Added a notebook to illustrate the use of feature importance. And also fixed a...

Added a notebook to illustrate the use of feature importance. And also fixed a little bug related to the calling of the callbacks.

pytorch_widedeep/callbacks.py

@@ -655,6 +655,7 @@ class EarlyStopping(Callback):
         if self.monitor_op(current - self.min_delta, self.best):
             self.best = current
             self.wait = 0
+            self.best_epoch = epoch
             if self.restore_best_weights:
                 self.state_dict = copy.deepcopy(self.model.state_dict())
         else:
@@ -665,7 +666,9 @@ class EarlyStopping(Callback):
 
     def on_train_end(self, logs: Optional[Dict] = None):
         if self.stopped_epoch > 0 and self.verbose > 0:
-            print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))
+            print(
+                f"Best Epoch: {self.best_epoch + 1}. Best {self.monitor}: {self.best:.5f}"
+            )
         if self.restore_best_weights and self.state_dict is not None:
             if self.verbose > 0:
                 print("Restoring model weights from the end of the best epoch")

pytorch_widedeep/training/trainer.py

@@ -524,7 +524,7 @@ class Trainer(BaseTrainer):
             self.callback_container.on_epoch_end(epoch, epoch_logs, on_epoch_end_metric)
 
             if self.early_stop:
-                self.callback_container.on_train_end(epoch_logs)
+                # self.callback_container.on_train_end(epoch_logs)
                 break
 
             if self.model.with_fds: