def benchmark(clf): t0 = time() clf.fit(X_train, y_train) train_time = time() - t0 t0 = time() pred = clf.predict(X_test) test_time = time() - t0 err = metrics.zero_one(y_test, pred) / float(pred.shape[0]) return err, train_time, test_time
def test_losses(): """test loss functions""" assert_equal(zero_one(y[half:], y_), 13) assert_almost_equal(mean_square_error(y[half:], y_), 12.999, 2) assert_almost_equal(explained_variance(y[half:], y_), -0.04, 2)
## } ## print("Training LinearSVC on training set") ## clf = LinearSVC(**parameters) print("Training SGD with alpha=0.001 and n_iter=2") clf = SGD(alpha=0.001, n_iter=2) t0 = time() clf.fit(X_train, y_train) print "done in %fs" % (time() - t0) print "Predicting the outcomes of the testing set" t0 = time() pred = clf.predict(X_test) print "done in %fs" % (time() - t0) print "Classification performance:" print print metrics.classification_report( y_test, pred, labels=[-1, 1], class_names=['any other types', 'cover type 1']) print "" err = metrics.zero_one(y_test, pred) / float(pred.shape[0]) print "Error rate: %.4f" % err print "" cm = metrics.confusion_matrix(y_test, pred) print "Confusion matrix:" print cm