class LassoLarsCVImpl():
def __init__(self, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=3, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, positive=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'verbose': verbose,
'max_iter': max_iter,
'normalize': normalize,
'precompute': precompute,
'cv': cv,
'max_n_alphas': max_n_alphas,
'n_jobs': n_jobs,
'eps': eps,
'copy_X': copy_X,
'positive': positive}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
# Transform into numpy object
x_train = poly_reg.fit_transform(X_train)
X_test = poly_reg.fit_transform(X_test)
y_test = np.array(y_test.ix[:, 0])
y_train = np.array(y_train.ix[:, 0])
# Build model with good params
model = LassoLarsCV(copy_X=True,
cv=None,
eps=2.2204460492503131e-16,
fit_intercept=True,
max_iter=500,
max_n_alphas=1000,
n_jobs=1,
normalize=True,
positive=False,
precompute='auto',
verbose=False)
# Fit the model
model.fit(x_train, y_train)
# Predict
y_pred = model.predict(X_test)
# Scoring
if regression:
print('Score on test set:', mean_absolute_error(y_test, y_pred))
else:
print('Score on test set:', accuracy_score(y_test, y_pred))