implemented zero-shot experiment code for VanillaFunGen and WordClassGen

main.py

@@ -7,6 +7,9 @@ from src.util.evaluation import evaluate
 from src.util.results_csv import CSVlog
 from src.view_generators import *
 
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
 
 def main(args):
     assert args.post_embedder or args.muse_embedder or args.wce_embedder or args.gru_embedder or args.bert_embedder, \
@@ -17,7 +20,7 @@ def main(args):
     print('Running generalized funnelling...')
 
     data = MultilingualDataset.load(args.dataset)
-    data.set_view(languages=['da'])
+    # data.set_view(languages=['da', 'nl'])
     data.show_dimensions()
     lX, ly = data.training()
     lXte, lyte = data.test()
@@ -189,8 +192,8 @@ if __name__ == '__main__':
                         default=25)
 
     parser.add_argument('--patience_bert', dest='patience_bert', type=int, metavar='',
-                        help='set early stop patience for the BertGen, default 5',
+                        help='set early stop patience for the BertGen, default 3',
-                        default=5)
+                        default=3)
 
     parser.add_argument('--batch_rnn', dest='batch_rnn', type=int, metavar='',
                         help='set batchsize for the RecurrentGen, default 64',

run.sh

@@ -2,15 +2,15 @@
 
 echo Running Zero-shot experiments [output at csv_logs/gfun/zero_shot_gfun.csv]
 
-python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl pt --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl pt --n_jobs 6
-#python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl pt sv --n_jobs 3
+python main.py ../datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -x -m -w -b -c --nepochs_bert 10 --batch_bert 8 --gpus 0 --muse_dir ../embeddings/MUSE/ -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl pt sv --n_jobs 6
 
 
 #for i in {0..10..1}

src/models/learners.py

@@ -1,4 +1,5 @@
 import time
+import src.util.disable_sklearn_warnings
 
 import numpy as np
 from joblib import Parallel, delayed

src/util/disable_sklearn_warnings.py

@@ -0,0 +1,8 @@
+import warnings
+
+
+def warn(*args, **kwargs):
+    pass
+
+
+warnings.warn = warn

src/util/standardizer.py

@@ -1,4 +1,5 @@
 import numpy as np
+import src.util.disable_sklearn_warnings
 
 
 class StandardizeTransformer:

src/view_generators.py

@@ -16,6 +16,7 @@ This module contains the view generators that take care of computing the view sp
 - View generator (-b): generates document embedding via mBERT model.
 """
 from abc import ABC, abstractmethod
+import src.util.disable_sklearn_warnings
 # from time import time
 
 from pytorch_lightning import Trainer
@@ -317,7 +318,7 @@ class RecurrentGen(ViewGen):
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         self.multilingualIndex.embedding_matrices(self.pretrained, supervised=self.wce)
         self.model = self._init_model()
-        self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False)
+        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')
         self.lr_monitor = LearningRateMonitor(logging_interval='epoch')
@@ -446,7 +447,7 @@ class BertGen(ViewGen):
         self.stored_path = stored_path
         self.model = self._init_model()
         self.patience = patience
-        self.logger = TensorBoardLogger(save_dir='../tb_logs', name='bert', default_hp_metric=False)
+        self.logger = TensorBoardLogger(save_dir='tb_logs', name='bert', default_hp_metric=False)
         self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
                                                  patience=self.patience, verbose=False, mode='max')
 
@@ -469,7 +470,7 @@ class BertGen(ViewGen):
         :param ly: dict {lang: target vectors}
         :return: self.
         """
-        print('\n# Fitting BertGen (M)...')
+        print('\n# Fitting BertGen (B)...')
         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,