Permalink
Branch: master
Find file Copy path
96 lines (83 sloc) 2.05 KB
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2016-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from scipy import stats
import os
import math
import argparse
def compat_splitting(line):
return line.decode('utf8').split()
def similarity(v1, v2):
n1 = np.linalg.norm(v1)
n2 = np.linalg.norm(v2)
return np.dot(v1, v2) / n1 / n2
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument(
'--model',
'-m',
dest='modelPath',
action='store',
required=True,
help='path to model'
)
parser.add_argument(
'--data',
'-d',
dest='dataPath',
action='store',
required=True,
help='path to data'
)
args = parser.parse_args()
vectors = {}
fin = open(args.modelPath, 'rb')
for _, line in enumerate(fin):
try:
tab = compat_splitting(line)
vec = np.array(tab[1:], dtype=float)
word = tab[0]
if np.linalg.norm(vec) == 0:
continue
if not word in vectors:
vectors[word] = vec
except ValueError:
continue
except UnicodeDecodeError:
continue
fin.close()
mysim = []
gold = []
drop = 0.0
nwords = 0.0
fin = open(args.dataPath, 'rb')
for line in fin:
tline = compat_splitting(line)
word1 = tline[0].lower()
word2 = tline[1].lower()
nwords = nwords + 1.0
if (word1 in vectors) and (word2 in vectors):
v1 = vectors[word1]
v2 = vectors[word2]
d = similarity(v1, v2)
mysim.append(d)
gold.append(float(tline[2]))
else:
drop = drop + 1.0
fin.close()
corr = stats.spearmanr(mysim, gold)
dataset = os.path.basename(args.dataPath)
print(
"{0:20s}: {1:2.0f} (OOV: {2:2.0f}%)"
.format(dataset, corr[0] * 100, math.ceil(drop / nwords * 100.0))
)