bringing brances up to date

metrics/attention_rollout.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+from einops import reduce
+
+
+import torch
+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
+
+
+class AttentionRollout(_BaseScores):
+
+    def __init__(self, *args):
+        super(AttentionRollout, self).__init__(*args)
+        pass
+
+    def __call__(self, outputs):
+        summary_dict = dict()
+        #######################################################################################
+        # Additional Score  -  Histogram Distances - Image Plotting
+        #######################################################################################
+        #
+        # INIT
+        attn_weights = [output['attn_weights'].cpu().numpy() for output in outputs]
+        attn_reduce_heads = [reduce(x, '') for x in attn_weights]
+
+        if self.model.params.use_residual:
+            residual_att = np.eye(att_mat.shape[1])[None, ...]
+            aug_att_mat = att_mat + residual_att
+            aug_att_mat = aug_att_mat / aug_att_mat.sum(axis=-1)[..., None]
+        else:
+            aug_att_mat = att_mat
+
+        joint_attentions = np.zeros(aug_att_mat.shape)
+
+        layers = joint_attentions.shape[0]
+        joint_attentions[0] = aug_att_mat[0]
+        for i in np.arange(1, layers):
+            joint_attentions[i] = aug_att_mat[i].dot(joint_attentions[i - 1])
+
+
+
+
+
+

metrics/multi_class_classification.py

@@ -113,17 +113,17 @@ class MultiClassScores(_BaseScores):
         #######################################################################################
         #
         # Confusion matrix
-
+        fig1, ax1 = plt.subplots(dpi=96)
         cm = confusion_matrix([class_names[x] for x in y_true], [class_names[x] for x in y_pred_max],
                               labels=[class_names[key] for key in class_names.keys()],
                               normalize='all')
         disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                                       display_labels=[class_names[i] for i in range(self.model.n_classes)]
                                       )
-        disp.plot(include_values=True)
+        disp.plot(include_values=True, ax=ax1)
 
-        self.model.logger.log_image('Confusion_Matrix', image=disp.figure_, step=self.model.current_epoch)
+        self.model.logger.log_image('Confusion_Matrix', image=fig1, step=self.model.current_epoch)
         # self.model.logger.log_image('Confusion_Matrix', image=disp.figure_, step=self.model.current_epoch, ext='pdf')
 
         plt.close('all')
-        return summary_dict
+        return summary_dict