from argparse import ArgumentParser from data.dataset_builder import MultilingualDataset from funnelling import * from util.common import MultilingualIndex, get_params, get_method_name from util.evaluation import evaluate from util.results_csv import CSVlog from view_generators import * def main(args): assert args.post_embedder or args.muse_embedder or args.wce_embedder or args.gru_embedder or args.bert_embedder, \ 'empty set of document embeddings is not allowed!' print('Running generalized funnelling...') data = MultilingualDataset.load(args.dataset) data.set_view(languages=['it', 'fr']) data.show_dimensions() lX, ly = data.training() lXte, lyte = data.test() # Init multilingualIndex - mandatory when deploying Neural View Generators... if args.gru_embedder or args.bert_embedder: multilingualIndex = MultilingualIndex() lMuse = MuseLoader(langs=sorted(lX.keys()), cache=args.muse_dir) multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary()) # Init ViewGenerators and append them to embedder_list embedder_list = [] if args.post_embedder: posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=args.n_jobs) embedder_list.append(posteriorEmbedder) if args.muse_embedder: museEmbedder = MuseGen(muse_dir=args.muse_dir, n_jobs=args.n_jobs) embedder_list.append(museEmbedder) if args.wce_embedder: wceEmbedder = WordClassGen(n_jobs=args.n_jobs) embedder_list.append(wceEmbedder) if args.gru_embedder: rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=args.gru_wce, batch_size=256, nepochs=args.nepochs, gpus=args.gpus, n_jobs=args.n_jobs) embedder_list.append(rnnEmbedder) if args.bert_embedder: bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=10, gpus=args.gpus, n_jobs=args.n_jobs) embedder_list.append(bertEmbedder) # Init DocEmbedderList (i.e., first-tier learners or view generators) and metaclassifier docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True) meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'), meta_parameters=get_params(optimc=args.optimc)) # Init Funnelling Architecture gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta) # Training --------------------------------------- print('\n[Training Generalized Funnelling]') time_init = time() time_tr = time() gfun.fit(lX, ly) time_tr = round(time() - time_tr, 3) print(f'Training completed in {time_tr} seconds!') # Testing ---------------------------------------- print('\n[Testing Generalized Funnelling]') time_te = time() ly_ = gfun.predict(lXte) l_eval = evaluate(ly_true=lyte, ly_pred=ly_) time_te = round(time() - time_te, 3) print(f'Testing completed in {time_te} seconds!') # Logging --------------------------------------- print('\n[Results]') results = CSVlog(args.csv_dir) metrics = [] for lang in lXte.keys(): macrof1, microf1, macrok, microk = l_eval[lang] metrics.append([macrof1, microf1, macrok, microk]) print(f'Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f}') if results is not None: _id, _dataset = get_method_name(args) results.add_row(method='gfun', setting=_id, optimc=args.optimc, sif='True', zscore='True', l2='True', dataset=_dataset, time_tr=time_tr, time_te=time_te, lang=lang, macrof1=macrof1, microf1=microf1, macrok=macrok, microk=microk, notes='') print('Averages: MF1, mF1, MK, mK', np.round(np.mean(np.array(metrics), axis=0), 3)) overall_time = round(time() - time_init, 3) exit(f'\nExecuted in: {overall_time} seconds!') if __name__ == '__main__': parser = ArgumentParser(description='Run generalized funnelling, A. Moreo, A. Pedrotti and F. Sebastiani') parser.add_argument('dataset', help='Path to the dataset') parser.add_argument('-o', '--output', dest='csv_dir', help='Result file (default ../csv_log/gfun_results.csv)', type=str, default='csv_logs/gfun/gfun_results.csv') parser.add_argument('-x', '--post_embedder', dest='post_embedder', action='store_true', help='deploy posterior probabilities embedder to compute document embeddings', default=False) parser.add_argument('-w', '--wce_embedder', dest='wce_embedder', action='store_true', help='deploy (supervised) Word-Class embedder to the compute document embeddings', default=False) parser.add_argument('-m', '--muse_embedder', dest='muse_embedder', action='store_true', help='deploy (pretrained) MUSE embedder to compute document embeddings', default=False) parser.add_argument('-b', '--bert_embedder', dest='bert_embedder', action='store_true', help='deploy multilingual Bert to compute document embeddings', default=False) parser.add_argument('-g', '--gru_embedder', dest='gru_embedder', action='store_true', help='deploy a GRU in order to compute document embeddings', default=False) parser.add_argument('-c', '--c_optimize', dest='optimc', action='store_true', help='Optimize SVMs C hyperparameter', default=False) parser.add_argument('-n', '--nepochs', dest='nepochs', type=str, help='Number of max epochs to train Recurrent embedder (i.e., -g)') parser.add_argument('-j', '--n_jobs', dest='n_jobs', type=int, help='Number of parallel jobs (default is -1, all)', default=-1) parser.add_argument('--muse_dir', dest='muse_dir', type=str, help='Path to the MUSE polylingual word embeddings (default ../embeddings)', default='../embeddings') parser.add_argument('--gru_wce', dest='gru_wce', action='store_true', help='Deploy WCE embedding as embedding layer of the GRU View Generator', default=False) parser.add_argument('--gru_dir', dest='gru_dir', type=str, help='Set the path to a pretrained GRU model (i.e., -g view generator)', default=None) parser.add_argument('--bert_dir', dest='bert_dir', type=str, help='Set the path to a pretrained mBERT model (i.e., -b view generator)', default=None) parser.add_argument('--gpus', help='specifies how many GPUs to use per node', default=None) args = parser.parse_args() main(args)