sklearn.cross_validation.permutation_test_score()

Warning

DEPRECATED

sklearn.cross_validation.permutation_test_score(estimator, X, y, cv=None, n_permutations=100, n_jobs=1, labels=None, random_state=0, verbose=0, scoring=None) [source]

Evaluate the significance of a cross-validated score with permutations

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.permutation_test_score instead.

Read more in the User Guide.

Parameters:

Parameters:	estimator : estimator object implementing ?fit? The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like The target variable to try to predict in the case of supervised learning. scoring : string, callable or None, optional, default: None A string (see model evaluation documentation) or a scorer callable object / function with signature `scorer(estimator, X, y)`. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 3-fold cross-validation, integer, to specify the number of folds. An object to be used as a cross-validation generator. An iterable yielding train/test splits. For integer/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. Refer User Guide for the various cross-validation strategies that can be used here. n_permutations : integer, optional Number of times to permute `y`. n_jobs : integer, optional The number of CPUs to use to do the computation. -1 means ?all CPUs?. labels : array-like of shape [n_samples] (optional) Labels constrain the permutation among groups of samples with a same label. random_state : RandomState or an int seed (0 by default) A random number generator instance to define the state of the random permutations generator. verbose : integer, optional The verbosity level.
Returns:	score : float The true score without permuting targets. permutation_scores : array, shape (n_permutations,) The scores obtained for each permutations. pvalue : float The returned value equals p-value if `scoring` returns bigger numbers for better scores (e.g., accuracy_score). If `scoring` is rather a loss function (i.e. when lower is better such as with `mean_squared_error`) then this is actually the complement of the p-value: 1 - p-value.

estimator : estimator object implementing ?fit?

The object to use to fit the data.

X : array-like of shape at least 2D

The data to fit.

y : array-like

The target variable to try to predict in the case of supervised learning.

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 3-fold cross-validation,
integer, to specify the number of folds.
An object to be used as a cross-validation generator.
An iterable yielding train/test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

n_permutations : integer, optional

Number of times to permute y.

n_jobs : integer, optional

The number of CPUs to use to do the computation. -1 means ?all CPUs?.

labels : array-like of shape [n_samples] (optional)

Labels constrain the permutation among groups of samples with a same label.

random_state : RandomState or an int seed (0 by default)

A random number generator instance to define the state of the random permutations generator.

verbose : integer, optional

The verbosity level.

Returns:

score : float

The true score without permuting targets.

permutation_scores : array, shape (n_permutations,)

The scores obtained for each permutations.

pvalue : float

The returned value equals p-value if scoring returns bigger numbers for better scores (e.g., accuracy_score). If scoring is rather a loss function (i.e. when lower is better such as with mean_squared_error) then this is actually the complement of the p-value: 1 - p-value.

Notes

This function implements Test 1 in:

Ojala and Garriga. Permutation Tests for Studying Classifier Performance. The Journal of Machine Learning Research (2010) vol. 11

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.permutation_test_score.html

doc_scikit_learn

2017-01-15 04:25:37

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