47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
from argparse import ArgumentParser
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from util.embeddings_manager import MuseLoader
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from view_generators import RecurrentGen, BertGen
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from data.dataset_builder import MultilingualDataset
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from util.common import MultilingualIndex
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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'], categories=[0,1])
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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=)
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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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# gFun = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
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# gFun = MuseGen(muse_dir='/home/andreapdr/funneling_pdr/embeddings', n_jobs=N_JOBS)
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# gFun = WordClassGen(n_jobs=N_JOBS)
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gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=512, gpus=args.gpus, n_jobs=N_JOBS)
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# gFun = BertGen(multilingualIndex, gpus=args.gpus, batch_size=128, n_jobs=N_JOBS)
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gFun.fit(lX, ly)
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# print('Projecting...')
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# y_ = gFun.transform(lX)
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exit('Executed!')
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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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