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())