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import argparse
import csv
import random
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import normalize
from Wasserstein_Distance import (WassersteinRetriever,
clean_corpus_using_embeddings_vocabulary,
load_embeddings)
def main(args):
np.seterr(divide='ignore') # POT has issues with divide by zero errors
source_lang = args.source_lang
target_lang = args.target_lang
source_vectors_filename = args.source_vector
target_vectors_filename = args.target_vector
vectors_source = load_embeddings(source_vectors_filename)
vectors_target = load_embeddings(target_vectors_filename)
source_defs_filename = args.source_defs
target_defs_filename = args.target_defs
batch = args.batch
mode = args.mode
runfor = list()
if mode == 'all':
runfor.extend(['wmd', 'snk'])
else:
runfor.append(mode)
defs_source = [
line.rstrip('\n')
for line in open(source_defs_filename, encoding='utf8')
]
defs_target = [
line.rstrip('\n')
for line in open(target_defs_filename, encoding='utf8')
]
clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary(
set(vectors_source.keys()),
defs_source,
vectors_source,
source_lang,
)
clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
set(vectors_target.keys()),
defs_target,
vectors_target,
target_lang,
)
take = args.instances
common_keys = set(src_keys).intersection(set(target_keys))
take = min(len(common_keys), take) # you can't sample more than length
experiment_keys = random.sample(common_keys, take)
instances = len(experiment_keys)
clean_src_corpus = list(clean_src_corpus[experiment_keys])
clean_target_corpus = list(clean_target_corpus[experiment_keys])
if not batch:
print(
f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}'
)
del vectors_source, vectors_target, defs_source, defs_target
vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
common = [
word for word in vec.get_feature_names()
if word in clean_src_vectors or word in clean_target_vectors
]
W_common = []
for w in common:
if w in clean_src_vectors:
W_common.append(np.array(clean_src_vectors[w]))
else:
W_common.append(np.array(clean_target_vectors[w]))
if not batch:
print(
f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}'
)
W_common = np.array(W_common)
W_common = normalize(W_common)
vect = TfidfVectorizer(vocabulary=common, dtype=np.double, norm=None)
vect.fit(clean_src_corpus + clean_target_corpus)
X_train_idf = vect.transform(clean_src_corpus)
X_test_idf = vect.transform(clean_target_corpus)
for metric in runfor:
if not batch:
print(f'{metric}: {source_lang} - {target_lang}')
clf = WassersteinRetriever(W_embed=W_common,
n_neighbors=5,
n_jobs=14,
sinkhorn=(metric == 'snk'))
clf.fit(X_train_idf[:instances], np.ones(instances))
p_at_one, percentage = clf.align(X_test_idf[:instances],
n_neighbors=instances)
if not batch:
print(f'P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%')
else:
fields = [
f'{source_lang}', f'{target_lang}', f'{instances}',
f'{p_at_one}', f'{percentage}'
]
with open(f'{metric}_retrieval_result.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='run retrieval using wmd or snk')
parser.add_argument('source_lang', help='source language short name')
parser.add_argument('target_lang', help='target language short name')
parser.add_argument('source_vector', help='path of the source vector')
parser.add_argument('target_vector', help='path of the target vector')
parser.add_argument('source_defs', help='path of the source definitions')
parser.add_argument('target_defs', help='path of the target definitions')
parser.add_argument(
'-b',
'--batch',
action='store_true',
help=
'running in batch (store results in csv) or running a single instance (output the results)'
)
parser.add_argument('mode',
choices=['all', 'wmd', 'snk'],
default='all',
help='which methods to run')
parser.add_argument(
'-n',
'--instances',
help='number of instances in each language to retrieve',
default=1000,
type=int)
args = parser.parse_args()
main(args)
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