Name | MAPIE JSON |
Version |
0.9.1
JSON |
| download |
home_page | https://github.com/scikit-learn-contrib/MAPIE |
Summary | A scikit-learn-compatible module for estimating prediction intervals. |
upload_time | 2024-09-13 08:23:18 |
maintainer | T. Cordier, V. Blot, L. Lacombe |
docs_url | None |
author | None |
requires_python | >=3.7 |
license | new BSD |
keywords |
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VCS |
 |
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
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coveralls test coverage |
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.. -*- 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 <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/scikit-learn-contrib/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 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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🔍 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 <https://github.com/scikit-learn-contrib/MAPIE/blob/master/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}
}
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[10-12].\n- Easily **wrap any model (scikit-learn, tensorflow, pytorch, ...) with, if needed, a scikit-learn-compatible wrapper**\n for the purposes just mentioned.\n\nHere's a quick instantiation of MAPIE models for regression and classification problems related to uncertainty quantification\n(more details in the Quickstart section):\n\n.. code:: python\n\n # Uncertainty quantification for regression problem\n from mapie.regression import MapieRegressor\n mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)\n\n.. code:: python\n\n # Uncertainty quantification for classification problem\n from mapie.classification import MapieClassifier\n mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)\n\nImplemented methods in **MAPIE** respect three fundamental pillars:\n\n- They are **model and use case agnostic**, \n- They possess **theoretical guarantees** under minimal assumptions on the data and the model,\n- They are based on **peer-reviewed algorithms** and respect programming standards.\n\n**MAPIE** relies notably on the field of *Conformal Prediction* and *Distribution-Free Inference*.\n\n\n\ud83d\udd17 Requirements\n===============\n\n- **MAPIE** runs on Python 3.7+.\n- **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/>`_.\n\n\n\ud83d\udee0 Installation\n===============\n\n**MAPIE** can be installed in different ways:\n\n.. code:: sh\n\n $ pip install mapie # installation via `pip`\n $ conda install -c conda-forge mapie # or via `conda`\n $ pip install git+https://github.com/scikit-learn-contrib/MAPIE # or directly from the github repository\n\n\n\u26a1 Quickstart\n=============\n\nHere we propose two basic uncertainty quantification problems for regression and classification tasks with scikit-learn.\n\nAs **MAPIE** is compatible with the standard scikit-learn API, you can see that with just these few lines of code:\n\n- How easy it is **to wrap your favorite scikit-learn-compatible model** around your model.\n- How easy it is **to follow the standard sequential** ``fit`` and ``predict`` process like any scikit-learn estimator.\n\n.. code:: python\n\n # Uncertainty quantification for regression problem\n import numpy as np\n from sklearn.linear_model import LinearRegression\n from sklearn.datasets import make_regression\n from sklearn.model_selection import train_test_split\n\n from mapie.regression import MapieRegressor\n\n\n X, y = make_regression(n_samples=500, n_features=1)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n\n regressor = LinearRegression()\n\n mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)\n\n mapie_regressor = mapie_regressor.fit(X_train, y_train)\n y_pred, y_pis = mapie_regressor.predict(X_test, alpha=[0.05, 0.32])\n\n.. code:: python\n\n # Uncertainty quantification for classification problem\n import numpy as np\n from sklearn.linear_model import LogisticRegression\n from sklearn.datasets import make_blobs\n from sklearn.model_selection import train_test_split\n\n from mapie.classification import MapieClassifier\n\n\n X, y = make_blobs(n_samples=500, n_features=2, centers=3)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n\n classifier = LogisticRegression()\n\n mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)\n\n mapie_classifier = mapie_classifier.fit(X_train, y_train)\n y_pred, y_pis = mapie_classifier.predict(X_test, alpha=[0.05, 0.32])\n\n\n\ud83d\udcd8 Documentation\n================\n\nThe full documentation can be found `on this link <https://mapie.readthedocs.io/en/latest/>`_.\n\n\n\ud83d\udcdd Contributing\n===============\n\nYou are welcome to propose and contribute new ideas.\nWe encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`_ so that we can align on the work to be done.\nIt is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.\nFor more information on the contribution process, please go `here <CONTRIBUTING.rst>`_.\n\n\n\ud83e\udd1d Affiliations\n================\n\nMAPIE has been developed through a collaboration between Capgemini, Quantmetry, Michelin, ENS Paris-Saclay,\nand with the financial support from R\u00e9gion Ile de France and Confiance.ai.\n\n|Capgemini| |Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|\n\n.. |Capgemini| image:: https://www.capgemini.com/wp-content/themes/capgemini2020/assets/images/logo.svg\n :height: 35px\n :width: 140px\n :target: https://www.capgemini.com/\n\n.. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg\n :height: 35px\n :width: 140px\n :target: https://www.quantmetry.com/\n\n.. |Michelin| image:: https://agngnconpm.cloudimg.io/v7/https://dgaddcosprod.blob.core.windows.net/corporate-production/attachments/cls05tqdd9e0o0tkdghwi9m7n-clooe1x0c3k3x0tlu4cxi6dpn-bibendum-salut.full.png\n :height: 50px\n :width: 45px\n :target: https://www.michelin.com/en/\n\n.. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg\n :height: 35px\n :width: 140px\n :target: https://ens-paris-saclay.fr/en/\n\n.. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg\n :height: 45px\n :width: 45px\n :target: https://www.confiance.ai/\n\n.. |IledeFrance| image:: https://www.iledefrance.fr/sites/default/files/logo/2024-02/logoGagnerok.svg\n :height: 35px\n :width: 140px\n :target: https://www.iledefrance.fr/\n\n\n\ud83d\udd0d References\n==============\n\n[1] Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. Algorithmic Learning in a Random World. Springer Nature, 2022.\n\n[2] Angelopoulos, Anastasios N., and Stephen Bates. \"Conformal prediction: A gentle introduction.\" Foundations and Trends\u00ae in Machine Learning 16.4 (2023): 494-591.\n\n[3] Rina Foygel Barber, Emmanuel J. Cand\u00e8s, Aaditya Ramdas, and Ryan J. Tibshirani. \"Predictive inference with the jackknife+.\" Ann. Statist., 49(1):486\u2013507, (2021).\n\n[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.\n\n[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.\n\n[6] Romano, Yaniv, Matteo Sesia, and Emmanuel Candes. \"Classification with valid and adaptive coverage.\" Advances in Neural Information Processing Systems 33 (2020): 3581-3591.\n\n[7] Angelopoulos, Anastasios, et al. \"Uncertainty sets for image classifiers using conformal prediction.\" International Conference on Learning Representations (2021).\n\n[8] Romano, Yaniv, Evan Patterson, and Emmanuel Candes. \"Conformalized quantile regression.\" Advances in neural information processing systems 32 (2019).\n\n[9] Xu, Chen, and Yao Xie. \"Conformal prediction interval for dynamic time-series.\" International Conference on Machine Learning. PMLR, (2021).\n\n[10] Bates, Stephen, et al. \"Distribution-free, risk-controlling prediction sets.\" Journal of the ACM (JACM) 68.6 (2021): 1-34.\n\n[11] Angelopoulos, Anastasios N., Stephen, Bates, Adam, Fisch, Lihua, Lei, and Tal, Schuster. \"Conformal Risk Control.\" (2022).\n\n[12] Angelopoulos, Anastasios N., Stephen, Bates, Emmanuel J. 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