code: def catboost_shap(self, model, df, y_variable=None):
      import catboost
      from catboost import CatBoostClassifier, Pool
      from catboost import CatBoostRegressor
      from catboost.utils import get_confusion_matrix
  
      # explain the model's predictions using SHAP
      if y_variable != None:
          try:
              df = df.drop(y_variable, axis=1)
          except:
              df = df
  
      # find categorical variables and it's index
      g = get_cols()
  
      cat, cat_index = g.cate_col_with_index(df)
      all_columns = list(df.columns)
  
      # convert dataframe in to array
      df_array = df.to_numpy()
  
      # call the function
      shap_values = self.get_shap_values(
          df_array, model, all_columns, cat_index
      )
  
      # append the results with the original file
  
      # final_df = df.join(shap_values)
  
      shap_columns = shap_values.columns
  
      Y = df.copy()
      for c in shap_columns:
          Y[c] = list(shap_values[c])
  
      return Y
  
file_path: explainx/lib/calculate_shap.py
imports: from imports import *
  from rescale_numeric_feature import *
label_desc: Use image node_id 3 for calling the calculate_shap obj's underlying member method code with example usage: obj.catboost_shap(model, df, y_variable) and returns: Y