def test_category_product_too_large(random_gen):
dfc = {}
for i in range(20):
dfc[str(i)] = random_cat(10, 1000)
cat = pd.DataFrame(dfc)
with pytest.raises(ValueError):
category_product(cat)
def test_interaction_cat_only(cat):
interact = Interaction(cat=cat)
assert interact.nobs == cat.shape[0]
assert_frame_equal(cat, interact.cat)
expected = category_interaction(category_product(cat), precondition=False)
actual = interact.sparse
assert isinstance(actual, csc_matrix)
assert_allclose(expected.A, actual.A)
def test_interaction_cat_cont(cat, cont):
interact = Interaction(cat=cat, cont=cont)
assert interact.nobs == cat.shape[0]
assert_frame_equal(cat, interact.cat)
assert_frame_equal(cont, interact.cont)
base = category_interaction(category_product(cat), precondition=False).A
expected = []
for i in range(cont.shape[1]):
element = base.copy()
element[np.where(element)] = cont.iloc[:, i].to_numpy()
expected.append(element)
expected = np.column_stack(expected)
actual = interact.sparse
assert isinstance(actual, csc_matrix)
assert_allclose(expected, interact.sparse.A)
def test_category_product(cat):
prod = category_product(cat)
if cat.shape[1] == 1:
assert_series_equal(prod, cat.iloc[:, 0], check_names=False)
else:
alt = cat.iloc[:, 0].astype("int64")
for i in range(1, cat.shape[1]):
alt += 10 ** (4 * i) * cat.iloc[:, i].astype("int64")
alt = pd.Categorical(alt)
alt = pd.Series(alt)
df = pd.DataFrame([prod.cat.codes, alt.cat.codes], index=["cat_prod", "alt"]).T
g = df.groupby("cat_prod").alt
assert (g.nunique() == 1).all()
g = df.groupby("alt").cat_prod
assert (g.nunique() == 1).all()
def test_category_product_not_cat(random_gen):
cat = pd.DataFrame(
{str(i): pd.Series(random_gen.randint(0, 10, 1000))
for i in range(3)})
with pytest.raises(TypeError):
category_product(cat)
