X_train = X_train[idx]
y_train = y_train[idx]
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)
# add noise
y += 0.01 * np.random.normal((n_samples, ))
# Split data in train set and test set
n_samples = X.shape[0]
X_train, y_train = X[:n_samples / 2], y[:n_samples / 2]
X_test, y_test = X[n_samples / 2:], y[n_samples / 2:]
################################################################################
# Lasso
from scikits.learn.linear_model import Lasso
alpha = 0.1
lasso = Lasso(alpha=alpha)
y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
print lasso
print "r^2 on test data : %f" % (
1 - np.linalg.norm(y_test - y_pred_lasso)**2 / np.linalg.norm(y_test)**2)
################################################################################
# ElasticNet
from scikits.learn.linear_model import ElasticNet
enet = ElasticNet(alpha=alpha, rho=0.7)
y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
print enet
print "r^2 on test data : %f" % (
1 - np.linalg.norm(y_test - y_pred_enet)**2 / np.linalg.norm(y_test)**2)
