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_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_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)