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from sklearn.datasets import fetch_mldata
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import scale
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report


    

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if __name__ == '__main__':
    data = fetch_mldata('MNIST original', data_home='./data/mnist')
    X, y = data.data, data.target
    X = X/255.0 * 2 - 1


    

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X_train, X_test, y_train, y_test = train_test_split(X,y)


    

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pipeline = Pipeline([
    ('clf', SVC(kernel='rbf', gamma=0.01, C=100))
])
print X_train.shape
parameters = {
    'clf__gamma': (0.01, 0.03, 0.1, 0.3, 1),
    'clf__C': (0.1, 0.3, 1, 3, 10, 30)
}

grid_search = GridSearchCV(pipeline, parameters, n_jobs=2, verbose=1, scoring='accuracy')
grid_search.fit(X_train[:10000], y_train[:10000])
print 'Best score: %0.3f' % grid_search.best_score_
print 'Best parameter set:'
best_parameters = grid_search.best_estimator_.get_params()


    

    




(52500, 784)
Fitting 3 folds for each of 30 candidates, totalling 90 fits

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for param_name in sorted(parameters.keys()):
    print '\t%s: %r' % (param_name, best_parameters[param_name])
predictions = grid_search.predict(X_test)
print classification_report(y_test, predictions)


    

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