unsupervised

mlmc/metrics/unsupervised.py

@@ -159,9 +159,9 @@ class KeywordCoherence():
         kw_acc = sum([(x.item() in y) for x,y in zip(counts.sum(-1).argmax(-1), predictions)]) / len(predictions)
         kw_sw = [sum([(x.item() in y) for x,y in zip(counts[:,:,(i-idx):i].sum(-1).argmax(-1), predictions)]) / len(predictions) for i in range(min(idx,ndx-1),ndx)]
 
-        import matplotlib.pyplot as plt
-        plt.plot(kw_sw)
-        plt.show()
+        # import matplotlib.pyplot as plt
+        # plt.plot(kw_sw)
+        # plt.show()
 
         return {"kw_acc": kw_acc, "kwsw_aoc":sum(kw_sw) / len(kw_sw), "kw_sw": kw_sw, }
 

mlmc/models/abstracts/abstract_textclassification.py

@@ -379,7 +379,7 @@ class TextClassificationAbstract(torch.nn.Module):
                     if log_mlflow:
                         import mlflow
                         mlflow.log_metric(f"{valid_prefix}_loss" ,valid_loss, step=e)
-                        result_metrics.log_mlflow(step=e, prefix=valid_prefix)
+                        result_metrics.log_mlflow(step=e, prefix=valid_prefix, model=self)
 
                     valid_loss_dict = {f"{valid_prefix}_loss": valid_loss}
                     valid_loss_dict.update(result_metrics.compute(model=self))