116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
from argparse import ArgumentParser
|
|
from funnelling import *
|
|
from view_generators import *
|
|
from data.dataset_builder import MultilingualDataset
|
|
from util.common import MultilingualIndex, get_params
|
|
from util.evaluation import evaluate
|
|
from util.results_csv import CSVlog
|
|
from time import time
|
|
|
|
|
|
def main(args):
|
|
OPTIMC = True # TODO
|
|
N_JOBS = 8
|
|
print('Running refactored...')
|
|
|
|
# _DATASET = '/homenfs/a.pedrotti1/datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
|
|
# EMBEDDINGS_PATH = '/homenfs/a.pedrotti1/embeddings/MUSE'
|
|
|
|
_DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
|
|
EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
|
|
data = MultilingualDataset.load(_DATASET)
|
|
data.set_view(languages=['it', 'fr'])
|
|
lX, ly = data.training()
|
|
lXte, lyte = data.test()
|
|
|
|
# Init multilingualIndex - mandatory when deploying Neural View Generators...
|
|
multilingualIndex = MultilingualIndex()
|
|
lMuse = MuseLoader(langs=sorted(lX.keys()), cache=EMBEDDINGS_PATH)
|
|
multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary())
|
|
|
|
embedder_list = []
|
|
if args.X:
|
|
posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
|
|
embedder_list.append(posteriorEmbedder)
|
|
|
|
if args.M:
|
|
museEmbedder = MuseGen(muse_dir=EMBEDDINGS_PATH, n_jobs=N_JOBS)
|
|
embedder_list.append(museEmbedder)
|
|
|
|
if args.W:
|
|
wceEmbedder = WordClassGen(n_jobs=N_JOBS)
|
|
embedder_list.append(wceEmbedder)
|
|
|
|
if args.G:
|
|
rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
|
|
nepochs=250, gpus=args.gpus, n_jobs=N_JOBS)
|
|
embedder_list.append(rnnEmbedder)
|
|
|
|
if args.B:
|
|
bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)
|
|
embedder_list.append(bertEmbedder)
|
|
|
|
# Init DocEmbedderList
|
|
docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
|
|
meta_parameters = None if not OPTIMC else [{'C': [1, 1e3, 1e2, 1e1, 1e-1]}]
|
|
meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf', C=meta_parameters),
|
|
meta_parameters=get_params(optimc=True))
|
|
|
|
# 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=ly, ly_pred=ly_)
|
|
time_te = round(time() - time_te, 3)
|
|
print(f'Testing completed in {time_te} seconds!')
|
|
|
|
# Logging ---------------------------------------
|
|
print('\n[Results]')
|
|
results = CSVlog('test_log.csv')
|
|
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}')
|
|
results.add_row(method='gfun',
|
|
setting='TODO',
|
|
sif='True',
|
|
zscore='True',
|
|
l2='True',
|
|
dataset='TODO',
|
|
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()
|
|
parser.add_argument('--X')
|
|
parser.add_argument('--M')
|
|
parser.add_argument('--W')
|
|
parser.add_argument('--G')
|
|
parser.add_argument('--B')
|
|
parser.add_argument('--gpus', default=None)
|
|
args = parser.parse_args()
|
|
main(args)
|