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_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)
