unittest
|
||||
|
||||
# build and push documentation to gh-pages (only if pushed to the master branch)
|
||||
docs:
|
||||
name: Documentation
|
||||
runs-on: ubuntu-latest
|
||||
if: github.ref == 'refs/heads/master'
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: 3.11
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
|
||||
python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@main"
|
||||
python -m pip install -e .[neural,docs]
|
||||
- name: Build documentation
|
||||
run: sphinx-build -M html docs/source docs/build
|
||||
- name: Publish documentation
|
||||
run: |
|
||||
git clone ${{ github.server_url }}/${{ github.repository }}.git --branch gh-pages --single-branch __gh-pages/
|
||||
cp -r docs/build/html/* __gh-pages/
|
||||
cd __gh-pages/
|
||||
git config --local user.email "action@github.com"
|
||||
git config --local user.name "GitHub Action"
|
||||
git add .
|
||||
git commit -am "Documentation based on ${{ github.sha }}" || true
|
||||
- name: Push changes
|
||||
uses: ad-m/github-push-action@master
|
||||
with:
|
||||
branch: gh-pages
|
||||
directory: __gh-pages/
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
release:
|
||||
name: Build & Publish Release
|
||||
runs-on: ubuntu-latest
|
||||
if: startsWith(github.ref, 'refs/tags/')
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install build dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip build twine
|
||||
- name: Build package
|
||||
run: python -m build
|
||||
- name: Publish to TestPyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
user: __token__
|
||||
# use these for TESTs!
|
||||
# password: ${{ secrets.TEST_PYPI_API_TOKEN }}
|
||||
# repository_url: https://test.pypi.org/legacy/
|
||||
password: ${{ secrets.PYPI_API_TOKEN }}
|
||||
repository_url: https://upload.pypi.org/legacy/
|
||||
- name: Create GitHub Release
|
||||
id: create_release
|
||||
uses: actions/create-release@v1
|
||||
with:
|
||||
tag_name: ${{ github.ref_name }}
|
||||
release_name: Release ${{ github.ref_name }}
|
||||
body: |
|
||||
Changes in this release:
|
||||
- see commit history for details
|
||||
draft: false
|
||||
prerelease: false
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
|
@ -69,12 +69,8 @@ instance/
|
|||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# vscode config:
|
||||
.vscode/
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/doctest
|
||||
docs/_build/doctrees
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
|
@ -89,11 +85,6 @@ ipython_config.py
|
|||
# pyenv
|
||||
.python-version
|
||||
|
||||
# poetry
|
||||
poetry.toml
|
||||
pyproject.toml
|
||||
poetry.lock
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
|
|
@ -139,33 +130,3 @@ dmypy.json
|
|||
.pyre/
|
||||
|
||||
*__pycache__*
|
||||
*.pdf
|
||||
*.zip
|
||||
*.png
|
||||
*.csv
|
||||
*.pkl
|
||||
*.dataframe
|
||||
|
||||
|
||||
# other projects
|
||||
LeQua2022
|
||||
MultiLabel
|
||||
NewMethods
|
||||
Ordinal
|
||||
Retrieval
|
||||
eDiscovery
|
||||
poster-cikm
|
||||
slides-cikm
|
||||
slides-short-cikm
|
||||
quick_experiment
|
||||
svm_perf_quantification/svm_struct
|
||||
svm_perf_quantification/svm_light
|
||||
TweetSentQuant
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
*.png
|
||||
.idea
|
||||
|
|
|
|||
249
CHANGE_LOG.txt
249
CHANGE_LOG.txt
|
|
@ -1,249 +0,0 @@
|
|||
Change Log 0.2.1
|
||||
-----------------
|
||||
|
||||
- Improved documentation of confidence regions. Added QuaPy logo :')
|
||||
|
||||
- Added Bayesian KDEy and Bayesian MAPLS quantifiers.
|
||||
|
||||
- Added temperature calibration utilities for Bayesian confidence-aware methods.
|
||||
|
||||
- Added compositional CLR and ILR transformations.
|
||||
|
||||
- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
|
||||
|
||||
- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST.
|
||||
|
||||
- Added TemperatureScalingFromLogits for calibrating pretrained logits.
|
||||
|
||||
- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
|
||||
|
||||
- Added ReadMe method by Daniel Hopkins and Gary King.
|
||||
|
||||
- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
|
||||
|
||||
- Improved unit testing and separated integration tests.
|
||||
|
||||
- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
|
||||
|
||||
- Added visualization tools for 3-class problems in the simplex, see also the new example no.19
|
||||
|
||||
- Deep code revision and improved codebase
|
||||
|
||||
- Added EDx/EDy from quantificationlib (thanks to Pablo and Juanjo!)
|
||||
|
||||
|
||||
|
||||
Change Log 0.2.0
|
||||
-----------------
|
||||
|
||||
- Base code Refactor:
|
||||
- Removing coupling between LabelledCollection and quantification methods; the fit interface changes:
|
||||
def fit(data:LabelledCollection): -> def fit(X, y):
|
||||
- Adding function "predict" (function "quantify" is still present as an alias, for the nostalgic)
|
||||
- Aggregative methods's behavior in terms of fit_classifier and how to treat the val_split is now
|
||||
indicated exclusively at construction time, and it is no longer possible to indicate it at fit time.
|
||||
This is because, in v<=0.1.9, one could create a method (e.g., ACC) and then indicate:
|
||||
my_acc.fit(tr_data, fit_classifier=False, val_split=val_data)
|
||||
in which case the first argument is unused, and this was ambiguous with
|
||||
my_acc.fit(the_data, fit_classifier=False)
|
||||
in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
|
||||
indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
|
||||
- This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
|
||||
|
||||
- EMQ has been modified, so that the representation function "classify" now only provides posterior
|
||||
probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
|
||||
- A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
|
||||
in case the abstention's calibration functions failed (which happens sometimes). Options include:
|
||||
- 'raise': raises a RuntimeException (default)
|
||||
- 'backup': reruns by silently avoiding calibration
|
||||
- Parameter "recalib" has been renamed "calib"
|
||||
|
||||
- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
|
||||
ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.
|
||||
This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or
|
||||
classify multiple times the instances of a sample. See:
|
||||
- quapy/method/confidence.py (new)
|
||||
- the new example no. 16.confidence_regions.py
|
||||
|
||||
- BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong.
|
||||
|
||||
- Improved documentation of qp.plot module.
|
||||
|
||||
|
||||
Change Log 0.1.9
|
||||
----------------
|
||||
|
||||
- Added LeQua 2024 datasets and normalized match distance to qp.error
|
||||
|
||||
- Improved data loaders for UCI binary and UCI multiclass datasets (thanks to Lorenzo Volpi!); these datasets
|
||||
can be loaded with standardised covariates (default)
|
||||
|
||||
- Added a default classifier for aggregative quantifiers, which now can be instantiated without specifying
|
||||
the classifier. The default classifier can be accessed in qp.environ['DEFAULT_CLS'] and is assigned to
|
||||
sklearn.linear_model.LogisticRegression(max_iter=3000). If the classifier is not specified, then a clone
|
||||
of said classifier is returned. E.g.:
|
||||
> pacc = PACC()
|
||||
is equivalent to:
|
||||
> pacc = PACC(classifier=LogisticRegression(max_iter=3000))
|
||||
|
||||
- Improved error loging in model selection. In v0.1.8 only Status.INVALID was reported; in v0.1.9 it is
|
||||
now accompanied by a textual description of the error
|
||||
|
||||
- The number of parallel workers can now be set via an environment variable by running, e.g.:
|
||||
> N_JOBS=10 python3 your_script.py
|
||||
which has the same effect as writing the following code at the beginning of your_script.py:
|
||||
> import quapy as qp
|
||||
> qp.environ["N_JOBS"] = 10
|
||||
|
||||
- Some examples have been added to the ./examples/ dir, which now contains numbered examples from basics (0)
|
||||
to advanced topics (higher numbers)
|
||||
|
||||
- Moved the wiki documents to the ./docs/ folder so that they become editable via PR for the community
|
||||
|
||||
- Added Composable methods from Mirko Bunse's qunfold library! (thanks to Mirko Bunse!)
|
||||
|
||||
- Added Continuous Integration with GitHub Actions (thanks to Mirko Bunse!)
|
||||
|
||||
- Added Bayesian CC method (thanks to Pawel Czyz!). The method is described in detail in the paper
|
||||
Ziegler, Albert, and Paweł Czyż. "Bayesian Quantification with Black-Box Estimators."
|
||||
arXiv preprint arXiv:2302.09159 (2023).
|
||||
|
||||
- Removed binary UCI datasets {acute.a, acute.b, balance.2} from the list qp.data.datasets.UCI_BINARY_DATASETS
|
||||
(the datasets are still loadable from the fetch_UCIBinaryLabelledCollection and fetch_UCIBinaryDataset
|
||||
functions, though). The reason is that these datasets tend to yield results (for all methods) that are
|
||||
one or two orders of magnitude greater than for other datasets, and this has a disproportionate impact in
|
||||
methods average (I suspect there is something wrong in those datasets).
|
||||
|
||||
|
||||
Change Log 0.1.8
|
||||
----------------
|
||||
|
||||
- Added Kernel Density Estimation methods (KDEyML, KDEyCS, KDEyHD) as proposed in the paper:
|
||||
Moreo, A., González, P., & del Coz, J. J. Kernel Density Estimation for Multiclass Quantification.
|
||||
arXiv preprint arXiv:2401.00490, 2024
|
||||
|
||||
- Substantial internal refactor: aggregative methods now inherit a pattern by which the fit method consists of:
|
||||
a) fitting the classifier and returning the representations of the training instances (typically the posterior
|
||||
probabilities, the label predictions, or the classifier scores, and typically obtained through kFCV).
|
||||
b) fitting an aggregation function
|
||||
The function implemented in step a) is inherited from the super class. Each new aggregative method now has to
|
||||
implement only the "aggregative_fit" of step b).
|
||||
This pattern was already implemented for the prediction (thus allowing evaluation functions to be performed
|
||||
very quicky), and is now available also for training. The main benefit is that model selection now can nestle
|
||||
the training of quantifiers in two levels: one for the classifier, and another for the aggregation function.
|
||||
As a result, a method with a param grid of 10 combinations for the classifier and 10 combinations for the
|
||||
quantifier, now implies 10 trainings of the classifier + 10*10 trainings of the aggregation function (this is
|
||||
typically much faster than the classifier training), whereas in versions <0.1.8 this amounted to training
|
||||
10*10 (classifiers+aggregations).
|
||||
|
||||
- Added different solvers for ACC and PACC quantifiers. In quapy < 0.1.8 these quantifiers try to solve the system
|
||||
of equations Ax=B exactly (by means of np.linalg.solve). As noted by Mirko Bunse (thanks!), such an exact solution
|
||||
does sometimes not exist. In cases like this, quapy < 0.1.8 resorted to CC for providing a plausible solution.
|
||||
ACC and PACC now resorts to an approximated solution in such cases (minimizing the L2-norm of the difference
|
||||
between Ax-B) as proposed by Mirko Bunse. A quick experiment reveals this heuristic greatly improves the results
|
||||
of ACC and PACC in T2A@LeQua.
|
||||
|
||||
- Fixed ThresholdOptimization methods (X, T50, MAX, MS and MS2). Thanks to Tobias Schumacher and colleagues for pointing
|
||||
this out in Appendix A of "Schumacher, T., Strohmaier, M., & Lemmerich, F. (2021). A comparative evaluation of
|
||||
quantification methods. arXiv:2103.03223v3 [cs.LG]"
|
||||
|
||||
- Added HDx and DistributionMatchingX to non-aggregative quantifiers (see also the new example "comparing_HDy_HDx.py")
|
||||
|
||||
- New UCI multiclass datasets added (thanks to Pablo González). The 5 UCI multiclass datasets are those corresponding
|
||||
to the following criteria:
|
||||
- >1000 instances
|
||||
- >2 classes
|
||||
- classification datasets
|
||||
- Python API available
|
||||
|
||||
- New IFCB (plankton) dataset added (thanks to Pablo González). See qp.datasets.fetch_IFCB.
|
||||
|
||||
- Added new evaluation measures NAE, NRAE (thanks to Andrea Esuli)
|
||||
|
||||
- Added new meta method "MedianEstimator"; an ensemble of binary base quantifiers that receives as input a dictionary
|
||||
of hyperparameters that will explore exhaustively, fitting and generating predictions for each combination of
|
||||
hyperparameters, and that returns, as the prevalence estimates, the median across all predictions.
|
||||
|
||||
- Added "custom_protocol.py" example.
|
||||
|
||||
- New API documentation template.
|
||||
|
||||
|
||||
Change Log 0.1.7
|
||||
----------------
|
||||
|
||||
- Protocols are now abstracted as instances of AbstractProtocol. There is a new class extending AbstractProtocol called
|
||||
AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings.
|
||||
There are some examples of protocols, APP, NPP, UPP, DomainMixer (experimental).
|
||||
The idea is to start the sample generation by simply calling the __call__ method.
|
||||
This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection,
|
||||
and sampling functions in LabelledCollection relied of the old functions. E.g., the functionality of
|
||||
qp.evaluation.artificial_prevalence_report or qp.evaluation.natural_prevalence_report is now obtained by means of
|
||||
qp.evaluation.report which takes a protocol as an argument. I have not maintained compatibility with the old
|
||||
interfaces because I did not really like them. Check the wiki guide and the examples for more details.
|
||||
|
||||
- Exploration of hyperparameters in Model selection can now be run in parallel (there was a n_jobs argument in
|
||||
QuaPy 0.1.6 but only the evaluation part for one specific hyperparameter was run in parallel).
|
||||
|
||||
- The prediction function has been refactored, so it applies the optimization for aggregative quantifiers (that
|
||||
consists in pre-classifying all instances, and then only invoking aggregate on the samples) only in cases in
|
||||
which the total number of classifications would be smaller than the number of classifications with the standard
|
||||
procedure. The user can now specify "force", "auto", True of False, in order to actively decide for applying it
|
||||
or not.
|
||||
|
||||
- examples directory created!
|
||||
|
||||
- DyS, Topsoe distance and binary search (thanks to Pablo González)
|
||||
|
||||
- Multi-thread reproducibility via seeding (thanks to Pablo González)
|
||||
|
||||
- n_jobs is now taken from the environment if set to None
|
||||
|
||||
- ACC, PACC, Forman's threshold variants have been parallelized.
|
||||
|
||||
- cross_val_predict (for quantification) added to model_selection: would be nice to allow the user specifies a
|
||||
test protocol maybe, or None for bypassing it?
|
||||
|
||||
- Bugfix: adding two labelled collections (with +) now checks for consistency in the classes
|
||||
|
||||
- newer versions of numpy raise a warning when accessing types (e.g., np.float). I have replaced all such instances
|
||||
with the plain python type (e.g., float).
|
||||
|
||||
- new dependency "abstention" (to add to the project requirements and setup). Calibration methods from
|
||||
https://github.com/kundajelab/abstention added.
|
||||
|
||||
- the internal classifier of aggregative methods is now called "classifier" instead of "learner"
|
||||
|
||||
- when optimizing the hyperparameters of an aggregative quantifier, the classifier's specific hyperparameters
|
||||
should be marked with a "classifier__" prefix (just like in scikit-learn with estimators), while the quantifier's
|
||||
specific hyperparameters are named directly. For example, PCC(LogisticRegression()) quantifier has hyperparameters
|
||||
"classifier__C", "classifier__class_weight", etc., instead of "C" and "class_weight" as in v0.1.6.
|
||||
|
||||
- hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations
|
||||
yielding to internal errors of surrogate functions are reported and skipped, without stopping the grid search.
|
||||
|
||||
- DistributionMatching methods added. This is a general framework for distribution matching methods that caters for
|
||||
multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
|
||||
method aligned with the Firat's formulation.
|
||||
|
||||
- internal method properties "binary", "aggregative", and "probabilistic" have been removed; these conditions are
|
||||
checked via isinstance
|
||||
|
||||
- quantifiers (i.e., classes that inherit from BaseQuantifier) are not forced to implement classes_ or n_classes;
|
||||
these can be used anyway internally, but the framework will not suppose (nor impose) that a quantifier implements
|
||||
them
|
||||
|
||||
- qp.evaluation.prediction has been optimized so that, if a quantifier is of type aggregative, and if the evaluation
|
||||
protocol is of type OnLabelledCollection, then the computation is faster. In this specific case, the predictions
|
||||
are issued only once and for all, and not for each sample. An exception to this (which is implement also), is
|
||||
when the number of instances across all samples is anyway smaller than the number of instances in the original
|
||||
labelled collection; in this case the heuristic is of no help, and is therefore not applied.
|
||||
|
||||
- the distinction between "classify" and "posterior_probabilities" has been removed in Aggregative quantifiers,
|
||||
so that probabilistic classifiers return posterior probabilities, while non-probabilistic quantifiers
|
||||
return crisp decisions.
|
||||
|
||||
- OneVsAll fixed. There are now two classes: a generic one OneVsAllGeneric that works with any quantifier (e.g.,
|
||||
any instance of BaseQuantifier), and a subclass of it called OneVsAllAggregative which implements the
|
||||
classify / aggregate interface. Both are instances of OneVsAll. There is a method getOneVsAll that returns the
|
||||
best instance based on the type of quantifier.
|
||||
145
README.md
145
README.md
|
|
@ -1,22 +1,15 @@
|
|||
# QuaPy
|
||||
|
||||
## version 0.2.1
|
||||
|
||||
QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify)
|
||||
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
|
||||
written in Python.
|
||||
|
||||
QuaPy is based on the concept of "data sample", and provides implementations of the
|
||||
most important aspects of the quantification workflow, such as (baseline and advanced)
|
||||
quantification methods,
|
||||
quantification-oriented model selection mechanisms, evaluation measures, and evaluations protocols
|
||||
QuaPy roots on the concept of data sample, and provides implementations of
|
||||
most important concepts in quantification literature, such as the most important
|
||||
quantification baselines, many advanced quantification methods,
|
||||
quantification-oriented model selection, many evaluation measures and protocols
|
||||
used for evaluating quantification methods.
|
||||
QuaPy also makes available commonly used datasets, and offers visualization tools
|
||||
for facilitating the analysis and interpretation of the experimental results.
|
||||
|
||||
### Last updates:
|
||||
|
||||
* Version 0.2.1 is released! major changes can be consulted [here](CHANGE_LOG.txt).
|
||||
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/index.html)
|
||||
QuaPy also integrates commonly used datasets and offers visualization tools
|
||||
for facilitating the analysis and interpretation of results.
|
||||
|
||||
### Installation
|
||||
|
||||
|
|
@ -24,69 +17,52 @@ for facilitating the analysis and interpretation of the experimental results.
|
|||
pip install quapy
|
||||
```
|
||||
|
||||
### Cite QuaPy
|
||||
|
||||
If you find QuaPy useful (and we hope you will), please consider citing the original paper in your research:
|
||||
|
||||
```
|
||||
@inproceedings{moreo2021quapy,
|
||||
title={QuaPy: a python-based framework for quantification},
|
||||
author={Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
|
||||
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
|
||||
pages={4534--4543},
|
||||
year={2021}
|
||||
}
|
||||
```
|
||||
|
||||
## A quick example:
|
||||
|
||||
The following script fetches a dataset of tweets, trains, applies, and evaluates a quantifier based on the
|
||||
_Adjusted Classify & Count_ quantification method, using, as the evaluation measure, the _Mean Absolute Error_ (MAE)
|
||||
between the predicted and the true class prevalence values
|
||||
The following script fetchs a Twitter dataset, trains and evaluates an
|
||||
_Adjusted Classify & Count_ model in terms of the _Mean Absolute Error_ (MAE)
|
||||
between the class prevalences estimated for the test set and the true prevalences
|
||||
of the test set.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
|
||||
dataset = qp.datasets.fetch_twitter('semeval16')
|
||||
|
||||
# create an "Adjusted Classify & Count" quantifier
|
||||
model = qp.method.aggregative.ACC()
|
||||
Xtr, ytr = training.Xy
|
||||
model.fit(Xtr, ytr)
|
||||
model = qp.method.aggregative.ACC(LogisticRegression())
|
||||
model.fit(dataset.training)
|
||||
|
||||
estim_prevalence = model.predict(test.X)
|
||||
true_prevalence = test.prevalence()
|
||||
estim_prevalences = model.quantify(dataset.test.instances)
|
||||
true_prevalences = dataset.test.prevalence()
|
||||
|
||||
error = qp.error.mae(true_prevalences, estim_prevalences)
|
||||
|
||||
error = qp.error.mae(true_prevalence, estim_prevalence)
|
||||
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
||||
```
|
||||
|
||||
Quantification is useful in scenarios characterized by prior probability shift. In other
|
||||
words, we would be little interested in estimating the class prevalence values of the test set if
|
||||
we could assume the IID assumption to hold, as this prevalence would be roughly equivalent to the
|
||||
class prevalence of the training set. For this reason, any quantification model
|
||||
should be tested across many samples, even ones characterized by class prevalence
|
||||
values different or very different from those found in the training set.
|
||||
QuaPy implements sampling procedures and evaluation protocols that automate this workflow.
|
||||
See the [documentation](https://hlt-isti.github.io/QuaPy/manuals.html) for detailed examples.
|
||||
Quantification is useful in scenarios of prior probability shift. In other
|
||||
words, we would not be interested in estimating the class prevalences of the test set if
|
||||
we could assume the IID assumption to hold, as this prevalence would simply coincide with the
|
||||
class prevalence of the training set. For this reason, any Quantification model
|
||||
should be tested across samples characterized by different class prevalences.
|
||||
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
|
||||
See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
|
||||
|
||||
## Features
|
||||
|
||||
* Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization,
|
||||
quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
|
||||
* Support for uncertainty quantification via bootstrap-based and Bayesian methods, including confidence intervals and simplex-aware confidence regions.
|
||||
* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
|
||||
* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
|
||||
* Datasets frequently used in quantification (textual and numeric), including:
|
||||
* Implementation of most popular quantification methods (Classify-&-Count variants, Expectation-Maximization,
|
||||
SVM-based variants for quantification, HDy, QuaNet, and Ensembles).
|
||||
* Versatile functionality for performing evaluation based on artificial sampling protocols.
|
||||
* Implementation of most commonly used evaluation metrics (e.g., MAE, MRAE, MSE, NKLD, etc.).
|
||||
* Popular datasets for Quantification (textual and numeric) available, including:
|
||||
* 32 UCI Machine Learning datasets.
|
||||
* 11 Twitter quantification-by-sentiment datasets.
|
||||
* 3 product reviews quantification-by-sentiment datasets.
|
||||
* 4 tasks from LeQua 2022 competition and 4 tasks from LeQua 2024 competition
|
||||
* IFCB for Plancton quantification
|
||||
* Native support for binary and single-label multiclass quantification scenarios.
|
||||
* Model selection functionality that minimizes quantification-oriented loss functions.
|
||||
* Visualization tools for analysing the experimental results.
|
||||
* 11 Twitter Sentiment datasets.
|
||||
* 3 Reviews Sentiment datasets.
|
||||
* Native supports for binary and single-label scenarios of quantification.
|
||||
* Model selection functionality targeting quantification-oriented losses.
|
||||
* Visualization tools for analysing results.
|
||||
|
||||
## Requirements
|
||||
|
||||
|
|
@ -98,29 +74,38 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
|
|||
* pandas, xlrd
|
||||
* matplotlib
|
||||
|
||||
## Contributing
|
||||
## SVM-perf with quantification-oriented losses
|
||||
In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
|
||||
SVM(AE), or SVM(RAE), you have to first download the
|
||||
[svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
|
||||
package, apply the patch
|
||||
[svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch), and compile the sources.
|
||||
The script [prepare_svmperf.sh](prepare_svmperf.sh) does all the job. Simply run:
|
||||
|
||||
In case you want to contribute improvements to quapy, please generate pull request to the "devel" branch.
|
||||
```
|
||||
./prepare_svmperf.sh
|
||||
```
|
||||
|
||||
The resulting directory [svm_perf_quantification](./svm_perf_quantification) contains the
|
||||
patched version of _svmperf_ with quantification-oriented losses.
|
||||
|
||||
The [svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch) is an extension of the patch made available by
|
||||
[Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0)
|
||||
that allows SVMperf to optimize for
|
||||
the _Q_ measure as proposed by [Barranquero et al. 2015](https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X)
|
||||
and for the _KLD_ and _NKLD_ as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0)
|
||||
for quantification.
|
||||
This patch extends the former by also allowing SVMperf to optimize for
|
||||
_AE_ and _RAE_.
|
||||
|
||||
## Documentation
|
||||
|
||||
## Wiki
|
||||
|
||||
Check out the [developer API documentation here](https://hlt-isti.github.io/QuaPy/index.html).
|
||||
|
||||
Check out the [Manuals](https://hlt-isti.github.io/QuaPy/manuals.html), in which many code examples
|
||||
Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) in which many examples
|
||||
are provided:
|
||||
|
||||
* [Datasets](https://hlt-isti.github.io/QuaPy/manuals/datasets.html)
|
||||
* [Evaluation](https://hlt-isti.github.io/QuaPy/manuals/evaluation.html)
|
||||
* [Protocols](https://hlt-isti.github.io/QuaPy/manuals/protocols.html)
|
||||
* [Methods](https://hlt-isti.github.io/QuaPy/manuals/methods.html)
|
||||
* [SVMperf](https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html)
|
||||
* [Model Selection](https://hlt-isti.github.io/QuaPy/manuals/model-selection.html)
|
||||
* [Plotting](https://hlt-isti.github.io/QuaPy/manuals/plotting.html)
|
||||
|
||||
## Acknowledgments:
|
||||
|
||||
<img src="docs/source/SoBigData.png" alt="SoBigData++" width="250"/>
|
||||
|
||||
This work has been supported by the QuaDaSh project
|
||||
_"Finanziato dall’Unione europea---Next Generation EU,
|
||||
Missione 4 Componente 2 CUP B53D23026250001"_.
|
||||
* [Datasets](https://github.com/HLT-ISTI/QuaPy/wiki/Datasets)
|
||||
* [Evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
|
||||
* [Methods](https://github.com/HLT-ISTI/QuaPy/wiki/Methods)
|
||||
* [Model Selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
|
||||
* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)
|
||||
91
TODO.txt
91
TODO.txt
|
|
@ -1,24 +1,75 @@
|
|||
Solve the warnings issue; right now there is a warning ignore in method/__init__.py:
|
||||
Packaging:
|
||||
==========================================
|
||||
Documentation with sphinx
|
||||
Document methods with paper references
|
||||
unit-tests
|
||||
clean wiki_examples!
|
||||
|
||||
Add 'platt' to calib options in EMQ?
|
||||
Refactor:
|
||||
==========================================
|
||||
Unify ThresholdOptimization methods, as an extension of PACC (and not ACC), the fit methods are almost identical and
|
||||
use a prob classifier (take into account that PACC uses pcc internally, whereas the threshold methods use cc
|
||||
instead). The fit method of ACC and PACC has a block for estimating the validation estimates that should be unified
|
||||
as well...
|
||||
Rename APP NPP
|
||||
Add NPP as an option for GridSearchQ
|
||||
|
||||
Allow n_prevpoints in APP to be specified by a user-defined grid?
|
||||
New features:
|
||||
==========================================
|
||||
Add NAE, NRAE
|
||||
Add "measures for evaluating ordinal"?
|
||||
Add datasets for topic.
|
||||
Do we want to cover cross-lingual quantification natively in QuaPy, or does it make more sense as an application on top?
|
||||
|
||||
Current issues:
|
||||
==========================================
|
||||
SVMperf-based learners do not remove temp files in __del__?
|
||||
In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
|
||||
negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
|
||||
an instance of single-label with 2 labels. Check
|
||||
Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
|
||||
OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
|
||||
Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
|
||||
Add random seed management to support replicability (see temp_seed in util.py).
|
||||
GridSearchQ is not trully parallelized. It only parallelizes on the predictions.
|
||||
In the context of a quantifier (e.g., QuaNet or CC), the parameters of the learner should be prefixed with "estimator__",
|
||||
in QuaNet this is resolved with a __check_params_colision, but this should be improved. It might be cumbersome to
|
||||
impose the "estimator__" prefix for, e.g., quantifiers like CC though... This should be changed everywhere...
|
||||
QuaNet needs refactoring. The base quantifiers ACC and PACC receive val_data with instances already transformed. This
|
||||
issue is due to a bad design.
|
||||
|
||||
Improvements:
|
||||
==========================================
|
||||
Explore the hyperparameter "number of bins" in HDy
|
||||
Rename EMQ to SLD ?
|
||||
Parallelize the kFCV in ACC and PACC?
|
||||
Parallelize model selection trainings
|
||||
We might want to think of (improving and) adding the class Tabular (it is defined and used on branch tweetsent). A more
|
||||
recent version is in the project ql4facct. This class is meant to generate latex tables from results (highligting
|
||||
best results, computing statistical tests, colouring cells, producing rankings, producing averages, etc.). Trying
|
||||
to generate tables is typically a bad idea, but in this specific case we do have pretty good control of what an
|
||||
experiment looks like. (Do we want to abstract experimental results? this could be useful not only for tables but
|
||||
also for plots).
|
||||
Add proper logging system. Currently we use print
|
||||
It might be good to simplify the number of methods that have to be implemented for any new Quantifier. At the moment,
|
||||
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.
|
||||
|
||||
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
|
||||
to one always?
|
||||
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.
|
||||
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.
|
||||
Would be nice to get a better integration with sklearn.
|
||||
|
||||
Add the fix suggested by Alexander?
|
||||
"For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
|
||||
based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
|
||||
positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
|
||||
the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
|
||||
we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
|
||||
scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
|
||||
- This affects LabelledCollection
|
||||
- This functionality should be accessible via sampling protocols and evaluation functions
|
||||
|
||||
- [TODO] document confidence in manuals
|
||||
- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
|
||||
- [TODO] add HistNetQ
|
||||
- [TODO] add CDE-iteration and Bayes-CDE methods
|
||||
- [TODO] add Friedman's method and DeBias
|
||||
- [TODO] check ignore warning stuff
|
||||
check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
|
||||
- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string
|
||||
|
|
@ -1 +0,0 @@
|
|||
build/
|
||||
|
|
@ -1 +0,0 @@
|
|||
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line, and also
|
||||
# from the environment for the first two.
|
||||
SPHINXOPTS ?=
|
||||
SPHINXBUILD ?= sphinx-build
|
||||
SOURCEDIR = source
|
||||
BUILDDIR = build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
|
@ -1,464 +0,0 @@
|
|||
|
||||
<!DOCTYPE html>
|
||||
|
||||
|
||||
<html lang="en" data-content_root="../" >
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Overview: module code — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
<script data-cfasync="false">
|
||||
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
|
||||
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
|
||||
</script>
|
||||
<!--
|
||||
this give us a css class that will be invisible only if js is disabled
|
||||
-->
|
||||
<noscript>
|
||||
<style>
|
||||
.pst-js-only { display: none !important; }
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</noscript>
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<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=8f2a1f02" />
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<link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=90905a2f556bf617f1a9" />
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<script src="../_static/documentation_options.js?v=37f418d5"></script>
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<script>DOCUMENTATION_OPTIONS.pagename = '_modules/index';</script>
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<script>DOCUMENTATION_OPTIONS.search_as_you_type = false;</script>
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<link rel="index" title="Index" href="../genindex.html" />
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<link rel="search" title="Search" href="../search.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docsearch:language" content="en"/>
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<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
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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="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </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="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </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">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelEncoder</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils.validation</span><span class="w"> </span><span class="kn">import</span> <span class="n">check_X_y</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </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>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_require_abstention_calibration</span><span class="p">():</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">abstention.calibration</span><span class="w"> </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="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
|
||||
<span class="s2">"Calibration methods in quapy.classification.calibration require the optional "</span>
|
||||
<span class="s2">"'abstention' package."</span>
|
||||
<span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
|
||||
<span class="k">return</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</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="w"> </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="w"> </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="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</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="w"> </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="w"> </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="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</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="w"> </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="w"> </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="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</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>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TemperatureScalingFromLogits">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">TemperatureScalingFromLogits</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"> Calibrates a matrix of logits by learning a temperature-scaling mapping</span>
|
||||
<span class="sd"> with the calibration methods from `abstention.calibration`.</span>
|
||||
|
||||
<span class="sd"> This estimator is useful when the inputs are already logits produced by a</span>
|
||||
<span class="sd"> pretrained classifier, and the goal is to transform them directly into</span>
|
||||
<span class="sd"> calibrated posterior probabilities without retraining the underlying model.</span>
|
||||
|
||||
<span class="sd"> :param bias_corrected: if True, uses Bias-Corrected Temperature Scaling</span>
|
||||
<span class="sd"> (BCTS); otherwise, uses standard Temperature Scaling (TS)</span>
|
||||
<span class="sd"> :param verbose: whether the underlying calibrator should display progress</span>
|
||||
<span class="sd"> information</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bias_corrected</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">bias_corrected</span> <span class="o">=</span> <span class="n">bias_corrected</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="TemperatureScalingFromLogits.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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 logits calibrator.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
|
||||
<span class="sd"> logits</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)` containing class labels</span>
|
||||
<span class="sd"> :return: self</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">check_X_y</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">label_encoder_</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
|
||||
<span class="n">y_enc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">fit_transform</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">label_encoder_</span><span class="o">.</span><span class="n">classes_</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">logits_dim</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">if</span> <span class="n">n_classes</span> <span class="o">!=</span> <span class="n">logits_dim</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">'mismatch between the number of classes (</span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">) and the '</span>
|
||||
<span class="sa">f</span><span class="s1">'dimensionality of the logits (</span><span class="si">{</span><span class="n">logits_dim</span><span class="si">}</span><span class="s1">)'</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</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="bp">self</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="k">else</span> <span class="p">[],</span>
|
||||
<span class="p">)</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">calibration_function_</span> <span class="o">=</span> <span class="n">calibrator</span><span class="p">(</span><span class="n">X</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">n_classes</span><span class="p">)[</span><span class="n">y_enc</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict_proba">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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"> Converts logits into calibrated posterior probabilities.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
|
||||
<span class="sd"> logits</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with calibrated</span>
|
||||
<span class="sd"> posterior probabilities</span>
|
||||
<span class="sd"> """</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">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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 after calibration.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
|
||||
<span class="sd"> logits</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_samples,)` with class label</span>
|
||||
<span class="sd"> predictions</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">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">label_encoder_</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</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></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
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<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">TruncatedSVD</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MockClassifierFromPosteriors</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"> Mock classifier that bypasses classifier training when the input instances</span>
|
||||
<span class="sd"> are already posterior probabilities produced by a pretrained probabilistic</span>
|
||||
<span class="sd"> classifier.</span>
|
||||
|
||||
<span class="sd"> :param X: arrays of shape `(n_samples, n_classes)` are interpreted as posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="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">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="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">X</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="MockClassifierFromPosteriors.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict_proba">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="k">return</span> <span class="n">X</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
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<article class="bd-article">
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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="w"> </span><span class="nn">logging</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">subprocess</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">os</span><span class="w"> </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="w"> </span><span class="nn">os.path</span><span class="w"> </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="w"> </span><span class="nn">subprocess</span><span class="w"> </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="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </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="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </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="w"> </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="w"> </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="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="sa">f</span><span class="s1">'did you install svm-perf? '</span>
|
||||
<span class="sa">f</span><span class="s1">'see instructions in https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html'</span><span class="p">)</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>
|
||||
|
||||
<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="w"> </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="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="n">tempfile</span><span class="o">.</span><span class="n">gettempdir</span><span class="p">(),</span> <span class="n">random_code</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">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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">'</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">PIPE</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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">error</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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</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="w"> </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="w"> </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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">'</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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</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="w"> </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>
|
||||
|
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</article>
|
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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>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
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|
||||
<footer>
|
||||
|
||||
<hr/>
|
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|
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<p>© Copyright 2024, Alejandro Moreo.</p>
|
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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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>
|
||||
|
||||
|
||||
|
||||
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|
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|
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<h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">spmatrix</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </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="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">map_parallel</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="instance_transformation">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.instance_transformation">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">instance_transformation</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">transformer</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"> Transforms a :class:`quapy.data.base.Dataset` applying the `fit_transform` and `transform` functions</span>
|
||||
<span class="sd"> of a (sklearn's) transformer.</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 transformer: TransformerMixin implementing `fit_transform` and `transform` functions</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` with transformed instances (if inplace=False) or a reference to the</span>
|
||||
<span class="sd"> current Dataset (if inplace=True) where the instances have been transformed</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">training_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</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">Xy</span><span class="p">)</span>
|
||||
<span class="n">test_transformed</span> <span class="o">=</span> <span class="n">transformer</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">X</span><span class="p">)</span>
|
||||
<span class="n">orig_name</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</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_transformed</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_transformed</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="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">'vocabulary_'</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">transformer</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_transformed</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_transformed</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="n">vocab</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">'vocabulary_'</span><span class="p">):</span>
|
||||
<span class="n">vocab</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</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">vocabulary</span><span class="o">=</span><span class="n">vocab</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">orig_name</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<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="w"> </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="k">return</span> <span class="n">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">inplace</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="w"> </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="w"> </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="w"> </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">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test</span>
|
||||
<span class="n">std_train_X</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">train</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">std_test_X</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">test</span><span class="o">.</span><span class="n">X</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">std_train_X</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">std_test_X</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">std_train_X</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="n">classes</span><span class="o">=</span><span class="n">train</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">std_test_X</span><span class="p">,</span> <span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</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="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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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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</div>
|
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|
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<article class="bd-article">
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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="w"> </span><span class="nn">logging</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">dok_matrix</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </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="w"> </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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">warning</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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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<h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </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="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </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="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </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="w"> </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="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </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">predict</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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>
|
||||
|
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|
||||
|
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|
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<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Real</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KernelDensity</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeSoftQuantifier</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.special</span><span class="w"> </span><span class="kn">import</span> <span class="n">logsumexp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics.pairwise</span><span class="w"> </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="w"> </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="n">KERNELS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">]</span>
|
||||
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span><span class="w"> </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="n">kernel</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: the bandwidth if the check is passed, or 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="n">Real</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="n">Real</span><span class="p">):</span>
|
||||
<span class="n">bandwidth</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">bandwidth</span>
|
||||
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_kernel</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">KERNELS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'unknown </span><span class="si">{</span><span class="n">kernel</span><span class="si">=}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="n">kernel</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="w"> </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="n">kernel</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"> :param kernel: the kernel family</span>
|
||||
<span class="sd"> :return: a scikit-learn's KernelDensity object</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_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="n">bandwidth</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">effective_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</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="w"> </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="n">kernel</span><span class="p">,</span> <span class="n">log_densities</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"> 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"> :param kernel: the kernel family</span>
|
||||
<span class="sd"> :return: np.ndarray with the densities</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_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="n">log_density</span> <span class="o">=</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>
|
||||
<span class="k">if</span> <span class="n">log_densities</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">log_density</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">log_density</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="w"> </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">classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</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"> :param kernel: the kernel family</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="n">class_cond_X</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
|
||||
<span class="n">selX</span> <span class="o">=</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">if</span> <span class="n">selX</span><span class="o">.</span><span class="n">size</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">'empty class </span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">class_cond_X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selX</span><span class="p">)</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_cond_yi</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">X_cond_yi</span> <span class="ow">in</span> <span class="n">class_cond_X</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.transform_posteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.transform_posteriors">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">transform_posteriors</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">kernel</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</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">shrink_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">'aitchison'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">'ilr'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">X</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.shrink_posteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.shrink_posteriors">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">shrink_posteriors</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">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'shrinkage'</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">shrinkage</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">X</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">n_classes</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="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">uniform</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">n_classes</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span> <span class="o">+</span> <span class="n">shrinkage</span> <span class="o">*</span> <span class="n">uniform</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.effective_bandwidth">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.effective_bandwidth">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">effective_bandwidth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'shrinkage'</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">shrinkage</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">}</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">bandwidth</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.clr_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.clr_transform">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">clr_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="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'clr'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">clr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">CLRtransformation</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.ilr_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.ilr_transform">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">ilr_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="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ilr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">ILRtransformation</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr</span><span class="p">(</span><span class="n">X</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="w"> </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://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param kernel: kernel of KDE, valid ones are in KDEBase.KERNELS</span>
|
||||
<span class="sd"> :param shrinkage: amount of shrinkage towards the uniform distribution to apply before</span>
|
||||
<span class="sd"> Aitchison/ILR transformations. Must be in ``[0,1)``.</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="w"> </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="o">=</span><span class="kc">None</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="n">val_split</span><span class="o">=</span><span class="mi">5</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">kernel</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="mf">0.0</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="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">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</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="n">kernel</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">shrinkage</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'shrinkage must be in [0,1)'</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">'gaussian'</span> <span class="ow">or</span> <span class="n">shrinkage</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> \
|
||||
<span class="s1">'shrinkage is only supported for Aitchison/ILR kernels'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">shrinkage</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="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="w"> </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">labels</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="n">classif_predictions</span><span class="p">,</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">bandwidth</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kernel</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="KDEyML.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="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">epsilon</span> <span class="o">=</span> <span class="mf">1e-12</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="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">'gaussian'</span> <span class="ow">and</span> <span class="n">n_classes</span> <span class="o">>=</span> <span class="mi">20</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n_classes</span> <span class="o">>=</span> <span class="mi">30</span><span class="p">:</span>
|
||||
<span class="n">test_log_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="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">True</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="w"> </span><span class="nf">neg_loglikelihood</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">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">smooth</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">epsilon</span><span class="p">)</span>
|
||||
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">logsumexp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prev</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">test_log_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="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">else</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="bp">self</span><span class="o">.</span><span class="n">kernel</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="w"> </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="n">prev</span> <span class="o">@</span> <span class="n">test_densities</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="w"> </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://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</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="w"> </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="o">=</span><span class="kc">None</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="n">val_split</span><span class="o">=</span><span class="mi">5</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">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="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">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</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">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="n">kernel</span><span class="o">=</span><span class="s1">'gaussian'</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="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="w"> </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">labels</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="n">classif_predictions</span><span class="p">,</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">bandwidth</span><span class="p">,</span> <span class="s1">'gaussian'</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="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="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="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="s1">'gaussian'</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="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="w"> </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="s1">'gaussian'</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="s1">'gaussian'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="w"> </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="w"> </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://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="n">val_split</span><span class="o">=</span><span class="mi">5</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="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">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</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="n">kernel</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">)</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="w"> </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="w"> </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">labels</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="p">,</span> <span class="n">labels</span>
|
||||
<span class="n">n</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">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</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="p">)</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">F</span><span class="o">.</span><span class="n">counts_from_labels</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="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="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="w"> </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="w"> </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"># recall 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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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">quapy.method._neural</span></li>
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<article class="bd-article">
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<h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="kn">import</span> <span class="n">MSELoss</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">relu</span>
|
||||
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">UPP</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">EarlyStop</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </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="w"> </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.Xy)</span>
|
||||
<span class="sd"> >>> estim_prevalence = model.predict(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 fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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="w"> </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">fit_classifier</span><span class="o">=</span><span class="kc">True</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">fit_classifier</span> <span class="o">=</span> <span class="n">fit_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="w"> </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 QuaNet.</span>
|
||||
|
||||
<span class="sd"> :param X: the training instances 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 y: the labels of X</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</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="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="bp">self</span><span class="o">.</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">debug</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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</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="w"> </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.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="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="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">X</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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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<article class="bd-article">
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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="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">abstractmethod</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="n">val_split</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="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">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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">FN</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FN</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">FN</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="w"> </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">labels</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="p">,</span> <span class="n">labels</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="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="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">fit_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="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="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">fit_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="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="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">fit_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="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="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">fit_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="w"> </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="w"> </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">labels</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="p">,</span> <span class="n">labels</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="w"> </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="w"> </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 scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</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 tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="o">=</span><span class="kc">None</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="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">fit_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="w"> </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>
|
||||
|
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<article class="bd-article">
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<h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </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="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </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="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </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="w"> </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"> a pair X, y, the method :meth:`predict`, 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="w"> </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"> Generates a quantifier.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like, the training instances</span>
|
||||
<span class="sd"> :param y: array-like, the labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.predict">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span><span class="w"> </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"> Generate class prevalence estimates for the sample's instances</span>
|
||||
|
||||
<span class="sd"> :param X: array-like, the test instances</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 class="viewcode-block" id="BaseQuantifier.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</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"> Alias to :meth:`predict`, for old compatibility</span>
|
||||
|
||||
<span class="sd"> :param X: array-like</span>
|
||||
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</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>
|
||||
|
||||
|
||||
|
||||
<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="w"> </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="w"> </span><span class="nf">_check_binary</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">quantifier_name</span><span class="p">):</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">y</span><span class="p">))</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="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="w"> </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="w"> </span><span class="nf">newOneVsAll</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="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="w"> </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 prevalence values sum up to 1.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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">BaseQuantifier</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="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'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="w"> </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="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="k">assert</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="o">!=</span><span class="mi">2</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="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="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">_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">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </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.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="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">X</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="c1"># @property</span>
|
||||
<span class="c1"># def classes_(self):</span>
|
||||
<span class="c1"># return sorted(self.dict_binary_quantifiers.keys())</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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">predict</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="w"> </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">X</span><span class="p">,</span> <span class="n">y</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">X</span><span class="p">,</span> <span class="n">y</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="o">*</span><span class="n">bindata</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span></div>
|
||||
|
||||
</pre></div>
|
||||
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">quapy.method.non_aggregative</span></li>
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<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </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">Counter</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">normalize</span>
|
||||
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.confidence</span><span class="w"> </span><span class="kn">import</span> <span class="n">WithConfidenceABC</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_divergence</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </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="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._energy</span><span class="w"> </span><span class="kn">import</span> <span class="n">_EnergyDistanceCore</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.optimize</span><span class="w"> </span><span class="kn">import</span> <span class="n">lsq_linear</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</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="w"> </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="w"> </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="w"> </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"> Computes the training prevalence and stores it.</span>
|
||||
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</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">F</span><span class="o">.</span><span class="n">classes_from_labels</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="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_labels</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="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
|
||||
|
||||
<span class="sd"> :param X: 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="w"> </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 search: string indicating the search strategy used to estimate the prevalence values.</span>
|
||||
<span class="sd"> Valid options are `optim_minimize` (default, works for binary and multiclass problems),</span>
|
||||
<span class="sd"> `linear_search` (binary only), and `ternary_search` (binary only)</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="w"> </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="w"> </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="w"> </span><span class="nn">quapy.method.meta</span><span class="w"> </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="w"> </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="w"> </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"> 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 X: array-like of shape `(n_samples, n_features)`, the training instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</span>
|
||||
<span class="sd"> """</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="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">y</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</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">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">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.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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"> 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 X: 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">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="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">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="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">X</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="w"> </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>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EDx">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">EDx</span><span class="p">(</span><span class="n">_EnergyDistanceCore</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"> Energy Distance x (EDx), a covariate-space distribution-matching</span>
|
||||
<span class="sd"> quantifier based on energy distance.</span>
|
||||
|
||||
<span class="sd"> EDx is the classifier-free counterpart of :class:`quapy.method.aggregative.EDy`.</span>
|
||||
<span class="sd"> Instead of representing each class through posterior-probability vectors, it</span>
|
||||
<span class="sd"> represents each class by the cloud of raw feature vectors observed in the</span>
|
||||
<span class="sd"> training set and estimates the test prevalence vector by solving the same</span>
|
||||
<span class="sd"> energy-distance quadratic program directly in feature space.</span>
|
||||
|
||||
<span class="sd"> This implementation works for binary and multiclass single-label</span>
|
||||
<span class="sd"> quantification and relies on the optional ``quadprog`` dependency. The</span>
|
||||
<span class="sd"> current QuaPy adaptation shares its numerical core with EDy and keeps</span>
|
||||
<span class="sd"> credit to the original implementation available in</span>
|
||||
<span class="sd"> `quantificationlib <https://github.com/AICGijon/quantificationlib>`_.</span>
|
||||
|
||||
<span class="sd"> The formulation follows the same references as EDy, namely:</span>
|
||||
|
||||
<span class="sd"> * Alberto Castaño, Laura Morán-Fernández, Jaime Alonso,</span>
|
||||
<span class="sd"> Verónica Bolón-Canedo, Amparo Alonso-Betanzos, and Juan José del Coz.</span>
|
||||
<span class="sd"> *An analysis of quantification methods based on matching distributions*.</span>
|
||||
<span class="sd"> * Hideko Kawakubo, Marthinus Christoffel du Plessis, and Masashi Sugiyama</span>
|
||||
<span class="sd"> (2016). *Computationally efficient class-prior estimation under class</span>
|
||||
<span class="sd"> balance change using energy distance*. IEICE Transactions on Information</span>
|
||||
<span class="sd"> and Systems, 99(1):176-186.</span>
|
||||
|
||||
<span class="sd"> :param distance: distance used to compare feature vectors. Valid string</span>
|
||||
<span class="sd"> aliases are ``'manhattan'`` (default) and ``'euclidean'``; a custom</span>
|
||||
<span class="sd"> callable compatible with pairwise-distance signatures can also be used</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers (default ``None``, meaning the</span>
|
||||
<span class="sd"> value is taken from the environment)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">distance</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">'manhattan'</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">distance</span> <span class="o">=</span> <span class="n">distance</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">classes_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">train_distrib_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">train_n_cls_i_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">K_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">G_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">C_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">b_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">a_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="EDx.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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">"""Fit class-conditional feature-space distributions from training data."""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_ed_init_parameters</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">asarray</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="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="bp">self</span><span class="o">.</span><span class="n">n_features_in_</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">train_distrib</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">[</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>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_energy_model</span><span class="p">(</span><span class="n">train_distrib</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EDx.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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">"""Estimate class prevalences for a test sample of raw instances."""</span>
|
||||
<span class="k">assert</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="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="p">,</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">n_features_in_</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</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="si">}</span><span class="s1">'</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict_energy</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> ReadMe is a non-aggregative quantification system proposed by</span>
|
||||
<span class="sd"> `Daniel Hopkins and Gary King, 2007. A method of automated nonparametric content analysis for</span>
|
||||
<span class="sd"> social science. American Journal of Political Science, 54(1):229–247.</span>
|
||||
<span class="sd"> <https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00428.x>`_.</span>
|
||||
<span class="sd"> The idea is to estimate `Q(Y=i)` directly from:</span>
|
||||
|
||||
<span class="sd"> :math:`Q(X)=\\sum_{i=1} Q(X|Y=i) Q(Y=i)`</span>
|
||||
|
||||
<span class="sd"> via least-squares regression, i.e., without incurring the cost of computing posterior probabilities.</span>
|
||||
<span class="sd"> However, this poses a very difficult representation in which the vector `Q(X)` and the matrix `Q(X|Y=i)`</span>
|
||||
<span class="sd"> can be of very high dimensions. In order to render the problem tracktable, ReadMe performs bagging in</span>
|
||||
<span class="sd"> the feature space. ReadMe also combines bagging with bootstrap in order to derive confidence intervals</span>
|
||||
<span class="sd"> around point estimations.</span>
|
||||
|
||||
<span class="sd"> We use the same default parameters as in the official</span>
|
||||
<span class="sd"> `R implementation <https://github.com/iqss-research/ReadMeV1/blob/master/R/prototype.R>`_.</span>
|
||||
|
||||
<span class="sd"> :param prob_model: str ('naive', or 'full'), selects the modality in which the probabilities `Q(X)` and</span>
|
||||
<span class="sd"> `Q(X|Y)` are to be modelled. Options include "full", which corresponds to the original formulation of</span>
|
||||
<span class="sd"> ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which</span>
|
||||
<span class="sd"> `Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where</span>
|
||||
<span class="sd"> `K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.</span>
|
||||
<span class="sd"> Of course, this approach is computationally prohibited for large `K`, so the authors advised against computing it</span>
|
||||
<span class="sd"> for matrices with `K>25` (although we recommend even smaller values of `K`). A much faster model is "naive", which</span>
|
||||
<span class="sd"> considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this</span>
|
||||
<span class="sd"> case, `bagging_range` can be set to much larger values. Default is "full" (i.e., original ReadMe behavior).</span>
|
||||
<span class="sd"> :param bootstrap_trials: int, number of bootstrap trials (default 300)</span>
|
||||
<span class="sd"> :param bagging_trials: int, number of bagging trials (default 300)</span>
|
||||
<span class="sd"> :param bagging_range: int, number of features to keep for each bagging trial (default 15)</span>
|
||||
<span class="sd"> :param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)</span>
|
||||
<span class="sd"> :param region: str in 'intervals', 'ellipse', 'ellipse-clr'; indicates the preferred method for</span>
|
||||
<span class="sd"> defining the confidence region (see :class:`WithConfidenceABC`)</span>
|
||||
<span class="sd"> :param bonferroni: bool (default False), whether to apply Bonferroni correction when</span>
|
||||
<span class="sd"> `region='intervals'`. This parameter has no effect for ellipse-based regions.</span>
|
||||
<span class="sd"> :param random_state: int or None, allows replicability (default None)</span>
|
||||
<span class="sd"> :param verbose: bool, whether to display information during the process (default False)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span> <span class="o">=</span> <span class="mi">25</span>
|
||||
<span class="n">PROBABILISTIC_MODELS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"naive"</span><span class="p">,</span> <span class="s2">"full"</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">prob_model</span><span class="o">=</span><span class="s2">"full"</span><span class="p">,</span>
|
||||
<span class="n">bootstrap_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
|
||||
<span class="n">bagging_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
|
||||
<span class="n">bagging_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
|
||||
<span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
|
||||
<span class="n">region</span><span class="o">=</span><span class="s1">'intervals'</span><span class="p">,</span>
|
||||
<span class="n">bonferroni</span><span class="o">=</span><span class="kc">False</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">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">prob_model</span> <span class="ow">in</span> <span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unknown </span><span class="si">{</span><span class="n">prob_model</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">=</span> <span class="n">prob_model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span> <span class="o">=</span> <span class="n">bootstrap_trials</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span> <span class="o">=</span> <span class="n">bagging_trials</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span> <span class="o">=</span> <span class="n">bagging_range</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span> <span class="o">=</span> <span class="n">confidence_level</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span> <span class="o">=</span> <span class="n">bonferroni</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">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="bp">self</span><span class="o">.</span><span class="n">_check_matrix</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">rng</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">default_rng</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="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">y</span><span class="p">)</span>
|
||||
|
||||
<span class="n">Xsize</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">0</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># Bootstrap loop</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span> <span class="o">=</span> <span class="p">[],</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">bootstrap_trials</span><span class="p">):</span>
|
||||
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">replace</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">Xboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.predict_conf">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict_conf">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict_conf</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">confidence_level</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></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">ConfidenceRegionABC</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">confidence_level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">confidence_level</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span>
|
||||
|
||||
<span class="n">n_features</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">boots_prevalences</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">Xboots</span><span class="p">,</span> <span class="n">yboots</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
|
||||
<span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="p">),</span>
|
||||
<span class="n">desc</span><span class="o">=</span><span class="s1">'bootstrap predictions'</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">bootstrap_trials</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span>
|
||||
<span class="p">):</span>
|
||||
<span class="n">bagging_estimates</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">bagging_trials</span><span class="p">):</span>
|
||||
<span class="n">feat_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_features</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">bagging_range</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">Xboots_bagging</span> <span class="o">=</span> <span class="n">Xboots</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
|
||||
<span class="n">X_boots_bagging</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">bagging_prev</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quantify_iteration</span><span class="p">(</span><span class="n">Xboots_bagging</span><span class="p">,</span> <span class="n">yboots</span><span class="p">,</span> <span class="n">X_boots_bagging</span><span class="p">)</span>
|
||||
<span class="n">bagging_estimates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bagging_prev</span><span class="p">)</span>
|
||||
|
||||
<span class="n">boots_prevalences</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">bagging_estimates</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">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</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">region</span><span class="p">,</span> <span class="n">bonferroni</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span><span class="p">)</span>
|
||||
<span class="n">prev_estim</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">point_estimate</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">prev_estim</span><span class="p">,</span> <span class="n">conf</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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="n">prev_estim</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">predict_conf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prev_estim</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</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">Xte</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Single ReadMe estimate."""</span>
|
||||
<span class="n">PX_given_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="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xtr</span><span class="p">[</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</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">PX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xte</span><span class="p">)</span>
|
||||
|
||||
<span class="n">res</span> <span class="o">=</span> <span class="n">lsq_linear</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="n">PX_given_Y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">PX</span><span class="p">,</span> <span class="n">bounds</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">pY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">pY</span> <span class="o">/</span> <span class="n">pY</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_matrix</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">"""the "full" model requires estimating empirical distributions; due to the high computational cost,</span>
|
||||
<span class="sd"> this function is only made available for binary matrices"""</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">'full'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_binary_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'the empirical distribution can only be computed efficiently on binary matrices'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_is_binary_matrix</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">data</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">data</span> <span class="k">if</span> <span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">else</span> <span class="n">X</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="n">data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_P</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">'naive'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multinomial_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">'full'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_empirical_distribution</span><span class="p">(</span><span class="n">X</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">'unknown </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span><span class="si">}</span><span class="s1">; valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_empirical_distribution</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="k">if</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="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</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 empirical distribution can only be computed efficiently for dimensions '</span>
|
||||
<span class="sa">f</span><span class="s1">'less or equal than </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># we first convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5);</span>
|
||||
<span class="c1"># this will speed up subsequent comparisons a lot</span>
|
||||
<span class="n">K</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">binary_powers</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">arange</span><span class="p">(</span><span class="n">K</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="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
|
||||
<span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span> <span class="c1"># e.g., [0 0 1 0 1] @ [16, 8, 4, 2, 1] = 5</span>
|
||||
|
||||
<span class="c1"># count occurrences and compute probs</span>
|
||||
<span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">X_as_binary_numbers</span><span class="p">,</span> <span class="n">minlength</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="n">K</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">probs</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">probs</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_multinomial_distribution</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">PX</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="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="n">PX</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">PX</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</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">PX</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </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">HDx</span> <span class="o">=</span> <span class="n">DMx</span><span class="o">.</span><span class="n">HDx</span>
|
||||
<span class="n">DistributionMatchingX</span> <span class="o">=</span> <span class="n">DMx</span>
|
||||
<span class="n">EnergyDistanceX</span> <span class="o">=</span> <span class="n">EDx</span>
|
||||
<span class="n">HellingerDistanceX</span> <span class="o">=</span> <span class="n">HDx</span>
|
||||
</pre></div>
|
||||
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">quapy.model_selection</span></li>
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<div id="searchbox"></div>
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<article class="bd-article">
|
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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="w"> </span><span class="nn">itertools</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </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="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">wraps</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">clone</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluation</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </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="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </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="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">timeout</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </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="w"> </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="w"> </span><span class="nc">ConfigStatus</span><span class="p">:</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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_measure</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="w"> </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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</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="w"> </span><span class="nf">__check_error_measure</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="w"> </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="w"> </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_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</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="w"> </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="w"> </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">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">predictions</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">y</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="w"> </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="w"> </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_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</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="w"> </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="w"> </span><span class="nf">_compute_scores_aggregative</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="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_X</span> <span class="o">=</span> <span class="n">X</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</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="n">asarray</span><span class="o">=</span><span class="kc">False</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="w"> </span><span class="nf">_compute_scores_nonaggregative</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">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_X</span> <span class="o">=</span> <span class="n">X</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</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="w"> </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="w"> </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">""" 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 X: array-like, training covariates</span>
|
||||
<span class="sd"> :param y: array-like, labels of training data</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">X</span><span class="p">,</span> <span class="n">y</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">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">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="bp">self</span><span class="o">.</span><span class="n">fit_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="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="bp">self</span><span class="o">.</span><span class="n">fit_time_</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="n">validation_collection</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">training_collection</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">classes</span><span class="o">=</span><span class="n">validation_collection</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
|
||||
<span class="n">devel_collection</span> <span class="o">=</span> <span class="n">training_collection</span> <span class="o">+</span> <span class="n">validation_collection</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="o">*</span><span class="n">devel_collection</span><span class="o">.</span><span class="n">Xy</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.predict">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </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">"""Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
|
||||
|
||||
<span class="sd"> :param X: 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">predict</span><span class="p">(</span><span class="n">X</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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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="n">msg</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">exception</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="w"> </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="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</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">predict</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="w"> </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="w"> </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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<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>
|
||||
|
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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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<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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<h1>Source code for quapy.tests.test_labelcollection</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">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>
|
||||
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|
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<h1>Source code for quapy.tests.test_methods</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">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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|
||||
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|
||||
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||||
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<li class="breadcrumb-item active">quapy.tests.test_protocols</li>
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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>
|
||||
</pre></div>
|
||||
|
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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="w"> </span><span class="nn">contextlib</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">pickle</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">urllib</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </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="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </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="w"> </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="w"> </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"> :param open_args: if True, then the delayed function is called on *args_i, instead of on args_i</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span><span class="w"> </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="parallel_unpack">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel_unpack">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">parallel_unpack</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="w"> </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="o">*</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="w"> </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="w"> </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="w"> </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="w"> </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"> Downloads a file (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="w"> </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="w"> </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="w"> </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="w"> </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">path</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="w"> </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="nb">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">with</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">as</span> <span class="n">fin</span><span class="p">:</span>
|
||||
<span class="n">instance</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fin</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="k">with</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="k">as</span> <span class="n">foo</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="n">foo</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="w"> </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="load_report">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.load_report">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">load_report</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">as_dict</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">str2prev_arr</span><span class="p">(</span><span class="n">strprev</span><span class="p">):</span>
|
||||
<span class="n">within</span> <span class="o">=</span> <span class="n">strprev</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="n">float_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">within</span><span class="p">]</span>
|
||||
<span class="n">float_list</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">1.</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">float_list</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">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">float_list</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="n">df</span><span class="p">[</span><span class="s1">'true-prev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'true-prev'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">as_dict</span><span class="p">:</span>
|
||||
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">col</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="n">vals</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">values</span>
|
||||
<span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'true-prev'</span><span class="p">,</span> <span class="s1">'estim-prev'</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">vstack</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
|
||||
<span class="n">d</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
|
||||
<span class="k">return</span> <span class="n">d</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">df</span></div>
|
||||
|
||||
|
||||
|
||||
<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="w"> </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="w"> </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="w"> </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="w"> </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="w"> </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>
|
||||
|
||||
</article>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<footer class="prev-next-footer d-print-none">
|
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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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|
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<div class="footer-item">
|
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|
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<p class="sphinx-version">
|
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Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 9.0.4.
|
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<br/>
|
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</p>
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|
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|
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|
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|
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<!-- # L10n: Setting the PST URL as an argument as this does not need to be localized -->
|
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Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.20.0.
|
||||
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|
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|
||||
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|
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|
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|
||||
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -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;
|
||||
}
|
||||
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|
||||
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|
|||
!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 @@
|
|||
/**
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||||
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||||
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|
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||||
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|
||||
rect.setAttribute("class", className);
|
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|
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|
||||
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|
||||
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|
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|
||||
let addItems = [];
|
||||
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|
||||
addItems.forEach((obj) =>
|
||||
obj.parent.insertAdjacentElement("beforebegin", obj.target),
|
||||
);
|
||||
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|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
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|
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|
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* highlight the search words provided in localstorage in the text
|
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*/
|
||||
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|
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|
||||
|
||||
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|
||||
const url = new URL(window.location);
|
||||
const highlight =
|
||||
localStorage.getItem("sphinx_highlight_terms")
|
||||
|| url.searchParams.get("highlight")
|
||||
|| "";
|
||||
localStorage.removeItem("sphinx_highlight_terms");
|
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|
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// clears text fragments (not set in window.location by the browser)
|
||||
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|
||||
url.searchParams.delete("highlight");
|
||||
window.history.replaceState({}, "", url);
|
||||
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|
||||
|
||||
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|
||||
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|
||||
.toLowerCase()
|
||||
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|
||||
.filter((x) => x);
|
||||
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|
||||
|
||||
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|
||||
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()
|
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|
||||
'<p class="highlight-link">'
|
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+ '<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.
|
||||
*
|
||||
* :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
@import url("basic.css");
|
||||
|
||||
/* -- page layout ----------------------------------------------------------- */
|
||||
|
||||
body {
|
||||
font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
|
||||
'Verdana', sans-serif;
|
||||
font-size: 14px;
|
||||
letter-spacing: -0.01em;
|
||||
line-height: 150%;
|
||||
text-align: center;
|
||||
background-color: #BFD1D4;
|
||||
color: black;
|
||||
padding: 0;
|
||||
border: 1px solid #aaa;
|
||||
|
||||
margin: 0px 80px 0px 80px;
|
||||
min-width: 740px;
|
||||
}
|
||||
|
||||
div.document {
|
||||
background-color: white;
|
||||
text-align: left;
|
||||
background-image: url(contents.png);
|
||||
background-repeat: repeat-x;
|
||||
}
|
||||
|
||||
div.documentwrapper {
|
||||
float: left;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.bodywrapper {
|
||||
margin: 0 calc(230px + 10px) 0 0;
|
||||
border-right: 1px solid #ccc;
|
||||
}
|
||||
|
||||
div.body {
|
||||
margin: 0;
|
||||
padding: 0.5em 20px 20px 20px;
|
||||
}
|
||||
|
||||
div.related {
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
background-image: url(navigation.png);
|
||||
height: 2em;
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
}
|
||||
|
||||
div.related ul li {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
height: 2em;
|
||||
float: left;
|
||||
}
|
||||
|
||||
div.related ul li.right {
|
||||
float: right;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
div.related ul li a {
|
||||
margin: 0;
|
||||
padding: 0 5px 0 5px;
|
||||
line-height: 1.75em;
|
||||
color: #EE9816;
|
||||
}
|
||||
|
||||
div.related ul li a:hover {
|
||||
color: #3CA8E7;
|
||||
}
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
div.sphinxsidebar {
|
||||
padding: 0.5em 15px 15px 0;
|
||||
width: calc(230px - 20px);
|
||||
float: right;
|
||||
font-size: 1em;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
div.sphinxsidebar h3, div.sphinxsidebar h4 {
|
||||
margin: 1em 0 0.5em 0;
|
||||
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|
||||
padding: 0.1em 0 0.1em 0.5em;
|
||||
color: white;
|
||||
border: 1px solid #86989B;
|
||||
background-color: #AFC1C4;
|
||||
}
|
||||
|
||||
div.sphinxsidebar h3 a {
|
||||
color: white;
|
||||
}
|
||||
|
||||
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|
||||
padding-left: 1.5em;
|
||||
margin-top: 7px;
|
||||
padding: 0;
|
||||
line-height: 130%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul {
|
||||
margin-left: 20px;
|
||||
}
|
||||
|
||||
div.footer {
|
||||
background-color: #E3EFF1;
|
||||
color: #86989B;
|
||||
padding: 3px 8px 3px 0;
|
||||
clear: both;
|
||||
font-size: 0.8em;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
div.footer a {
|
||||
color: #86989B;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
/* -- body styles ----------------------------------------------------------- */
|
||||
|
||||
p {
|
||||
margin: 0.8em 0 0.5em 0;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #CA7900;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #2491CF;
|
||||
}
|
||||
|
||||
a:visited {
|
||||
color: #551A8B;
|
||||
}
|
||||
|
||||
div.body a {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
h1 {
|
||||
margin: 0;
|
||||
padding: 0.7em 0 0.3em 0;
|
||||
font-size: 1.5em;
|
||||
color: #11557C;
|
||||
}
|
||||
|
||||
h2 {
|
||||
margin: 1.3em 0 0.2em 0;
|
||||
font-size: 1.35em;
|
||||
padding: 0;
|
||||
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|
||||
|
||||
h3 {
|
||||
margin: 1em 0 -0.3em 0;
|
||||
font-size: 1.2em;
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<a href="index.html">QuaPy: A Python-based open-source framework for quantification</a>
|
||||
</nav>
|
||||
|
||||
<div class="wy-nav-content">
|
||||
<div class="rst-content">
|
||||
<div role="navigation" aria-label="Page navigation">
|
||||
<ul class="wy-breadcrumbs">
|
||||
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
|
||||
<li class="breadcrumb-item active">quapy.benchmarking package</li>
|
||||
<li class="wy-breadcrumbs-aside">
|
||||
<a href="_sources/quapy.benchmarking.rst.txt" rel="nofollow"> View page source</a>
|
||||
</li>
|
||||
</ul>
|
||||
<hr/>
|
||||
</div>
|
||||
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
|
||||
<div itemprop="articleBody">
|
||||
|
||||
<section id="quapy-benchmarking-package">
|
||||
<h1>quapy.benchmarking package<a class="headerlink" href="#quapy-benchmarking-package" title="Link to this heading"></a></h1>
|
||||
<section id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
|
||||
</section>
|
||||
<section id="module-quapy.benchmarking.typical">
|
||||
<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>
|
||||
<dl class="py function">
|
||||
<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>
|
||||
<dd></dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-quapy.benchmarking">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.benchmarking" title="Link to this heading"></a></h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
</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>
|
||||
|
|
@ -1,10 +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
|
||||
|
|
@ -1,35 +0,0 @@
|
|||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=source
|
||||
set BUILDDIR=build
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.https://www.sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
|
||||
:end
|
||||
popd
|
||||
|
|
@ -1 +0,0 @@
|
|||
!*.png
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 111 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 128 KiB |
|
|
@ -1,53 +0,0 @@
|
|||
.navbar-brand.logo .title.logo__title {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.navbar-brand img {
|
||||
max-height: 2.2rem;
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.navbar-brand.logo .title.logo__title {
|
||||
font-size: 1.05rem;
|
||||
line-height: 1.15;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
@media (max-width: 1200px) {
|
||||
.bd-header .navbar-header-items {
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.bd-header .navbar-header-items__center {
|
||||
overflow-x: auto;
|
||||
}
|
||||
|
||||
.bd-header .bd-navbar-elements {
|
||||
flex-wrap: nowrap;
|
||||
}
|
||||
}
|
||||
|
||||
.hero-copy {
|
||||
font-size: 1.15rem;
|
||||
line-height: 1.7;
|
||||
max-width: 56rem;
|
||||
margin: 0 0 1.5rem 0;
|
||||
}
|
||||
|
||||
.landing-grid {
|
||||
margin: 1.2rem 0 2rem 0;
|
||||
}
|
||||
|
||||
.landing-card {
|
||||
border-radius: 1rem;
|
||||
border: 1px solid var(--pst-color-border, #d0d7de);
|
||||
box-shadow: 0 10px 24px rgba(15, 23, 42, 0.08);
|
||||
}
|
||||
|
||||
.landing-card .sd-card-title {
|
||||
font-size: 1.1rem;
|
||||
}
|
||||
|
||||
[data-theme="dark"] .landing-card {
|
||||
box-shadow: 0 10px 24px rgba(0, 0, 0, 0.22);
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB |
|
|
@ -1,92 +0,0 @@
|
|||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# For the full list of built-in configuration values, see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
|
||||
|
||||
import pathlib
|
||||
import sys
|
||||
from os.path import join
|
||||
quapy_path = join(pathlib.Path(__file__).parents[2].resolve().as_posix(), 'quapy')
|
||||
wiki_path = join(pathlib.Path(__file__).parents[0].resolve().as_posix(), 'wiki')
|
||||
source_path = pathlib.Path(__file__).parents[2].resolve().as_posix()
|
||||
print(f'quapy path={quapy_path}')
|
||||
print(f'quapy source path={source_path}')
|
||||
sys.path.insert(0, quapy_path)
|
||||
sys.path.insert(0, wiki_path)
|
||||
sys.path.insert(0, source_path)
|
||||
|
||||
print(sys.path)
|
||||
|
||||
|
||||
project = 'QuaPy: A Python-based open-source framework for quantification'
|
||||
copyright = '2024, Alejandro Moreo'
|
||||
author = 'Alejandro Moreo'
|
||||
|
||||
|
||||
|
||||
import quapy
|
||||
|
||||
release = quapy.__version__
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
|
||||
|
||||
extensions = [
|
||||
'sphinx.ext.autosectionlabel',
|
||||
'sphinx.ext.duration',
|
||||
'sphinx.ext.doctest',
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.autosummary',
|
||||
'sphinx.ext.viewcode',
|
||||
'sphinx.ext.napoleon',
|
||||
'sphinx.ext.intersphinx',
|
||||
'myst_parser',
|
||||
'sphinx_design',
|
||||
]
|
||||
|
||||
autosectionlabel_prefix_document = True
|
||||
|
||||
source_suffix = ['.rst', '.md']
|
||||
|
||||
myst_enable_extensions = ['colon_fence']
|
||||
|
||||
templates_path = ['_templates']
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
|
||||
|
||||
#html_theme = 'sphinx_rtd_theme'
|
||||
html_theme = 'pydata_sphinx_theme'
|
||||
# html_theme = 'furo'
|
||||
# need to be installed: pip install furo (not working...)
|
||||
html_static_path = ['_static']
|
||||
html_css_files = ['custom.css']
|
||||
html_theme_options = {
|
||||
'logo': {
|
||||
'image_light': '_static/quapy_logo.png',
|
||||
'image_dark': '_static/quapy_logo_dark.png',
|
||||
},
|
||||
'icon_links': [
|
||||
{
|
||||
'name': 'GitHub',
|
||||
'url': 'https://github.com/HLT-ISTI/QuaPy',
|
||||
'icon': 'fa-brands fa-github',
|
||||
'type': 'fontawesome',
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
# intersphinx configuration
|
||||
intersphinx_mapping = {
|
||||
"sklearn": ("https://scikit-learn.org/stable/", None),
|
||||
}
|
||||
|
||||
|
|
@ -1,148 +0,0 @@
|
|||
```{toctree}
|
||||
:hidden:
|
||||
|
||||
Home <self>
|
||||
manuals
|
||||
API <quapy>
|
||||
```
|
||||
|
||||
# QuaPy
|
||||
|
||||
```{div} hero-copy
|
||||
QuaPy is an open-source Python framework for quantification, also known as
|
||||
supervised prevalence estimation or learning to quantify. It is designed with
|
||||
research and experimental analysis in mind, and combines datasets, protocols,
|
||||
evaluation measures, visualization tools, and a broad collection of
|
||||
quantification methods in a single workflow.
|
||||
```
|
||||
|
||||
`````{grid} 1 1 2 2
|
||||
:gutter: 3
|
||||
:class-container: landing-grid
|
||||
|
||||
````{grid-item-card} Quickstart
|
||||
:class-card: landing-card
|
||||
Install QuaPy and run your first quantifier in a few lines of code.
|
||||
+++
|
||||
```{button-link} #installation
|
||||
:color: primary
|
||||
Get Started
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} Manuals
|
||||
:class-card: landing-card
|
||||
Hands-on guides with methodological context, literature pointers, and reproducible workflows.
|
||||
+++
|
||||
```{button-ref} manuals
|
||||
:ref-type: doc
|
||||
:color: primary
|
||||
Open Manuals
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} API
|
||||
:class-card: landing-card
|
||||
Browse the full reference for `quapy`, including methods, datasets, utilities, and research-oriented extensions.
|
||||
+++
|
||||
```{button-ref} quapy
|
||||
:ref-type: doc
|
||||
:color: primary
|
||||
Browse API
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} GitHub
|
||||
:class-card: landing-card
|
||||
Explore the source code, open issues, and current development branch activity.
|
||||
+++
|
||||
```{button-link} https://github.com/HLT-ISTI/QuaPy
|
||||
:color: primary
|
||||
Open GitHub
|
||||
```
|
||||
````
|
||||
|
||||
`````
|
||||
|
||||
## Installation
|
||||
|
||||
```sh
|
||||
pip install quapy
|
||||
```
|
||||
|
||||
## Why QuaPy
|
||||
|
||||
QuaPy is built around the concept of a data sample and supports the main tasks
|
||||
in the quantification workflow: training quantifiers, generating evaluation
|
||||
samples, measuring quantification error, selecting models under distribution
|
||||
shift, and visualizing experimental behaviour. The framework is especially
|
||||
suited for research settings, where one often needs not only implementations,
|
||||
but also methodological context, literature links, and reproducible evaluation
|
||||
procedures.
|
||||
|
||||
Some of the main features are:
|
||||
|
||||
* Implementation of many popular quantification methods, including Classify & Count and its variants,
|
||||
Expectation Maximization, HDy, QuaNet, quantification ensembles, and Bayesian extensions.
|
||||
* Evaluation protocols for generating test samples under prior probability shift.
|
||||
* A broad set of quantification-oriented evaluation metrics.
|
||||
* Ready-to-use textual, numeric, and benchmark competition datasets.
|
||||
* Method documentation that points back to the relevant literature and original papers.
|
||||
* Native support for binary and single-label multiclass quantification.
|
||||
* Visualization tools for analysing predictions, drift, confidence regions, and ternary prevalences.
|
||||
|
||||
## First Example
|
||||
|
||||
The following script fetches a binary dataset, trains an Adjusted Classify & Count quantifier,
|
||||
and evaluates the resulting prevalence prediction with Mean Absolute Error.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
|
||||
|
||||
model = qp.method.aggregative.ACC()
|
||||
Xtr, ytr = training.Xy
|
||||
model.fit(Xtr, ytr)
|
||||
|
||||
estim_prevalence = model.predict(test.X)
|
||||
true_prevalence = test.prevalence()
|
||||
|
||||
error = qp.error.mae(true_prevalence, estim_prevalence)
|
||||
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
||||
```
|
||||
|
||||
Quantification is especially useful when the class prevalence of the test data
|
||||
may differ from that of the training data. QuaPy implements protocols that make
|
||||
it easy to evaluate methods across many such shifts. See the [](./manuals) for
|
||||
worked examples.
|
||||
|
||||
## Citing QuaPy
|
||||
|
||||
If you find QuaPy useful, please consider citing the original paper.
|
||||
|
||||
```bibtex
|
||||
@inproceedings{moreo2021quapy,
|
||||
title={QuaPy: a python-based framework for quantification},
|
||||
author={Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
|
||||
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
|
||||
pages={4534--4543},
|
||||
year={2021}
|
||||
}
|
||||
```
|
||||
|
||||
## Contributing
|
||||
|
||||
If you want to contribute improvements to QuaPy, please open a pull request
|
||||
against the `devel` branch.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
```{image} SoBigData.png
|
||||
:width: 250px
|
||||
:alt: SoBigData++
|
||||
```
|
||||
|
||||
This work has been supported by the QuaDaSh project
|
||||
_"Finanziato dall'Unione europea---Next Generation EU,
|
||||
Missione 4 Componente 2 CUP B53D23026250001"_.
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
.. QuaPy: A Python-based open-source framework for quantification documentation master file, created by
|
||||
sphinx-quickstart on Wed Feb 7 16:26:46 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to QuaPy's documentation!
|
||||
==========================================================================================
|
||||
|
||||
QuaPy is a Python-based open-source framework for quantification.
|
||||
|
||||
This document contains the API of the modules included in QuaPy.
|
||||
|
||||
Installation
|
||||
------------
|
||||
|
||||
`pip install quapy`
|
||||
|
||||
GitHub
|
||||
------------
|
||||
|
||||
QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
|
||||
Contents
|
||||
--------
|
||||
|
||||
.. toctree::
|
||||
|
||||
modules
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
Manuals
|
||||
=======
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:numbered:
|
||||
|
||||
manuals/datasets
|
||||
manuals/evaluation
|
||||
manuals/methods
|
||||
manuals/model-selection
|
||||
manuals/plotting
|
||||
manuals/protocols
|
||||
|
|
@ -1,600 +0,0 @@
|
|||
# Datasets
|
||||
|
||||
QuaPy makes available several datasets that have been used in
|
||||
quantification literature, as well as an interface to allow
|
||||
anyone import their custom datasets.
|
||||
|
||||
A _Dataset_ object in QuaPy is roughly a pair of _LabelledCollection_ objects,
|
||||
one playing the role of the training set, another the test set.
|
||||
_LabelledCollection_ is a data class consisting of the (iterable)
|
||||
instances and labels. This class handles most of the sampling functionality in QuaPy.
|
||||
Take a look at the following code:
|
||||
|
||||
```python
|
||||
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
|
||||
instances = [
|
||||
'1st positive document', '2nd positive document',
|
||||
'the only negative document',
|
||||
'1st neutral document', '2nd neutral document', '3rd neutral document'
|
||||
]
|
||||
labels = [2, 2, 0, 1, 1, 1]
|
||||
|
||||
data = qp.data.LabelledCollection(instances, labels)
|
||||
print(F.strprev(data.prevalence(), prec=2))
|
||||
|
||||
```
|
||||
|
||||
Output the class prevalences (showing 2 digit precision):
|
||||
```
|
||||
[0.17, 0.50, 0.33]
|
||||
```
|
||||
|
||||
One can easily produce new samples at desired class prevalence values:
|
||||
|
||||
```python
|
||||
sample_size = 10
|
||||
prev = [0.4, 0.1, 0.5]
|
||||
sample = data.sampling(sample_size, *prev)
|
||||
|
||||
print('instances:', sample.instances)
|
||||
print('labels:', sample.labels)
|
||||
print('prevalence:', F.strprev(sample.prevalence(), prec=2))
|
||||
```
|
||||
|
||||
Which outputs:
|
||||
```
|
||||
instances: ['the only negative document' '2nd positive document'
|
||||
'2nd positive document' '2nd neutral document' '1st positive document'
|
||||
'the only negative document' 'the only negative document'
|
||||
'the only negative document' '2nd positive document'
|
||||
'1st positive document']
|
||||
labels: [0 2 2 1 2 0 0 0 2 2]
|
||||
prevalence: [0.40, 0.10, 0.50]
|
||||
```
|
||||
|
||||
Samples can be made consistent across different runs (e.g., to test
|
||||
different methods on the same exact samples) by sampling and retaining
|
||||
the indexes, that can then be used to generate the sample:
|
||||
|
||||
```python
|
||||
index = data.sampling_index(sample_size, *prev)
|
||||
for method in methods:
|
||||
sample = data.sampling_from_index(index)
|
||||
...
|
||||
```
|
||||
|
||||
However, generating samples for evaluation purposes is tackled in QuaPy
|
||||
by means of the evaluation protocols (see the dedicated entries in the manuals
|
||||
for [evaluation](./evaluation) and [protocols](./protocols)).
|
||||
|
||||
|
||||
## Reviews Datasets
|
||||
|
||||
Three datasets of reviews about Kindle devices, Harry Potter's series, and
|
||||
the well-known IMDb movie reviews can be fetched using a unified interface.
|
||||
For example:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_reviews('kindle')
|
||||
```
|
||||
|
||||
These datasets have been used in:
|
||||
```
|
||||
Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
|
||||
A recurrent neural network for sentiment quantification.
|
||||
In Proceedings of the 27th ACM International Conference on
|
||||
Information and Knowledge Management (pp. 1775-1778).
|
||||
```
|
||||
|
||||
The list of reviews ids is available in:
|
||||
|
||||
```python
|
||||
qp.datasets.REVIEWS_SENTIMENT_DATASETS
|
||||
```
|
||||
|
||||
Some statistics of the fhe available datasets are summarized below:
|
||||
|
||||
| Dataset | classes | train size | test size | train prev | test prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| hp | 2 | 9533 | 18399 | \[0.018, 0.982\] | \[0.065, 0.935\] | text |
|
||||
| kindle | 2 | 3821 | 21591 | \[0.081, 0.919\] | \[0.063, 0.937\] | text |
|
||||
| imdb | 2 | 25000 | 25000 | \[0.500, 0.500\] | \[0.500, 0.500\] | text |
|
||||
|
||||
## Twitter Sentiment Datasets
|
||||
|
||||
11 Twitter datasets for sentiment analysis.
|
||||
Text is not accessible, and the documents were made available
|
||||
in tf-idf format. Each dataset presents two splits: a train/val
|
||||
split for model selection purposes, and a train+val/test split
|
||||
for model evaluation. The following code exemplifies how to load
|
||||
a twitter dataset for model selection.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_twitter('gasp', for_model_selection=True)
|
||||
```
|
||||
|
||||
The datasets were used in:
|
||||
|
||||
```
|
||||
Gao, W., & Sebastiani, F. (2015, August).
|
||||
Tweet sentiment: From classification to quantification.
|
||||
In 2015 IEEE/ACM International Conference on Advances in
|
||||
Social Networks Analysis and Mining (ASONAM) (pp. 97-104). IEEE.
|
||||
```
|
||||
|
||||
Three of the datasets (semeval13, semeval14, and semeval15) share the
|
||||
same training set (semeval), meaning that the training split one would get
|
||||
when requesting any of them is the same. The dataset "semeval" can only
|
||||
be requested with "for_model_selection=True".
|
||||
The lists of the Twitter dataset's ids can be consulted in:
|
||||
|
||||
```python
|
||||
# a list of 11 dataset ids that can be used for model selection or model evaluation
|
||||
qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
|
||||
|
||||
# 9 dataset ids in which "semeval13", "semeval14", and "semeval15" are replaced with "semeval"
|
||||
qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
|
||||
```
|
||||
|
||||
Some details can be found below:
|
||||
|
||||
| Dataset | classes | train size | test size | features | train prev | test prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| gasp | 3 | 8788 | 3765 | 694582 | [0.421, 0.496, 0.082] | [0.407, 0.507, 0.086] | sparse |
|
||||
| hcr | 3 | 1594 | 798 | 222046 | [0.546, 0.211, 0.243] | [0.640, 0.167, 0.193] | sparse |
|
||||
| omd | 3 | 1839 | 787 | 199151 | [0.463, 0.271, 0.266] | [0.437, 0.283, 0.280] | sparse |
|
||||
| sanders | 3 | 2155 | 923 | 229399 | [0.161, 0.691, 0.148] | [0.164, 0.688, 0.148] | sparse |
|
||||
| semeval13 | 3 | 11338 | 3813 | 1215742 | [0.159, 0.470, 0.372] | [0.158, 0.430, 0.412] | sparse |
|
||||
| semeval14 | 3 | 11338 | 1853 | 1215742 | [0.159, 0.470, 0.372] | [0.109, 0.361, 0.530] | sparse |
|
||||
| semeval15 | 3 | 11338 | 2390 | 1215742 | [0.159, 0.470, 0.372] | [0.153, 0.413, 0.434] | sparse |
|
||||
| semeval16 | 3 | 8000 | 2000 | 889504 | [0.157, 0.351, 0.492] | [0.163, 0.341, 0.497] | sparse |
|
||||
| sst | 3 | 2971 | 1271 | 376132 | [0.261, 0.452, 0.288] | [0.207, 0.481, 0.312] | sparse |
|
||||
| wa | 3 | 2184 | 936 | 248563 | [0.305, 0.414, 0.281] | [0.282, 0.446, 0.272] | sparse |
|
||||
| wb | 3 | 4259 | 1823 | 404333 | [0.270, 0.392, 0.337] | [0.274, 0.392, 0.335] | sparse |
|
||||
|
||||
|
||||
## UCI Machine Learning
|
||||
|
||||
### Binary datasets
|
||||
|
||||
A set of 32 datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php)
|
||||
used in:
|
||||
|
||||
```
|
||||
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.
|
||||
Information Fusion, 34, 87-100.
|
||||
```
|
||||
|
||||
The list does not exactly coincide with that used in Pérez-Gállego et al. 2017
|
||||
since we were unable to find the datasets with ids "diabetes" and "phoneme".
|
||||
|
||||
These dataset can be loaded by calling, e.g.:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
data = qp.datasets.fetch_UCIBinaryDataset('yeast', verbose=True)
|
||||
```
|
||||
|
||||
This call will return a _Dataset_ object in which the training and
|
||||
test splits are randomly drawn, in a stratified manner, from the whole
|
||||
collection at 70% and 30%, respectively. The _verbose=True_ option indicates
|
||||
that the dataset description should be printed in standard output.
|
||||
The original data is not split,
|
||||
and some papers submit the entire collection to a kFCV validation.
|
||||
In order to accommodate with these practices, one could first instantiate
|
||||
the entire collection, and then creating a generator that will return one
|
||||
training+test dataset at a time, following a kFCV protocol:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
collection = qp.datasets.fetch_UCIBinaryLabelledCollection("yeast")
|
||||
for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
|
||||
...
|
||||
```
|
||||
|
||||
Above code will allow to conduct a 2x5FCV evaluation on the "yeast" dataset.
|
||||
|
||||
All datasets come in numerical form (dense matrices); some statistics
|
||||
are summarized below.
|
||||
|
||||
| Dataset | classes | instances | features | prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|---|
|
||||
| acute.a | 2 | 120 | 6 | [0.508, 0.492] | dense |
|
||||
| acute.b | 2 | 120 | 6 | [0.583, 0.417] | dense |
|
||||
| balance.1 | 2 | 625 | 4 | [0.539, 0.461] | dense |
|
||||
| balance.2 | 2 | 625 | 4 | [0.922, 0.078] | dense |
|
||||
| balance.3 | 2 | 625 | 4 | [0.539, 0.461] | dense |
|
||||
| breast-cancer | 2 | 683 | 9 | [0.350, 0.650] | dense |
|
||||
| cmc.1 | 2 | 1473 | 9 | [0.573, 0.427] | dense |
|
||||
| cmc.2 | 2 | 1473 | 9 | [0.774, 0.226] | dense |
|
||||
| cmc.3 | 2 | 1473 | 9 | [0.653, 0.347] | dense |
|
||||
| ctg.1 | 2 | 2126 | 21 | [0.222, 0.778] | dense |
|
||||
| ctg.2 | 2 | 2126 | 21 | [0.861, 0.139] | dense |
|
||||
| ctg.3 | 2 | 2126 | 21 | [0.917, 0.083] | dense |
|
||||
| german | 2 | 1000 | 24 | [0.300, 0.700] | dense |
|
||||
| haberman | 2 | 306 | 3 | [0.735, 0.265] | dense |
|
||||
| ionosphere | 2 | 351 | 34 | [0.641, 0.359] | dense |
|
||||
| iris.1 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| iris.2 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| iris.3 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| mammographic | 2 | 830 | 5 | [0.514, 0.486] | dense |
|
||||
| pageblocks.5 | 2 | 5473 | 10 | [0.979, 0.021] | dense |
|
||||
| semeion | 2 | 1593 | 256 | [0.901, 0.099] | dense |
|
||||
| sonar | 2 | 208 | 60 | [0.534, 0.466] | dense |
|
||||
| spambase | 2 | 4601 | 57 | [0.606, 0.394] | dense |
|
||||
| spectf | 2 | 267 | 44 | [0.794, 0.206] | dense |
|
||||
| tictactoe | 2 | 958 | 9 | [0.653, 0.347] | dense |
|
||||
| transfusion | 2 | 748 | 4 | [0.762, 0.238] | dense |
|
||||
| wdbc | 2 | 569 | 30 | [0.627, 0.373] | dense |
|
||||
| wine.1 | 2 | 178 | 13 | [0.669, 0.331] | dense |
|
||||
| wine.2 | 2 | 178 | 13 | [0.601, 0.399] | dense |
|
||||
| wine.3 | 2 | 178 | 13 | [0.730, 0.270] | dense |
|
||||
| wine-q-red | 2 | 1599 | 11 | [0.465, 0.535] | dense |
|
||||
| wine-q-white | 2 | 4898 | 11 | [0.335, 0.665] | dense |
|
||||
| yeast | 2 | 1484 | 8 | [0.711, 0.289] | dense |
|
||||
|
||||
#### Notes:
|
||||
All datasets will be downloaded automatically the first time they are requested, and
|
||||
stored in the _quapy_data_ folder for faster further reuse.
|
||||
|
||||
However, notice that it is a good idea to ignore datasets:
|
||||
* _acute.a_ and _acute.b_: these are very easy and many classifiers would score 100% accuracy
|
||||
* _balance.2_: this is extremely difficult; probably there is some problem with this dataset,
|
||||
the errors it tends to produce are orders of magnitude greater than for other datasets,
|
||||
and this has a disproportionate impact in the average performance.
|
||||
|
||||
### Multiclass datasets
|
||||
|
||||
A collection of 24 multiclass datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php).
|
||||
Some of the datasets were first used in [this paper](https://arxiv.org/abs/2401.00490) and can be instantiated as follows:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', verbose=True)
|
||||
```
|
||||
|
||||
A dataset can be instantiated filtering classes with a minimum number of instances using the `min_class_support` parameter
|
||||
(default: `100`) as folows:
|
||||
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', min_class_support=50, verbose=True)
|
||||
```
|
||||
|
||||
There are no pre-defined train-test partitions for these datasets, but you can easily create your own with the
|
||||
`split_stratified` method, e.g., `data.split_stratified()`. This can be also achieved using the method `fetch_UCIMulticlassDataset`
|
||||
as shown below:
|
||||
|
||||
```python
|
||||
data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, verbose=True)
|
||||
train, test = data.train_test
|
||||
```
|
||||
|
||||
This method tries to respect the `min_test_split` value while generating the train-test partition, but the resulting training set
|
||||
will not be bigger than `max_train_instances`, which defaults to `25000`. A bigger value can be passed as a parameter:
|
||||
|
||||
```python
|
||||
data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, max_train_instances=30000, verbose=True)
|
||||
train, test = data.train_test
|
||||
```
|
||||
|
||||
The datasets correspond to a part of the datasets that can be retrieved from the platform using the following filters:
|
||||
* datasets for classification
|
||||
* more than 2 classes
|
||||
* containing at least 1,000 instances
|
||||
* can be imported using the Python API.
|
||||
|
||||
Some statistics about these datasets are displayed below :
|
||||
|
||||
| **Dataset** | **classes** | **instances** | **features** | **prevs** | **type** |
|
||||
|:------------|:-----------:|:-------------:|:------------:|:----------|:--------:|
|
||||
| dry-bean | 7 | 13611 | 16 | [0.097, 0.038, 0.120, 0.261, 0.142, 0.149, 0.194] | dense |
|
||||
| wine-quality | 5 | 6462 | 11 | [0.033, 0.331, 0.439, 0.167, 0.030] | dense |
|
||||
| academic-success | 3 | 4424 | 36 | [0.321, 0.179, 0.499] | dense |
|
||||
| digits | 10 | 5620 | 64 | [0.099, 0.102, 0.099, 0.102, 0.101, 0.099, 0.099, 0.101, 0.099, 0.100] | dense |
|
||||
| letter | 26 | 20000 | 16 | [0.039, 0.038, 0.037, 0.040, 0.038, 0.039, 0.039, 0.037, 0.038, 0.037, 0.037, 0.038, 0.040, 0.039, 0.038, 0.040, 0.039, 0.038, 0.037, 0.040, 0.041, 0.038, 0.038, 0.039, 0.039, 0.037] | dense |
|
||||
| abalone | 11 | 3842 | 9 | [0.030, 0.067, 0.102, 0.148, 0.179, 0.165, 0.127, 0.069, 0.053, 0.033, 0.027] | dense |
|
||||
| obesity | 7 | 2111 | 23 | [0.129, 0.136, 0.166, 0.141, 0.153, 0.137, 0.137] | dense |
|
||||
| nursery | 4 | 12958 | 19 | [0.333, 0.329, 0.312, 0.025] | dense |
|
||||
| yeast | 4 | 1299 | 8 | [0.356, 0.125, 0.188, 0.330] | dense |
|
||||
| hand_digits | 10 | 10992 | 16 | [0.104, 0.104, 0.104, 0.096, 0.104, 0.096, 0.096, 0.104, 0.096, 0.096] | dense |
|
||||
| satellite | 6 | 6435 | 36 | [0.238, 0.109, 0.211, 0.097, 0.110, 0.234] | dense |
|
||||
| shuttle | 4 | 57927 | 7 | [0.787, 0.003, 0.154, 0.056] | dense |
|
||||
| cmc | 3 | 1473 | 9 | [0.427, 0.226, 0.347] | dense |
|
||||
| isolet | 26 | 7797 | 617 | [0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038, 0.038] | dense |
|
||||
| waveform-v1 | 3 | 5000 | 21 | [0.331, 0.329, 0.339] | dense |
|
||||
| molecular | 3 | 3190 | 227 | [0.240, 0.241, 0.519] | dense |
|
||||
| poker_hand | 8 | 1024985 | 10 | [0.501, 0.423, 0.048, 0.021, 0.004, 0.002, 0.001, 0.000] | dense |
|
||||
| connect-4 | 3 | 67557 | 84 | [0.095, 0.246, 0.658] | dense |
|
||||
| mhr | 3 | 1014 | 6 | [0.268, 0.400, 0.331] | dense |
|
||||
| chess | 15 | 27870 | 20 | [0.100, 0.051, 0.102, 0.078, 0.017, 0.007, 0.163, 0.061, 0.025, 0.021, 0.014, 0.071, 0.150, 0.129, 0.009] | dense |
|
||||
| page_block | 3 | 5357 | 10 | [0.917, 0.061, 0.021] | dense |
|
||||
| phishing | 3 | 1353 | 9 | [0.519, 0.076, 0.405] | dense |
|
||||
| image_seg | 7 | 2310 | 19 | [0.143, 0.143, 0.143, 0.143, 0.143, 0.143, 0.143] | dense |
|
||||
| hcv | 4 | 1385 | 28 | [0.243, 0.240, 0.256, 0.261] | dense |
|
||||
|
||||
Values shown above refer to datasets obtained through `fetchUCIMulticlassLabelledCollection` using all default parameters.
|
||||
|
||||
## LeQua 2022 Datasets
|
||||
|
||||
QuaPy also provides the datasets used for the LeQua 2022 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.
|
||||
Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B
|
||||
are multiclass quantification problems consisting of estimating the class prevalence
|
||||
values of 28 different merchandise products.
|
||||
|
||||
Every task consists of a training set, a set of validation samples (for model selection)
|
||||
and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection
|
||||
(training) and two generation protocols (for validation and test samples), as follows:
|
||||
|
||||
```python
|
||||
training, val_generator, test_generator = qp.datasets.fetch_lequa2022(task=task)
|
||||
```
|
||||
|
||||
See the `5a.lequa2022_experiments.py` in the examples folder for further details on how to
|
||||
carry out experiments using these datasets.
|
||||
|
||||
The datasets are downloaded only once, and stored for fast reuse.
|
||||
|
||||
Some statistics are summarized below:
|
||||
|
||||
| Dataset | classes | train size | validation samples | test samples | docs by sample | type |
|
||||
|---------|:-------:|:----------:|:------------------:|:------------:|:----------------:|:--------:|
|
||||
| T1A | 2 | 5000 | 1000 | 5000 | 250 | vector |
|
||||
| T1B | 28 | 20000 | 1000 | 5000 | 1000 | vector |
|
||||
| T2A | 2 | 5000 | 1000 | 5000 | 250 | text |
|
||||
| T2B | 28 | 20000 | 1000 | 5000 | 1000 | text |
|
||||
|
||||
For further details on the datasets, we refer to the original
|
||||
[paper](https://ceur-ws.org/Vol-3180/paper-146.pdf):
|
||||
|
||||
```
|
||||
Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
|
||||
A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.
|
||||
```
|
||||
|
||||
## LeQua 2024 Datasets
|
||||
|
||||
QuaPy also provides the datasets used for the [LeQua 2024 competition](https://lequa2024.github.io/).
|
||||
In brief, there are 4 tasks:
|
||||
* T1: binary quantification (by sentiment)
|
||||
* T2: multiclass quantification (28 classes, merchandise products)
|
||||
* T3: ordinal quantification (5-stars sentiment ratings)
|
||||
* T4: binary sentiment quantification under a combination of covariate shift and prior shift
|
||||
|
||||
In all cases, the covariate space has 256 dimensions (extracted using the `ELECTRA-Small` model).
|
||||
|
||||
Every task consists of a training set, a set of validation samples (for model selection)
|
||||
and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection
|
||||
(training bags) and sampling generation protocols (for validation and test bags).
|
||||
T3 also offers the possibility to obtain a series of training bags (in form of a
|
||||
sampling generation protocol) instead of one single training bag. Use it as follows:
|
||||
|
||||
```python
|
||||
training, val_generator, test_generator = qp.datasets.fetch_lequa2024(task=task)
|
||||
```
|
||||
|
||||
See the `5b.lequa2024_experiments.py` in the examples folder for further details on how to
|
||||
carry out experiments using these datasets.
|
||||
|
||||
The datasets are downloaded only once, and stored for fast reuse.
|
||||
|
||||
Some statistics are summarized below:
|
||||
|
||||
| Dataset | classes | train size | validation samples | test samples | docs by sample | type |
|
||||
|---------|:-------:|:-----------:|:------------------:|:------------:|:--------------:|:--------:|
|
||||
| T1 | 2 | 5000 | 1000 | 5000 | 250 | vector |
|
||||
| T2 | 28 | 20000 | 1000 | 5000 | 1000 | vector |
|
||||
| T3 | 5 | 100 samples | 1000 | 5000 | 200 | vector |
|
||||
| T4 | 2 | 5000 | 1000 | 5000 | 250 | vector |
|
||||
|
||||
For further details on the datasets or the competition, we refer to
|
||||
[the official site](https://lequa2024.github.io/data/) and
|
||||
[the overview paper](http://nmis.isti.cnr.it/sebastiani/Publications/LQ2024.pdf).
|
||||
|
||||
```
|
||||
Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
|
||||
An Overview of LeQua 2024, the 2nd International Data Challenge on Learning to Quantify,
|
||||
Proceedings of the 4th International Workshop on Learning to Quantify (LQ 2024),
|
||||
ECML-PKDD 2024, Vilnius, Lithuania.
|
||||
```
|
||||
|
||||
|
||||
## Image Embedding Datasets
|
||||
|
||||
QuaPy also provides a collection of image datasets in the form of pre-generated
|
||||
embeddings.
|
||||
These
|
||||
embeddings were generated using [this extraction script](https://github.com/pglez82/visiondatasets_quapy)
|
||||
and are hosted in [Zenodo](https://zenodo.org/records/21131944).
|
||||
|
||||
An example of current public interface is:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
data = qp.datasets.fetch_image_embeddings(
|
||||
dataset_name='cifar10',
|
||||
embedding='features',
|
||||
heldout_only=True,
|
||||
)
|
||||
train, test = data.train_test
|
||||
```
|
||||
|
||||
The available datasets are in `qp.datasets.IMAGE_DATASETS`, and include 6 datasets:
|
||||
|
||||
* `cifar10`, `cifar100`, and `cifar100coarse`:
|
||||
[Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009.](https://cave.cs.toronto.edu/kriz/learning-features-2009-TR.pdf)
|
||||
* `mnist`:
|
||||
[Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. The MNIST database of handwritten digits. 1998.](http://yann.lecun.com/exdb/mnist/)
|
||||
* `fashionmnist`:
|
||||
[Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.](https://arxiv.org/abs/1708.07747)
|
||||
* `svhn`:
|
||||
[Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y. Ng, et al. Reading digits in natural images with unsupervised feature learning. NIPS Workshop, 2011.](https://static.googleusercontent.com/media/research.google.com/es//pubs/archive/37648.pdf)
|
||||
|
||||
|
||||
The available embedding types are in `qp.datasets.IMAGE_EMBEDDINGS`, and include:
|
||||
|
||||
* `features` are the penultimate-layer representations
|
||||
* `logits` are the pre-activation outputs of the neural model
|
||||
* `predictions` are the post-softmax posterior probabilities
|
||||
|
||||
The datasets correspond to frozen neural representations extracted from models
|
||||
trained on image classification tasks. QuaPy downloads them automatically on
|
||||
first use and stores them locally for fast reuse.
|
||||
|
||||
Each dataset is internally organised into three splits: `train`, `val`, and
|
||||
`test`. The `train` split was used to train the neural model that produced the
|
||||
embeddings, while `val` and `test` were not seen during neural training.
|
||||
For this reason, the default setting indicates `heldout_only=True`, meaning
|
||||
that the returned dataset will take the validation partition as the training
|
||||
set, and the test partition as the test set.
|
||||
This is often the most convenient choice for quantification experiments, since
|
||||
it avoids training quantifiers on examples that were already used to train the
|
||||
embedding model.
|
||||
|
||||
If instead you want to use all the available non-test data, you can set `heldout_only=False`,
|
||||
in which case, the returned training set is the union of the original neural
|
||||
training split and the validation split.
|
||||
|
||||
Some statistics are shown in the following table:
|
||||
|
||||
| Dataset | backbone | classes | neural network train size | validation size | test size | feature dim | logit dim | prediction dim | type |
|
||||
|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| cifar100 | resnet18 | 100 | 35000 | 15000 | 10000 | 512 | 100 | 100 | dense |
|
||||
| cifar10 | resnet18 | 10 | 35000 | 15000 | 10000 | 512 | 10 | 10 | dense |
|
||||
| cifar100coarse | resnet18 | 20 | 35000 | 15000 | 10000 | 512 | 20 | 20 | dense |
|
||||
| mnist | basiccnn | 10 |42000 | 18000 | 10000 | 128 | 10 | 10 | dense |
|
||||
| fashionmnist | basiccnn | 10 | 42000 | 18000 | 10000 | 128 | 10 | 10 | dense |
|
||||
| svhn | resnet18 | 10 | 51280 | 21977 | 26032 | 512 | 10 | 10 | dense |
|
||||
|
||||
|
||||
|
||||
|
||||
## IFCB Plankton dataset
|
||||
|
||||
IFCB is a dataset of plankton species in water samples hosted in [Zenodo](https://zenodo.org/records/10036244).
|
||||
This dataset is based on the data available publicly at [WHOI-Plankton repo](https://github.com/hsosik/WHOI-Plankton)
|
||||
and the scripts for the processing are available at [P. González's repo](https://github.com/pglez82/IFCB_Zenodo).
|
||||
|
||||
This dataset comes with precomputed features for testing quantification algorithms.
|
||||
|
||||
Some statistics:
|
||||
|
||||
| | **Training** | **Validation** | **Test** |
|
||||
|-----------------|:------------:|:--------------:|:--------:|
|
||||
| samples | 200 | 86 | 678 |
|
||||
| total instances | 584474 | 246916 | 2626429 |
|
||||
| mean per sample | 2922.3 | 2871.1 | 3873.8 |
|
||||
| min per sample | 266 | 59 | 33 |
|
||||
| max per sample | 6645 | 7375 | 9112 |
|
||||
|
||||
The number of features is 512, while the number of classes is 50.
|
||||
In terms of prevalence, the mean is 0.020, the minimum is 0, and the maximum is 0.978.
|
||||
|
||||
The dataset can be loaded for model selection (`for_model_selection=True`, thus returning the training and validation)
|
||||
or for test (`for_model_selection=False`, thus returning the training+validation and the test).
|
||||
|
||||
Additionally, the training can be interpreted as a list (a generator) of samples (`single_sample_train=False`)
|
||||
or as a single training set (`single_sample_train=True`).
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
train, val_gen = qp.datasets.fetch_IFCB(for_model_selection=True, single_sample_train=True)
|
||||
# ... model selection
|
||||
|
||||
train, test_gen = qp.datasets.fetch_IFCB(for_model_selection=False, single_sample_train=True)
|
||||
# ... train and evaluation
|
||||
```
|
||||
|
||||
See also [Automatic plankton quantification using deep features
|
||||
P González, A Castaño, EE Peacock, J Díez, JJ Del Coz, HM Sosik
|
||||
Journal of Plankton Research 41 (4), 449-463](https://par.nsf.gov/servlets/purl/10172325).
|
||||
|
||||
|
||||
|
||||
## Adding Custom Datasets
|
||||
|
||||
It is straightforward to import your own datasets into QuaPy.
|
||||
I what follows, there are some code snippets for doing so; see also the example
|
||||
[3.custom_collection.py](https://github.com/HLT-ISTI/QuaPy/blob/master/examples/3.custom_collection.py).
|
||||
|
||||
QuaPy provides data loaders for simple formats dealing with
|
||||
text; for example, use `qp.data.reader.from_text` for the following the format:
|
||||
|
||||
```
|
||||
class-id \t first document's pre-processed text \n
|
||||
class-id \t second document's pre-processed text \n
|
||||
...
|
||||
```
|
||||
|
||||
or `qp.data.reader.from_sparse` for sparse representations of the form:
|
||||
|
||||
```
|
||||
{-1, 0, or +1} col(int):val(float) col(int):val(float) ... \n
|
||||
...
|
||||
```
|
||||
|
||||
both functions return a tuple `X, y` containing a list of strings and the corresponding
|
||||
labels, respectively.
|
||||
|
||||
The code in charge in loading a LabelledCollection is:
|
||||
|
||||
```python
|
||||
@classmethod
|
||||
def load(cls, path:str, loader_func:callable):
|
||||
return LabelledCollection(*loader_func(path))
|
||||
```
|
||||
|
||||
indicating that any `loader_func` (e.g., `from_text`, `from_sparse`, `from_csv`, or a user-defined one) which
|
||||
returns valid arguments for initializing a _LabelledCollection_ object will allow
|
||||
to load any collection. More specifically, the _LabelledCollection_ receives as
|
||||
arguments the _instances_ (iterable) and the _labels_ (iterable) and,
|
||||
optionally, the number of classes (it would be
|
||||
inferred from the labels if not indicated, but this requires at least one
|
||||
positive example for
|
||||
all classes to be present in the collection).
|
||||
|
||||
The same _loader_func_ can be passed to a Dataset, along with two
|
||||
paths, in order to create a training and test pair of _LabelledCollection_,
|
||||
e.g.:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
train_path = '../my_data/train.dat'
|
||||
test_path = '../my_data/test.dat'
|
||||
|
||||
def my_custom_loader(path, **custom_kwargs):
|
||||
with open(path, 'rb') as fin:
|
||||
...
|
||||
return instances, labels
|
||||
|
||||
data = qp.data.Dataset.load(train_path, test_path, my_custom_loader, **custom_kwargs)
|
||||
```
|
||||
|
||||
### Data Processing
|
||||
|
||||
QuaPy implements a number of preprocessing functions in the package `qp.data.preprocessing`, including:
|
||||
|
||||
* _text2tfidf_: tfidf vectorization
|
||||
* _reduce_columns_: reducing the number of columns based on term frequency
|
||||
* _standardize_: transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so
|
||||
that the column values have zero mean and unit variance).
|
||||
* _index_: transforms textual tokens into lists of numeric ids
|
||||
|
||||
These functions are applied to `Dataset` objects, and offer the possibility to apply the transformation
|
||||
inline (thus modifying the original dataset), or to return a modified copy.
|
||||
|
|
@ -1,272 +0,0 @@
|
|||
# Evaluation
|
||||
|
||||
Quantification is an appealing tool in scenarios of dataset shift,
|
||||
and particularly in scenarios of prior-probability shift.
|
||||
That is, the interest in estimating the class prevalences arises
|
||||
under the belief that those class prevalences might have changed
|
||||
with respect to the ones observed during training.
|
||||
In other words, one could simply return the training prevalence
|
||||
as a predictor of the test prevalence if this change is assumed
|
||||
to be unlikely (as is the case in general scenarios of
|
||||
machine learning governed by the iid assumption).
|
||||
In brief, quantification requires dedicated evaluation protocols,
|
||||
which are implemented in QuaPy and explained here.
|
||||
|
||||
## Error Measures
|
||||
|
||||
The module quapy.error implements the most popular error measures for quantification, e.g., mean absolute error (_mae_), mean relative absolute error (_mrae_), among others. For each such measure (e.g., _mrae_) there are corresponding functions (e.g., _rae_) that do not average the results across samples.
|
||||
|
||||
Some errors of classification are also available, e.g., accuracy error (_acce_) or F-1 error (_f1e_).
|
||||
|
||||
The error functions implement the following interface, e.g.:
|
||||
|
||||
```python
|
||||
mae(true_prevs, prevs_hat)
|
||||
```
|
||||
|
||||
in which the first argument is a ndarray containing the true
|
||||
prevalences, and the second argument is another ndarray with
|
||||
the estimations produced by some method.
|
||||
|
||||
Some error functions, e.g., _mrae_, _mkld_, and _mnkld_, are
|
||||
smoothed for numerical stability. In those cases, there is a
|
||||
third argument, e.g.:
|
||||
|
||||
```python
|
||||
def mrae(true_prevs, prevs_hat, eps=None): ...
|
||||
```
|
||||
|
||||
indicating the value for the smoothing parameter epsilon.
|
||||
Traditionally, this value is set to 1/(2T) in past literature,
|
||||
with T the sampling size. One could either pass this value
|
||||
to the function each time, or to set a QuaPy's environment
|
||||
variable _SAMPLE_SIZE_ once, and omit this argument
|
||||
thereafter (recommended);
|
||||
e.g.:
|
||||
|
||||
```python
|
||||
qp.environ['SAMPLE_SIZE'] = 100 # once for all
|
||||
true_prev = [0.5, 0.3, 0.2] # let's assume 3 classes
|
||||
estim_prev = [0.1, 0.3, 0.6]
|
||||
error = qp.error.mrae(true_prev, estim_prev)
|
||||
print(f'mrae({true_prev}, {estim_prev}) = {error:.3f}')
|
||||
```
|
||||
|
||||
will print:
|
||||
```
|
||||
mrae([0.5, 0.3, 0.2], [0.1, 0.3, 0.6]) = 0.914
|
||||
```
|
||||
|
||||
It is also possible to instantiate QuaPy's quantification
|
||||
error functions from strings using, e.g.:
|
||||
|
||||
```python
|
||||
error_function = qp.error.from_name('mse')
|
||||
error = error_function(true_prev, estim_prev)
|
||||
```
|
||||
|
||||
The main quantification measures currently available in `qp.error` are the
|
||||
following. As a rule of thumb, names starting with `m` indicate the mean value
|
||||
across many sample pairs, while the corresponding unprefixed function returns
|
||||
the sample-wise quantity. Let `p` denote the true prevalence vector,
|
||||
`\hat{p}` the predicted prevalence vector, `\mathcal{Y}` the set of classes,
|
||||
and `p^{tr}` the training prevalence vector.
|
||||
|
||||
### Prevalence-vector measures
|
||||
|
||||
Absolute error and its mean version:
|
||||
|
||||
```{math}
|
||||
AE(p,\hat{p}) = \frac{1}{|\mathcal{Y}|}\sum_{y \in \mathcal{Y}} |\hat{p}(y)-p(y)|
|
||||
```
|
||||
|
||||
Implemented as `ae` and `mae`.
|
||||
|
||||
Normalized absolute error and its mean version:
|
||||
|
||||
```{math}
|
||||
NAE(p,\hat{p}) = \frac{AE(p,\hat{p})}{z_{AE}},\qquad
|
||||
z_{AE}=\frac{2(1-\min_{y \in \mathcal{Y}} p(y))}{|\mathcal{Y}|}
|
||||
```
|
||||
|
||||
Implemented as `nae` and `mnae`.
|
||||
|
||||
Squared error and its mean version:
|
||||
|
||||
```{math}
|
||||
SE(p,\hat{p}) = \frac{1}{|\mathcal{Y}|}\sum_{y \in \mathcal{Y}} (\hat{p}(y)-p(y))^2
|
||||
```
|
||||
|
||||
Implemented as `se` and `mse`.
|
||||
|
||||
Relative absolute error and its mean version:
|
||||
|
||||
```{math}
|
||||
RAE(p,\hat{p}) = \frac{1}{|\mathcal{Y}|}\sum_{y \in \mathcal{Y}}\frac{|\hat{p}(y)-p(y)|}{p(y)}
|
||||
```
|
||||
|
||||
Implemented as `rae` and `mrae`.
|
||||
|
||||
Normalized relative absolute error and its mean version:
|
||||
|
||||
```{math}
|
||||
NRAE(p,\hat{p}) = \frac{RAE(p,\hat{p})}{z_{RAE}},\qquad
|
||||
z_{RAE}=\frac{|\mathcal{Y}|-1+\frac{1-\min_{y \in \mathcal{Y}} p(y)}{\min_{y \in \mathcal{Y}} p(y)}}{|\mathcal{Y}|}
|
||||
```
|
||||
|
||||
Implemented as `nrae` and `mnrae`.
|
||||
|
||||
Kullback-Leibler divergence and its mean version:
|
||||
|
||||
```{math}
|
||||
KLD(p,\hat{p}) = \sum_{y \in \mathcal{Y}} p(y)\log\frac{p(y)}{\hat{p}(y)}
|
||||
```
|
||||
|
||||
Implemented as `kld` and `mkld`.
|
||||
|
||||
Normalized Kullback-Leibler divergence and its mean version:
|
||||
|
||||
```{math}
|
||||
NKLD(p,\hat{p}) = 2\frac{e^{KLD(p,\hat{p})}}{e^{KLD(p,\hat{p})}+1}-1
|
||||
```
|
||||
|
||||
Implemented as `nkld` and `mnkld`.
|
||||
|
||||
Squared ratio error and its mean version:
|
||||
|
||||
```{math}
|
||||
SRE(p,\hat{p},p^{tr}) = \frac{1}{|\mathcal{Y}|}\sum_{i \in \mathcal{Y}} (w_i-\hat{w}_i)^2,\qquad
|
||||
w_i=\frac{p_i}{p^{tr}_i}
|
||||
```
|
||||
|
||||
Implemented as `sre` and `msre`.
|
||||
|
||||
The Aitchison Quantification Error (AQE) and its mean version (MAQE) are implemented as `aqe` and `maqe` using the
|
||||
Aitchison Distance (available in `qp.functional.AitchisonDistance`, here denoted `d_A`):
|
||||
|
||||
```{math}
|
||||
d_A(p,\hat{p}) = \|\mathrm{clr}(p)-\mathrm{clr}(\hat{p})\|_2
|
||||
```
|
||||
|
||||
### Additional measures
|
||||
|
||||
Match distance computes the cumulative-distribution discrepancy under the
|
||||
assumption that moving mass from class `i` to class `i+1` has unit cost:
|
||||
|
||||
```{math}
|
||||
MD(p,\hat{p}) = \sum_{i=1}^{|\mathcal{Y}|-1} \left|\sum_{j=1}^{i} p_j - \sum_{j=1}^{i} \hat{p}_j\right|
|
||||
```
|
||||
|
||||
Implemented as `md`. Its normalized variant `nmd` rescales this quantity by
|
||||
`1/(|\mathcal{Y}|-1)`.
|
||||
|
||||
For binary quantification, QuaPy also provides the signed bias of the positive
|
||||
class and its mean value:
|
||||
|
||||
```{math}
|
||||
bias(p,\hat{p}) = \hat{p}_1 - p_1
|
||||
```
|
||||
|
||||
Implemented as `bias_binary` and `mean_bias_binary`.
|
||||
|
||||
### Classification measures
|
||||
|
||||
The same module also exposes two classification-oriented error measures, which
|
||||
can occasionally be useful for diagnostics: `acce` (accuracy error, i.e.,
|
||||
`1-accuracy`) and `f1e` (macro-`F_1` error, i.e., `1-F_1^M`).
|
||||
|
||||
## Evaluation Protocols
|
||||
|
||||
An _evaluation protocol_ is an evaluation procedure that uses
|
||||
one specific _sample generation procotol_ to genereate many
|
||||
samples, typically characterized by widely varying amounts of
|
||||
_shift_ with respect to the original distribution, that are then
|
||||
used to evaluate the performance of a (trained) quantifier.
|
||||
These protocols are explained in more detail in a dedicated [manual](./protocols.md).
|
||||
For the moment being, let us assume we already have
|
||||
chosen and instantiated one specific such protocol, that we here
|
||||
simply call _prot_. Let also assume our model is called
|
||||
_quantifier_ and that our evaluatio measure of choice is
|
||||
_mae_. The evaluation comes down to:
|
||||
|
||||
```python
|
||||
mae = qp.evaluation.evaluate(quantifier, protocol=prot, error_metric='mae')
|
||||
print(f'MAE = {mae:.4f}')
|
||||
```
|
||||
|
||||
It is often desirable to evaluate our system using more than one
|
||||
single evaluation measure. In this case, it is convenient to generate
|
||||
a _report_. A report in QuaPy is a dataframe accounting for all the
|
||||
true prevalence values with their corresponding prevalence values
|
||||
as estimated by the quantifier, along with the error each has given
|
||||
rise.
|
||||
|
||||
```python
|
||||
report = qp.evaluation.evaluation_report(quantifier, protocol=prot, error_metrics=['mae', 'mrae', 'mkld'])
|
||||
```
|
||||
|
||||
From a pandas' dataframe, it is straightforward to visualize all the results,
|
||||
and compute the averaged values, e.g.:
|
||||
|
||||
```python
|
||||
pd.set_option('display.expand_frame_repr', False)
|
||||
report['estim-prev'] = report['estim-prev'].map(F.strprev)
|
||||
print(report)
|
||||
|
||||
print('Averaged values:')
|
||||
print(report.mean(numeric_only=True))
|
||||
```
|
||||
|
||||
This will produce an output like:
|
||||
|
||||
```
|
||||
true-prev estim-prev mae mrae mkld
|
||||
0 [0.308, 0.692] [0.314, 0.686] 0.005649 0.013182 0.000074
|
||||
1 [0.896, 0.104] [0.909, 0.091] 0.013145 0.069323 0.000985
|
||||
2 [0.848, 0.152] [0.809, 0.191] 0.039063 0.149806 0.005175
|
||||
3 [0.016, 0.984] [0.033, 0.967] 0.017236 0.487529 0.005298
|
||||
4 [0.728, 0.272] [0.751, 0.249] 0.022769 0.057146 0.001350
|
||||
... ... ... ... ... ...
|
||||
4995 [0.72, 0.28] [0.698, 0.302] 0.021752 0.053631 0.001133
|
||||
4996 [0.868, 0.132] [0.888, 0.112] 0.020490 0.088230 0.001985
|
||||
4997 [0.292, 0.708] [0.298, 0.702] 0.006149 0.014788 0.000090
|
||||
4998 [0.24, 0.76] [0.220, 0.780] 0.019950 0.054309 0.001127
|
||||
4999 [0.948, 0.052] [0.965, 0.035] 0.016941 0.165776 0.003538
|
||||
|
||||
[5000 rows x 5 columns]
|
||||
Averaged values:
|
||||
mae 0.023588
|
||||
mrae 0.108779
|
||||
mkld 0.003631
|
||||
dtype: float64
|
||||
|
||||
Process finished with exit code 0
|
||||
```
|
||||
|
||||
Alternatively, we can simply generate all the predictions by:
|
||||
|
||||
```python
|
||||
true_prevs, estim_prevs = qp.evaluation.prediction(quantifier, protocol=prot)
|
||||
```
|
||||
|
||||
All the evaluation functions implement specific optimizations for speeding-up
|
||||
the evaluation of aggregative quantifiers (i.e., of instances of _AggregativeQuantifier_).
|
||||
|
||||
The optimization comes down to generating classification predictions (either crisp or soft)
|
||||
only once for the entire test set, and then applying the sampling procedure to the
|
||||
predictions, instead of generating samples of instances and then computing the
|
||||
classification predictions every time. This is only possible when the protocol
|
||||
is an instance of _OnLabelledCollectionProtocol_.
|
||||
|
||||
The optimization is only
|
||||
carried out when the number of classification predictions thus generated would be
|
||||
smaller than the number of predictions required for the entire protocol; e.g.,
|
||||
if the original dataset contains 1M instances, but the protocol is such that it would
|
||||
at most generate 20 samples of 100 instances, then it would be preferable to postpone the
|
||||
classification for each sample. This behaviour is indicated by setting
|
||||
_aggr_speedup="auto"_. Conversely, when indicating _aggr_speedup="force"_ QuaPy will
|
||||
precompute all the predictions irrespectively of the number of instances and number of samples.
|
||||
Finally, this can be deactivated by setting _aggr_speedup=False_. Note that this optimization
|
||||
is not only applied for the final evaluation, but also for the internal evaluations carried
|
||||
out during _model selection_. Since these are typically many, the heuristic can help reduce the
|
||||
execution time significatively.
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,145 +0,0 @@
|
|||
# Model Selection
|
||||
|
||||
As a supervised machine learning task, quantification methods
|
||||
can strongly depend on a good choice of model hyper-parameters.
|
||||
The process whereby those hyper-parameters are chosen is
|
||||
typically known as _Model Selection_, and typically consists of
|
||||
testing different settings and picking the one that performed
|
||||
best in a held-out validation set in terms of any given
|
||||
evaluation measure.
|
||||
|
||||
## Targeting a Quantification-oriented loss
|
||||
|
||||
The task being optimized determines the evaluation protocol,
|
||||
i.e., the criteria according to which the performance of
|
||||
any given method for solving is to be assessed.
|
||||
As a task on its own right, quantification should impose
|
||||
its own model selection strategies, i.e., strategies
|
||||
aimed at finding appropriate configurations
|
||||
specifically designed for the task of quantification.
|
||||
|
||||
Quantification has long been regarded as an add-on of
|
||||
classification, and thus the model selection strategies
|
||||
customarily adopted in classification have simply been
|
||||
applied to quantification (see the next section).
|
||||
It has been argued in [Moreo, Alejandro, and Fabrizio Sebastiani.
|
||||
Re-Assessing the "Classify and Count" Quantification Method.
|
||||
ECIR 2021: Advances in Information Retrieval pp 75–91.](https://link.springer.com/chapter/10.1007/978-3-030-72240-1_6)
|
||||
that specific model selection strategies should
|
||||
be adopted for quantification. That is, model selection
|
||||
strategies for quantification should target
|
||||
quantification-oriented losses and be tested in a variety
|
||||
of scenarios exhibiting different degrees of prior
|
||||
probability shift.
|
||||
|
||||
The class _qp.model_selection.GridSearchQ_ implements a grid-search exploration over the space of
|
||||
hyper-parameter combinations that [evaluates](./evaluation)
|
||||
each combination of hyper-parameters by means of a given quantification-oriented
|
||||
error metric (e.g., any of the error functions implemented
|
||||
in _qp.error_) and according to a
|
||||
[sampling generation protocol](./protocols).
|
||||
|
||||
The following is an example (also included in the examples folder) of model selection for quantification:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.protocol import APP
|
||||
from quapy.method.aggregative import DMy
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
import numpy as np
|
||||
|
||||
"""
|
||||
In this example, we show how to perform model selection on a DistributionMatching quantifier.
|
||||
"""
|
||||
|
||||
model = DMy(LogisticRegression())
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = 100
|
||||
qp.environ['N_JOBS'] = -1 # explore hyper-parameters in parallel
|
||||
|
||||
training, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
|
||||
|
||||
# The model will be returned by the fit method of GridSearchQ.
|
||||
# Every combination of hyper-parameters will be evaluated by confronting the
|
||||
# quantifier thus configured against a series of samples generated by means
|
||||
# of a sample generation protocol. For this example, we will use the
|
||||
# artificial-prevalence protocol (APP), that generates samples with prevalence
|
||||
# values in the entire range of values from a grid (e.g., [0, 0.1, 0.2, ..., 1]).
|
||||
# We devote 30% of the dataset for this exploration.
|
||||
training, validation = training.split_stratified(train_prop=0.7)
|
||||
protocol = APP(validation)
|
||||
|
||||
# We will explore a classification-dependent hyper-parameter (e.g., the 'C'
|
||||
# hyper-parameter of LogisticRegression) and a quantification-dependent hyper-parameter
|
||||
# (e.g., the number of bins in a DistributionMatching quantifier.
|
||||
# Classifier-dependent hyper-parameters have to be marked with a prefix "classifier__"
|
||||
# in order to let the quantifier know this hyper-parameter belongs to its underlying
|
||||
# classifier.
|
||||
param_grid = {
|
||||
'classifier__C': np.logspace(-3, 3, 7),
|
||||
'nbins': [8, 16, 32, 64],
|
||||
}
|
||||
|
||||
model = qp.model_selection.GridSearchQ(
|
||||
model=model,
|
||||
param_grid=param_grid,
|
||||
protocol=protocol,
|
||||
error='mae', # the error to optimize is the MAE (a quantification-oriented loss)
|
||||
refit=True, # retrain on the whole labelled set once done
|
||||
verbose=True # show information as the process goes on
|
||||
).fit(*training.Xy)
|
||||
|
||||
print(f'model selection ended: best hyper-parameters={model.best_params_}')
|
||||
model = model.best_model_
|
||||
|
||||
# evaluation in terms of MAE
|
||||
# we use the same evaluation protocol (APP) on the test set
|
||||
mae_score = qp.evaluation.evaluate(model, protocol=APP(test), error_metric='mae')
|
||||
|
||||
print(f'MAE={mae_score:.5f}')
|
||||
```
|
||||
|
||||
In this example, the system outputs:
|
||||
```
|
||||
[GridSearchQ]: starting model selection with self.n_jobs =-1
|
||||
[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 64} got mae score 0.04021 [took 1.1356s]
|
||||
[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 32} got mae score 0.04286 [took 1.2139s]
|
||||
[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 16} got mae score 0.04888 [took 1.2491s]
|
||||
[GridSearchQ]: hyperparams={'classifier__C': 0.001, 'nbins': 8} got mae score 0.05163 [took 1.5372s]
|
||||
[...]
|
||||
[GridSearchQ]: hyperparams={'classifier__C': 1000.0, 'nbins': 32} got mae score 0.02445 [took 2.9056s]
|
||||
[GridSearchQ]: optimization finished: best params {'classifier__C': 100.0, 'nbins': 32} (score=0.02234) [took 7.3114s]
|
||||
[GridSearchQ]: refitting on the whole development set
|
||||
model selection ended: best hyper-parameters={'classifier__C': 100.0, 'nbins': 32}
|
||||
MAE=0.03102
|
||||
```
|
||||
|
||||
|
||||
## Targeting a Classification-oriented loss
|
||||
|
||||
Optimizing a model for quantification could rather be
|
||||
computationally costly.
|
||||
In aggregative methods, one could alternatively try to optimize
|
||||
the classifier's hyper-parameters for classification.
|
||||
Although this is theoretically suboptimal, many articles in
|
||||
quantification literature have opted for this strategy.
|
||||
|
||||
In QuaPy, this is achieved by simply instantiating the
|
||||
classifier learner as a GridSearchCV from scikit-learn.
|
||||
The following code illustrates how to do that:
|
||||
|
||||
```python
|
||||
learner = GridSearchCV(
|
||||
LogisticRegression(),
|
||||
param_grid={'C': np.logspace(-4, 5, 10), 'class_weight': ['balanced', None]},
|
||||
cv=5)
|
||||
model = DistributionMatching(learner).fit(*dataset.train.Xy)
|
||||
```
|
||||
|
||||
However, this is conceptually flawed, since the model should be
|
||||
optimized for the task at hand (quantification), and not for a surrogate task (classification),
|
||||
i.e., the model should be requested to deliver low quantification errors, rather
|
||||
than low classification errors.
|
||||
|
||||
|
||||
|
||||
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Reference in New Issue