def test_wrap_polynomial_features():
# load data
X = fetch_california_housing(as_frame=True).frame
# prepare selectors
tr = PolynomialFeatures()
tr_wrap = SklearnTransformerWrapper(transformer=PolynomialFeatures())
# Test:
# When passing variable list
varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
tr_wrap.set_params(variables=varlist)
Xt = pd.DataFrame(tr.fit_transform(X[varlist]),
columns=tr.get_feature_names_out(varlist))
Xw = tr_wrap.fit_transform(X)
pd.testing.assert_frame_equal(Xw, pd.concat([X, Xt], axis=1))
assert Xw.shape[1] == len(X.columns) + len(
tr.get_feature_names_out(varlist))
# when variable list is None
tr_wrap.set_params(variables=None)
Xt = pd.DataFrame(tr.fit_transform(X), columns=tr.get_feature_names_out())
Xw = tr_wrap.fit_transform(X)
pd.testing.assert_frame_equal(Xw, pd.concat([X, Xt], axis=1))
assert Xw.shape[1] == len(X.columns) + len(
tr.get_feature_names_out(X.columns))
def test_wrap_transformers(transformer):
# load data
X = fetch_california_housing(as_frame=True).frame
# prepare selectors
tr = clone(transformer)
tr_wrap = SklearnTransformerWrapper(transformer=transformer)
# Test:
# When passing variable list
varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
tr_wrap.set_params(variables=varlist)
Xt = pd.DataFrame(tr.fit_transform(X[varlist]), columns=X[varlist].columns)
Xw = tr_wrap.fit_transform(X)
remaining = [f for f in X.columns if f not in varlist]
assert Xt.shape[1] == 4
assert Xw.shape[1] == 9
pd.testing.assert_frame_equal(Xt, Xw[varlist])
pd.testing.assert_frame_equal(X[remaining], Xw[remaining])
# when variable list is None
tr_wrap.set_params(variables=None)
Xt = pd.DataFrame(tr.fit_transform(X), columns=X.columns)
Xw = tr_wrap.fit_transform(X)
pd.testing.assert_frame_equal(Xt, Xw)
def test_sklearn_standardscaler_object(df_vartypes):
variables_to_scale = ["Name"]
transformer = SklearnTransformerWrapper(transformer=StandardScaler(),
variables=variables_to_scale)
with pytest.raises(TypeError):
transformer.fit_transform(df_vartypes)
# init params
assert isinstance(transformer.transformer, StandardScaler)
assert transformer.variables == variables_to_scale
def test_sklearn_ohe_all_features(df_vartypes):
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64))
ref = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob": pd.date_range("2020-02-24", periods=4, freq="T"),
"Name_jack": [0, 0, 0, 1],
"Name_krish": [0, 0, 1, 0],
"Name_nick": [0, 1, 0, 0],
"Name_tom": [1, 0, 0, 0],
"City_Bristol": [0, 0, 0, 1],
"City_Liverpool": [0, 0, 1, 0],
"City_London": [1, 0, 0, 0],
"City_Manchester": [0, 1, 0, 0],
"Age_18": [0, 0, 0, 1],
"Age_19": [0, 0, 1, 0],
"Age_20": [1, 0, 0, 0],
"Age_21": [0, 1, 0, 0],
"Marks_0.6": [0, 0, 0, 1],
"Marks_0.7": [0, 0, 1, 0],
"Marks_0.8": [0, 1, 0, 0],
"Marks_0.9": [1, 0, 0, 0],
"dob_2020-02-24T00:00:00.000000000": [1, 0, 0, 0],
"dob_2020-02-24T00:01:00.000000000": [0, 1, 0, 0],
"dob_2020-02-24T00:02:00.000000000": [0, 0, 1, 0],
"dob_2020-02-24T00:03:00.000000000": [0, 0, 0, 1],
})
transformed_df = transformer.fit_transform(df_vartypes)
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_imputer_numeric_with_constant(dataframe_na):
variables_to_impute = ['Age', 'Marks']
na_variables_left_after_imputation = [
col for col in dataframe_na.loc[:, dataframe_na.isna().any()].columns
if col not in variables_to_impute
]
transformer = SklearnTransformerWrapper(transformer=SimpleImputer(
fill_value=-999, strategy='constant'),
variables=variables_to_impute)
# transformed dataframe
ref = dataframe_na.copy()
ref[variables_to_impute] = ref[variables_to_impute].fillna(-999)
dataframe_na_transformed = transformer.fit_transform(dataframe_na)
# init params
assert isinstance(transformer.transformer, SimpleImputer)
assert transformer.variables == variables_to_impute
# fit params
assert transformer.input_shape_ == (8, 6)
# transformed output
assert all(dataframe_na_transformed[na_variables_left_after_imputation].
isna().sum() != 0)
assert all(dataframe_na_transformed[variables_to_impute].isna().sum() == 0)
pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
def test_sklearn_ohe_numeric(df_vartypes):
variables_to_encode = ["Age"]
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64),
variables=variables_to_encode,
)
ref = pd.DataFrame({
"Age": [20, 21, 19, 18],
"Age_18": [0, 0, 0, 1],
"Age_19": [0, 0, 1, 0],
"Age_20": [1, 0, 0, 0],
"Age_21": [0, 1, 0, 0],
})
transformed_df = transformer.fit_transform(
df_vartypes[variables_to_encode])
# init params
assert isinstance(transformer.transformer, OneHotEncoder)
assert transformer.variables == variables_to_encode
# fit params
assert transformer.input_shape_ == (4, 1)
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_ohe_object_many_features(df_vartypes):
variables_to_encode = ["Name", "City"]
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64),
variables=variables_to_encode,
)
ref = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Name_jack": [0, 0, 0, 1],
"Name_krish": [0, 0, 1, 0],
"Name_nick": [0, 1, 0, 0],
"Name_tom": [1, 0, 0, 0],
"City_Bristol": [0, 0, 0, 1],
"City_Liverpool": [0, 0, 1, 0],
"City_London": [1, 0, 0, 0],
"City_Manchester": [0, 1, 0, 0],
})
transformed_df = transformer.fit_transform(
df_vartypes[variables_to_encode])
# init params
assert isinstance(transformer.transformer, OneHotEncoder)
assert transformer.variables == variables_to_encode
# fit params
assert transformer.input_shape_ == (4, 2)
pd.testing.assert_frame_equal(ref, transformed_df)
def test_get_feature_names_out_polynomialfeatures(varlist):
X = fetch_california_housing(as_frame=True).frame
tr_wrap = SklearnTransformerWrapper(transformer=PolynomialFeatures(),
variables=varlist)
Xw = tr_wrap.fit_transform(X)
assert Xw.columns.tolist() == tr_wrap.get_feature_names_out()
if varlist is not None:
output_feat = [
"1",
"MedInc",
"HouseAge",
"AveRooms",
"AveBedrms",
"MedInc^2",
"MedInc HouseAge",
"MedInc AveRooms",
"MedInc AveBedrms",
"HouseAge^2",
"HouseAge AveRooms",
"HouseAge AveBedrms",
"AveRooms^2",
"AveRooms AveBedrms",
"AveBedrms^2",
]
assert output_feat == tr_wrap.get_feature_names_out(varlist)
def test_sklearn_ohe_object_one_feature(df_vartypes):
variables_to_encode = ["Name"]
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64),
variables=variables_to_encode,
)
ref = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack"],
"Name_jack": [0, 0, 0, 1],
"Name_krish": [0, 0, 1, 0],
"Name_nick": [0, 1, 0, 0],
"Name_tom": [1, 0, 0, 0],
})
transformed_df = transformer.fit_transform(
df_vartypes[variables_to_encode])
# init params
assert isinstance(transformer.transformer, OneHotEncoder)
assert transformer.variables == variables_to_encode
# fit params
assert transformer.n_features_in_ == 1
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_ohe_object_many_features(dataframe_vartypes):
variables_to_encode = ['Name', 'City']
transformer = SklearnTransformerWrapper(transformer=OneHotEncoder(
sparse=False, dtype=np.int64),
variables=variables_to_encode)
ref = pd.DataFrame({
'Name': ['tom', 'nick', 'krish', 'jack'],
'City': ['London', 'Manchester', 'Liverpool', 'Bristol'],
'Name_jack': [0, 0, 0, 1],
'Name_krish': [0, 0, 1, 0],
'Name_nick': [0, 1, 0, 0],
'Name_tom': [1, 0, 0, 0],
'City_Bristol': [0, 0, 0, 1],
'City_Liverpool': [0, 0, 1, 0],
'City_London': [1, 0, 0, 0],
'City_Manchester': [0, 1, 0, 0]
})
transformed_df = transformer.fit_transform(
dataframe_vartypes[variables_to_encode])
# init params
assert isinstance(transformer.transformer, OneHotEncoder)
assert transformer.variables == variables_to_encode
# fit params
assert transformer.input_shape_ == (4, 2)
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_imputer_object_with_constant(df_na):
variables_to_impute = ["Name", "City"]
na_variables_left_after_imputation = [
col for col in df_na.loc[:, df_na.isna().any()].columns
if col not in variables_to_impute
]
transformer = SklearnTransformerWrapper(
transformer=SimpleImputer(fill_value="missing", strategy="constant"),
variables=variables_to_impute,
)
# transformed dataframe
ref = df_na.copy()
ref[variables_to_impute] = ref[variables_to_impute].fillna("missing")
dataframe_na_transformed = transformer.fit_transform(df_na)
# init params
assert isinstance(transformer.transformer, SimpleImputer)
assert transformer.variables == variables_to_impute
# fit params
assert transformer.input_shape_ == (8, 6)
# transformed output
assert all(dataframe_na_transformed[na_variables_left_after_imputation].
isna().sum() != 0)
assert all(dataframe_na_transformed[variables_to_impute].isna().sum() == 0)
pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
def test_get_feature_names_out_transformers(varlist, transformer):
X = fetch_california_housing(as_frame=True).frame
tr_wrap = SklearnTransformerWrapper(transformer=transformer,
variables=varlist)
Xw = tr_wrap.fit_transform(X)
assert Xw.columns.to_list() == tr_wrap.get_feature_names_out()
assert Xw.columns.to_list() == tr_wrap.get_feature_names_out(
["MedInc", "HouseAge"])
def test_function_transformer_works_with_numericals():
X = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
X_expected = pd.DataFrame({"col1": [2, 3, 4], "col2": ["a", "b", "c"]})
transformer = SklearnTransformerWrapper(
FunctionTransformer(lambda x: x + 1), variables=["col1"])
X_tf = transformer.fit_transform(X)
pd.testing.assert_frame_equal(X_expected, X_tf)
def test_get_feature_names_out_selectors(varlist, transformer):
X = fetch_california_housing(as_frame=True).frame
y = X["MedHouseVal"]
X = X.drop(["MedHouseVal"], axis=1)
tr_wrap = SklearnTransformerWrapper(transformer=transformer,
variables=varlist)
Xw = tr_wrap.fit_transform(X, y)
assert Xw.columns.to_list() == tr_wrap.get_feature_names_out()
assert Xw.columns.to_list() == tr_wrap.get_feature_names_out(
["MedInc", "HouseAge"])
def test_inverse_transform(transformer):
X = fetch_california_housing(as_frame=True).frame
X = X.drop(["Longitude"], axis=1)
tr_wrap = SklearnTransformerWrapper(transformer=transformer)
# When passing variable list
varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
tr_wrap.set_params(variables=varlist)
X_tr = tr_wrap.fit_transform(X)
X_inv = tr_wrap.inverse_transform(X_tr)
pd.testing.assert_frame_equal(X_inv, X)
# when variable list is None
tr_wrap.set_params(variables=None)
X_tr = tr_wrap.fit_transform(X)
X_inv = tr_wrap.inverse_transform(X_tr)
pd.testing.assert_frame_equal(X_inv, X)
def test_sklearn_imputer_allfeatures_with_constant(df_na):
transformer = SklearnTransformerWrapper(
transformer=SimpleImputer(fill_value="missing", strategy="constant"))
# transformed dataframe
ref = df_na.copy()
ref = ref.fillna("missing")
dataframe_na_transformed = transformer.fit_transform(df_na)
# transformed output
assert all(dataframe_na_transformed.isna().sum() == 0)
pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
def test_function_transformer_works_with_categoricals():
X = pd.DataFrame({"col1": ["1", "2", "3"], "col2": ["a", "b", "c"]})
X_expected = pd.DataFrame({
"col1": [1.0, 2.0, 3.0],
"col2": ["a", "b", "c"]
})
transformer = SklearnTransformerWrapper(
FunctionTransformer(lambda x: x.astype(np.float64)),
variables=["col1"])
X_tf = transformer.fit_transform(X)
pd.testing.assert_frame_equal(X_expected, X_tf)
def test_wrap_selectors(transformer):
# load data
X = fetch_california_housing(as_frame=True).frame
y = X["MedHouseVal"]
X = X.drop(["MedHouseVal"], axis=1)
# prepare selectors
sel = clone(transformer)
sel_wrap = SklearnTransformerWrapper(transformer=transformer)
# Test:
# When passing variable list
varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
sel_wrap.set_params(variables=varlist)
Xt = pd.DataFrame(
sel.fit_transform(X[varlist], y),
columns=X[varlist].columns[(sel.get_support())],
)
Xw = sel_wrap.fit_transform(X, y)
selected = X[varlist].columns[(sel.get_support())]
remaining = [f for f in X.columns if f not in varlist]
pd.testing.assert_frame_equal(Xt, Xw[selected])
pd.testing.assert_frame_equal(X[remaining], Xw[remaining])
assert Xw.shape[1] == len(remaining) + len(selected)
# when variable list is None
sel_wrap.set_params(variables=None)
Xt = pd.DataFrame(sel.fit_transform(X, y),
columns=X.columns[(sel.get_support())])
Xw = sel_wrap.fit_transform(X, y)
pd.testing.assert_frame_equal(Xt, Xw)
def test_sklearn_imputer_allfeatures_with_constant(dataframe_na):
transformer = SklearnTransformerWrapper(
transformer=SimpleImputer(fill_value='missing', strategy='constant'))
# transformed dataframe
ref = dataframe_na.copy()
ref = ref.fillna('missing')
dataframe_na_transformed = transformer.fit_transform(dataframe_na)
# init params
assert isinstance(transformer.transformer, SimpleImputer)
# fit params
assert transformer.input_shape_ == (8, 6)
# transformed output
assert all(dataframe_na_transformed.isna().sum() == 0)
pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
def test_sklearn_standardscaler_allfeatures(dataframe_vartypes):
transformer = SklearnTransformerWrapper(transformer=StandardScaler())
ref = dataframe_vartypes.copy()
variables_to_scale = list(ref.select_dtypes(include='number').columns)
ref[variables_to_scale] = (
ref[variables_to_scale] -
ref[variables_to_scale].mean()) / ref[variables_to_scale].std(ddof=0)
transformed_df = transformer.fit_transform(dataframe_vartypes)
# init params
assert isinstance(transformer.transformer, StandardScaler)
assert transformer.variables == variables_to_scale
# fit params
assert transformer.input_shape_ == (4, 5)
assert (transformer.transformer.mean_.round(6) == np.array([19.5,
0.75])).all()
assert all(transformer.transformer.scale_.round(6) == [1.118034, 0.111803])
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_standardscaler_numeric(dataframe_vartypes):
variables_to_scale = ['Age', 'Marks']
transformer = SklearnTransformerWrapper(transformer=StandardScaler(),
variables=variables_to_scale)
ref = dataframe_vartypes.copy()
ref[variables_to_scale] = (
ref[variables_to_scale] -
ref[variables_to_scale].mean()) / ref[variables_to_scale].std(ddof=0)
transformed_df = transformer.fit_transform(dataframe_vartypes)
# init params
assert isinstance(transformer.transformer, StandardScaler)
assert transformer.variables == variables_to_scale
# fit params
assert transformer.input_shape_ == (4, 5)
assert (transformer.transformer.mean_.round(6) == np.array([19.5,
0.75])).all()
assert all(transformer.transformer.scale_.round(6) == [1.118034, 0.111803])
pd.testing.assert_frame_equal(ref, transformed_df)
def test_sklearn_ohe_all_features(dataframe_vartypes):
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64))
ref = pd.DataFrame({
'Name': ['tom', 'nick', 'krish', 'jack'],
'City': ['London', 'Manchester', 'Liverpool', 'Bristol'],
'Age': [20, 21, 19, 18],
'Marks': [0.9, 0.8, 0.7, 0.6],
'dob': pd.date_range('2020-02-24', periods=4, freq='T'),
'Name_jack': [0, 0, 0, 1],
'Name_krish': [0, 0, 1, 0],
'Name_nick': [0, 1, 0, 0],
'Name_tom': [1, 0, 0, 0],
'City_Bristol': [0, 0, 0, 1],
'City_Liverpool': [0, 0, 1, 0],
'City_London': [1, 0, 0, 0],
'City_Manchester': [0, 1, 0, 0],
'Age_18': [0, 0, 0, 1],
'Age_19': [0, 0, 1, 0],
'Age_20': [1, 0, 0, 0],
'Age_21': [0, 1, 0, 0],
'Marks_0.6': [0, 0, 0, 1],
'Marks_0.7': [0, 0, 1, 0],
'Marks_0.8': [0, 1, 0, 0],
'Marks_0.9': [1, 0, 0, 0],
'dob_2020-02-24T00:00:00.000000000': [1, 0, 0, 0],
'dob_2020-02-24T00:01:00.000000000': [0, 1, 0, 0],
'dob_2020-02-24T00:02:00.000000000': [0, 0, 1, 0],
'dob_2020-02-24T00:03:00.000000000': [0, 0, 0, 1]
})
transformed_df = transformer.fit_transform(dataframe_vartypes)
# init params
assert isinstance(transformer.transformer, OneHotEncoder)
# fit params
assert transformer.input_shape_ == (4, 5)
pd.testing.assert_frame_equal(ref, transformed_df)
def test_wrap_simple_imputer(df_na):
variables_to_impute = ["Age", "Marks"]
na_variables_left_after_imputation = [
col for col in df_na.loc[:, df_na.isna().any()].columns
if col not in variables_to_impute
]
transformer = SklearnTransformerWrapper(
transformer=SimpleImputer(fill_value=-999, strategy="constant"),
variables=variables_to_impute,
)
# transformed dataframe
ref = df_na.copy()
ref[variables_to_impute] = ref[variables_to_impute].fillna(-999)
dataframe_na_transformed = transformer.fit_transform(df_na)
# transformed output
assert all(dataframe_na_transformed[na_variables_left_after_imputation].
isna().sum() != 0)
assert all(dataframe_na_transformed[variables_to_impute].isna().sum() == 0)
pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
def test_get_feature_names_out_ohe(varlist, df_vartypes):
transformer = SklearnTransformerWrapper(
transformer=OneHotEncoder(sparse=False, dtype=np.int64),
variables=varlist,
)
df_tr = transformer.fit_transform(df_vartypes)
assert df_tr.columns.to_list() == transformer.get_feature_names_out()
if varlist is not None:
output_feat = [
"Name_jack",
"Name_krish",
"Name_nick",
"Name_tom",
"City_Bristol",
"City_Liverpool",
"City_London",
"City_Manchester",
]
assert output_feat == transformer.get_feature_names_out(varlist)