docstr: Unit test get_names 1
docstr_tok: ["Unit","test","get_names","1"]
code: def test_get_names_1(self):
      """
      Unit test get_names 1
      """
      train = pd.DataFrame(
          {"num1": [0, 1], "num2": [0, 2], "other": ["A", "B"]}
      )
  
      enc_1 = ColumnTransformer(
          transformers=[
              (
                  "quantile",
                  skp.QuantileTransformer(n_quantiles=2),
                  ["num1", "num2"],
              )
          ],
          remainder="drop",
      )
  
      enc_2 = ColumnTransformer(
          transformers=[
              (
                  "quantile",
                  skp.QuantileTransformer(n_quantiles=2),
                  ["num1", "num2"],
              )
          ],
          remainder="passthrough",
      )
  
      enc_3 = ColumnTransformer(
          transformers=[("onehot", skp.OneHotEncoder(), ["other"])],
          remainder="drop",
      )
  
      enc_4 = ColumnTransformer(
          transformers=[("onehot", skp.OneHotEncoder(), ["other"])],
          remainder="passthrough",
      )
  
      enc_1.fit(train)
      enc_2.fit(train)
      enc_3.fit(train)
      enc_4.fit(train)
  
      feature_names_1 = []
      l_transformers = list(enc_1._iter(fitted=True))
  
      for name, trans, column, _ in l_transformers:
          feature_names_1.extend(get_names(name, trans, column, enc_1))
  
      feature_names_2 = []
      l_transformers = list(enc_2._iter(fitted=True))
  
      for name, trans, column, _ in l_transformers:
          feature_names_2.extend(get_names(name, trans, column, enc_2))
  
      feature_names_3 = []
      l_transformers = list(enc_3._iter(fitted=True))
  
      for name, trans, column, _ in l_transformers:
          feature_names_3.extend(get_names(name, trans, column, enc_3))
  
      feature_names_4 = []
      l_transformers = list(enc_4._iter(fitted=True))
  
      for name, trans, column, _ in l_transformers:
          feature_names_4.extend(get_names(name, trans, column, enc_4))
  
      assert len(feature_names_1) == 2
      assert len(feature_names_2) == 3
      assert len(feature_names_3) == 2
      assert len(feature_names_4) == 4
  
imports: import unittest
  import pandas
  import numpy
  import category_encoders
  from sklearn.compose import ColumnTransformer
  import sklearn.preprocessing
  import catboost
  import sklearn
  import lightgbm
  import xgboost
  from shapash.utils.transform import inverse_transform
  from shapash.utils.transform import apply_preprocessing
  from shapash.utils.columntransformer_backend import get_feature_names, get_names, get_list_features_names, get_col_mapping_ct
  from sklearn.ensemble import GradientBoostingClassifier
label_desc: Use image node_id 33 for calling the TestInverseTransformColumnsTransformer obj's underlying member method code with example usage: obj.test_get_names_1() without return types