tracking best val micro F1

src/main.py

@@ -83,7 +83,7 @@ def main(opt):
         method = opt.name
 
     # train
-    cls.fit(Xtr, ytr,
+    val_microf1 = cls.fit(Xtr, ytr,
             batch_size=opt.batchsize, epochs=opt.epochs, alpha=opt.alpha, lr=opt.lr,
             log=f'{opt.log}/{method}-{dataset_name}.csv',
             checkpointpath=opt.checkpoint
@@ -94,7 +94,7 @@ def main(opt):
     acc, macrof1, microf1 = evaluation(yte, yte_)
 
     results = Results(opt.output)
-    results.add(dataset_name, method, acc, macrof1, microf1)
+    results.add(dataset_name, method, acc, macrof1, microf1, val_microf1)
 
 
     # verification
@@ -121,10 +121,10 @@ class Results:
         addheader = not os.path.exists(path)
         self.foo = open(path, 'at')
         if addheader:
-            self.add('Dataset', 'Method', 'Accuracy', 'MacroF1', 'microF1')
+            self.add('Dataset', 'Method', 'Accuracy', 'MacroF1', 'microF1', 'val_microF1')
 
-    def add(self, dataset, method, acc, macrof1, microf1):
-        self.foo.write(f'{dataset}\t{method}\t{acc}\t{macrof1}\t{microf1}\n')
+    def add(self, dataset, method, acc, macrof1, microf1, val_microF1):
+        self.foo.write(f'{dataset}\t{method}\t{acc}\t{macrof1}\t{microf1}\t{val_microF1}\n')
         self.foo.flush()
 
     def close(self):

src/model/classifiers.py

@@ -100,6 +100,7 @@ class AuthorshipAttributionClassifier(nn.Module):
                     break
         print(f'training ended; loading best model parameters in {checkpointpath} for epoch {early_stop.best_epoch}')
         self.load_state_dict(torch.load(checkpointpath))
+        return early_stop.best_score
 
     def predict(self, x, batch_size=100):
         self.eval()