forked from moreo/QuaPy
119 lines
5.4 KiB
Markdown
119 lines
5.4 KiB
Markdown
# QuaPy
|
|
|
|
QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify)
|
|
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
|
|
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:
|
|
|
|
* A detailed documentation is now available [here](https://hlt-isti.github.io/QuaPy/)
|
|
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
|
|
|
|
### Installation
|
|
|
|
```commandline
|
|
pip install quapy
|
|
```
|
|
|
|
## 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
|
|
of the test set.
|
|
|
|
```python
|
|
import quapy as qp
|
|
from sklearn.linear_model import LogisticRegression
|
|
|
|
dataset = qp.datasets.fetch_twitter('semeval16')
|
|
|
|
# create an "Adjusted Classify & Count" quantifier
|
|
model = qp.method.aggregative.ACC(LogisticRegression())
|
|
model.fit(dataset.training)
|
|
|
|
estim_prevalence = model.quantify(dataset.test.instances)
|
|
true_prevalence = dataset.test.prevalence()
|
|
|
|
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 [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, and quantification ensembles).
|
|
* Versatile functionality for performing evaluation based on artificial sampling protocols.
|
|
* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, SE, KLD, NKLD, etc.).
|
|
* Datasets frequently used in quantification (textual and numeric), including:
|
|
* 32 UCI Machine Learning datasets.
|
|
* 11 Twitter quantification-by-sentiment datasets.
|
|
* 3 product reviews quantification-by-sentiment datasets.
|
|
* 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.
|
|
|
|
## Requirements
|
|
|
|
* scikit-learn, numpy, scipy
|
|
* pytorch (for QuaNet)
|
|
* svmperf patched for quantification (see below)
|
|
* joblib
|
|
* tqdm
|
|
* pandas, xlrd
|
|
* matplotlib
|
|
|
|
## 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:
|
|
|
|
```
|
|
./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_ measures as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0).
|
|
This patch extends the above one by also allowing SVMperf to optimize for
|
|
_AE_ and _RAE_.
|
|
|
|
|
|
## Documentation
|
|
|
|
The [developer API documentation](https://hlt-isti.github.io/QuaPy/build/html/modules.html) is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html).
|
|
|
|
Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples
|
|
are provided:
|
|
|
|
* [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)
|