minor fixes

src/learning/transformers.py

@@ -822,10 +822,12 @@ def clip_gradient(model, clip_value=1e-1):
 
 
 def init_logfile_nn(method_name, opt):
+    import os
     logfile = CSVLog(opt.logfile_gru, ['dataset', 'method', 'epoch', 'measure', 'value', 'run', 'timelapse'])
     logfile.set_default('dataset', opt.dataset)
     logfile.set_default('run', opt.seed)
-    logfile.set_default('method', method_name)
+    logfile.set_default('method', get_method_name(os.path.basename(opt.dataset), opt.posteriors, opt.supervised, opt.pretrained, opt.mbert,
+                                                opt.gruViewGenerator, opt.gruMUSE, opt.gruWCE, opt.agg, opt.allprob))
     assert opt.force or not logfile.already_calculated(), f'results for dataset {opt.dataset} method {method_name} ' \
                                                           f'and run {opt.seed} already calculated'
     return logfile

src/main_gFun.py

@@ -14,7 +14,8 @@ 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 ' \
+    if op.gruViewGenerator:
+        assert op.gruWCE or op.gruMUSE, 'Initializing Gated Recurrent embedding layer without ' \
                                                               'explicit initialization of GRU View Generator'
 
     l2 = op.l2
@@ -35,7 +36,7 @@ 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()
@@ -86,7 +87,6 @@ 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)
@@ -95,7 +95,6 @@ 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/parser_options.py

@@ -24,7 +24,7 @@ parser.add_option('-G', dest='gruViewGenerator',  action='store_true',
 
 parser.add_option("--l2", dest="l2", action='store_true',
                   help="Activates l2 normalization as a post-processing for the document embedding views",
-                  default=False)
+                  default=True)
 
 parser.add_option("--allprob", dest="allprob", action='store_true',
                   help="All views are generated as posterior probabilities. This affects the supervised and pretrained"
@@ -51,10 +51,10 @@ parser.add_option("-p", "--pca", dest="max_labels_S", type=int,
                   default=300)
 
 parser.add_option("-r", "--remove-pc", dest="sif", action='store_true',
-                  help="Remove common component when computing dot product of word embedding matrices", default=False)
+                  help="Remove common component when computing dot product of word embedding matrices", default=True)
 
 parser.add_option("-z", "--zscore", dest="zscore", action='store_true',
-                  help="Z-score normalize matrices (WCE and MUSE)", default=False)
+                  help="Z-score normalize matrices (WCE and MUSE)", default=True)
 
 parser.add_option("-a", "--agg", dest="agg", action='store_true',
                   help="Set aggregation function of the common Z-space to average (Default: concatenation)",