main.py

@@ -49,16 +49,16 @@ def main(args):
     if args.bert_embedder:
         bertEmbedder = BertGen(multilingualIndex, batch_size=args.batch_bert, nepochs=args.nepochs_bert,
                                patience=args.patience_bert, gpus=args.gpus, n_jobs=args.n_jobs)
+        bertEmbedder.transform(lX)
         embedder_list.append(bertEmbedder)
 
     # Init DocEmbedderList (i.e., first-tier learners or view generators) and metaclassifier
     docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
     meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'),
-                          meta_parameters=get_params(optimc=args.optimc),
-                          n_jobs=args.n_jobs)
+                          meta_parameters=get_params(optimc=args.optimc))
 
     # Init Funnelling Architecture
-    gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta, n_jobs=args.n_jobs)
+    gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
 
     # Training ---------------------------------------
     print('\n[Training Generalized Funnelling]')
@@ -71,7 +71,7 @@ def main(args):
     print('\n[Testing Generalized Funnelling]')
     time_te = time.time()
     ly_ = gfun.predict(lXte)
-    l_eval = evaluate(ly_true=lyte, ly_pred=ly_, n_jobs=args.n_jobs)
+    l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
     time_te = round(time.time() - time_te, 3)
     print(f'Testing completed in {time_te} seconds!')
 

src/view_generators.py

@@ -18,7 +18,6 @@ This module contains the view generators that take care of computing the view sp
 from abc import ABC, abstractmethod
 # from time import time
 
-import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@@ -242,10 +241,6 @@ class RecurrentGen(ViewGen):
         self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False)
         self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
                                                  patience=self.patience, verbose=False, mode='max')
-
-        # modifying EarlyStopping global var in order to compute >= with respect to the best score
-        self.early_stop_callback.mode_dict['max'] = torch.ge
-
         self.lr_monitor = LearningRateMonitor(logging_interval='epoch')
 
     def _init_model(self):
@@ -353,9 +348,6 @@ class BertGen(ViewGen):
         self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
                                                  patience=self.patience, verbose=False, mode='max')
 
-        # modifying EarlyStopping global var in order to compute >= with respect to the best score
-        self.early_stop_callback.mode_dict['max'] = torch.ge
-
     def _init_model(self):
         output_size = self.multilingualIndex.get_target_dim()
         return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
@@ -369,7 +361,7 @@ class BertGen(ViewGen):
         :param ly: dict {lang: target vectors}
         :return: self.
         """
-        print('# Fitting BertGen (B)...')
+        print('# Fitting BertGen (M)...')
         create_if_not_exist(self.logger.save_dir)
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512)