running comparison with refactored

src/learning/transformers.py

@@ -193,13 +193,14 @@ class WordClassEmbedder:
 
 class MBertEmbedder:
 
-    def __init__(self, doc_embed_path=None, patience=10, checkpoint_dir='../hug_checkpoint/', path_to_model=None,
-                 nC=None):
+    def __init__(self, options, doc_embed_path=None, patience=10, checkpoint_dir='../hug_checkpoint/', path_to_model=None,
+                 nC=None, ):
         self.doc_embed_path = doc_embed_path
         self.patience = patience
         self.checkpoint_dir = checkpoint_dir
         self.fitted = False
         self.requires_tfidf = False
+        self.options = options
         if path_to_model is None and nC is not None:
             self.model = None
         else:
@@ -238,12 +239,13 @@ class MBertEmbedder:
         # Training loop
         logfile = '../log/log_mBert_extractor.csv'
         method_name = 'mBert_feature_extractor'
+        logfile = init_logfile_nn(method_name, self.options)
 
-        tinit = time()
+        tinit = time.time()
         lang_ids = va_dataset.lang_ids
         for epoch in range(1, nepochs + 1):
             print('# Start Training ...')
-            train(model, tr_dataloader, epoch, criterion, optim, method_name, tinit, logfile)
+            train(model, tr_dataloader, epoch, criterion, optim, method_name, tinit, logfile, self.options)
             lr_scheduler.step()  # reduces the learning rate # TODO arg epoch?
 
             # Validation
@@ -260,7 +262,7 @@ class MBertEmbedder:
         if val_epochs > 0:
             print(f'running last {val_epochs} training epochs on the validation set')
             for val_epoch in range(1, val_epochs + 1):
-                train(self.model, va_dataloader, epoch + val_epoch, criterion, optim, method_name, tinit, logfile)
+                train(self.model, va_dataloader, epoch + val_epoch, criterion, optim, method_name, tinit, logfile, self.options)
 
         self.fitted = True
         return self

src/main_gFun.py

@@ -7,6 +7,10 @@ from util.results import PolylingualClassificationResults
 from util.common import *
 from util.parser_options import *
 
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
+
 if __name__ == '__main__':
     (op, args) = parser.parse_args()
     dataset = op.dataset
@@ -14,8 +18,8 @@ if __name__ == '__main__':
     assert not (op.set_c != 1. and op.optimc), 'Parameter C cannot be defined along with optim_c option'
     assert op.posteriors or op.supervised or op.pretrained or op.mbert or op.gruViewGenerator, \
         'empty set of document embeddings is not allowed'
-    assert (op.gruWCE or op.gruMUSE) and op.gruViewGenerator, 'Initializing Gated Recurrent embedding layer without ' \
-                                                              'explicit initialization of GRU View Generator'
+    assert not ((op.gruWCE or op.gruMUSE) and op.gruViewGenerator), 'Initializing Gated Recurrent embedding layer without ' \
+                                                                    'explicit initialization of GRU View Generator'
 
     l2 = op.l2
     dataset_file = os.path.basename(dataset)
@@ -35,11 +39,20 @@ if __name__ == '__main__':
 
     # load dataset
     data = MultilingualDataset.load(dataset)
-    data.set_view(languages=['nl', 'it'])   # TODO: DEBUG SETTING
+    # data.set_view(languages=['nl', 'it'])   # TODO: DEBUG SETTING
     data.show_dimensions()
     lXtr, lytr = data.training()
     lXte, lyte = data.test()
 
+    # DEBUGGING
+    ratio = 0.01
+    lXtr = {k:v[:50] for k,v in lXtr.items()}
+    lytr = {k: v[:50] for k, v in lytr.items()}
+    lXte = {k: v[:50] for k, v in lXte.items()}
+    lyte = {k: v[:50] for k, v in lyte.items()}
+
+
+
     # text preprocessing
     tfidfvectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
@@ -97,8 +110,9 @@ if __name__ == '__main__':
         View generator (-B): generates document embedding via mBERT model. 
         """
         op.bert_path = '/home/andreapdr/funneling_pdr/hug_checkpoint/mBERT-rcv1-2_run0'    # TODO DEBUG
+        op.bert_path = None
         mbert = MBertEmbedder(path_to_model=op.bert_path,
-                              nC=data.num_categories())
+                              nC=data.num_categories(), options=op)
         if op.allprob:
             mbert = FeatureSet2Posteriors(mbert, l2=l2)
         doc_embedder.append(mbert)

src/models/mBert.py

@@ -153,10 +153,10 @@ def do_tokenization(l_dataset, max_len=512, verbose=True):
     return l_tokenized
 
 
-def train(model, train_dataloader, epoch, criterion, optim, method_name, tinit, logfile, log_interval=10):
-    # _dataset_path = opt.dataset.split('/')[-1].split('_')
-    # dataset_id = _dataset_path[0] + _dataset_path[-1]
-    dataset_id = 'TODO fix this!'
+def train(model, train_dataloader, epoch, criterion, optim, method_name, tinit, logfile, opt, log_interval=10):
+    _dataset_path = opt.dataset.split('/')[-1].split('_')
+    dataset_id = _dataset_path[0] + _dataset_path[-1]
+    # dataset_id = 'TODO fix this!'
 
     loss_history = []
     model.train()