branching to devel

src/learning/transformers.py

@@ -330,6 +330,9 @@ class RecurrentEmbedder:
 
     def fit(self, lX, ly, lV=None, batch_size=64, nepochs=200, val_epochs=1):
         print('### Gated Recurrent Unit View Generator (G)')
+        # could be better to init model here at first .fit() call!
+        if self.model is None:
+            print('TODO: Init model!')
         if not self.is_trained:
             # Batchify input
             self.multilingual_index.train_val_split(val_prop=0.2, max_val=2000, seed=self.seed)

src/main_gFun.py

@@ -27,7 +27,6 @@ if __name__ == '__main__':
                                                 op.gruViewGenerator, op.gruMUSE, op.gruWCE, op.agg, op.allprob)
     print(f'Method: gFun{method_name}\nDataset: {dataset_name}')
     print('-'*50)
-    exit()
     
     # set zscore range - is slice(0, 0) mean will be equal to 0 and std to 1, thus normalization will have no effect
     standardize_range = slice(0, 0)
@@ -36,7 +35,7 @@ if __name__ == '__main__':
 
     # load dataset
     data = MultilingualDataset.load(dataset)
-    # data.set_view(languages=['fr', 'it'])   # TODO: DEBUG SETTING
+    data.set_view(languages=['nl', 'it'])   # TODO: DEBUG SETTING
     data.show_dimensions()
     lXtr, lytr = data.training()
     lXte, lyte = data.test()
@@ -87,6 +86,7 @@ if __name__ == '__main__':
         document embeddings are then casted into vectors of posterior probabilities via a set of SVM.
         NB: --allprob won't have any effect on this View Gen since output is already encoded as post prob
         """
+        op.gru_path = '/home/andreapdr/funneling_pdr/checkpoint/gru_viewgen_-rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'    # TODO DEBUG
         rnn_embedder = RecurrentEmbedder(pretrained=op.gruMUSE, supervised=op.gruWCE, multilingual_dataset=data,
                                          options=op, model_path=op.gru_path)
         doc_embedder.append(rnn_embedder)
@@ -95,6 +95,7 @@ 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
         mbert = MBertEmbedder(path_to_model=op.bert_path,
                               nC=data.num_categories())
         if op.allprob:

src/util/evaluation.py

@@ -45,7 +45,7 @@ def average_results(l_eval, show=True):
 
 
 def evaluate_method(polylingual_method, lX, ly, predictor=None, soft=False, return_time=False):
-    tinit=time.time()
+    tinit = time.time()
     print('prediction for test')
     assert set(lX.keys()) == set(ly.keys()), 'inconsistent dictionaries in evaluate'
     n_jobs = polylingual_method.n_jobs if hasattr(polylingual_method, 'n_jobs') else -1