def test_intercept(self, distribution, n_categories):
graph = StructureModel()
graph.add_node("A")
data_noint = generate_categorical_dataframe(
graph,
100000,
distribution,
noise_scale=0.1,
n_categories=n_categories,
seed=10,
intercept=False,
)
data_intercept = generate_categorical_dataframe(
graph,
100000,
distribution,
noise_scale=0.1,
n_categories=n_categories,
seed=10,
intercept=True,
)
assert np.all(~np.isclose(data_intercept.mean(axis=0),
data_noint.mean(axis=0),
atol=0.05,
rtol=0))
def test_intercept(self, distribution):
graph = StructureModel()
graph.add_node("123")
data_noint = generate_binary_data(graph,
100000,
distribution,
noise_scale=0,
seed=10,
intercept=False)
data_intercept = generate_binary_data(graph,
100000,
distribution,
noise_scale=0,
seed=10,
intercept=True)
assert not np.isclose(data_noint[:, 0].mean(),
data_intercept[:, 0].mean())
def test_intercept(self, distribution):
graph = StructureModel()
graph.add_node("123")
data_noint = generate_continuous_data(
graph,
n_samples=100000,
distribution=distribution,
noise_scale=0,
seed=10,
intercept=False,
)
data_intercept = generate_continuous_data(
graph,
n_samples=100000,
distribution=distribution,
noise_scale=0,
seed=10,
intercept=True,
)
assert not np.isclose(data_noint[:, 0].mean(),
data_intercept[:, 0].mean())
assert np.isclose(data_noint[:, 0].std(), data_intercept[:, 0].std())