def test_simple_imputation_inverse_transform(missing_value):
# Test inverse_transform feature for np.nan
X_1 = np.array(
[
[9, missing_value, 3, -1],
[4, -1, 5, 4],
[6, 7, missing_value, -1],
[8, 9, 0, missing_value],
]
)
X_2 = np.array(
[
[5, 4, 2, 1],
[2, 1, missing_value, 3],
[9, missing_value, 7, 1],
[6, 4, 2, missing_value],
]
)
X_3 = np.array(
[
[1, missing_value, 5, 9],
[missing_value, 4, missing_value, missing_value],
[2, missing_value, 7, missing_value],
[missing_value, 3, missing_value, 8],
]
)
X_4 = np.array(
[
[1, 1, 1, 3],
[missing_value, 2, missing_value, 1],
[2, 3, 3, 4],
[missing_value, 4, missing_value, 2],
]
)
imputer = SimpleImputer(
missing_values=missing_value, strategy="mean", add_indicator=True
)
X_1_trans = imputer.fit_transform(X_1)
X_1_inv_trans = imputer.inverse_transform(X_1_trans)
X_2_trans = imputer.transform(X_2) # test on new data
X_2_inv_trans = imputer.inverse_transform(X_2_trans)
assert_array_equal(X_1_inv_trans, X_1)
assert_array_equal(X_2_inv_trans, X_2)
for X in [X_3, X_4]:
X_trans = imputer.fit_transform(X)
X_inv_trans = imputer.inverse_transform(X_trans)
assert_array_equal(X_inv_trans, X)
def test_simple_imputation_inverse_transform_exceptions(missing_value):
X_1 = np.array([
[9, missing_value, 3, -1],
[4, -1, 5, 4],
[6, 7, missing_value, -1],
[8, 9, 0, missing_value]
])
imputer = SimpleImputer(missing_values=missing_value, strategy="mean")
X_1_trans = imputer.fit_transform(X_1)
with pytest.raises(ValueError,
match=f"Got 'add_indicator={imputer.add_indicator}'"):
imputer.inverse_transform(X_1_trans)
