70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
"""
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=================================================================
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Test with permutations the significance of a classification score
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=================================================================
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In order to test if a classification score is significative a technique
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in repeating the classification procedure after randomizing, permuting,
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the labels. The p-value is then given by the percentage of runs for
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which the score obtained is greater than the classification score
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obtained in the first place.
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"""
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD 3 clause
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.svm import SVC
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import permutation_test_score
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from sklearn import datasets
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# #############################################################################
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# Loading a dataset
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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n_classes = np.unique(y).size
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# Some noisy data not correlated
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random = np.random.RandomState(seed=0)
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E = random.normal(size=(len(X), 2200))
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# Add noisy data to the informative features for make the task harder
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X = np.c_[X, E]
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svm = SVC(kernel='linear')
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cv = StratifiedKFold(2)
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score, permutation_scores, pvalue = permutation_test_score(
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svm, X, y, scoring="accuracy", cv=cv, n_permutations=100, n_jobs=1)
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print("Classification score %s (pvalue : %s)" % (score, pvalue))
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# #############################################################################
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# View histogram of permutation scores
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plt.hist(permutation_scores, 20, label='Permutation scores',
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edgecolor='black')
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ylim = plt.ylim()
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# BUG: vlines(..., linestyle='--') fails on older versions of matplotlib
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# plt.vlines(score, ylim[0], ylim[1], linestyle='--',
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# color='g', linewidth=3, label='Classification Score'
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# ' (pvalue %s)' % pvalue)
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# plt.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
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# color='k', linewidth=3, label='Luck')
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plt.plot(2 * [score], ylim, '--g', linewidth=3,
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label='Classification Score'
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' (pvalue %s)' % pvalue)
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plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
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plt.ylim(ylim)
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plt.legend()
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plt.xlabel('Score')
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plt.show()
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