def __init__(self,
input_wvectors,
input_word2id,
input_id2word,
input_vocabulary,
pair_file_path,
kn_file_name,
output_file_name,
topn = 20):
word2id = dict()
with codecs.open(input_word2id, 'r', encoding='utf-8') as f:
for lines in f:
word2id[lines.strip().split()[0]] = int(lines.strip().split()[1])
id2word = dict()
with codecs.open(input_id2word, 'r', encoding='utf-8') as f:
for lines in f:
id2word[int(lines.strip().split()[0])] = lines.strip().split()[1]
vocabulary = []
with codecs.open(input_vocabulary, 'r', encoding='utf-8') as f:
for lines in f:
vocabulary.append(int(lines.strip()))
self.topn = topn
kneighbor = KNeighbor(input_wvectors, vocabulary, word2id, id2word)
dump_to_pkl(kneighbor, kn_file_name)
logging_set('NSselect.log')
files = os.listdir(pair_file_path)
pairs = dict()
for file in tqdm(files):
if not os.path.isdir(file):
path = pair_file_path + "/" + file
pair = load_from_pkl(path)
logging.info("pair size: %d" % (len(pair)))
if len(pairs) == 0:
pairs = pair
else:
for key in pair.keys():
if key in pairs:
pairs[key] += pair[key]
else:
pairs[key] = pair[key]
logging.info("current total pair size: %d" % (len(pairs)))
logging.info("start calculate score")
score = self.select_new(pairs, kneighbor, self.topn)
#score1 = self.select(pairs, kneighbor)
logging.info("start saving")
dump_to_pkl(score, output_file_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="Philly arguments parser")
parser.add_argument('emb_file_name', type=str)
parser.add_argument('--similarity_test_paths',
type=str,
default='data/240.txt|data/297.txt')
parser.add_argument('--synset_paths',
type=str,
default='data/nsem3-adjusted.txt')
parser.add_argument('--analogy_test_paths',
type=str,
default='data/analogy.txt')
parser.add_argument('--log_path', type=str, default='evaluation.log')
args, _ = parser.parse_known_args()
logging_set(args.log_path)
if args.similarity_test_paths == 'None':
args.similarity_test_paths = None
if args.synset_paths == 'None':
args.synset_paths = None
if args.analogy_test_paths == 'None':
args.analogy_test_paths = None
best_scores, save_flag = evaluation(args.emb_file_name,
args.similarity_test_paths,
args.synset_paths,
args.analogy_test_paths)
def select(self, pairs, kneighbor):
score = dict()
for keyn in tqdm(kneighbor.keys()):
score[keyn] = []
for value in kneighbor[keyn]:
s = 0
i = 0
for keyp in pairs.keys():
if keyp[0] == keyn:
replace = tuple([value] + list(keyp[1:]))
if replace in pairs:
s += pairs[replace] / pairs[keyp]
i += 1
else:
s += 0
i += 1
score[keyn].append(s / i)
return score
if __name__ == '__main__':
logging_set('NSselect.log')
ns = NSselect(input_wvectors=sys.argv[1],
input_word2id=sys.argv[2],
input_id2word=sys.argv[3],
input_vocabulary=sys.argv[4],
pair_file_path=sys.argv[5],
kn_file_name=sys.argv[6],
output_file_name=sys.argv[7])
import os
from utils import load_from_pkl, dump_to_pkl, logging_set
from tqdm import tqdm
import logging
import gc
logging_set('merge_pair.log')
path = 'data/pair'
files = os.listdir(path)[1:]
pairs = dict()
for idx, file in enumerate(tqdm(files)):
if idx % 20 == 0:
gc.collect() #手动触发 内存回收
if not os.path.isdir(file):
pair_file_path = path + "/" + file
pair = load_from_pkl(pair_file_path)
logging.info("pair size: %d" % (len(pair)))
if len(pairs) == 0:
pairs = pair
else:
for key in pair.keys():
if key in pairs:
pairs[key] += pair[key]
else:
pairs[key] = pair[key]
logging.info("current total pair size: %d" % (len(pairs)))
output_file_name = 'data/pairs.pkl'
dump_to_pkl(pairs, output_file_name)
