# 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):

```
# Uncertainty quantification for regression problem
from mapie.regression import MapieRegressor
mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)
```

```
# 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:

```
$ 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.

```
# 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])
```

```
# 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.

# 🔍 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).