def test_automatically_finds_variables(df_vartypes):
# test case 1: automatically select variables
transformer = BoxCoxTransformer(variables=None)
X = transformer.fit_transform(df_vartypes)
# expected output
transf_df = df_vartypes.copy()
transf_df["Age"] = [9.78731, 10.1666, 9.40189, 9.0099]
transf_df["Marks"] = [-0.101687, -0.207092, -0.316843, -0.431788]
# test init params
assert transformer.variables is None
# test fit attr
assert transformer.variables_ == ["Age", "Marks"]
assert transformer.n_features_in_ == 5
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
# test inverse_transform
Xit = transformer.inverse_transform(X)
# convert numbers to original format.
Xit["Age"] = Xit["Age"].round().astype("int64")
Xit["Marks"] = Xit["Marks"].round(1)
# test
pd.testing.assert_frame_equal(Xit, df_vartypes)
def test_error_if_df_contains_negative_values(df_vartypes):
# test error when data contains negative values
df_neg = df_vartypes.copy()
df_neg.loc[1, "Age"] = -1
# test case 4: when variable contains negative value, fit
with pytest.raises(ValueError):
transformer = BoxCoxTransformer()
transformer.fit(df_neg)
# test case 5: when variable contains negative value, transform
with pytest.raises(ValueError):
transformer = BoxCoxTransformer()
transformer.fit(df_vartypes)
transformer.transform(df_neg)
def test_automatically_finds_variables(df_vartypes):
# test case 1: automatically select variables
transformer = BoxCoxTransformer(variables=None)
X = transformer.fit_transform(df_vartypes)
# expected output
transf_df = df_vartypes.copy()
transf_df["Age"] = [9.78731, 10.1666, 9.40189, 9.0099]
transf_df["Marks"] = [-0.101687, -0.207092, -0.316843, -0.431788]
# test init params
assert transformer.variables == ["Age", "Marks"]
# test fit attr
assert transformer.input_shape_ == (4, 5)
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
def test_non_fitted_error(df_vartypes):
with pytest.raises(NotFittedError):
transformer = BoxCoxTransformer()
transformer.transform(df_vartypes)
def test_transform_raises_error_if_df_contains_na(df_vartypes, df_na):
# test case 3: when dataset contains na, transform method
with pytest.raises(ValueError):
transformer = BoxCoxTransformer()
transformer.fit(df_vartypes)
transformer.transform(df_na[["Name", "City", "Age", "Marks", "dob"]])
def test_fit_raises_error_if_df_contains_na(df_na):
# test case 2: when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = BoxCoxTransformer()
transformer.fit(df_na)
import pytest
from sklearn.utils.estimator_checks import check_estimator
from feature_engine.transformation import (
BoxCoxTransformer,
LogTransformer,
PowerTransformer,
ReciprocalTransformer,
YeoJohnsonTransformer,
)
@pytest.mark.parametrize(
"Estimator",
[
BoxCoxTransformer(),
LogTransformer(),
PowerTransformer(),
ReciprocalTransformer(),
YeoJohnsonTransformer(),
],
)
def test_all_transformers(Estimator):
return check_estimator(Estimator)
