def test_automatically_find_variables_and_gaussian_imputation_on_right_tail(
df_na):
# set up transformer
imputer = EndTailImputer(imputation_method="gaussian",
tail="right",
fold=3,
variables=None)
X_transformed = imputer.fit_transform(df_na)
# set up expected output
X_reference = df_na.copy()
X_reference["Age"] = X_reference["Age"].fillna(58.94908118478389)
X_reference["Marks"] = X_reference["Marks"].fillna(1.3244261503263175)
# test init params
assert imputer.imputation_method == "gaussian"
assert imputer.tail == "right"
assert imputer.fold == 3
assert imputer.variables is None
# test fit attr
assert imputer.variables_ == ["Age", "Marks"]
assert imputer.n_features_in_ == 6
imputer.imputer_dict_ = {
key: round(value, 3)
for (key, value) in imputer.imputer_dict_.items()
}
assert imputer.imputer_dict_ == {
"Age": 58.949,
"Marks": 1.324,
}
# transform output: indicated vars ==> no NA, not indicated vars with NA
assert X_transformed[["Age", "Marks"]].isnull().sum().sum() == 0
assert X_transformed[["City", "Name"]].isnull().sum().sum() > 0
pd.testing.assert_frame_equal(X_transformed, X_reference)
def test_user_enters_variables_and_max_value_imputation(df_na):
imputer = EndTailImputer(imputation_method="max",
tail="right",
fold=2,
variables=["Age", "Marks"])
imputer.fit(df_na)
assert imputer.imputer_dict_ == {"Age": 82.0, "Marks": 1.8}
def test_user_enters_variables_and_iqr_imputation_on_left_tail(df_na):
# test case 5: IQR + left tail
imputer = EndTailImputer(imputation_method="iqr",
tail="left",
fold=1.5,
variables=["Age", "Marks"])
imputer.fit(df_na)
assert imputer.imputer_dict_ == {"Age": -6.5, "Marks": 0.36249999999999993}
def test_automatically_select_variables_and_gaussian_imputation_on_left_tail(
df_na):
imputer = EndTailImputer(imputation_method="gaussian", tail="left", fold=3)
imputer.fit(df_na)
assert imputer.imputer_dict_ == {
"Age": -1.520509756212462,
"Marks": 0.04224051634034898,
}
def test_automatically_select_variables_and_gaussian_imputation_on_left_tail(
df_na):
imputer = EndTailImputer(imputation_method="gaussian", tail="left", fold=3)
imputer.fit(df_na)
imputer.imputer_dict_ = {
key: round(value, 3)
for (key, value) in imputer.imputer_dict_.items()
}
assert imputer.imputer_dict_ == {
"Age": -1.521,
"Marks": 0.042,
}
def test_user_enters_variables_and_iqr_imputation_on_right_tail(df_na):
# set up transformer
imputer = EndTailImputer(imputation_method="iqr",
tail="right",
fold=1.5,
variables=["Age", "Marks"])
X_transformed = imputer.fit_transform(df_na)
# set up expected result
X_reference = df_na.copy()
X_reference["Age"] = X_reference["Age"].fillna(65.5)
X_reference["Marks"] = X_reference["Marks"].fillna(1.0625)
# test fit and transform attr and output
assert imputer.imputer_dict_ == {"Age": 65.5, "Marks": 1.0625}
assert X_transformed[["Age", "Marks"]].isnull().sum().sum() == 0
pd.testing.assert_frame_equal(X_transformed, X_reference)
def test_error_when_imputation_method_is_not_permitted():
with pytest.raises(ValueError):
EndTailImputer(imputation_method="arbitrary")
def test_non_fitted_error(df_na):
with pytest.raises(NotFittedError):
imputer = EndTailImputer()
imputer.transform(df_na)
def test_error_when_fold_is_1():
with pytest.raises(ValueError):
EndTailImputer(fold=-1)
def test_error_when_tail_is_string():
with pytest.raises(ValueError):
EndTailImputer(tail="arbitrary")
from feature_engine.transformation import (
BoxCoxTransformer,
LogTransformer,
PowerTransformer,
ReciprocalTransformer,
YeoJohnsonTransformer,
)
from feature_engine.wrappers import SklearnTransformerWrapper
# imputation
@parametrize_with_checks([
MeanMedianImputer(),
ArbitraryNumberImputer(),
CategoricalImputer(fill_value=0, ignore_format=True),
EndTailImputer(),
AddMissingIndicator(),
RandomSampleImputer(),
DropMissingData(),
])
def test_sklearn_compatible_imputer(estimator, check):
check(estimator)
# encoding
@parametrize_with_checks([
CountFrequencyEncoder(ignore_format=True),
DecisionTreeEncoder(regression=False, ignore_format=True),
MeanEncoder(ignore_format=True),
OneHotEncoder(ignore_format=True),
OrdinalEncoder(ignore_format=True),
