

.. _sphx_glr_auto_examples_feature_selection_plot_permutation_test_for_classification.py:


=================================================================
Test with permutations the significance of a classification score
=================================================================

In order to test if a classification score is significative a technique
in repeating the classification procedure after randomizing, permuting,
the labels. The p-value is then given by the percentage of runs for
which the score obtained is greater than the classification score
obtained in the first place.



.. code-block:: python


    # Author:  Alexandre Gramfort <alexandre.gramfort@inria.fr>
    # License: BSD 3 clause

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.svm import SVC
    from sklearn.model_selection import StratifiedKFold
    from sklearn.model_selection import permutation_test_score
    from sklearn import datasets



Loading a dataset


.. code-block:: python

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    n_classes = np.unique(y).size

    # Some noisy data not correlated
    random = np.random.RandomState(seed=0)
    E = random.normal(size=(len(X), 2200))

    # Add noisy data to the informative features for make the task harder
    X = np.c_[X, E]

    svm = SVC(kernel='linear')
    cv = StratifiedKFold(2)

    score, permutation_scores, pvalue = permutation_test_score(
        svm, X, y, scoring="accuracy", cv=cv, n_permutations=100, n_jobs=1)

    print("Classification score %s (pvalue : %s)" % (score, pvalue))


View histogram of permutation scores


.. code-block:: python

    plt.hist(permutation_scores, 20, label='Permutation scores')
    ylim = plt.ylim()
    # BUG: vlines(..., linestyle='--') fails on older versions of matplotlib
    #plt.vlines(score, ylim[0], ylim[1], linestyle='--',
    #          color='g', linewidth=3, label='Classification Score'
    #          ' (pvalue %s)' % pvalue)
    #plt.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
    #          color='k', linewidth=3, label='Luck')
    plt.plot(2 * [score], ylim, '--g', linewidth=3,
             label='Classification Score'
             ' (pvalue %s)' % pvalue)
    plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')

    plt.ylim(ylim)
    plt.legend()
    plt.xlabel('Score')
    plt.show()

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_permutation_test_for_classification.py <plot_permutation_test_for_classification.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_permutation_test_for_classification.ipynb <plot_permutation_test_for_classification.ipynb>`
