def test_predict_df_multitask(self):
c = ChildClassifier(features=["x", "y"], targets=["c", "d"])
res = c.predict(DATA_FRAME)
assert is_frame(res)
assert np.array_equal(res.columns, ["c", "d"])
assert np.array_equal(res.index, DATA_FRAME.index)
def test_predict_df_multitask(self):
c = ChildRegressor(features=['x', 'y'], targets=['c', 'd'])
res = c.predict(DATA_FRAME)
assert is_frame(res)
assert np.array_equal(res.columns, ['c', 'd'])
assert np.array_equal(res.index, DATA_FRAME.index)
def test_predict_log_proba_df(self):
c = ChildClassifier(features=["x", "y"], targets=["c"])
res = c.predict_log_proba(DATA_FRAME)
assert is_frame(res)
assert np.array_equal(res.index, DATA_FRAME.index)
assert isinstance(res.columns, pd.MultiIndex)
assert np.array_equal(res.columns.get_level_values(0), ["c", "c"])
assert np.array_equal(res.columns.get_level_values(1), [0, 1])
def test_predict_log_proba_arr_pdmode(self):
c = ChildClassifier(features=["x", "y"], targets=["c"])
with pytest.warns(CompatabilityWarning):
res = c.predict_log_proba(TWO_D_ARRAY)
assert is_frame(res)
assert np.array_equal(res.index, range(len(TWO_D_ARRAY)))
assert isinstance(res.columns, pd.MultiIndex)
assert np.array_equal(res.columns.get_level_values(0), ["c", "c"])
assert np.array_equal(res.columns.get_level_values(1), [0, 1])
def test_transform_df(self):
res = ChildTransformer(features=['x', 'y']).transform(DATA_FRAME)
assert is_frame(res)
assert np.array_equal(res.columns, ['z', 'y'])
assert np.array_equal(DATA_FRAME.index, res.index)
def test_transform_arr_pdmode(self):
with pytest.warns(CompatabilityWarning):
res = ChildTransformer(features=['x', 'y']).transform(TWO_D_ARRAY)
assert is_frame(res)
assert np.array_equal(res.columns, ['z', 'y'])
def test_removes_novariance_column():
v_t = VarianceThreshold(0)
res_df = v_t.fit_transform(DATA)
assert is_frame(res_df)
assert "zero_var" not in res_df.columns
