def test_weighted_decision_path_regression():
X_train, X_test, y_train, y_test = load_scaled_boston()
mtr = MondrianTreeRegressor(random_state=0)
mtr.fit(X_train, y_train)
check_weighted_decision_path_regression(mtr, X_test)
mtr.partial_fit(X_train, y_train)
check_weighted_decision_path_regression(mtr, X_test)
def test_tree_attributes():
rng = np.random.RandomState(0)
X = rng.randn(20, 5)
y = np.sum(X[:, :4], axis=1)
mr = MondrianTreeRegressor(random_state=0)
mr.fit(X, y)
check_tree_attributes(X, y, 0, mr.tree_)
mr.partial_fit(X, y)
check_tree_attributes(X, y, mr.tree_.root, mr.tree_, False)
def test_mean_std_reg_convergence():
X_train, _, y_train, _ = load_scaled_boston()
mr = MondrianTreeRegressor(random_state=0)
mr.fit(X_train, y_train)
check_mean_std_reg_convergence(mr, X_train, y_train)
n_s = int(len(X_train) / 2)
mr.partial_fit(X_train[:n_s], y_train[:n_s])
mr.partial_fit(X_train[n_s:], y_train[n_s:])
check_mean_std_reg_convergence(mr, X_train, y_train)
def test_reg_boston():
"""Consistency on boston house prices"""
mtr = MondrianTreeRegressor(random_state=0)
boston = load_boston()
X, y = boston.data, boston.target
mtr.fit(X, y)
score = mean_squared_error(mtr.predict(X), y)
assert_less(score, 1, "Failed with score = {0}".format(score))
mtr.partial_fit(X, y)
score = mean_squared_error(mtr.predict(X), y)
assert_less(score, 1, "Failed with score = {0}".format(score))
