Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/scikit-learn-contrib/MAPIE

A scikit-learn-compatible module for estimating prediction intervals.
https://github.com/scikit-learn-contrib/MAPIE

classification confidence-intervals conformal-prediction data-science python regression sklearn

Last synced: about 1 month ago
JSON representation

A scikit-learn-compatible module for estimating prediction intervals.

Lists

README 

        
.. -*- mode: rst -*-



|GitHubActions| |Codecov| |ReadTheDocs| |License| |PythonVersion| |PyPi| |Conda| |Release| |Commits| |DOI|



.. |GitHubActions| image:: https://github.com/scikit-learn-contrib/MAPIE/actions/workflows/test.yml/badge.svg

   :target: https://github.com/scikit-learn-contrib/MAPIE/actions



.. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/MAPIE/branch/master/graph/badge.svg?token=F2S6KYH4V1

   :target: https://codecov.io/gh/scikit-learn-contrib/MAPIE



.. |ReadTheDocs| image:: https://readthedocs.org/projects/mapie/badge/?version=stable

    :target: https://mapie.readthedocs.io/en/stable/?badge=stable

    :alt: Documentation Status



.. |License| image:: https://img.shields.io/github/license/simai-ml/MAPIE

   :target: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE



.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/mapie

   :target: https://pypi.org/project/mapie/



.. |PyPi| image:: https://img.shields.io/pypi/v/mapie

   :target: https://pypi.org/project/mapie/



.. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/mapie

   :target: https://anaconda.org/conda-forge/mapie



.. |Release| image:: https://img.shields.io/github/v/release/scikit-learn-contrib/mapie

   :target: https://github.com/scikit-learn-contrib/MAPIE/releases



.. |Commits| image:: https://img.shields.io/github/commits-since/scikit-learn-contrib/mapie/latest/master

   :target: https://github.com/scikit-learn-contrib/MAPIE/commits/master



.. |DOI| image:: https://img.shields.io/badge/10.48550/arXiv.2207.12274-B31B1B.svg

   :target: https://arxiv.org/abs/2207.12274



.. image:: https://github.com/simai-ml/MAPIE/raw/master/doc/images/mapie_logo_nobg_cut.png

    :width: 400

    :align: center



MAPIE - Model Agnostic Prediction Interval Estimator

====================================================



**MAPIE** is an open-source Python library for quantifying uncertainties and controlling the risks of machine learning models.

It is a scikit-learn-contrib project that allows you to:



- Easily **compute conformal prediction intervals** (or prediction sets) with controlled (or guaranteed) marginal coverage rate

  for regression [3,4,8], classification (binary and multi-class) [5-7] and time series [9].

- Easily **control risks** of more complex tasks such as multi-label classification,

  semantic segmentation in computer vision (probabilistic guarantees on recall, precision, ...) [10-12].

- Easily **wrap any model (scikit-learn, tensorflow, pytorch, ...) with, if needed, a scikit-learn-compatible wrapper**

  for the purposes just mentioned.



Here's a quick instantiation of MAPIE models for regression and classification problems related to uncertainty quantification

(more details in the Quickstart section):



.. code:: python



    # Uncertainty quantification for regression problem

    from mapie.regression import MapieRegressor

    mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)



.. code:: python



    # Uncertainty quantification for classification problem

    from mapie.classification import MapieClassifier

    mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)



Implemented methods in **MAPIE** respect three fundamental pillars:



- They are **model and use case agnostic**, 

- They possess **theoretical guarantees** under minimal assumptions on the data and the model,

- They are based on **peer-reviewed algorithms** and respect programming standards.



**MAPIE** relies notably on the field of *Conformal Prediction* and *Distribution-Free Inference*.



🔗 Requirements

===============



- **MAPIE** runs on Python 3.7+.

- **MAPIE** stands on the shoulders of giants. Its only internal dependencies are `scikit-learn `_ and `numpy=>1.21 `_.



🛠 Installation

===============



**MAPIE** can be installed in different ways:



.. code:: sh



    $ pip install mapie  # installation via `pip`

    $ conda install -c conda-forge mapie  # or via `conda`

    $ pip install git+https://github.com/scikit-learn-contrib/MAPIE  # or directly from the github repository



⚡ Quickstart

=============



Here we propose two basic uncertainty quantification problems for regression and classification tasks with scikit-learn.



As **MAPIE** is compatible with the standard scikit-learn API, you can see that with just these few lines of code:



- How easy it is **to wrap your favorite scikit-learn-compatible model** around your model.

- How easy it is **to follow the standard sequential** ``fit`` and ``predict`` process like any scikit-learn estimator.



.. code:: python



    # Uncertainty quantification for regression problem

    import numpy as np

    from sklearn.linear_model import LinearRegression

    from sklearn.datasets import make_regression

    from sklearn.model_selection import train_test_split



    from mapie.regression import MapieRegressor



    X, y = make_regression(n_samples=500, n_features=1)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)



    regressor = LinearRegression()



    mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)



    mapie_regressor = mapie_regressor.fit(X_train, y_train)

    y_pred, y_pis = mapie_regressor.predict(X_test, alpha=[0.05, 0.32])



.. code:: python



    # Uncertainty quantification for classification problem

    import numpy as np

    from sklearn.linear_model import LogisticRegression

    from sklearn.datasets import make_blobs

    from sklearn.model_selection import train_test_split



    from mapie.classification import MapieClassifier



    X, y = make_blobs(n_samples=500, n_features=2, centers=3)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)



    classifier = LogisticRegression()



    mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)



    mapie_classifier = mapie_classifier.fit(X_train, y_train)

    y_pred, y_pis = mapie_classifier.predict(X_test, alpha=[0.05, 0.32])



📘 Documentation

================



The full documentation can be found `on this link `_.



📝 Contributing

===============



You are welcome to propose and contribute new ideas.

We encourage you to `open an issue `_ so that we can align on the work to be done.

It is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.

For more information on the contribution process, please go `here `_.



🤝  Affiliations

================



MAPIE has been developed through a collaboration between Quantmetry, Michelin, ENS Paris-Saclay,

and with the financial support from Région Ile de France and Confiance.ai.



|Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|



.. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg

    :height: 35

    :target: https://www.quantmetry.com/



.. |Michelin| image:: https://agngnconpm.cloudimg.io/v7/https://dgaddcosprod.blob.core.windows.net/corporate-production/attachments/cls05tqdd9e0o0tkdghwi9m7n-clooe1x0c3k3x0tlu4cxi6dpn-bibendum-salut.full.png

    :height: 35

    :target: https://www.michelin.com/en/



.. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg

    :height: 35

    :target: https://ens-paris-saclay.fr/en



.. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg

    :height: 35

    :target: https://www.confiance.ai/



.. |IledeFrance| image:: https://www.iledefrance.fr/sites/default/files/logo/2024-02/logoGagnerok.svg

    :height: 35

    :target: https://www.iledefrance.fr/



🔍  References

==============



[1] Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. Algorithmic Learning in a Random World. Springer Nature, 2022.



[2] Angelopoulos, Anastasios N., and Stephen Bates. "Conformal prediction: A gentle introduction." Foundations and Trends® in Machine Learning 16.4 (2023): 494-591.



[3] Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani. "Predictive inference with the jackknife+." Ann. Statist., 49(1):486–507, (2021).



[4] Kim, Byol, Chen Xu, and Rina Barber. "Predictive inference is free with the jackknife+-after-bootstrap." Advances in Neural Information Processing Systems 33 (2020): 4138-4149.



[5] Sadinle, Mauricio, Jing Lei, and Larry Wasserman. "Least ambiguous set-valued classifiers with bounded error levels." Journal of the American Statistical Association 114.525 (2019): 223-234.



[6] Romano, Yaniv, Matteo Sesia, and Emmanuel Candes. "Classification with valid and adaptive coverage." Advances in Neural Information Processing Systems 33 (2020): 3581-3591.



[7] Angelopoulos, Anastasios, et al. "Uncertainty sets for image classifiers using conformal prediction." International Conference on Learning Representations (2021).



[8] Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019).



[9] Xu, Chen, and Yao Xie. "Conformal prediction interval for dynamic time-series." International Conference on Machine Learning. PMLR, (2021).



[10] Bates, Stephen, et al. "Distribution-free, risk-controlling prediction sets." Journal of the ACM (JACM) 68.6 (2021): 1-34.



[11] Angelopoulos, Anastasios N., Stephen, Bates, Adam, Fisch, Lihua, Lei, and Tal, Schuster. "Conformal Risk Control." (2022).



[12] Angelopoulos, Anastasios N., Stephen, Bates, Emmanuel J. Candès, et al. "Learn Then Test: Calibrating Predictive Algorithms to Achieve Risk Control." (2022).



📝 License

==========



MAPIE is free and open-source software licensed under the `3-clause BSD license `_.