implemented zero-shot experiment code for VanillaFunGen and WordClassGen
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andrea
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c65c91fc27
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31
main.py
31
main.py
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@ -11,17 +11,19 @@ from src.view_generators import *
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def main(args):
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assert args.post_embedder or args.muse_embedder or args.wce_embedder or args.gru_embedder or args.bert_embedder, \
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'empty set of document embeddings is not allowed!'
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assert not (args.zero_shot and (args.zscl_langs is None)), \
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'--zscl_langs cannot be empty when setting --zero_shot to True'
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print('Running generalized funnelling...')
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data = MultilingualDataset.load(args.dataset)
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data.set_view(languages=['it', 'da', 'nl'])
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data.set_view(languages=['nl'])
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data.show_dimensions()
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lX, ly = data.training()
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lXte, lyte = data.test()
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zero_shot = True
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zscl_train_langs = ['it'] # Todo: testing zero shot
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zero_shot = args.zero_shot
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zscl_train_langs = args.zscl_langs
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# Init multilingualIndex - mandatory when deploying Neural View Generators...
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if args.gru_embedder or args.bert_embedder:
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@ -37,24 +39,24 @@ def main(args):
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if args.muse_embedder:
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museEmbedder = MuseGen(muse_dir=args.muse_dir, n_jobs=args.n_jobs,
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zero_shot=zero_shot, train_langs=zscl_train_langs) # Todo: testing zero shot
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zero_shot=zero_shot, train_langs=zscl_train_langs)
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embedder_list.append(museEmbedder)
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if args.wce_embedder:
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wceEmbedder = WordClassGen(n_jobs=args.n_jobs,
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zero_shot=zero_shot, train_langs=zscl_train_langs) # Todo: testing zero shot
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zero_shot=zero_shot, train_langs=zscl_train_langs)
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embedder_list.append(wceEmbedder)
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if args.gru_embedder:
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rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=args.rnn_wce,
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batch_size=args.batch_rnn, nepochs=args.nepochs_rnn, patience=args.patience_rnn,
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zero_shot=zero_shot, train_langs=zscl_train_langs, # Todo: testing zero shot
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zero_shot=zero_shot, train_langs=zscl_train_langs,
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gpus=args.gpus, n_jobs=args.n_jobs)
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embedder_list.append(rnnEmbedder)
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if args.bert_embedder:
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bertEmbedder = BertGen(multilingualIndex, batch_size=args.batch_bert, nepochs=args.nepochs_bert,
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zero_shot=zero_shot, train_langs=zscl_train_langs, # Todo: testing zero shot
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zero_shot=zero_shot, train_langs=zscl_train_langs,
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patience=args.patience_bert, gpus=args.gpus, n_jobs=args.n_jobs)
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embedder_list.append(bertEmbedder)
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@ -76,7 +78,7 @@ def main(args):
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# Testing ----------------------------------------
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print('\n[Testing Generalized Funnelling]')
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time_te = time.time()
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# TODO: Zero shot scenario -> setting first tier learners zero_shot param to False
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if args.zero_shot:
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gfun.set_zero_shot(val=False)
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ly_ = gfun.predict(lXte)
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l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
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@ -85,7 +87,7 @@ def main(args):
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# Logging ---------------------------------------
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print('\n[Results]')
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results = CSVlog(args.csv_dir)
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results = CSVlog(f'csv_logs/gfun/{args.csv_dir}')
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metrics = []
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for lang in lXte.keys():
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macrof1, microf1, macrok, microk = l_eval[lang]
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@ -120,8 +122,8 @@ if __name__ == '__main__':
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parser.add_argument('dataset', help='Path to the dataset')
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parser.add_argument('-o', '--output', dest='csv_dir', metavar='',
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help='Result file (default csv_logs/gfun/gfun_results.csv)', type=str,
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default='csv_logs/gfun/gfun_results.csv')
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help='Result file saved in csv_logs/gfun/dir, default is gfun_results.csv)', type=str,
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default='gfun_results.csv')
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parser.add_argument('-x', '--post_embedder', dest='post_embedder', action='store_true',
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help='deploy posterior probabilities embedder to compute document embeddings',
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@ -194,5 +196,12 @@ if __name__ == '__main__':
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parser.add_argument('--gpus', metavar='', help='specifies how many GPUs to use per node',
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default=None)
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parser.add_argument('--zero_shot', dest='zero_shot', action='store_true',
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help='run zero-shot experiments',
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default=False)
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parser.add_argument('--zscl_langs', dest='zscl_langs', metavar='', nargs='*',
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help='set the languages to be used in training in zero shot experiments')
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args = parser.parse_args()
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main(args)
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11
run.sh
11
run.sh
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@ -2,7 +2,16 @@
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echo Running Zero-shot experiments [output at csv_logs/gfun/zero_shot_gfun.csv]
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -o csv_logs/gfun/zero_shot_gfun.csv --gpus 0
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -m -w -o csv_logs/gfun/zero_shot_gfun.csv --zero_shot --zscl_langs da de en es fr it nl pt --n_jobs 6
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python main.py /home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle -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 6
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#for i in {0..10..1}
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#do
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@ -128,6 +128,9 @@ class Funnelling:
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def set_zero_shot(self, val: bool):
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for embedder in self.first_tier.embedders:
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if isinstance(embedder, VanillaFunGen):
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embedder.set_zero_shot(val)
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else:
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embedder.embedder.set_zero_shot(val)
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return
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@ -56,7 +56,7 @@ class VanillaFunGen(ViewGen):
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View Generator (x): original funnelling architecture proposed by Moreo, Esuli and
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Sebastiani in DOI: https://doi.org/10.1145/3326065
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"""
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def __init__(self, base_learner, first_tier_parameters=None, n_jobs=-1):
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def __init__(self, base_learner, first_tier_parameters=None, zero_shot=False, train_langs: list = None, n_jobs=-1):
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"""
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Init Posterior Probabilities embedder (i.e., VanillaFunGen)
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:param base_learner: naive monolingual learners to be deployed as first-tier learners. Should be able to
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@ -71,9 +71,19 @@ class VanillaFunGen(ViewGen):
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self.doc_projector = NaivePolylingualClassifier(base_learner=self.learners,
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parameters=self.first_tier_parameters, n_jobs=self.n_jobs)
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self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
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# Zero shot parameters
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self.zero_shot = zero_shot
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if train_langs is None:
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train_langs = ['it']
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self.train_langs = train_langs
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def fit(self, lX, lY):
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print('# Fitting VanillaFunGen (X)...')
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if self.zero_shot:
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self.langs = sorted(self.train_langs)
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lX = self.zero_shot_experiments(lX)
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lX = self.vectorizer.fit_transform(lX)
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else:
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lX = self.vectorizer.fit_transform(lX)
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self.doc_projector.fit(lX, lY)
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return self
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@ -93,9 +103,19 @@ class VanillaFunGen(ViewGen):
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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def zero_shot_experiments(self, lX):
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print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
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_lX = {}
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for lang in self.langs:
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if lang in self.train_langs:
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_lX[lang] = lX[lang]
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else:
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_lX[lang] = None
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lX = _lX
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return lX
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def set_zero_shot(self, val: bool):
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self.zero_shot = val
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print('# TODO: PosteriorsGen has not been configured for zero-shot experiments')
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return
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@ -205,8 +225,14 @@ class WordClassGen(ViewGen):
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:return: self.
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"""
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print('# Fitting WordClassGen (W)...')
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if self.zero_shot:
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self.langs = sorted(self.train_langs)
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lX = self.zero_shot_experiments(lX)
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lX = self.vectorizer.fit_transform(lX)
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else:
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lX = self.vectorizer.fit_transform(lX)
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self.langs = sorted(lX.keys())
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wce = Parallel(n_jobs=self.n_jobs)(
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delayed(wce_matrix)(lX[lang], ly[lang]) for lang in self.langs)
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self.lWce = {l: wce[i] for i, l in enumerate(self.langs)}
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@ -220,15 +246,10 @@ class WordClassGen(ViewGen):
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:param lX: dict {lang: indexed documents}
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:return: document projection to the common latent space.
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"""
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# Testing zero-shot experiments
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if self.zero_shot:
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lX = self.zero_shot_experiments(lX)
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lX = {l: self.vectorizer.vectorizer[l].transform(lX[l]) for l in self.langs if lX[l] is not None}
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else:
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lX = self.vectorizer.transform(lX)
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XdotWce = Parallel(n_jobs=self.n_jobs)(
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delayed(XdotM)(lX[lang], self.lWce[lang], sif=True) for lang in sorted(lX.keys()))
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lWce = {l: XdotWce[i] for i, l in enumerate(sorted(lX.keys()))}
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delayed(XdotM)(lX[lang], self.lWce[lang], sif=True) for lang in sorted(lX.keys()) if lang in self.lWce.keys())
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lWce = {l: XdotWce[i] for i, l in enumerate(sorted(lX.keys())) if l in self.lWce.keys()}
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lWce = _normalize(lWce, l2=True)
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return lWce
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@ -339,7 +360,7 @@ class RecurrentGen(ViewGen):
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print('# Fitting RecurrentGen (G)...')
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create_if_not_exist(self.logger.save_dir)
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recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size, n_jobs=self.n_jobs,
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zero_shot=self.zero_shot, zscl_langs=self.train_langs) # Todo: zero shot settings
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zero_shot=self.zero_shot, zscl_langs=self.train_langs)
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trainer = Trainer(gradient_clip_val=1e-1, gpus=self.gpus, logger=self.logger, max_epochs=self.nepochs,
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callbacks=[self.early_stop_callback, self.lr_monitor], checkpoint_callback=False)
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@ -350,7 +371,7 @@ class RecurrentGen(ViewGen):
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# self.model.linear2 = vanilla_torch_model.linear2
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# self.model.rnn = vanilla_torch_model.rnn
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if self.zero_shot: # Todo: zero shot experiment setting
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if self.zero_shot:
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print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
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trainer.fit(self.model, datamodule=recurrentDataModule)
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@ -451,7 +472,7 @@ class BertGen(ViewGen):
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bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512,
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zero_shot=self.zero_shot, zscl_langs=self.train_langs)
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if self.zero_shot: # Todo: zero shot experiment setting
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if self.zero_shot:
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print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
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trainer = Trainer(gradient_clip_val=1e-1, max_epochs=self.nepochs, gpus=self.gpus,
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