class TestNumpyXyMatrices:
@given(data(), shape_X_y_matrices())
def test_input_as_tuples(self, data, shape_X_y):
X, y = data.draw(numpy_X_y_matrices(shape_X_y))
assert X.shape == shape_X_y[0]
assert y.shape == shape_X_y[1]
assert len(y.shape) == 1
@given(data(), shape_X_y_matrices(y_as_vector=False))
def test_input_as_tuples_y_matrix(self, data, shape_X_y):
X, y = data.draw(numpy_X_y_matrices(shape_X_y))
assert X.shape == shape_X_y[0]
assert y.shape == shape_X_y[1]
assert len(y.shape) == 2
@given(data())
def test_input_as_strategy(self, data):
data.draw(numpy_X_y_matrices(shape_X_y_matrices()))
@given(data())
def test_error_shape_0_smaller_shape_1(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_y_matrices([[10, 20], [10, 1]]))
@given(data())
def test_error_shape_0_different(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_y_matrices([[10, 5], [4, 1]]))
@given(data(), shape_X_y_matrices(), ordered_pair(32, 47))
def test_min_max_values(self, data, shape_X_y, min_max_values):
min_value, max_value = min_max_values
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, min_value=min_value, max_value=max_value)
)
assert X.min() >= min_value
assert y.min() >= min_value
assert X.max() <= max_value
assert y.max() <= max_value
@given(data(), shape_X_y_matrices())
def test_no_nan(self, data, shape_X_y):
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, allow_nan=False, allow_infinity=True)
)
assert not np.isnan(X).any()
assert not np.isnan(y).any()
@given(data(), shape_X_y_matrices())
def test_no_infinity(self, data, shape_X_y):
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, allow_nan=True, allow_infinity=False)
)
assert not np.isinf(X).any()
assert not np.isinf(y).any()
def numpy_X_y_matrices(
draw,
X_y_shapes=shape_X_y_matrices(),
min_value: float = None,
max_value: float = None,
allow_nan: bool = False,
allow_infinity: bool = False,
):
if isinstance(X_y_shapes, tuple) or isinstance(X_y_shapes, list):
X_shape, y_shape = X_y_shapes
else:
X_shape, y_shape = draw(X_y_shapes)
if X_shape[0] != y_shape[0]:
raise ValueError(
f"X.shape[0] must be == y.shape[0]: {X_shape}, {y_shape}")
if X_shape[0] <= X_shape[1]:
raise ValueError(f"X.shape[0] must be <= X.shape[1]: {X_shape}")
elements = floats(
min_value=min_value,
max_value=max_value,
allow_nan=allow_nan,
allow_infinity=allow_infinity,
)
X = draw(arrays(
dtype=float,
shape=X_shape,
elements=elements,
))
y = draw(arrays(
dtype=float,
shape=y_shape,
elements=elements,
))
return X, y
def test_input_as_strategy(self, data):
data.draw(numpy_X_y_matrices(shape_X_y_matrices()))
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(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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_matrices(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(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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_matrices([100, X.shape[1]]))
multi_feature_multi_output_regressor.predict(X_predict)
@given(
data=data(),
X_y=numpy_X_y_matrices(
X_y_shapes=shape_X_y_matrices(y_as_vector=False),
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_matrices([100, 30]))
with pytest.raises(ValueError):
multi_feature_multi_output_regressor.predict(X_predict)
def test_shape_X_Y_value_error(data):
with pytest.raises(ValueError):
data.draw(shape_X_y_matrices(1, 8, 9, 10, 10, 20))
@given(data=data(), value=integers(0, 10))
def test_ordered_pair_min_equal_max(data, value):
with pytest.raises(ValueError):
data.draw(ordered_pair(value, value))
@given(data=data(), shape_0=ordered_pair(10, 100), shape_1=ordered_pair(1, 8))
def test_shape_X(data, shape_0, shape_1):
shape = data.draw(shape_matrix(*shape_0, *shape_1))
assert shape_0[0] <= shape[0] <= shape_0[1]
assert shape_1[0] <= shape[1] <= shape_1[1]
@given(shape_X_y_matrices(123, 243, 12, 34, 1, 6))
def test_shape_X_y_matrices(shape_X_y):
shape_X, shape_y = shape_X_y
assert shape_X[0] == shape_y[0]
assert 12 <= shape_X[1] <= 34
assert 1 <= shape_y[1] <= 6
@given(shape_X_y_matrices(10, 20, 10, 20, 1, 6))
def test_shape_1_X_smaller_shape_0(shape_X_y):
shape_X, shape_y = shape_X_y
assert shape_X[0] > shape_X[1]
@given(data=data())
def test_shape_X_Y_value_error(data):
@given(data=data(), value=integers(0, 10))
def test_ordered_pair_min_equal_max(data, value):
with pytest.raises(ValueError):
data.draw(ordered_pair(value, value))
@given(data=data(), shape_0=ordered_pair(10, 100), shape_1=ordered_pair(1, 8))
def test_shape_X(data, shape_0, shape_1):
shape = data.draw(shape_matrix(*shape_0, *shape_1))
assert shape_0[0] <= shape[0] <= shape_0[1]
assert shape_1[0] <= shape[1] <= shape_1[1]
@given(shape_X_y_matrices(123, 243, 12, 34, 1, 6, y_as_vector=False))
def test_shape_X_y_matrices_y_matrix(shape_X_y):
shape_X, shape_y = shape_X_y
assert shape_X[0] == shape_y[0]
assert 12 <= shape_X[1] <= 34
assert 1 <= shape_y[1] <= 6
@given(shape_X_y_matrices(123, 243, 12, 34, 1, 6, y_as_vector=True))
def test_shape_X_y_matrices_y_vector(shape_X_y):
shape_X, shape_y = shape_X_y
assert shape_X[0] == shape_y[0]
assert 12 <= shape_X[1] <= 34
assert len(shape_y) == 1
