class_docstr: Unit test Smart Predictor class
class_docstr_tok: ["Unit","test","Smart","Predictor","class"]
docstr: Unit test apply_postprocessing 2
docstr_tok: ["Unit","test","apply_postprocessing","2"]
code: def test_apply_postprocessing_2(self):
      """
      Unit test apply_postprocessing 2
      """
      postprocessing = {
          "x1": {"type": "suffix", "rule": " t"},
          "x2": {"type": "prefix", "rule": "test"},
      }
  
      predictor_1 = self.predictor_3
      predictor_1.postprocessing = postprocessing
  
      predictor_1.data = {
          "x": None,
          "ypred": None,
          "contributions": None,
      }
      predictor_1.data["x"] = pd.DataFrame(
          [[1, 2], [3, 4]], columns=["x1", "x2"], index=["Id1", "Id2"]
      )
      expected_output = pd.DataFrame(
          data=[["1 t", "test2"], ["3 t", "test4"]],
          columns=["x1", "x2"],
          index=["Id1", "Id2"],
      )
      output = predictor_1.apply_postprocessing()
      assert np.array_equal(output, expected_output)
  
imports: import unittest
  from shapash.explainer.smart_predictor import SmartPredictor
  from shapash import SmartExplainer
  from shapash.explainer.smart_state import SmartState
  from shapash.explainer.multi_decorator import MultiDecorator
  from shapash.backend import ShapBackend
  import os
  from os import path
  from pathlib import Path
  import pandas
  import numpy
  import catboost
  from catboost import Pool
  import category_encoders
  from unittest.mock import patch
  import types
  from sklearn.compose import ColumnTransformer
  import sklearn.preprocessing
  import shap
label_desc: Use image node_id 32 for calling the TestSmartPredictor obj's underlying member method code with example usage: obj.test_apply_postprocessing_2() without return types