diff --git a/fedot_ind/core/models/detection/anomaly/algorithms/convolutional_autoencoder_detector.py b/fedot_ind/core/models/detection/anomaly/algorithms/convolutional_autoencoder_detector.py
index 006561ae7..e9621d864 100644
--- a/fedot_ind/core/models/detection/anomaly/algorithms/convolutional_autoencoder_detector.py
+++ b/fedot_ind/core/models/detection/anomaly/algorithms/convolutional_autoencoder_detector.py
@@ -74,9 +74,9 @@ def _build_decoder(self):
                 out_channels = 32
 
             decoder_layer_dict.update({f'conv{i}':
-                                           ConvTranspose1d(in_channels=in_channels,
-                                                           out_channels=out_channels,
-                                                           output_padding=1, **self.convolutional_params)})
+                                       ConvTranspose1d(in_channels=in_channels,
+                                                       out_channels=out_channels,
+                                                       output_padding=1, **self.convolutional_params)})
             decoder_layer_dict.update({f'relu{i}': self.activation_func()})
             in_channels = out_channels
         self.decoder = Sequential(decoder_layer_dict)
@@ -110,7 +110,7 @@ def fit(self,
         self._init_model()
         train_loader, valid_loader = self._create_dataloader(data, batch_size, validation_split)
         train_steps, early_stopping, best_model, best_val_loss = max(1, len(train_loader)), EarlyStopping(), \
-                                                                 None, float('inf')
+            None, float('inf')
         scheduler = lr_scheduler.OneCycleLR(optimizer=self.optimizer,
                                             steps_per_epoch=train_steps,
                                             epochs=epochs,
@@ -132,7 +132,6 @@ def val_one_epoch(batch):
             return loss.data.item() * inputs.size(0)
 
         for epoch in tqdm(range(epochs)):
-            iter_count = 0
             self.train()
             train_loss = list(map(lambda batch_tuple: train_one_batch(batch_tuple), train_loader))
             train_loss = np.average(train_loss)
diff --git a/fedot_ind/core/tuning/search_space.py b/fedot_ind/core/tuning/search_space.py
index e26dcbe19..ac46cc740 100644
--- a/fedot_ind/core/tuning/search_space.py
+++ b/fedot_ind/core/tuning/search_space.py
@@ -99,7 +99,7 @@
          "n_seasonality_blocks": {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 4, 1)]]},
          "n_of_harmonics": {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 3, 1)]]}},
     'bagging': {'method':
-                    {'hyperopt-dist': hp.choice, 'sampling-scope': [['max', 'min', 'mean', 'median']]}},
+                {'hyperopt-dist': hp.choice, 'sampling-scope': [['max', 'min', 'mean', 'median']]}},
     'stat_detector':
         {'anomaly_thr': {'hyperopt-dist': hp.choice, 'sampling-scope': [list(np.arange(0.75, 0.99, 0.05))]},
          'window_length': {'hyperopt-dist': hp.choice,