def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-src_vecs',
default='embeddings/original/google.txt',
help=" source language vectors (default: GoogleNewsVecs )")
parser.add_argument(
'-trg_vecs',
default='embeddings/original/sg-300-{0}.txt',
help=" target language vectors (default: SGNS on Wikipedia)")
parser.add_argument(
'-trans',
help=
'translation pairs (default: Bing Liu Sentiment Lexicon Translations)',
default='lexicons/bingliu/en-{0}.txt')
parser.add_argument('-dataset',
default='opener_sents',
help="dataset to train and test on (default: opener)")
parser.add_argument(
'-bi',
help=
'List of booleans. True is only binary, False is only 4 class. True False is both. (default: [True, False])',
default=[True, False],
nargs='+',
type=str2bool)
args = parser.parse_args()
# Loop over the three languages
for lang in ['es', 'ca', 'eu']:
print('################ {0} ##############'.format(lang))
# Import monolingual vectors
print('importing word embeddings')
src_vecs = WordVecs(args.src_vecs)
src_vecs.mean_center()
src_vecs.normalize()
trg_vecs = WordVecs(args.trg_vecs.format(lang))
trg_vecs.mean_center()
trg_vecs.normalize()
# Setup projection dataset
pdataset = ProjectionDataset(args.trans.format(lang), src_vecs,
trg_vecs)
# learn the translation matrix W
W = get_W(pdataset, src_vecs, trg_vecs)
# project the source matrix to the new shared space
src_vecs._matrix = np.dot(src_vecs._matrix, W)
# Import datasets (representation will depend on final classifier)
print('importing datasets')
binary_dataset = General_Dataset(os.path.join('datasets', 'en',
args.dataset),
src_vecs,
binary=True,
rep=ave_vecs,
one_hot=False,
lowercase=False)
binary_cross_dataset = General_Dataset(os.path.join(
'datasets', lang, args.dataset),
trg_vecs,
binary=True,
rep=ave_vecs,
one_hot=False,
lowercase=False)
fine_dataset = General_Dataset(os.path.join('datasets', 'en',
args.dataset),
src_vecs,
binary=False,
rep=ave_vecs,
one_hot=False,
lowercase=False)
fine_cross_dataset = General_Dataset(os.path.join(
'datasets', lang, args.dataset),
trg_vecs,
binary=False,
rep=ave_vecs,
one_hot=False,
lowercase=False)
# Train linear SVM classifier
if True in args.bi:
best_c, best_f1 = get_best_C(binary_dataset, binary_cross_dataset)
clf = LinearSVC(C=best_c)
clf.fit(binary_dataset._Xtrain, binary_dataset._ytrain)
cpred = clf.predict(binary_cross_dataset._Xtest)
cf1 = macro_f1(binary_cross_dataset._ytest, cpred)
print_prediction(
clf, binary_cross_dataset,
os.path.join('predictions', lang, 'artetxe',
'{0}-bi.txt'.format(args.dataset)))
print('-binary-')
print('Acc: {0:.3f}'.format(
clf.score(binary_cross_dataset._Xtest,
binary_cross_dataset._ytest)))
print('Macro F1: {0:.3f}'.format(cf1))
print()
if False in args.bi:
best_c, best_f1 = get_best_C(fine_dataset, fine_cross_dataset)
clf = LinearSVC(C=best_c)
clf.fit(fine_dataset._Xtrain, fine_dataset._ytrain)
cpred = clf.predict(fine_cross_dataset._Xtest)
cf1 = macro_f1(fine_cross_dataset._ytest, cpred)
print_prediction(
clf, fine_cross_dataset,
os.path.join('predictions', lang, 'artetxe',
'{0}-4cls.txt'.format(args.dataset)))
print('-fine-')
print('Acc: {0:.3f}'.format(
clf.score(fine_cross_dataset._Xtest,
fine_cross_dataset._ytest)))
print('Macro F1: {0:.3f}'.format(cf1))
print()
'--trg_embedding',
default="../../embeddings/BLSE/sg-300-es.txt")
parser.add_argument('-sd',
'--src_dataset',
default="datasets/training/en/raw")
parser.add_argument('-td',
'--trg_dataset',
default="datasets/training/es/raw")
args = parser.parse_args()
# Import monolingual vectors
print('importing word embeddings')
src_vecs = WordVecs(args.src_embedding)
src_vecs.mean_center()
src_vecs.normalize()
trg_vecs = WordVecs(args.trg_embedding)
trg_vecs.mean_center()
trg_vecs.normalize()
# Setup projection dataset
trans = 'lexicons/bingliu_en_{0}.one-2-one.txt'.format(args.lang)
pdataset = ProjectionDataset(trans, src_vecs, trg_vecs)
# learn the translation matrix W
print('Projecting src embeddings to trg space...')
W = get_projection_matrix(pdataset, src_vecs, trg_vecs)
print('W done')
# project the source matrix to the new shared space
src_vecs._matrix = np.dot(src_vecs._matrix, W)
