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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

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

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README 

        
.. -*- mode: rst -*-



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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 Capgemini, Quantmetry, Michelin, ENS Paris-Saclay,

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



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



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    :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 `license `_.



📚 Citation

===========



If you use MAPIE in your research, please cite using:



.. code:: latex



    @inproceedings{Cordier_Flexible_and_Systematic_2023,

    author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},

    booktitle = {Conformal and Probabilistic Prediction with Applications},

    title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},

    year = {2023}

    }