111 lines
4.7 KiB
Markdown
111 lines
4.7 KiB
Markdown
# QuaPy
|
|
|
|
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
|
|
written in Python.
|
|
|
|
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 integrates commonly used datasets and offers visualization tools
|
|
for facilitating the analysis and interpretation of results.
|
|
|
|
### Installation
|
|
|
|
```commandline
|
|
pip install quapy
|
|
```
|
|
|
|
## A quick example:
|
|
|
|
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
|
|
|
|
dataset = qp.datasets.fetch_twitter('semeval16')
|
|
|
|
# create an "Adjusted Classify & Count" quantifier
|
|
model = qp.method.aggregative.ACC(LogisticRegression())
|
|
model.fit(dataset.training)
|
|
|
|
estim_prevalences = model.quantify(dataset.test.instances)
|
|
true_prevalences = dataset.test.prevalence()
|
|
|
|
error = qp.error.mae(true_prevalences, estim_prevalences)
|
|
|
|
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
|
```
|
|
|
|
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 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 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
|
|
|
|
* 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_ 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_.
|
|
|
|
|
|
## Wiki
|
|
|
|
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) |