TensorFlow-Models/swivel/wordsim.py

#!/usr/bin/env python
#
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Computes Spearman's rho with respect to human judgements.

Given a set of row (and potentially column) embeddings, this computes Spearman's
rho between the rank ordering of predicted word similarity and human judgements.

Usage:

  wordim.py --embeddings=<binvecs> --vocab=<vocab> eval1.tab eval2.tab ...

Options:

  --embeddings=<filename>: the vectors to test
  --vocab=<filename>: the vocabulary file

Evaluation files are assumed to be tab-separated files with exactly three
columns.  The first two columns contain the words, and the third column contains
the scored human judgement.

"""

import scipy.stats
import sys
from getopt import GetoptError, getopt

from vecs import Vecs

try:
  opts, args = getopt(sys.argv[1:], '', ['embeddings=', 'vocab='])
except GetoptError, e:
  print >> sys.stderr, e
  sys.exit(2)

opt_embeddings = None
opt_vocab = None

for o, a in opts:
  if o == '--embeddings':
    opt_embeddings = a
  if o == '--vocab':
    opt_vocab = a

if not opt_vocab:
  print >> sys.stderr, 'please specify a vocabulary file with "--vocab"'
  sys.exit(2)

if not opt_embeddings:
  print >> sys.stderr, 'please specify the embeddings with "--embeddings"'
  sys.exit(2)

try:
  vecs = Vecs(opt_vocab, opt_embeddings)
except IOError, e:
  print >> sys.stderr, e
  sys.exit(1)

def evaluate(lines):
  acts, preds = [], []

  with open(filename, 'r') as lines:
    for line in lines:
      w1, w2, act = line.strip().split('\t')
      pred = vecs.similarity(w1, w2)
      if pred is None:
        continue

      acts.append(float(act))
      preds.append(pred)

  rho, _ = scipy.stats.spearmanr(acts, preds)
  return rho

for filename in args:
  with open(filename, 'r') as lines:
    print '%0.3f %s' % (evaluate(lines), filename)