code: def test_transform_with_new_values(self):
      # training data
      d = {"col1": ["a", "a", "c"], "col2": ["w", "e", "d"]}
      df = pd.DataFrame(data=d)
      # fit binarizer
      lb1 = LabelBinarizer()
      lb1.fit(df, "col1")
      lb2 = LabelBinarizer()
      lb2.fit(df, "col2")
      # test data
      d_test = {
          "col1": ["c", "d", "d"],
          "col2": ["g", "e", "f"],
          "col3": [2, 3, 4],
      }
      df_test = pd.DataFrame(data=d_test)
      # transform
      df_test = lb1.transform(df_test, "col1")
      df_test = lb2.transform(df_test, "col2")
      self.assertTrue("col1_c" in df_test.columns)
      self.assertTrue("col1_d" not in df_test.columns)
      self.assertTrue("col2_w" in df_test.columns)
      self.assertTrue("col2_e" in df_test.columns)
      self.assertTrue("col2_d" in df_test.columns)
      self.assertTrue("col2_g" not in df_test.columns)
      self.assertTrue("col2_f" not in df_test.columns)
      self.assertEqual(df_test["col1_c"][0], 1)
      self.assertEqual(df_test["col1_c"][1], 0)
      self.assertEqual(df_test["col1_c"][2], 0)
      self.assertEqual(np.sum(df_test["col2_w"]), 0)
      self.assertEqual(np.sum(df_test["col2_d"]), 0)
      self.assertEqual(df_test["col2_e"][0], 0)
      self.assertEqual(df_test["col2_e"][1], 1)
      self.assertEqual(df_test["col2_e"][2], 0)
  
file_path: mljar-supervised/tests/tests_preprocessing/test_label_binarizer.py
imports: import unittest
  import tempfile
  import json
  import numpy
  import pandas
  from supervised.preprocessing.label_binarizer import LabelBinarizer
label_desc: Use image node_id 3 for calling the LabelBinarizerTest obj's underlying member method code with example usage: obj.test_transform_with_new_values() without return types