bug in metric calculation

audio_toolset/audio_io.py

@@ -88,10 +88,7 @@ class LibrosaAudioToMel(object):
     def __init__(self, amplitude_to_db=False, power_to_db=False, **mel_kwargs):
         assert not all([amplitude_to_db, power_to_db]), "Choose amplitude_to_db or power_to_db, not both!"
         # Mel kwargs are:
-        #   sr
-        #   n_mels
-        #   n_fft
-        #   hop_length
+        #   sr   n_mels   n_fft   hop_length
 
         self.mel_kwargs = mel_kwargs
         self.amplitude_to_db = amplitude_to_db

audio_toolset/audio_to_mel_dataset.py

@@ -34,10 +34,10 @@ class LibrosaAudioToMelDataset(Dataset):
         self.audio_path = Path(audio_file_path)
 
         mel_folder_suffix = self.audio_path.parent.parent.name
+        self.mel_folder = Path(str(self.audio_path)
+                               .replace(mel_folder_suffix, f'{mel_folder_suffix}_mel_folder')).parent.parent
 
-        self.mel_file_path = Path(str(self.audio_path)
-                                  .replace(mel_folder_suffix, f'{mel_folder_suffix}_mel_folder')
-                                  .replace(self.audio_path.suffix, '.npy'))
+        self.mel_file_path = self.mel_folder / f'{self.audio_path.stem}.npy'
 
         self.audio_augmentations = audio_augmentations
 
@@ -45,7 +45,7 @@ class LibrosaAudioToMelDataset(Dataset):
                                        self.audio_file_duration, mel_kwargs['sr'], mel_kwargs['hop_length'],
                                        mel_kwargs['n_mels'], transform=mel_augmentations)
 
-        self._mel_transform = Compose([LibrosaAudioToMel(**mel_kwargs),
+        self._mel_transform = Compose([LibrosaAudioToMel(power_to_db=False, **mel_kwargs),
                                        MelToImage()
                                        ])
 

audio_toolset/mel_augmentation.py

@@ -64,9 +64,9 @@ class ShiftTime(_BaseTransformation):
             # Set to silence for heading/ tailing
             shift = int(shift)
             if shift > 0:
-                augmented_data[:shift, :] = 0
+                augmented_data[:, :shift] = 0
             else:
-                augmented_data[shift:, :] = 0
+                augmented_data[:, shift:] = 0
             return augmented_data
         else:
             return x

metrics/binary_class_classifictaion.py

@@ -5,6 +5,7 @@ from sklearn.ensemble import IsolationForest
 from sklearn.metrics import recall_score, roc_auc_score, average_precision_score
 
 from ml_lib.metrics._base_score import _BaseScores
+from ml_lib.utils.tools import to_one_hot
 
 
 class BinaryScores(_BaseScores):
@@ -17,16 +18,27 @@ class BinaryScores(_BaseScores):
 
         # Additional Score like the unweighted Average Recall:
         #########################
+        # INIT
+        if isinstance(outputs['batch_y'], torch.Tensor):
+            y_true = outputs['batch_y'].cpu().numpy()
+        else:
+            y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+
+        if isinstance(outputs['y'], torch.Tensor):
+            y_pred = outputs['y'].cpu().numpy()
+        else:
+            y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
+
         # UnweightedAverageRecall
-        y_true = torch.cat([output['batch_y'] for output in outputs]) .cpu().numpy()
-        y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
+        # y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+        # y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
 
         # How to apply a threshold manualy
         # y_pred = (y_pred >= 0.5).astype(np.float32)
 
         # How to apply a threshold by IF (Isolation Forest)
-        clf = IsolationForest(random_state=self.model.seed)
-        y_score = clf.fit_predict(y_pred.reshape(-1,1))
+        clf = IsolationForest()
+        y_score = clf.fit_predict(y_pred.reshape(-1, 1))
         y_score = (np.asarray(y_score) == -1).astype(np.float32)
 
         uar_score = recall_score(y_true, y_score, labels=[0, 1], average='macro',

modules/util.py

@@ -44,7 +44,21 @@ try:
 
         def update(self, preds, target) -> None:
             for _, metric in self:
-                metric.update(preds, target)
+                try:
+                    if self.n_classes <= 2:
+                        metric.update(preds.unsqueeze(-1), target.unsqueeze(-1))
+                    else:
+                        metric.update(preds, target)
+                except ValueError:
+                    print(f'error was: {ValueError}')
+                    print(f'Metric is: {metric}')
+                    print(f'Shape is: preds - {preds.unsqueeze(-1).shape}, target - {target.shape}')
+                    metric.update(preds.unsqueeze(-1), target)
+                except AssertionError:
+                    print(f'error was: {AssertionError}')
+                    print(f'Metric is: {metric}')
+                    print(f'Shape is: preds - {preds.shape}, target - {target.unsqueeze(-1).shape}')
+                    metric.update(preds, target.unsqueeze(-1))
 
         def reset(self) -> None:
             for _, metric in self: