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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 <https://scikit-learn.org/stable/>_ and numpy=>1.21 <https://numpy.org/>_.

🛠 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 <https://mapie.readthedocs.io/en/latest/>_.

📝 Contributing

You are welcome to propose and contribute new ideas. We encourage you to open an issue <https://github.com/simai-ml/MAPIE/issues>_ 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 <CONTRIBUTING.rst>_.

🤝 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 :width: 150 .. _Quantmetry: https://www.quantmetry.com/

.. |Michelin| image:: https://www.michelin.com/wp-content/themes/michelin/public/img/michelin-logo-en.svg :width: 100 .. _Michelin: https://www.michelin.com/en/

.. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg :width: 100 .. _ENS: https://ens-paris-saclay.fr/en

.. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg :width: 100 .. _Confiance.ai: https://www.confiance.ai/

.. |IledeFrance| image:: https://www.iledefrance.fr/themes/custom/portail_idf/logo.svg :width: 100 .. _IledeFrance: 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 <https://github.com/simai-ml/MAPIE/blob/master/LICENSE>_.