def test_compute_p_value(self, scale_contingency_table, expected):
X = pd.Series(data=(["c1"] * 70 + ["c2"] * 20 + ["c3"] * 10))
y = pd.Series(data=([0] * 35 + [1] * 35 + [0] * 15 + [1] * 5 +
[0] * 8 + [1] * 2))
category = "c1"
actual = (CategoricalDataProcessor._compute_p_value(
X, y, category, scale_contingency_table))
assert pytest.approx(actual, abs=1e-5) == expected
def test_compute_p_value_regression(self, seed, expected):
np.random.seed(seed)
X = pd.Series(data=(["c1"] * 70 + ["c2"] * 20 + ["c3"] * 10))
y = pd.Series(data=np.random.uniform(0, 1, 100) * 5)
category = "c1"
actual = (CategoricalDataProcessor._compute_p_value(
X, y, category, "regression", None))
assert pytest.approx(actual, abs=1e-5) == expected