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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_covariance_plot_lw_vs_oas.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py:


=============================
Ledoit-Wolf vs OAS estimation
=============================

The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding the Ledoit-Wolf covariance estimate.

Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage
parameter, the OAS coefficient, whose convergence is significantly
better under the assumption that the data are Gaussian.

This example, inspired from Chen's publication [1], shows a comparison
of the estimated MSE of the LW and OAS methods, using Gaussian
distributed data.

[1] "Shrinkage Algorithms for MMSE Covariance Estimation"
Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010.

.. GENERATED FROM PYTHON SOURCE LINES 23-31

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from scipy.linalg import cholesky, toeplitz

    from sklearn.covariance import OAS, LedoitWolf

    np.random.seed(0)







.. GENERATED FROM PYTHON SOURCE LINES 32-106

.. code-block:: Python

    n_features = 100
    # simulation covariance matrix (AR(1) process)
    r = 0.1
    real_cov = toeplitz(r ** np.arange(n_features))
    coloring_matrix = cholesky(real_cov)

    n_samples_range = np.arange(6, 31, 1)
    repeat = 100
    lw_mse = np.zeros((n_samples_range.size, repeat))
    oa_mse = np.zeros((n_samples_range.size, repeat))
    lw_shrinkage = np.zeros((n_samples_range.size, repeat))
    oa_shrinkage = np.zeros((n_samples_range.size, repeat))
    for i, n_samples in enumerate(n_samples_range):
        for j in range(repeat):
            X = np.dot(np.random.normal(size=(n_samples, n_features)), coloring_matrix.T)

            lw = LedoitWolf(store_precision=False, assume_centered=True)
            lw.fit(X)
            lw_mse[i, j] = lw.error_norm(real_cov, scaling=False)
            lw_shrinkage[i, j] = lw.shrinkage_

            oa = OAS(store_precision=False, assume_centered=True)
            oa.fit(X)
            oa_mse[i, j] = oa.error_norm(real_cov, scaling=False)
            oa_shrinkage[i, j] = oa.shrinkage_

    # plot MSE
    plt.subplot(2, 1, 1)
    plt.errorbar(
        n_samples_range,
        lw_mse.mean(1),
        yerr=lw_mse.std(1),
        label="Ledoit-Wolf",
        color="navy",
        lw=2,
    )
    plt.errorbar(
        n_samples_range,
        oa_mse.mean(1),
        yerr=oa_mse.std(1),
        label="OAS",
        color="darkorange",
        lw=2,
    )
    plt.ylabel("Squared error")
    plt.legend(loc="upper right")
    plt.title("Comparison of covariance estimators")
    plt.xlim(5, 31)

    # plot shrinkage coefficient
    plt.subplot(2, 1, 2)
    plt.errorbar(
        n_samples_range,
        lw_shrinkage.mean(1),
        yerr=lw_shrinkage.std(1),
        label="Ledoit-Wolf",
        color="navy",
        lw=2,
    )
    plt.errorbar(
        n_samples_range,
        oa_shrinkage.mean(1),
        yerr=oa_shrinkage.std(1),
        label="OAS",
        color="darkorange",
        lw=2,
    )
    plt.xlabel("n_samples")
    plt.ylabel("Shrinkage")
    plt.legend(loc="lower right")
    plt.ylim(plt.ylim()[0], 1.0 + (plt.ylim()[1] - plt.ylim()[0]) / 10.0)
    plt.xlim(5, 31)

    plt.show()



.. image-sg:: /auto_examples/covariance/images/sphx_glr_plot_lw_vs_oas_001.png
   :alt: Comparison of covariance estimators
   :srcset: /auto_examples/covariance/images/sphx_glr_plot_lw_vs_oas_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.152 seconds)


.. _sphx_glr_download_auto_examples_covariance_plot_lw_vs_oas.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/covariance/plot_lw_vs_oas.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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        :alt: Launch JupyterLite
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    .. container:: sphx-glr-download sphx-glr-download-jupyter

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


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