def test_invalid_inputs():
with pytest.raises(ValueError,
match="Invalid input 'test' for handle_unknown"):
TargetEncoder(handle_unknown='test')
with pytest.raises(ValueError,
match="Invalid input 'test2' for handle_missing"):
TargetEncoder(handle_missing='test2')
with pytest.raises(
ValueError,
match="Smoothing value needs to be strictly larger than 0"):
TargetEncoder(smoothing=0)
def test_transform():
X = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': ["r", "t", "s", "t", "t"],
'col_3': ["a", "a", "a", "b", "a"]
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder()
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': [0.6, 0.65872, 0.6, 0.65872, 0.65872],
'col_3': [0.504743, 0.504743, 0.504743, 0.6, 0.504743]
})
pd.testing.assert_frame_equal(X_t, X_expected)
def test_parameters():
encoder = TargetEncoder(cols=['a'])
expected_parameters = {
"cols": ['a'],
"smoothing": 1.0,
"handle_unknown": "value",
"handle_missing": "value"
}
assert encoder.parameters == expected_parameters
def test_init():
parameters = {
"cols": None,
"smoothing": 1.0,
"handle_unknown": "value",
"handle_missing": "value"
}
encoder = TargetEncoder()
assert encoder.parameters == parameters
def test_target_encoder_woodwork_custom_overrides_returned_by_components(X_df):
y = pd.Series([1, 2, 1])
override_types = [
Integer, Double, Categorical, NaturalLanguage, Boolean, Datetime
]
for logical_type in override_types:
try:
X = ww.DataTable(X_df, logical_types={0: logical_type})
except TypeError:
continue
encoder = TargetEncoder()
encoder.fit(X, y)
transformed = encoder.transform(X, y)
assert isinstance(transformed, ww.DataTable)
if logical_type == Categorical:
assert transformed.logical_types == {0: Double}
else:
assert transformed.logical_types == {0: logical_type}
def test_pandas_numpy(mock_fit, X_y_binary):
X, y = X_y_binary
X = pd.DataFrame(X).sample(frac=1)
encoder = TargetEncoder()
encoder.fit(X, y)
assert_frame_equal(mock_fit.call_args[0][0], X)
X_numpy = X.to_numpy()
encoder.fit(X_numpy, y)
def test_get_feature_names():
X = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': ["r", "t", "s", "t", "t"],
'col_3': ["a", "a", "a", "b", "a"]
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder()
with pytest.raises(
ComponentNotYetFittedError,
match='This TargetEncoder is not fitted yet. You must fit'):
encoder.get_feature_names()
encoder.fit(X, y)
np.testing.assert_array_equal(encoder.get_feature_names(),
np.array(['col_1', 'col_2', 'col_3']))
def test_pandas_numpy(mock_fit, X_y_binary):
X, y = X_y_binary
X = pd.DataFrame(X).sample(frac=1)
encoder = TargetEncoder()
X_t = pd.DataFrame(X).reset_index(drop=True, inplace=False)
encoder.fit(X, y)
assert_frame_equal(mock_fit.call_args[0][0], X_t)
X_numpy = X.to_numpy()
encoder.fit(X_numpy, y)
def test_null_values_in_dataframe():
X = pd.DataFrame({
'col_1': ["a", "b", "c", "d", np.nan],
'col_2': ["a", "b", "a", "c", "b"],
'col_3': ["a", "a", "a", "a", "a"]
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder(handle_missing='value')
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1': [0.6, 0.6, 0.6, 0.6, 0.6],
'col_2': [0.526894, 0.526894, 0.526894, 0.6, 0.526894],
'col_3': [
0.6,
0.6,
0.6,
0.6,
0.6,
]
})
assert_frame_equal(X_expected, X_t.to_dataframe())
encoder = TargetEncoder(handle_missing='return_nan')
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1': [0.6, 0.6, 0.6, 0.6, np.nan],
'col_2': [0.526894, 0.526894, 0.526894, 0.6, 0.526894],
'col_3': [
0.6,
0.6,
0.6,
0.6,
0.6,
]
})
assert_frame_equal(X_expected, X_t.to_dataframe())
encoder = TargetEncoder(handle_missing='error')
with pytest.raises(ValueError,
match='Columns to be encoded can not contain null'):
encoder.fit(X, y)
def test_categories():
encoder = TargetEncoder()
with pytest.raises(AttributeError,
match="'TargetEncoder' object has no attribute"):
encoder.categories
def test_smoothing():
# larger smoothing values should bring the values closer to the global mean
X = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': [2, 1, 1, 1, 1],
'col_3': ["a", "a", "a", "a", "b"]
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder(smoothing=1)
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1':
pd.Series([1, 2, 1, 1, 2], dtype="Int64"),
'col_2':
pd.Series([2, 1, 1, 1, 1], dtype="Int64"),
'col_3': [0.742886, 0.742886, 0.742886, 0.742886, 0.6]
})
assert_frame_equal(X_expected, X_t.to_dataframe())
encoder = TargetEncoder(smoothing=10)
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1':
pd.Series([1, 2, 1, 1, 2], dtype="Int64"),
'col_2':
pd.Series([2, 1, 1, 1, 1], dtype="Int64"),
'col_3': [0.686166, 0.686166, 0.686166, 0.686166, 0.6]
})
assert_frame_equal(X_expected, X_t.to_dataframe())
encoder = TargetEncoder(smoothing=100)
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1':
pd.Series([1, 2, 1, 1, 2], dtype="Int64"),
'col_2':
pd.Series([2, 1, 1, 1, 1], dtype="Int64"),
'col_3': [0.676125, 0.676125, 0.676125, 0.676125, 0.6]
})
assert_frame_equal(X_expected, X_t.to_dataframe())
def test_cols():
X = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': ['2', '1', '1', '1', '1'],
'col_3': ["a", "a", "a", "a", "a"]
})
X_expected = X.astype({
'col_1': 'Int64',
'col_2': 'category',
'col_3': 'category'
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder(cols=[])
encoder.fit(X, y)
X_t = encoder.transform(X)
assert_frame_equal(X_expected, X_t.to_dataframe())
encoder = TargetEncoder(cols=['col_2'])
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1':
pd.Series([1, 2, 1, 1, 2], dtype="Int64"),
'col_2': [0.60000, 0.742886, 0.742886, 0.742886, 0.742886],
'col_3':
pd.Series(["a", "a", "a", "a", "a"], dtype="category")
})
assert_frame_equal(X_expected, X_t.to_dataframe(), check_less_precise=True)
encoder = TargetEncoder(cols=['col_2', 'col_3'])
encoder.fit(X, y)
X_t = encoder.transform(X)
encoder2 = TargetEncoder()
encoder2.fit(X, y)
X_t2 = encoder2.transform(X)
assert_frame_equal(X_t.to_dataframe(), X_t2.to_dataframe())
def test_cols():
X = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': ['2', '1', '1', '1', '1'],
'col_3': ["a", "a", "a", "a", "a"]
})
y = pd.Series([0, 1, 1, 1, 0])
encoder = TargetEncoder(cols=[])
encoder.fit(X, y)
X_t = encoder.transform(X)
pd.testing.assert_frame_equal(X, X_t)
encoder = TargetEncoder(cols=['col_2'])
encoder.fit(X, y)
X_t = encoder.transform(X)
X_expected = pd.DataFrame({
'col_1': [1, 2, 1, 1, 2],
'col_2': [0.60000, 0.742886, 0.742886, 0.742886, 0.742886],
'col_3': ["a", "a", "a", "a", "a"]
})
pd.testing.assert_frame_equal(X_t, X_expected, check_less_precise=True)
encoder = TargetEncoder(cols=['col_2', 'col_3'])
encoder.fit(X, y)
X_t = encoder.transform(X)
encoder2 = TargetEncoder()
encoder2.fit(X, y)
X_t2 = encoder2.transform(X)
pd.testing.assert_frame_equal(X_t, X_t2)
