sklearn.grid_search
.GridSearchCV¶
Warning
DEPRECATED

class
sklearn.grid_search.
GridSearchCV
(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]¶ Exhaustive search over specified parameter values for an estimator.
Deprecated since version 0.18: This module will be removed in 0.20. Use
sklearn.model_selection.GridSearchCV
instead.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 crossvalidated gridsearch over a parameter grid.
Read more in the User Guide.
Parameters:  estimator : estimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikitlearn estimator interface. Either estimator needs to provide a
score
function, orscoring
must be passed. param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
 scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. IfNone
, thescore
method of the estimator is used. fit_params : dict, optional
Parameters to pass to the fit method.
 n_jobs: int, default: 1 :
The maximum number of estimators fit in parallel.
 If 1 all CPUs are used.
 If 1 is given, no parallel computing code is used at all, which is useful for debugging.
 For
n_jobs
below 1,(n_cpus + n_jobs + 1)
are used. For example, withn_jobs = 2
all CPUs but one are used.
Changed in version 0.17: Upgraded to joblib 0.9.3.
 pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
 None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
 An int, giving the exact number of total jobs that are spawned
 A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if the estimator is a classifier and
y
is either binary or multiclass,sklearn.model_selection.StratifiedKFold
is used. In all other cases,sklearn.model_selection.KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance after fitting.
 verbose : integer
Controls the verbosity: the higher, the more messages.
 error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
Attributes:  grid_scores_ : list of named tuples
Contains scores for all parameter combinations in param_grid. Each entry corresponds to one parameter setting. Each named tuple has the attributes:
parameters
, a dict of parameter settingsmean_validation_score
, the mean score over the crossvalidation foldscv_validation_scores
, the list of scores for each fold
 best_estimator_ : estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.
 best_score_ : float
Score of best_estimator on the left out data.
 best_params_ : dict
Parameter setting that gave the best results on the hold out data.
 scorer_ : function
Scorer function used on the held out data to choose the best parameters for the model.
See also
ParameterGrid
 generates all the combinations of a hyperparameter grid.
sklearn.cross_validation.train_test_split
 utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.
sklearn.metrics.make_scorer
 Make a scorer from a performance metric or loss function.
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='ovr', 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
decision_function
(X)Call decision_function on the estimator with the best found parameters. fit
(X[, y])Run fit with all sets of parameters. get_params
([deep])Get parameters for this estimator. inverse_transform
(Xt)Call inverse_transform on the estimator with the best found parameters. predict
(X)Call predict on the estimator with the best found parameters. predict_log_proba
(X)Call predict_log_proba on the estimator with the best found parameters. predict_proba
(X)Call predict_proba on the estimator with the best found parameters. score
(X[, y])Returns the score on the given data, if the estimator has been refit. set_params
(**params)Set the parameters of this estimator. transform
(X)Call transform on the estimator with the best found parameters. 
__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
(X)[source]¶ Call decision_function on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportsdecision_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 : arraylike, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, 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
(Xt)[source]¶ Call inverse_transform on the estimator with the best found parameters.
Only available if the underlying estimator implements
inverse_transform
andrefit=True
.Parameters:  Xt : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

predict
(X)[source]¶ Call predict on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

predict_log_proba
(X)[source]¶ Call predict_log_proba on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict_log_proba
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

predict_proba
(X)[source]¶ Call predict_proba on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict_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 thebest_estimator_.score
method otherwise.Parameters:  X : arraylike, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, 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 longstanding behavior of this method changed in version 0.16.
 It no longer uses the metric provided by
estimator.score
if thescoring
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