This package allows you to easily estimate uncertainties in both regression and classification settings. In regression settings, MAPIE provides prediction intervals on single-output data. In classification settings, MAPIE provides prediction sets on multi-class data. In any case, MAPIE is compatible with any scikit-learn-compatible estimator.

Install via `pip`:

```pip install mapie
```

or via conda:

```\$ conda install -c conda-forge mapie
```

To install directly from the github repository :

```pip install git+https://github.com/simai-ml/MAPIE
```

### 2. Run MapieRegressor¶

Let us start with a basic regression problem. Here, we generate one-dimensional noisy data with normal distribution that we fit with a linear model.

```import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

regressor = LinearRegression()
X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
```

Since MAPIE is compliant with the standard scikit-learn API, we follow the standard sequential `fit` and `predict` process like any scikit-learn regressor. We set two values for alpha to estimate prediction intervals at approximately one and two standard deviations from the mean.

```from mapie.regression import MapieRegressor
alpha = [0.05, 0.32]
mapie = MapieRegressor(regressor)
mapie.fit(X, y)
y_pred, y_pis = mapie.predict(X, alpha=alpha)
```

### 3. Show the results¶

MAPIE returns a `np.ndarray` of shape (n_samples, 3, len(alpha)) giving the predictions, as well as the lower and upper bounds of the prediction intervals for the target quantile for each desired alpha value. The estimated prediction intervals can then be plotted as follows.

```from matplotlib import pyplot as plt
from mapie.metrics import coverage_score

coverage_scores = [
coverage_score(y, y_pis[:, 0, i], y_pis[:, 1, i])
for i, _ in enumerate(alpha)
]

plt.xlabel("x")
plt.ylabel("y")
plt.scatter(X, y, alpha=0.3)
plt.plot(X, y_preds[:, 0, 0], color="C1")
order = np.argsort(X[:, 0])
plt.plot(X[order], y_pis[order][:, 0, 1], color="C1", ls="--")
plt.plot(X[order], y_pis[order][:, 1, 1], color="C1", ls="--")
plt.fill_between(
X[order].ravel(),
y_pis[order][:, 0, 0].ravel(),
y_pis[order][:, 1, 0].ravel(),
alpha=0.2
)
plt.title(
f"Target and effective coverages for "
f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_scores:.3f})\n"
f"Target and effective coverages for "
f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_scores:.3f})"
)
plt.show()
``` The title of the plot compares the target coverages with the effective coverages. The target coverage, or the confidence interval, is the fraction of true labels lying in the prediction intervals that we aim to obtain for a given dataset. It is given by the alpha parameter defined in `MapieRegressor`, here equal to `0.05` and `0.32`, thus giving target coverages of 0.95 and 0.68. The effective coverage is the actual fraction of true labels lying in the prediction intervals.