Grid Search

Tables of contents

• Grid Search
  • GridSearchCV
    • Examples

scikits.learn.grid_search is a package to optimize the parameters of a model (e.g. Support Vector Classifier) using cross-validation

It is implemented in python, and uses the numpy and scipy packages. The computation can be run in parallel using the multiprocessing package.

GridSearchCV

class scikits.learn.grid_search.GridSearchCV(estimator, param_grid, loss_func=None, fit_params={}, n_jobs=1, iid=True)

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 instanciated for each grid point

param_grid: dict :

a dictionary of parameters that are used the generate the grid

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

fit_params : dict, optional

parameters to pass to the fit method

n_jobs: int, optional :

number of jobs to run in parallel (default 1)

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.

Examples

>>> import numpy as np
>>> from scikits.learn.cross_val import LeaveOneOut
>>> from scikits.learn.svm import SVR
>>> from scikits.learn.grid_search import GridSearchCV
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = SVR()
>>> clf = GridSearchCV(svr, parameters, n_jobs=1)
>>> clf.fit(X, y).predict([[-0.8, -1]])
array([ 1.14])

Methods

fit(X, Y)	self	Fit the model
predict(X)	array	Predict using the model.

fit(X, y, refit=True, cv=None, **kw)

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, [n_samples] :

Target vector relative to X

cv : crossvalidation generator

see scikits.learn.cross_val module

refit: boolean :

refit the best estimator with the entire dataset

Examples

See Parameter estimation using grid search with a nested cross-validation for an example of Grid Search computation on the digits dataset.