std = X_train.std(axis=0)
mean = X_train.mean(axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
std = y_train.std(axis=0)
mean = y_train.mean(axis=0)
y_train = (y_train - mean) / std
y_test = (y_test - mean) / std
gc.collect()
print "- benching ElasticNet"
clf = ElasticNet(alpha=alpha, rho=0.5, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
elnet_results[i, j, 0] = mean_square_error(clf.predict(X_test),
y_test)
elnet_results[i, j, 1] = time() - tstart
gc.collect()
print "- benching SGD"
n_iter = np.ceil(10 ** 4.0 / n_train)
clf = SGDRegressor(alpha=alpha, fit_intercept=False,
n_iter=n_iter, learning_rate="invscaling",
eta0=.01, power_t=0.25)
tstart = time()
clf.fit(X_train, y_train)
sgd_results[i, j, 0] = mean_square_error(clf.predict(X_test),
y_test)
sgd_results[i, j, 1] = time() - tstart
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)
std = X_train.std(axis=0)
mean = X_train.mean(axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
std = y_train.std(axis=0)
mean = y_train.mean(axis=0)
y_train = (y_train - mean) / std
y_test = (y_test - mean) / std
gc.collect()
print "- benching ElasticNet"
clf = ElasticNet(alpha=alpha, rho=0.5, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
elnet_results[i, j, 0] = mean_square_error(clf.predict(X_test),
y_test)
elnet_results[i, j, 1] = time() - tstart
gc.collect()
print "- benching SGD"
n_iter = np.ceil(10**4.0 / n_train)
clf = SGDRegressor(alpha=alpha,
fit_intercept=False,
n_iter=n_iter,
learning_rate="invscaling",
eta0=.01,
power_t=0.25)
tstart = time()
clf.fit(X_train, y_train)
sgd_results[i, j, 0] = mean_square_error(clf.predict(X_test),