130 lines
5.2 KiB
Markdown
130 lines
5.2 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:
|
|
|
|
* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
|
|
* A detailed wiki is available [here](https://github.com/HLT-ISTI/QuaPy/wiki)
|
|
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
|
|
|
|
### Installation
|
|
|
|
```commandline
|
|
pip install quapy
|
|
```
|
|
|
|
### Cite QuaPy
|
|
|
|
If you find QuaPy useful (and we hope you will), plese 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
|
|
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, quantification ensembles, among others).
|
|
* 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:
|
|
* 32 UCI Machine Learning binary datasets.
|
|
* 5 UCI Machine Learning multiclass datasets (_new in v0.1.8!_).
|
|
* 11 Twitter quantification-by-sentiment datasets.
|
|
* 3 product reviews quantification-by-sentiment datasets.
|
|
* 4 tasks from LeQua competition (_new in v0.1.7!_)
|
|
* IFCB dataset of plankton water samples (_new in v0.1.8!_).
|
|
* 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
|
|
* ucimlrepo
|
|
|
|
## Contributing
|
|
|
|
In case you want to contribute improvements to quapy, please generate pull request to the "devel" branch.
|
|
|
|
## Documentation
|
|
|
|
The developer API documentation 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)
|
|
* [Protocols](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)
|
|
* [Methods](https://github.com/HLT-ISTI/QuaPy/wiki/Methods)
|
|
* [SVMperf](https://github.com/HLT-ISTI/QuaPy/wiki/ExplicitLossMinimization)
|
|
* [Model Selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
|
|
* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)
|
|
|
|
## Acknowledgments:
|
|
|
|
|
|
|
|
<img src="logo/SoBigData.png" alt="SoBigData++" width="250"/>
|
|
|
|
<img src="logo/LogoQuaDaSh.png" alt="QuaDaSh" width="250"/>
|
|
|
|
<img src="logo/NextGenerationEU.jpg" alt="QuaDaSh" width="250"/>
|