title: Catboost: Why is multiclass classification internally transforming to regression/single class classification problem
body: <p>While using a custom loss function in a multi class classification, I am getting an error that my custom objective function is not having <code>calc_ders_range</code> attribute. However, as per my discussion in Telegram channel of catboost, <code>calc_ders_range</code> is for single classification/regression. I am puzzled to get the error below even when I am passing my custom objective to <code>CatBoostClassifier</code>.</p>
  <p>My Code:
  Output label is of <code>int64</code> type with values from <code>0</code> to <code>25</code> representing 26 classes. Custom objective and accuracy metric are taken from <code>Usage Examples</code> for sample <a href="https://catboost.ai/docs/concepts/python-usages-examples.html#user-defined-loss-function" rel="nofollow noreferrer">https://catboost.ai/docs/concepts/python-usages-examples.html#user-defined-loss-function</a></p>
  <pre><code>class MyObjective(object):
      def calc_ders_multi(self, approx, target, weight):
          approx = np.array(approx) - max(approx)
          exp_approx = np.exp(approx)
          exp_sum = exp_approx.sum()
          grad = []
          hess = []
          for j in range(len(approx)):
              der1 = -exp_approx[j] / exp_sum
              if j == target:
                  der1 += 1
              hess_row = []
              for j2 in range(len(approx)):
                  der2 = exp_approx[j] * exp_approx[j2] / (exp_sum**2)
                  if j2 == j:
                      der2 -= exp_approx[j] / exp_sum
                  hess_row.append(der2 * weight)
                  
              grad.append(der1 * weight)
              hess.append(hess_row)
              
          return (grad, hess)
  
  class AccuracyMetric(object):
      def get_final_error(self, error, weight):
          return error / (weight + 1e-38)
  
      def is_max_optimal(self):
          return True
  
      def evaluate(self, approxes, target, weight):
          best_class = np.argmax(approxes, axis=0)
          
          accuracy_sum = 0
          weight_sum = 0 
  
          for i in range(len(target)):
              w = 1.0 if weight is None else weight[i]
              weight_sum += w
              accuracy_sum += w * (best_class[i] == target[i])
  
          return accuracy_sum, weight_sum
  
  def get_pipeline(args):
      &quot;&quot;&quot;Create a pipeline.&quot;&quot;&quot;
      pipeline_feat1 = Pipeline([
          ('selector', ColumnSelector(cols='feat1', drop_axis=True)),
          ('vec', TfidfVectorizer(tokenizer=word_tokenize)),
      ])
      pipeline_feat2 = Pipeline([
          ('selector', ColumnSelector(cols='feat2', drop_axis=False)),
          ('imputer', SimpleImputer(missing_values=np.nan, strategy=&quot;constant&quot;, fill_value=0, copy=False)),
          ('ohe', OneHotEncoder(handle_unknown='ignore')),
      ])
      pipeline_feat3 = Pipeline([
          ('selector', ColumnSelector(cols='feat3', drop_axis=False)),
          ('imputer', SimpleImputer(missing_values=np.nan, strategy=&quot;constant&quot;, fill_value=0, copy=False)),
          ('ohe', OneHotEncoder(handle_unknown='ignore')),
      ])
      features = FeatureUnion([
          ('f1', pipeline_feat1),
          ('f2', pipeline_feat2),
          ('f3', pipeline_feat3),
      ])
  
      steps = [
          ('features', features),
          ('clf', CatBoostClassifier(task_type='CPU', iterations=5000, random_seed=0, 
  loss_function=MyObjective(), eval_metric=AccuracyMetric(), verbose=100))
      ]
      train_pipeline = Pipeline(steps)
  
      params = {
          &quot;features__f1__vec__max_features&quot;: args.f1_max_features,
          &quot;features__f1__vec__ngram_range&quot;: (1, args.f1_max_ngram)
      }
      params = {k: v for k, v in params.items() if v is not None}
  
      train_pipeline.set_params(**params)
      return train_pipeline
  </code></pre>
  <pre><code># Train model.
  pipeline = get_pipeline()
  # Split train and test data.
  X_train, X_val, y_train, y_val = train_test_split(df_train[['feat1', 'feat2', 'feat3']], df_train['label'], train_size=0.8, random_state=21)
  model = pipeline.fit(X_train, y_train)
  </code></pre>
  <p>Error Message:</p>
  <pre><code>AttributeError                            Traceback (most recent call last)
  _catboost.pyx in _catboost._ObjectiveCalcDersRange()
  
  AttributeError: 'MyObjective' object has no attribute 'calc_ders_range'
  
  During handling of the above exception, another exception occurred:
  
  CatBoostError                             Traceback (most recent call last)
  &lt;ipython-input-12-ea20f154d788&gt; in &lt;module&gt;
       10                                                     train_size=0.8,
       11                                                     random_state=21)
  ---&gt; 12 model = pipeline.fit(X_train, y_train)
       13 
  
  /opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
      333             if self._final_estimator != 'passthrough':
      334                 fit_params_last_step = fit_params_steps[self.steps[-1][0]]
  --&gt; 335                 self._final_estimator.fit(Xt, y, **fit_params_last_step)
      336 
      337         return self
  
  /opt/conda/lib/python3.7/site-packages/catboost/core.py in fit(self, X, y, cat_features, text_features, embedding_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)
     4296         self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,
     4297                   eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period,
  -&gt; 4298                   silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)
     4299         return self
     4300 
  
  /opt/conda/lib/python3.7/site-packages/catboost/core.py in _fit(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model)
     1807                 params,
     1808                 allow_clear_pool,
  -&gt; 1809                 train_params[&quot;init_model&quot;]
     1810             )
     1811 
  
  /opt/conda/lib/python3.7/site-packages/catboost/core.py in _train(self, train_pool, test_pool, params, allow_clear_pool, init_model)
     1256 
     1257     def _train(self, train_pool, test_pool, params, allow_clear_pool, init_model):
  -&gt; 1258         self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)
     1259         self._set_trained_model_attributes()
     1260 
  
  _catboost.pyx in _catboost._CatBoost._train()
  
  _catboost.pyx in _catboost._CatBoost._train()
  
  CatBoostError: catboost/python-package/catboost/helpers.cpp:42: Traceback (most recent call last):
    File &quot;_catboost.pyx&quot;, line 1345, in _catboost._ObjectiveCalcDersRange
  AttributeError: 'MyObjective' object has no attribute 'calc_ders_range'
  </code></pre>