#!/usr/bin/python
# -*- coding: utf-8 -*-
# Author: Rico Sennrich
"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
Unlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary
of a text to a configurable number of symbols, with only a small increase in the number of tokens.
Reference:
Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
"""
# Dec. 2016: Edited by Raj Dabre
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import re
import copy
import argparse
from collections import defaultdict, Counter
import six
# hack for python2/3 compatibility
from io import open
argparse.open = open
# python 2/3 compatibility
# if sys.version_info < (3, 0):
# sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
# sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
# sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
def create_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="learn BPE-based word segmentation")
parser.add_argument(
'--input', '-i', type=argparse.FileType('r'), default=sys.stdin,
metavar='PATH',
help="Input text (default: standard input).")
parser.add_argument(
'--output', '-o', type=argparse.FileType('w'), default=sys.stdout,
metavar='PATH',
help="Output file for BPE codes (default: standard output)")
parser.add_argument(
'--symbols', '-s', type=int, default=10000,
help="Create this many new symbols (each representing a character n-gram) (default: %(default)s))")
parser.add_argument(
'--min-frequency', type=int, default=2, metavar='FREQ',
help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s))')
parser.add_argument(
'--verbose', '-v', action="store_true",
help="verbose mode.")
return parser
def get_vocabulary(fobj):
"""Read text and return dictionary that encodes vocabulary
"""
vocab = Counter()
for line in fobj:
for word in line.split():
vocab[word] += 1
return vocab
def get_vocabulary_and_totals(fobj):
"""Read text and return dictionary that encodes vocabulary along with total word count
"""
total_words = 0
total_lines = 0
vocab = Counter()
for line in fobj:
total_lines += 1
for word in line.split():
vocab[word] += 1
total_words += 1
return vocab, total_words, total_lines
def update_pair_statistics(pair, changed, stats, indices):
"""Minimally update the indices and frequency of symbol pairs
if we merge a pair of symbols, only pairs that overlap with occurrences
of this pair are affected, and need to be updated.
"""
stats[pair] = 0
indices[pair] = defaultdict(int)
first, second = pair
new_pair = first + second
for j, word, old_word, freq in changed:
# find all instances of pair, and update frequency/indices around it
i = 0
while True:
try:
i = old_word.index(first, i)
except ValueError:
break
if i < len(old_word) - 1 and old_word[i + 1] == second:
if i:
prev = old_word[i - 1:i + 1]
stats[prev] -= freq
indices[prev][j] -= 1
if i < len(old_word) - 2:
# don't double-count consecutive pairs
if old_word[i + 2] != first or i >= len(old_word) - 3 or old_word[i + 3] != second:
nex = old_word[i + 1:i + 3]
stats[nex] -= freq
indices[nex][j] -= 1
i += 2
else:
i += 1
i = 0
while True:
try:
i = word.index(new_pair, i)
except ValueError:
break
if i:
prev = word[i - 1:i + 1]
stats[prev] += freq
indices[prev][j] += 1
# don't double-count consecutive pairs
if i < len(word) - 1 and word[i + 1] != new_pair:
nex = word[i:i + 2]
stats[nex] += freq
indices[nex][j] += 1
i += 1
def get_pair_statistics(vocab):
"""Count frequency of all symbol pairs, and create index"""
# data structure of pair frequencies
stats = defaultdict(int)
# index from pairs to words
indices = defaultdict(lambda: defaultdict(int))
for i, (word, freq) in enumerate(vocab):
prev_char = word[0]
for char in word[1:]:
stats[prev_char, char] += freq
indices[prev_char, char][i] += 1
prev_char = char
return stats, indices
def replace_pair(pair, vocab, indices):
"""Replace all occurrences of a symbol pair ('A', 'B') with a new symbol 'AB'"""
first, second = pair
pair_str = ''.join(pair)
pair_str = pair_str.replace('\\', '\\\\')
changes = []
pattern = re.compile(
r'(?<!\S)' +
re.escape(
first +
' ' +
second) +
r'(?!\S)')
for j, freq in six.iteritems(indices[pair]):
if freq < 1:
continue
word, freq = vocab[j]
new_word = ' '.join(word)
new_word = pattern.sub(pair_str, new_word)
new_word = tuple(new_word.split())
vocab[j] = (new_word, freq)
changes.append((j, new_word, word, freq))
return changes
def prune_stats(stats, big_stats, threshold):
"""Prune statistics dict for efficiency of max()
The frequency of a symbol pair never increases, so pruning is generally safe
(until we the most frequent pair is less frequent than a pair we previously pruned)
big_stats keeps full statistics for when we need to access pruned items
"""
for item, freq in list(six.iteritems(stats)):
if freq < threshold:
del stats[item]
if freq < 0:
big_stats[item] += freq
else:
big_stats[item] = freq
def get_vocabulary_from_iterable(iterable):
"""Read text and return dictionary that encodes vocabulary
iterable can be iterated and return sequences of "words"
"""
vocab = Counter()
for line in iterable:
for word in line:
vocab[word] += 1
return vocab
def learn_bpe_from_sentence_iterable(iterable, output, symbols=10000, min_frequency=2, verbose=True):
vocab = get_vocabulary_from_iterable(iterable)
vocab = dict([(tuple(x) + ('</w>',), y) for (x, y) in six.iteritems(vocab)])
sorted_vocab = sorted(six.iteritems(vocab), key=lambda x: x[1], reverse=True)
stats, indices = get_pair_statistics(sorted_vocab)
big_stats = copy.deepcopy(stats)
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = max(six.itervalues(stats)) / 10
for i in six.moves.range(symbols):
if stats:
most_frequent = max(stats, key=stats.get)
# we probably missed the best pair because of pruning; go back to full
# statistics
if not stats or (i and stats[most_frequent] < threshold):
prune_stats(stats, big_stats, threshold)
stats = copy.deepcopy(big_stats)
most_frequent = max(stats, key=stats.get)
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = stats[most_frequent] * i / (i + 10000.0)
prune_stats(stats, big_stats, threshold)
if stats[most_frequent] < min_frequency:
sys.stderr.write(
'no pair has frequency >= {0}. Stopping\n'.format(min_frequency))
break
if verbose:
sys.stderr.write('pair {0}: {1} {2} -> {1}{2} (frequency {3})\n'.format(
i, most_frequent[0], most_frequent[1], stats[most_frequent]))
output.write('{0} {1}\n'.format(*most_frequent))
changes = replace_pair(most_frequent, sorted_vocab, indices)
update_pair_statistics(most_frequent, changes, stats, indices)
stats[most_frequent] = 0
if not i % 100:
prune_stats(stats, big_stats, threshold)
if __name__ == '__main__':
parser = create_parser()
args = parser.parse_args()
vocab = get_vocabulary(args.input)
vocab = dict([(tuple(x) + ('</w>',), y) for (x, y) in six.iteritems(vocab)])
sorted_vocab = sorted(six.iteritems(vocab), key=lambda x: x[1], reverse=True)
stats, indices = get_pair_statistics(sorted_vocab)
big_stats = copy.deepcopy(stats)
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = max(six.itervalues(stats)) / 10
for i in six.moves.range(args.symbols):
if stats:
most_frequent = max(stats, key=stats.get)
# we probably missed the best pair because of pruning; go back to full
# statistics
if not stats or (i and stats[most_frequent] < threshold):
prune_stats(stats, big_stats, threshold)
stats = copy.deepcopy(big_stats)
most_frequent = max(stats, key=stats.get)
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = stats[most_frequent] * i / (i + 10000.0)
prune_stats(stats, big_stats, threshold)
if stats[most_frequent] < args.min_frequency:
sys.stderr.write(
'no pair has frequency >= {0}. Stopping\n'.format(
args.min_frequency))
break
if args.verbose:
sys.stderr.write('pair {0}: {1} {2} -> {1}{2} (frequency {3})\n'.format(
i, most_frequent[0], most_frequent[1], stats[most_frequent]))
args.output.write('{0} {1}\n'.format(*most_frequent))
changes = replace_pair(most_frequent, sorted_vocab, indices)
update_pair_statistics(most_frequent, changes, stats, indices)
stats[most_frequent] = 0
if not i % 100:
prune_stats(stats, big_stats, threshold)
