def test_raises_error_if_na_in_df(df_na, df_vartypes):
# when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = MatchVariables()
transformer.fit(df_na)
# when dataset contains na, transform method
with pytest.raises(ValueError):
transformer = MatchVariables()
transformer.fit(df_vartypes)
transformer.transform(df_na)
def test_drop_and_add_columns(fill_value, expected_studies, expected_age,
df_vartypes, df_na):
train = df_na.copy()
test = df_vartypes.copy()
test = test.drop("Age", axis=1) # to add more than one column
# adding columns to test if they are removed
for new_col in ["test1", "test2"]:
test.loc[:, new_col] = new_col
match_columns = MatchVariables(
fill_value=fill_value,
missing_values="ignore",
)
match_columns.fit(train)
transformed_df = match_columns.transform(test)
expected_result = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Studies":
expected_studies,
"Age":
expected_age,
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob":
pd.date_range("2020-02-24", periods=4, freq="T"),
})
# test init params
if fill_value is np.nan:
assert match_columns.fill_value is np.nan
else:
assert match_columns.fill_value == fill_value
assert match_columns.verbose is True
assert match_columns.missing_values == "ignore"
# test fit attrs
assert list(match_columns.input_features_) == list(train.columns)
assert match_columns.n_features_in_ == 6
# test transform output
pd.testing.assert_frame_equal(expected_result, transformed_df)
def test_drop_columns_when_more_columns_in_test_than_train(df_vartypes, df_na):
train = df_vartypes.copy()
train = train.drop("City", axis=1) # to remove more than one column
test = df_na.copy()
match_columns = MatchVariables(missing_values="ignore")
match_columns.fit(train)
transformed_df = match_columns.transform(test)
expected_result = test.drop(columns=["Studies", "City"])
# test init params
assert match_columns.fill_value is np.nan
assert match_columns.verbose is True
assert match_columns.missing_values == "ignore"
# test fit attrs
assert list(match_columns.input_features_) == list(train.columns)
assert match_columns.n_features_in_ == 4
# test transform output
pd.testing.assert_frame_equal(expected_result, transformed_df)
def test_verbose_print_out(capfd, df_vartypes, df_na):
match_columns = MatchVariables(missing_values="ignore", verbose=True)
train = df_na.copy()
train.loc[:, "new_variable"] = 5
match_columns.fit(train)
match_columns.transform(df_vartypes)
out, err = capfd.readouterr()
assert (out == "The following variables are added to the DataFrame: "
"['new_variable', 'Studies']\n"
or out == "The following variables are added to the DataFrame: "
"['Studies', 'new_variable']\n")
match_columns.fit(df_vartypes)
match_columns.transform(train)
out, err = capfd.readouterr()
assert (out == "The following variables are dropped from the DataFrame: "
"['new_variable', 'Studies']\n" or out
== "The following variables are dropped from the DataFrame: "
"['Studies', 'new_variable']\n")
import pytest
from sklearn.utils.estimator_checks import check_estimator
from tests.estimator_checks.estimator_checks import check_feature_engine_estimator
from feature_engine.preprocessing import MatchCategories, MatchVariables
_estimators = [MatchCategories(ignore_format=True), MatchVariables()]
@pytest.mark.parametrize("estimator", _estimators)
def test_check_estimator_from_sklearn(estimator):
return check_estimator(estimator)
@pytest.mark.parametrize("estimator", [MatchCategories(), MatchVariables()])
def test_check_estimator_from_feature_engine(estimator):
return check_feature_engine_estimator(estimator)
import pytest
from sklearn.utils.estimator_checks import check_estimator
from feature_engine.preprocessing import MatchVariables
@pytest.mark.parametrize(
"Estimator",
[
MatchVariables(),
],
)
def test_all_transformers(Estimator):
return check_estimator(Estimator)
def test_non_fitted_error(df_vartypes):
with pytest.raises(NotFittedError):
transformer = MatchVariables()
transformer.transform(df_vartypes)
def test_error_if_param_values_not_allowed(fill_value, missing_values,
verbose):
with pytest.raises(ValueError):
MatchVariables(fill_value=fill_value,
missing_values=missing_values,
verbose=verbose)