sklearn call

call.py

if __name__ == '__main__':
    from sklearn.cross_validation import StratifiedShuffleSplit
    # svc = LinearSVC(class_weight='auto', penalty='l1', dual=False, verbose = 15, C=0.1)
    svc = Perceptron(penalty='l1', alpha=0.05, fit_intercept=True, n_iter = 100, shuffle=True, n_jobs=-1, class_weight='auto', verbose=15)
    sf = StratifiedShuffleSplit(y,n_iter = 2, train_size =0.9, test_size=0.1);
    for train,test in sf:
        X_train, X_test = X[train], X[test]
        y_train, y_test = y[train], y[test]
        print 'Train set size: ', X_train.shape
        print 'Test set size: ', X_test.shape
        svc.fit(X_train, y_train)
        svc.score(X_test, y_test)