GridSearchCV (original) (raw)
class sklearn.model_selection.GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)[source]#
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 “score_samples”, “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.
Parameters:
estimatorestimator object
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_griddict or list of dictionaries
Dictionary with parameters names (str) 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.
scoringstr, callable, list, tuple or dict, default=None
Strategy to evaluate the performance of the cross-validated model on the test set.
If scoring represents a single score, one can use:
- a single string (see The scoring parameter: defining model evaluation rules);
- a callable (see Callable scorers) that returns a single value.
If scoring represents multiple scores, one can use:
- a list or tuple of unique strings;
- a callable returning a dictionary where the keys are the metric names and the values are the metric scores;
- a dictionary with metric names as keys and callables as values.
See Specifying multiple metrics for evaluation for an example.
n_jobsint, default=None
Number of jobs to run in parallel.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details.
Changed in version v0.20: n_jobs default changed from 1 to None
refitbool, str, or callable, default=True
Refit an estimator using the best found parameters on the whole dataset.
For multiple metric evaluation, this needs to be a str denoting the scorer that would be used to find the best parameters for refitting the estimator at the end.
Where there are considerations other than maximum score in choosing a best estimator, refit can be set to a function which returns the selected best_index_ given cv_results_. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_attribute will not be available.
The refitted estimator is made available at the best_estimator_attribute and permits using predict directly on thisGridSearchCV instance.
Also for multiple metric evaluation, the attributes best_index_,best_score_ and best_params_ will only be available ifrefit is set and all of them will be determined w.r.t this specific scorer.
See scoring parameter to know more about multiple metric evaluation.
See Custom refit strategy of a grid search with cross-validationto see how to design a custom selection strategy using a callable via refit.
Changed in version 0.20: Support for callable added.
cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a
(Stratified)KFold, - CV splitter,
- An iterable yielding (train, test) splits as arrays of indices.
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. These splitters are instantiated with shuffle=False so the splits will be the same across calls.
Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.
verboseint
Controls the verbosity: the higher, the more messages.
- >1 : the computation time for each fold and parameter candidate is displayed;
- >2 : the score is also displayed;
- >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation.
pre_dispatchint, or str, default=’2*n_jobs’
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 fast-running jobs, to avoid delays due to on-demand spawning of the jobs
- An int, giving the exact number of total jobs that are spawned
- A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
error_score‘raise’ or numeric, default=np.nan
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.
return_train_scorebool, default=False
If False, the cv_results_ attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.
Added in version 0.19.
Changed in version 0.21: Default value was changed from True to False
Attributes:
**cv_results_**dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.
For instance the below given table
will be represented by a cv_results_ dict of:
{ 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], mask = [False False False False]...) 'param_gamma': masked_array(data = [-- -- 0.1 0.2], mask = [ True True False False]...), 'param_degree': masked_array(data = [2.0 3.0 -- --], mask = [False False True True]...), 'split0_test_score' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.70, 0.78], 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'split0_train_score' : [0.80, 0.92, 0.70, 0.93], 'split1_train_score' : [0.82, 0.55, 0.70, 0.87], 'mean_train_score' : [0.81, 0.74, 0.70, 0.90], 'std_train_score' : [0.01, 0.19, 0.00, 0.03], 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49], 'std_fit_time' : [0.01, 0.02, 0.01, 0.01], 'mean_score_time' : [0.01, 0.06, 0.04, 0.04], 'std_score_time' : [0.00, 0.00, 0.00, 0.01], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], }
NOTE
The key 'params' is used to store a list of parameter settings dicts for all the parameter candidates.
The mean_fit_time, std_fit_time, mean_score_time andstd_score_time are all in seconds.
For multi-metric evaluation, the scores for all the scorers are available in the cv_results_ dict at the keys ending with that scorer’s name ('_<scorer_name>') instead of '_score' shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.)
**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.
See refit parameter for more information on allowed values.
**best_score_**float
Mean cross-validated score of the best_estimator
For multi-metric evaluation, this is present only if refit is specified.
This attribute is not available if refit is a function.
**best_params_**dict
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is present only if refit is specified.
**best_index_**int
The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.
The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).
For multi-metric evaluation, this is present only if refit is specified.
**scorer_**function or a dict
Scorer function used on the held out data to choose the best parameters for the model.
For multi-metric evaluation, this attribute holds the validatedscoring dict which maps the scorer key to the scorer callable.
**n_splits_**int
The number of cross-validation splits (folds/iterations).
**refit_time_**float
Seconds used for refitting the best model on the whole dataset.
This is present only if refit is not False.
Added in version 0.20.
**multimetric_**bool
Whether or not the scorers compute several metrics.
classes_ndarray of shape (n_classes,)
Class labels.
Number of features seen during fit.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Only defined ifbest_estimator_ is defined (see the documentation for the refitparameter for more details) and that best_estimator_ exposesfeature_names_in_ when fit.
Added in version 1.0.
See also
Generates all the combinations of a hyperparameter grid.
Utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.
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 onlypre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.
Examples
from sklearn import svm, datasets from sklearn.model_selection import GridSearchCV iris = datasets.load_iris() parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} svc = svm.SVC() clf = GridSearchCV(svc, parameters) clf.fit(iris.data, iris.target) GridSearchCV(estimator=SVC(), param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')}) sorted(clf.cv_results_.keys()) ['mean_fit_time', 'mean_score_time', 'mean_test_score',... 'param_C', 'param_kernel', 'params',... 'rank_test_score', 'split0_test_score',... 'split2_test_score', ... 'std_fit_time', 'std_score_time', 'std_test_score']
property classes_#
Class labels.
Only available when refit=True and the estimator is a classifier.
Call decision_function on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supportsdecision_function.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Returns:
y_scorendarray of shape (n_samples,) or (n_samples, n_classes) or (n_samples, n_classes * (n_classes-1) / 2)
Result of the decision function for X based on the estimator with the best found parameters.
fit(X, y=None, **params)[source]#
Run fit with all sets of parameters.
Parameters:
Xarray-like of shape (n_samples, n_features) or (n_samples, n_samples)
Training vectors, where n_samples is the number of samples andn_features is the number of features. For precomputed kernel or distance matrix, the expected shape of X is (n_samples, n_samples).
yarray-like of shape (n_samples, n_output) or (n_samples,), default=None
Target relative to X for classification or regression; None for unsupervised learning.
**paramsdict of str -> object
Parameters passed to the fit method of the estimator, the scorer, and the CV splitter.
If a fit parameter is an array-like whose length is equal tonum_samples then it will be split by cross-validation along withX and y. For example, the sample_weight parameter is split because len(sample_weights) = len(X). However, this behavior does not apply to groups which is passed to the splitter configured via the cv parameter of the constructor. Thus, groups is used_to perform the split_ and determines which samples are assigned to the each side of the a split.
Returns:
selfobject
Instance of fitted estimator.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.4.
Returns:
routingMetadataRouter
A MetadataRouter encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
inverse_transform(X=None, Xt=None)[source]#
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implementsinverse_transform and refit=True.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Xtindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Deprecated since version 1.5: Xt was deprecated in 1.5 and will be removed in 1.7. Use X instead.
Returns:
X{ndarray, sparse matrix} of shape (n_samples, n_features)
Result of the inverse_transform function for Xt based on the estimator with the best found parameters.
property n_features_in_#
Number of features seen during fit.
Only available when refit=True.
Call predict on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supportspredict.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Returns:
y_predndarray of shape (n_samples,)
The predicted labels or values for X based on the estimator with the best found parameters.
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:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Returns:
y_predndarray of shape (n_samples,) or (n_samples, n_classes)
Predicted class log-probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.
Call predict_proba on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supportspredict_proba.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Returns:
y_predndarray of shape (n_samples,) or (n_samples, n_classes)
Predicted class probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.
score(X, y=None, **params)[source]#
Return 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:
Xarray-like of shape (n_samples, n_features)
Input data, where n_samples is the number of samples andn_features is the number of features.
yarray-like of shape (n_samples, n_output) or (n_samples,), default=None
Target relative to X for classification or regression; None for unsupervised learning.
**paramsdict
Parameters to be passed to the underlying scorer(s).
Added in version 1.4: Only available if enable_metadata_routing=True. SeeMetadata Routing User Guide for more details.
Returns:
scorefloat
The score defined by scoring if provided, and thebest_estimator_.score method otherwise.
Call score_samples on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supportsscore_samples.
Added in version 0.24.
Parameters:
Xiterable
Data to predict on. Must fulfill input requirements of the underlying estimator.
Returns:
y_scorendarray of shape (n_samples,)
The best_estimator_.score_samples method.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Parameters:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform andrefit=True.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Returns:
Xt{ndarray, sparse matrix} of shape (n_samples, n_features)
X transformed in the new space based on the estimator with the best found parameters.