.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_regression/1-quickstart/plot_toy_model.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_regression_1-quickstart_plot_toy_model.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_regression_1-quickstart_plot_toy_model.py:


======================================================
Plotting MAPIE prediction intervals with a toy dataset
======================================================
An example plot of :class:`~mapie.regression.MapieRegressor` used
in the Quickstart.

.. GENERATED FROM PYTHON SOURCE LINES 8-52



.. image-sg:: /examples_regression/1-quickstart/images/sphx_glr_plot_toy_model_001.png
   :alt: Target and effective coverages for alpha=0.05: (0.950, 0.950) Target and effective coverages for alpha=0.32: (0.680, 0.682)
   :srcset: /examples_regression/1-quickstart/images/sphx_glr_plot_toy_model_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn.datasets import make_regression
    from sklearn.linear_model import LinearRegression

    from mapie.metrics import regression_coverage_score
    from mapie.regression import MapieRegressor

    RANDOM_STATE = 42
    regressor = LinearRegression()
    X, y = make_regression(
        n_samples=500, n_features=1, noise=20, random_state=RANDOM_STATE
    )

    alpha = [0.05, 0.32]
    mapie = MapieRegressor(regressor, method="plus", random_state=RANDOM_STATE)
    mapie.fit(X, y)
    y_pred, y_pis = mapie.predict(X, alpha=alpha)

    coverage_scores = [
        regression_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_pred, 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[0]:.2f}: ({1-alpha[0]:.3f}, {coverage_scores[0]:.3f})\n"
        f"Target and effective coverages for "
        f"alpha={alpha[1]:.2f}: ({1-alpha[1]:.3f}, {coverage_scores[1]:.3f})"
    )
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.191 seconds)


.. _sphx_glr_download_examples_regression_1-quickstart_plot_toy_model.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_toy_model.py <plot_toy_model.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_toy_model.ipynb <plot_toy_model.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_