8.11.1. sklearn.grid_search.GridSearchCV

class sklearn.grid_search.GridSearchCV(estimator, param_grid, 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')

Grid search on the parameters of a classifier

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.

loss_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediciton (small is good) if None is passed, the score of the estimator is maximized

score_func: callable, optional :

A function that takes 2 arguments and compares them in order to evaluate the performance of prediction (high is good). If None is passed, the score of the estimator is maximized.

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 immediatly 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 crossvalidation generator, optional

If an integer is passed, it is the number of fold (default 3). Specific crossvalidation 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 GridSearch instance after fitting.

verbose: integer :

Controls the verbosity: the higher, the more messages.

See also

IterGrid
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.

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score_func is passed in which case it is used instead. If a loss function loss_func is passed, it overrides the score functions and is minimized.

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=..., coef0=..., degree=...,
        gamma=..., kernel='rbf', max_iter=-1, probability=False,
        shrinking=True, tol=...),
    fit_params={}, iid=True, loss_func=None, n_jobs=1,
        param_grid=...,
        ...)

Attributes

grid_scores_ dict of any to float Contains scores for all parameter combinations in param_grid.
best_estimator_ estimator Estimator that was choosen by grid 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.

Methods

fit(X[, y]) Run fit with all sets of parameters
get_params([deep]) Get parameters for the estimator
score(X[, y])
set_params(**params) Set the parameters of the estimator.
__init__(estimator, param_grid, 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, **params)

Run fit with all sets of parameters

Returns the best classifier

Parameters:

X: array, [n_samples, n_features] :

Training vector, where n_samples in the number of samples and n_features is the number of features.

y: array-like, shape = [n_samples], optional :

Target vector relative to X for classification; None for unsupervised learning.

get_params(deep=True)

Get parameters for the estimator

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

set_params(**params)

Set the parameters of the 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 :
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