def test_all_binary_operation(df_vartypes):
# case 2: selected only one operation:
transformer = CombineWithReferenceFeature(
variables_to_combine=["Age"],
reference_variables=["Marks"],
operations=["sub", "div", "add", "mul"],
)
X = transformer.fit_transform(df_vartypes)
ref = pd.DataFrame.from_dict({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob":
pd.date_range("2020-02-24", periods=4, freq="T"),
"Age_sub_Marks": [19.1, 20.2, 18.3, 17.4],
"Age_div_Marks": [22.22222222222222, 26.25, 27.142857142857146, 30.0],
"Age_add_Marks": [20.9, 21.8, 19.7, 18.6],
"Age_mul_Marks": [18.0, 16.8, 13.299999999999999, 10.799999999999999],
})
# init params
assert transformer.variables_to_combine == ["Age"]
assert transformer.reference_variables == ["Marks"]
assert transformer.operations == ["sub", "div", "add", "mul"]
# fit params
assert transformer.n_features_in_ == 5
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_user_enters_output_variable_names(df_vartypes):
transformer = CombineWithReferenceFeature(
variables_to_combine=["Age", "Marks"],
reference_variables=["Age", "Marks"],
operations=["sub"],
new_variables_names=["Juan", "Pedro", "Fasola", "GranAmigo"],
)
X = transformer.fit_transform(df_vartypes)
ref = pd.DataFrame.from_dict({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob":
pd.date_range("2020-02-24", periods=4, freq="T"),
"Juan": [0, 0, 0, 0],
"Pedro": [-19.1, -20.2, -18.3, -17.4],
"Fasola": [19.1, 20.2, 18.3, 17.4],
"GranAmigo": [0.0, 0.0, 0.0, 0.0],
})
# init params
assert transformer.variables_to_combine == ["Age", "Marks"]
assert transformer.reference_variables == ["Age", "Marks"]
# fit params
assert transformer.operations == ["sub"]
assert transformer.n_features_in_ == 5
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_operations_with_multiple_variables(df_vartypes):
transformer = CombineWithReferenceFeature(
variables_to_combine=["Age", "Marks"],
reference_variables=["Age", "Marks"],
operations=["sub"],
)
X = transformer.fit_transform(df_vartypes)
ref = pd.DataFrame.from_dict(
{
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob": pd.date_range("2020-02-24", periods=4, freq="T"),
"Age_sub_Age": [0, 0, 0, 0],
"Marks_sub_Age": [-19.1, -20.2, -18.3, -17.4],
"Age_sub_Marks": [19.1, 20.2, 18.3, 17.4],
"Marks_sub_Marks": [0.0, 0.0, 0.0, 0.0],
}
)
# init params
assert transformer.variables_to_combine == ["Age", "Marks"]
assert transformer.reference_variables == ["Age", "Marks"]
# fit params
assert transformer.operations == ["sub"]
assert transformer.input_shape_ == (4, 5)
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_error_when_entered_variables_not_in_df(df_vartypes):
transformer = CombineWithReferenceFeature(
variables_to_combine=["FeatOutsideDataset", "Age"],
reference_variables=["Age", "Name"],
operations=["sub"],
)
with pytest.raises(KeyError):
transformer.fit_transform(df_vartypes)
def test_error_when_variables_to_combine_not_numeric(df_vartypes):
transformer = CombineWithReferenceFeature(
variables_to_combine=["Name", "Age", "Marks"],
reference_variables=["Age", "Name"],
operations=["sub"],
)
with pytest.raises(TypeError):
transformer.fit_transform(df_vartypes)
def test_error_if_new_variable_names_of_wrong_type():
with pytest.raises(ValueError):
CombineWithReferenceFeature(
variables_to_combine=["Age", "Name"],
reference_variables=["Age", "Name"],
new_variables_names=[4],
)
with pytest.raises(ValueError):
CombineWithReferenceFeature(
variables_to_combine=["Age", "Name"],
reference_variables=["Age", "Name"],
new_variables_names=("var1", "var2"),
)
def test_error_if_operation_is_wrong_type():
with pytest.raises(ValueError):
CombineWithReferenceFeature(
variables_to_combine=["Age", "Name"],
reference_variables=["Age", "Name"],
operations=[sum],
)
with pytest.raises(ValueError):
CombineWithReferenceFeature(
variables_to_combine=["Age", "Name"],
reference_variables=["Age", "Name"],
operations=("sub", "div"),
)
def test_error_when_variables_to_combine_wrong_type():
with pytest.raises(ValueError):
CombineWithReferenceFeature(variables_to_combine=("Age", "Name"),
reference_variables=["Age", "Name"])
with pytest.raises(ValueError):
CombineWithReferenceFeature(variables_to_combine=["Age", 0, 4.5],
reference_variables=["Age", "Name"])
with pytest.raises(ValueError):
CombineWithReferenceFeature(variables_to_combine=["Age", "Name"],
reference_variables=("Age", "Name"))
with pytest.raises(ValueError):
CombineWithReferenceFeature(variables_to_combine=["Age", "Name"],
reference_variables=["Age", 0, 4.5])
def test_error_when_drop_original_not_bool():
with pytest.raises(TypeError):
CombineWithReferenceFeature(
variables_to_combine=["Age"],
reference_variables=["Marks"],
drop_original="not_a_bool",
)
def test_error_if_operation_not_supported():
with pytest.raises(ValueError):
CombineWithReferenceFeature(
variables_to_combine=["Age", "Name"],
reference_variables=["Age", "Name"],
operations=["an_operation"],
)
def test_drop_original_variables(df_vartypes):
transformer = CombineWithReferenceFeature(
variables_to_combine=["Age", "Marks"],
reference_variables=["Age", "Marks"],
drop_original=True,
)
X = transformer.fit_transform(df_vartypes)
ref = pd.DataFrame.from_dict({
"Name": ["tom", "nick", "krish", "jack"],
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"dob":
pd.date_range("2020-02-24", periods=4, freq="T"),
"Age_sub_Age": [0, 0, 0, 0],
"Marks_sub_Age": [-19.1, -20.2, -18.3, -17.4],
"Age_sub_Marks": [19.1, 20.2, 18.3, 17.4],
"Marks_sub_Marks": [0.0, 0.0, 0.0, 0.0],
})
pd.testing.assert_frame_equal(X, ref)
import pytest
from sklearn.utils.estimator_checks import check_estimator
from feature_engine.creation import (
CombineWithReferenceFeature,
CyclicalTransformer,
MathematicalCombination,
)
@pytest.mark.parametrize(
"Estimator",
[
MathematicalCombination(variables_to_combine=["0", "1"]),
CombineWithReferenceFeature(variables_to_combine=["0"],
reference_variables=["1"]),
CyclicalTransformer(),
],
)
def test_all_transformers(Estimator):
return check_estimator(Estimator)
def test_error_when_param_variables_not_entered():
with pytest.raises(TypeError):
CombineWithReferenceFeature()