docstr: test get_features_transform_mapping with list of category encoders
docstr_tok: ["test","get_features_transform_mapping","with","list","of","category","encoders"]
code: def test_get_features_transform_mapping_4(self):
      """
      test get_features_transform_mapping with list of category encoders
      """
      test = pd.DataFrame(
          {
              "city": ["chicago", "paris", "chicago"],
              "state": ["US", "FR", "FR"],
              "other": ["A", "B", "B"],
          }
      )
      y = pd.DataFrame(data=[0, 1, 1], columns=["y"])
  
      enc = ce.OneHotEncoder(cols=["state"], use_cat_names=True)
      test_encoded = pd.DataFrame(enc.fit_transform(test, y))
  
      enc2 = ce.OrdinalEncoder(cols=["city"])
      test_encoded = enc2.fit_transform(test_encoded, y)
  
      x_encoded = pd.DataFrame(
          {
              "city": ["chicago", "paris", "chicago"],
              "state": ["US", "FR", "FR"],
              "other": ["A", "B", "B"],
          }
      )
  
      mapping = get_features_transform_mapping(
          x_encoded, test_encoded, [enc, enc2]
      )
      expected_mapping = {
          "city": ["city"],
          "state": ["state_US", "state_FR"],
          "other": ["other"],
      }
  
      self.assertDictEqual(mapping, expected_mapping)
  
file_path: shapash/tests/unit_tests/utils/test_transform.py
imports: import unittest
  import pandas
  import numpy
  from sklearn.compose import ColumnTransformer
  import sklearn.preprocessing
  import category_encoders
  from shapash.utils.transform import get_preprocessing_mapping, get_features_transform_mapping, handle_categorical_missing
  from pandas.testing import assert_frame_equal
label_desc: Use image node_id 6 for calling the TestInverseTransformCaterogyEncoder obj's underlying member method code with example usage: obj.test_get_features_transform_mapping_4() without return types