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)
class TestMultiFeatureMultiOutputRegressor:
def test_constructor(self, estimator):
multi_feature_multi_output_regressor = MultiFeatureMultiOutputRegressor(
estimator
)
assert multi_feature_multi_output_regressor.n_jobs == 1
@given(data=data(), X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000))
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
)
@given(X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000))
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),
)
@given(X=numpy_X_matrix(min_value=-10000, max_value=10000))
def test_error_predict_with_no_fit(self, estimator, X):
regressor = MultiFeatureMultiOutputRegressor(estimator)
with pytest.raises(NotFittedError):
regressor.predict(X)
@given(data=data(), X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000))
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
)
@given(
data=data(), X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000),
)
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
@given(
data=data(), X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000),
)
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)
@given(
data=data(), X_y=numpy_X_y_matrices(min_value=-10000, max_value=10000),
)
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_no_infinity(self, data, shape):
X = data.draw(numpy_X_matrix(shape, allow_nan=True, allow_infinity=False))
assert not np.isinf(X).any()
def test_min_max_values(self, data, shape, min_max_values):
min_value, max_value = min_max_values
X = data.draw(numpy_X_matrix(shape, min_value=min_value, max_value=max_value))
assert X.min() >= min_value
assert X.max() <= max_value
def test_error_shape_0_smaller_shape_1(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_matrix([10, 20]))
def test_input_as_strategy(self, data):
data.draw(numpy_X_matrix(shape_matrix()))
def test_input_as_tuples(self, data, shape):
X = data.draw(numpy_X_matrix(shape))
assert X.shape == shape