Word2vec accuracy

gistfile1.py

    def accuracy(self, questions, restrict_vocab=30000, most_similar=most_similar, lowercase=True):
        """
        Compute accuracy of the model. `questions` is a filename where lines are
        4-tuples of words, split into sections by ": SECTION NAME" lines.
        See https://code.google.com/p/word2vec/source/browse/trunk/questions-words.txt for an example.

        The accuracy is reported (=printed to log and returned as a list) for each
        section separately, plus there's one aggregate summary at the end.

        Use `restrict_vocab` to ignore all questions containing a word whose frequency
        is not in the top-N most frequent words (default top 30,000).

        This method corresponds to the `compute-accuracy` script of the original C word2vec.

        """
        ok_vocab = dict(sorted(iteritems(self.vocab),
                               key=lambda item: -item[1].count)[:restrict_vocab])
        ok_index = set(v.index for v in itervalues(ok_vocab))

        sections, section = [], None
        for line_no, line in enumerate(utils.smart_open(questions)):
            # TODO: use level3 BLAS (=evaluate multiple questions at once), for speed
            line = utils.to_unicode(line)
            if line.startswith(': '):
                # a new section starts => store the old section
                if section:
                    sections.append(section)
                    self.log_accuracy(section)
                section = {'section': line.lstrip(': ').strip(), 'correct': [], 'incorrect': []}
            else:
                if not section:
                    raise ValueError("missing section header before line #%i in %s" % (line_no, questions))
                try:
                    if lowercase:
                        a, b, c, expected = [word.lower() for word in line.split()]
                    else:
                        a, b, c, expected = line.split()
                except:
                    logger.info("skipping invalid line #%i in %s" % (line_no, questions))
                if a not in ok_vocab or b not in ok_vocab or c not in ok_vocab or expected not in ok_vocab:
                    logger.debug("skipping line #%i with OOV words: %s" % (line_no, line.strip()))
                    continue

                ignore = {a.lower(), b.lower(), c.lower()} # words to ignore
                correct = False
                expected = expected.lower()
                # find the most likely prediction, ignoring OOV words and input words
                for index in argsort(most_similar(self, positive=[b, c], negative=[a], topn=False))[::-1]:
                    predicted = self.index2word[index].lower()
                    if index in ok_index and predicted not in ignore:
                        if predicted != expected:
                            logger.debug("%s: expected %s, predicted %s" % (line.strip(), expected, predicted))
                        else:
                            correct = True
                        break
                if correct:
                    section['correct'].append((a, b, c, expected))
                else:
                    section['incorrect'].append((a, b, c, expected))
        if section:
            # store the last section, too
            sections.append(section)
            self.log_accuracy(section)

        total = {
            'section': 'total',
            'correct': sum((s['correct'] for s in sections), []),
            'incorrect': sum((s['incorrect'] for s in sections), []),
        }
        self.log_accuracy(total)
        sections.append(total)
        return sections