glm.fit(X, y, weights)
# 1d but wrong length
weights = [1, 0]
msg = r"sample_weight.shape == \(2,\), expected \(1,\)!"
with pytest.raises(ValueError, match=msg):
glm.fit(X, y, weights)
@pytest.mark.parametrize(
"name, instance",
[
("normal", NormalDistribution()),
("poisson", PoissonDistribution()),
("gamma", GammaDistribution()),
("inverse-gaussian", InverseGaussianDistribution()),
],
)
def test_glm_family_argument(name, instance):
"""Test GLM family argument set as string."""
y = np.array([0.1, 0.5]) # in range of all distributions
X = np.array([[1], [2]])
glm = GeneralizedLinearRegressor(family=name, alpha=0).fit(X, y)
assert isinstance(glm._family_instance, instance.__class__)
glm = GeneralizedLinearRegressor(family="not a family")
with pytest.raises(ValueError, match="family must be"):
glm.fit(X, y)
@pytest.mark.parametrize(
NormalDistribution,
PoissonDistribution,
GammaDistribution,
InverseGaussianDistribution,
DistributionBoundary,
)
@pytest.mark.parametrize(
"family, expected",
[
(NormalDistribution(), [True, True, True]),
(PoissonDistribution(), [False, True, True]),
(TweedieDistribution(power=1.5), [False, True, True]),
(GammaDistribution(), [False, False, True]),
(InverseGaussianDistribution(), [False, False, True]),
(TweedieDistribution(power=4.5), [False, False, True]),
],
)
def test_family_bounds(family, expected):
"""Test the valid range of distributions at -1, 0, 1."""
result = family.in_y_range([-1, 0, 1])
assert_array_equal(result, expected)
def test_invalid_distribution_bound():
dist = TweedieDistribution()
dist._lower_bound = 0
with pytest.raises(TypeError,
match="must be of type DistributionBoundary"):
dist.in_y_range([-1, 0, 1])
weights = [[0]]
with pytest.raises(ValueError, match="must be 1D array or scalar"):
glm.fit(X, y, weights)
# 1d but wrong length
weights = [1, 0]
msg = r"sample_weight.shape == \(2,\), expected \(1,\)!"
with pytest.raises(ValueError, match=msg):
glm.fit(X, y, weights)
@pytest.mark.parametrize('name, instance',
[('normal', NormalDistribution()),
('poisson', PoissonDistribution()),
('gamma', GammaDistribution()),
('inverse-gaussian', InverseGaussianDistribution())])
def test_glm_family_argument(name, instance):
"""Test GLM family argument set as string."""
y = np.array([0.1, 0.5]) # in range of all distributions
X = np.array([[1], [2]])
glm = GeneralizedLinearRegressor(family=name, alpha=0).fit(X, y)
assert isinstance(glm._family_instance, instance.__class__)
glm = GeneralizedLinearRegressor(family='not a family')
with pytest.raises(ValueError, match="family must be"):
glm.fit(X, y)
@pytest.mark.parametrize('name, instance', [('identity', IdentityLink()),
('log', LogLink())])
def test_glm_link_argument(name, instance):