def test_right_end_capping(df_normal_dist):
# test case 1: right end capping
transformer = ArbitraryOutlierCapper(
max_capping_dict={"var": 0.10727677848029868}, min_capping_dict=None
)
X = transformer.fit_transform(df_normal_dist)
# expected output
df_transf = df_normal_dist.copy()
df_transf["var"] = np.where(
df_transf["var"] > 0.10727677848029868, 0.10727677848029868, df_transf["var"]
)
# test init params
assert np.round(transformer.max_capping_dict["var"], 3) == np.round(
0.10727677848029868, 3
)
assert transformer.min_capping_dict is None
assert transformer.variables_ == ["var"]
# test fit attrs
assert np.round(transformer.right_tail_caps_["var"], 3) == np.round(
0.10727677848029868, 3
)
assert transformer.left_tail_caps_ == {}
assert transformer.n_features_in_ == 1
# test transform output
pd.testing.assert_frame_equal(X, df_transf)
assert np.round(X["var"].max(), 3) <= np.round(0.10727677848029868, 3)
assert np.round(df_normal_dist["var"].max(), 3) > np.round(0.10727677848029868, 3)
def test_left_tail_capping(df_normal_dist):
# test case 3: left tail
transformer = ArbitraryOutlierCapper(
max_capping_dict=None, min_capping_dict={"var": -0.17486039103044}
)
X = transformer.fit_transform(df_normal_dist)
# expected output
df_transf = df_normal_dist.copy()
df_transf["var"] = np.where(
df_transf["var"] < -0.17486039103044, -0.17486039103044, df_transf["var"]
)
# test init param
assert transformer.max_capping_dict is None
assert np.round(transformer.min_capping_dict["var"], 3) == np.round(
-0.17486039103044, 3
)
# test fit attr
assert transformer.right_tail_caps_ == {}
assert np.round(transformer.left_tail_caps_["var"], 3) == np.round(
-0.17486039103044, 3
)
# test transform output
pd.testing.assert_frame_equal(X, df_transf)
assert np.round(X["var"].min(), 3) >= np.round(-0.17486039103044, 3)
assert np.round(df_normal_dist["var"].min(), 3) < np.round(-0.17486039103044, 3)
def test_both_ends_capping(df_normal_dist):
# test case 2: both tails
transformer = ArbitraryOutlierCapper(
max_capping_dict={"var": 0.20857275540714884},
min_capping_dict={"var": -0.19661115230025186},
)
X = transformer.fit_transform(df_normal_dist)
# expected output
df_transf = df_normal_dist.copy()
df_transf["var"] = np.where(
df_transf["var"] > 0.20857275540714884, 0.20857275540714884, df_transf["var"]
)
df_transf["var"] = np.where(
df_transf["var"] < -0.19661115230025186, -0.19661115230025186, df_transf["var"]
)
# test fit params
assert np.round(transformer.right_tail_caps_["var"], 3) == np.round(
0.20857275540714884, 3
)
assert np.round(transformer.left_tail_caps_["var"], 3) == np.round(
-0.19661115230025186, 3
)
# test transform output
pd.testing.assert_frame_equal(X, df_transf)
assert np.round(X["var"].max(), 3) <= np.round(0.20857275540714884, 3)
assert np.round(X["var"].min(), 3) >= np.round(-0.19661115230025186, 3)
assert np.round(df_normal_dist["var"].max(), 3) > np.round(0.20857275540714884, 3)
assert np.round(df_normal_dist["var"].min(), 3) < np.round(-0.19661115230025186, 3)
def test_ignores_na_in_input_df(df_na):
# test case 4: dataset contains na and transformer is asked to ignore them
transformer = ArbitraryOutlierCapper(max_capping_dict=None,
min_capping_dict={"Age": 20},
missing_values="ignore")
X = transformer.fit_transform(df_na)
# expected output
df_transf = df_na.copy()
df_transf["Age"] = np.where(df_transf["Age"] < 20, 20, df_transf["Age"])
# test fit params
assert transformer.max_capping_dict is None
assert transformer.min_capping_dict == {"Age": 20}
assert transformer.input_shape_ == (8, 6)
# test transform output
pd.testing.assert_frame_equal(X, df_transf)
assert X["Age"].min() >= 20
assert df_na["Age"].min() < 20
def test_right_end_capping(df_normal_dist):
# test case 1: right end capping
transformer = ArbitraryOutlierCapper(
max_capping_dict={"var": 0.10727677848029868}, min_capping_dict=None)
X = transformer.fit_transform(df_normal_dist)
# expected output
df_transf = df_normal_dist.copy()
df_transf["var"] = np.where(df_transf["var"] > 0.10727677848029868,
0.10727677848029868, df_transf["var"])
# test init params
assert transformer.max_capping_dict == {"var": 0.10727677848029868}
assert transformer.min_capping_dict is None
assert transformer.variables == ["var"]
# test fit attrs
assert transformer.right_tail_caps_ == {"var": 0.10727677848029868}
assert transformer.left_tail_caps_ == {}
assert transformer.input_shape_ == (100, 1)
# test transform output
pd.testing.assert_frame_equal(X, df_transf)
assert X["var"].max() <= 0.10727677848029868
assert df_normal_dist["var"].max() > 0.10727677848029868