grid_search.GridSearchCV()

Exhaustive search over specified parameter values for an estimator.

Important members are fit, predict.

GridSearchCV implements a ?fit? and a ?score? method. It also implements ?predict?, ?predict_proba?, ?decision_function?, ?transform? and ?inverse_transform? if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

Read more in the User Guide.

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.

If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

Examples

>>> from sklearn import svm, grid_search, datasets
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> clf = grid_search.GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
...                             
GridSearchCV(cv=None, error_score=...,
       estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
                     decision_function_shape=None, degree=..., gamma=...,
                     kernel='rbf', max_iter=-1, probability=False,
                     random_state=None, shrinking=True, tol=...,
                     verbose=False),
       fit_params={}, iid=..., n_jobs=1,
       param_grid=..., pre_dispatch=..., refit=...,
       scoring=..., verbose=...)

Methods

__init__(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise') [source]

decision_function(*args, **kwargs) [source]

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters:	X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

fit(X, y=None) [source]

Run fit with all sets of parameters.

Parameters:	X : array-like, shape = [n_samples, n_features] Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

inverse_transform(*args, **kwargs) [source]

Call inverse_transform on the estimator with the best found parameters.

Only available if the underlying estimator implements inverse_transform and refit=True.

Parameters:	Xt : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

predict(*args, **kwargs) [source]

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters:	X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

predict_log_proba(*args, **kwargs) [source]

Call predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

Parameters:	X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

predict_proba(*args, **kwargs) [source]

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters:	X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.

score(X, y=None) [source]

Returns the score on the given data, if the estimator has been refit.

This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.

Parameters:	X : array-like, shape = [n_samples, n_features] Input data, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning.
Returns:	score : float

Notes

• The long-standing behavior of this method changed in version 0.16.
• It no longer uses the metric provided by estimator.score if the scoring parameter was set when fitting.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.

Returns:	self :

transform(*args, **kwargs) [source]

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters:	X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator.