def predict_features(self, df_features, df_target, idx=0, **kwargs):
"""For one variable, predict its neighbouring nodes.
Args:
df_features (pandas.DataFrame):
df_target (pandas.Series):
idx (int): (optional) for printing purposes
kwargs (dict): additional options for algorithms
Returns:
list: scores of each feature relatively to the target
"""
X = df_features.values
y = df_target.values
clf = ard(compute_score=True)
clf.fit(X, y.ravel())
return np.abs(clf.coef_)
model.__class__.__name__
if __name__ == "__main__":
br = '\n'
X = np.load('data/X_boston.npy')
y = np.load('data/y_boston.npy')
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
regressors = [
lr(),
bay(),
rr(alpha=.5, random_state=0),
l(alpha=0.1, random_state=0),
ll(),
knn(),
ard(),
rfr(random_state=0, n_estimators=100),
SVR(gamma='scale', kernel='rbf'),
rcv(fit_intercept=False),
en(random_state=0),
dtr(random_state=0),
ada(random_state=0),
gbr(random_state=0)
]
print('unscaled:', br)
for reg in regressors:
reg.fit(X_train, y_train)
rmse, name = get_error(reg, X_test, y_test)
name = reg.__class__.__name__
print(name + '(rmse):', end=' ')
print(rmse)