sklearn.cross_validation.permutation_test_score

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

Evaluate the significance of a cross-validated score with permutations

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 : integer or cross-validation generator, optional

If an integer is passed, it is the number of fold (default 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects.

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 score_func returns bigger numbers for better scores (e.g., accuracy_score). If score_func 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