def test_fit_target_to_feature_dict_working(self, data, X_y, estimator):
X, y = X_y
target_to_feature_dict = data.draw(
numeric_target_to_feature_dicts(n_targets=y.shape[1], n_features=X.shape[1])
)
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
multi_feature_multi_output_regressor.fit(
X, y, target_to_features_dict=target_to_feature_dict
)
def test_fit_bad_y(self, data, estimator, X_y):
X, y = X_y
y = y[:, 0].flatten()
target_to_feature_dict = data.draw(
numeric_target_to_feature_dicts(n_targets=1, n_features=X.shape[1])
)
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
with pytest.raises(ValueError):
multi_feature_multi_output_regressor.fit(
X, y, target_to_features_dict=target_to_feature_dict
)
def test_fit_target_to_feature_dict_consistent(self, data, X_y, estimator):
X, y = X_y
target_to_feature_dict = data.draw(
numeric_target_to_feature_dicts(n_targets=y.shape[1], n_features=X.shape[1])
)
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
multi_feature_multi_output_regressor.fit(
X, y, target_to_features_dict=target_to_feature_dict
)
for i, estimator_ in enumerate(
multi_feature_multi_output_regressor.estimators_
):
expected_n_features = len(target_to_feature_dict[i])
assert len(estimator_.coef_) == expected_n_features
def test_fit_as_multi_output_regressor_if_target_to_feature_none(
self, estimator, X_y
):
X, y = X_y
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
multi_feature_multi_output_regressor.fit(X, y)
multi_output_regressor = MultiOutputRegressor(estimator)
multi_output_regressor.fit(X, y)
assert_almost_equal(
multi_feature_multi_output_regressor.predict(X),
multi_output_regressor.predict(X),
)
def test_predict_target_to_feature_dict(self, data, X_y, estimator):
X, y = X_y
target_to_feature_dict = data.draw(
numeric_target_to_feature_dicts(n_targets=y.shape[1], n_features=X.shape[1])
)
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
multi_feature_multi_output_regressor.fit(
X, y, target_to_features_dict=target_to_feature_dict
)
X_predict = data.draw(numpy_X_matrix([100, X.shape[1]]))
multi_feature_multi_output_regressor.predict(X_predict)
def test_error_predict_target_to_feature_dict_wrong_X_shape(
self, data, X_y, estimator
):
X, y = X_y
target_to_feature_dict = data.draw(
numeric_target_to_feature_dicts(n_targets=y.shape[1], n_features=X.shape[1])
)
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
multi_feature_multi_output_regressor.fit(
X, y, target_to_features_dict=target_to_feature_dict
)
X_predict = data.draw(numpy_X_matrix([100, 30]))
with pytest.raises(ValueError):
multi_feature_multi_output_regressor.predict(X_predict)
def test_error_predict_with_no_fit(self, estimator, X):
regressor = MultiFeatureMultiOutputRegressor(estimator)
with pytest.raises(NotFittedError):
regressor.predict(X)
def test_constructor(self, estimator):
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
assert multi_feature_multi_output_regressor.n_jobs == 1