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 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
