sklearn.grid_search.GridSearchCV¶
- class sklearn.grid_search.GridSearchCV(estimator, param_grid, scoring=None, loss_func=None, score_func=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs')¶
Exhaustive search over specified parameter values for an estimator.
Important members are fit, predict.
GridSearchCV implements a “fit” method and a “predict” method like any classifier except that the parameters of the classifier used to predict is optimized by cross-validation.
Parameters: estimator : object type that implements the “fit” and “predict” methods
A object of that type is instantiated for each grid point.
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, optional, default: None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, optional
Number of jobs to run in parallel (default 1).
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 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 string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, optional
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 : integer or cross-validation generator, optional
If an integer is passed, it is the number of folds (default 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects
refit : boolean
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.
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 settings
- mean_validation_score, the mean score over the cross-validation folds
- cv_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.
`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 an 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, estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., degree=..., gamma=..., kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params={}, iid=..., loss_func=..., n_jobs=1, param_grid=..., pre_dispatch=..., refit=..., score_func=..., scoring=..., verbose=...)
Methods
fit(X[, y]) Run fit with all sets of parameters. get_params([deep]) Get parameters for this estimator. score(X[, y]) Returns the score on the given test data and labels, if the search estimator has been refit. set_params(**params) Set the parameters of this estimator. - __init__(estimator, param_grid, scoring=None, loss_func=None, score_func=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs')¶
- fit(X, y=None)¶
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)¶
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.
- score(X, y=None)¶
Returns the score on the given test data and labels, if the search estimator has been refit. The score function of the best estimator is used, or the scoring parameter where unavailable.
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
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
