class TestNumpyXMatrix:
@given(data(), shape_matrix())
def test_input_as_tuples(self, data, shape):
X = data.draw(numpy_X_matrices(shape))
assert X.shape == shape
@given(data())
def test_input_as_strategy(self, data):
data.draw(numpy_X_matrices(shape_matrix()))
@given(data())
def test_error_shape_0_smaller_shape_1(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_matrices([10, 20]))
@given(data(), shape_matrix(), ordered_pair(32, 47))
def test_min_max_values(self, data, shape, min_max_values):
min_value, max_value = min_max_values
X = data.draw(numpy_X_matrices(shape, min_value=min_value, max_value=max_value))
assert X.min() >= min_value
assert X.max() <= max_value
@given(data(), shape_matrix())
def test_no_nan(self, data, shape):
X = data.draw(numpy_X_matrices(shape, allow_nan=False, allow_infinity=True))
assert not np.isnan(X).any()
@given(data(), shape_matrix())
def test_no_infinity(self, data, shape):
X = data.draw(numpy_X_matrices(shape, allow_nan=True, allow_infinity=False))
assert not np.isinf(X).any()
def numpy_X_matrices(
draw,
shape=shape_matrix(),
min_value: float = None,
max_value: float = None,
allow_nan: bool = False,
allow_infinity: bool = False,
):
if not isinstance(shape, tuple) and not isinstance(shape, list):
shape = draw(shape)
if shape[0] <= shape[1]:
raise ValueError(f"X.shape[0] must be <= X.shape[1]: {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=shape,
elements=elements,
))
return X
def test_input_as_strategy(self, data):
data.draw(numpy_X_matrices(shape_matrix()))
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]
