68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
from argparse import ArgumentParser
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from funnelling import *
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from view_generators import *
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from data.dataset_builder import MultilingualDataset
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from util.common import MultilingualIndex
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from util.evaluation import evaluate
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from time import time
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def main(args):
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N_JOBS = 8
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print('Running refactored...')
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# _DATASET = '/homenfs/a.pedrotti1/datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
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# EMBEDDINGS_PATH = '/homenfs/a.pedrotti1/embeddings/MUSE'
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_DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
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EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
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data = MultilingualDataset.load(_DATASET)
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data.set_view(languages=['it', 'fr'])
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lX, ly = data.training()
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lXte, lyte = data.test()
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# Init multilingualIndex - mandatory when deploying Neural View Generators...
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multilingualIndex = MultilingualIndex()
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lMuse = MuseLoader(langs=sorted(lX.keys()), cache=EMBEDDINGS_PATH)
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multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary())
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# posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
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museEmbedder = MuseGen(muse_dir=EMBEDDINGS_PATH, n_jobs=N_JOBS)
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wceEmbedder = WordClassGen(n_jobs=N_JOBS)
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# rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
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# nepochs=250, gpus=args.gpus, n_jobs=N_JOBS)
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# bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)
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docEmbedders = DocEmbedderList([museEmbedder, wceEmbedder])
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gfun = Funnelling(first_tier=docEmbedders)
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# Training ---------------------------------------
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print('\n[Training Generalized Funnelling]')
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time_init = time()
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time_tr = time()
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gfun.fit(lX, ly)
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time_tr = round(time() - time_tr, 3)
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print(f'Training completed in {time_tr} seconds!')
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# Testing ----------------------------------------
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print('\n[Testing Generalized Funnelling]')
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time_te = time()
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ly_ = gfun.predict(lXte)
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l_eval = evaluate(ly_true=ly, ly_pred=ly_)
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print(l_eval)
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time_te = round(time() - time_te, 3)
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print(f'Testing completed in {time_te} seconds!')
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overall_time = round(time() - time_init, 3)
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exit(f'\nExecuted in: {overall_time } seconds!')
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if __name__ == '__main__':
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parser = ArgumentParser()
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parser.add_argument('--gpus', default=None)
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args = parser.parse_args()
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main(args)
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