forked from moreo/QuaPy
120 lines
4.9 KiB
Markdown
120 lines
4.9 KiB
Markdown
# QuaPy
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QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify)
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written in Python.
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QuaPy is based on the concept of "data sample", and provides implementations of the
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most important aspects of the quantification workflow, such as (baseline and advanced)
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quantification methods,
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quantification-oriented model selection mechanisms, evaluation measures, and evaluations protocols
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used for evaluating quantification methods.
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QuaPy also makes available commonly used datasets, and offers visualization tools
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for facilitating the analysis and interpretation of the experimental results.
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### Last updates:
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* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
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* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
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### Installation
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```commandline
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pip install quapy
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```
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### Cite QuaPy
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If you find QuaPy useful (and we hope you will), plese consider citing the original paper in your research:
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```
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@inproceedings{moreo2021quapy,
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title={QuaPy: a python-based framework for quantification},
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author={Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
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booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
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pages={4534--4543},
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year={2021}
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}
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```
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## A quick example:
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The following script fetches a dataset of tweets, trains, applies, and evaluates a quantifier based on the
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_Adjusted Classify & Count_ quantification method, using, as the evaluation measure, the _Mean Absolute Error_ (MAE)
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between the predicted and the true class prevalence values
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of the test set.
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```python
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import quapy as qp
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from sklearn.linear_model import LogisticRegression
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dataset = qp.datasets.fetch_twitter('semeval16')
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# create an "Adjusted Classify & Count" quantifier
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model = qp.method.aggregative.ACC(LogisticRegression())
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model.fit(dataset.training)
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estim_prevalence = model.quantify(dataset.test.instances)
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true_prevalence = dataset.test.prevalence()
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error = qp.error.mae(true_prevalence, estim_prevalence)
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print(f'Mean Absolute Error (MAE)={error:.3f}')
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```
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Quantification is useful in scenarios characterized by prior probability shift. In other
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words, we would be little interested in estimating the class prevalence values of the test set if
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we could assume the IID assumption to hold, as this prevalence would be roughly equivalent to the
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class prevalence of the training set. For this reason, any quantification model
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should be tested across many samples, even ones characterized by class prevalence
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values different or very different from those found in the training set.
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QuaPy implements sampling procedures and evaluation protocols that automate this workflow.
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See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
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## Features
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* Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization,
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quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
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* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
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* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
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* Datasets frequently used in quantification (textual and numeric), including:
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* 32 UCI Machine Learning datasets.
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* 11 Twitter quantification-by-sentiment datasets.
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* 3 product reviews quantification-by-sentiment datasets.
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* 4 tasks from LeQua competition (_new in v0.1.7!_)
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* Native support for binary and single-label multiclass quantification scenarios.
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* Model selection functionality that minimizes quantification-oriented loss functions.
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* Visualization tools for analysing the experimental results.
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## Requirements
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* scikit-learn, numpy, scipy
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* pytorch (for QuaNet)
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* svmperf patched for quantification (see below)
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* joblib
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* tqdm
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* pandas, xlrd
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* matplotlib
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## Contributing
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In case you want to contribute improvements to quapy, please generate pull request to the "devel" branch.
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## Documentation
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The [developer API documentation](https://hlt-isti.github.io/QuaPy/build/html/modules.html) is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html).
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Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples
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are provided:
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* [Datasets](https://github.com/HLT-ISTI/QuaPy/wiki/Datasets)
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* [Evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
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* [Protocols](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)
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* [Methods](https://github.com/HLT-ISTI/QuaPy/wiki/Methods)
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* [SVMperf](https://github.com/HLT-ISTI/QuaPy/wiki/ExplicitLossMinimization)
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* [Model Selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
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* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)
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## Acknowledgments:
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<img src="SoBigData.png" alt="SoBigData++" width="250"/>
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