def test_move_and_back(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_counts = numpy.copy(numpy_optimizer._class_counts)
orig_cc_counts = numpy.copy(numpy_optimizer._cc_counts)
orig_cw_counts = numpy.copy(numpy_optimizer._cw_counts)
orig_wc_counts = numpy.copy(numpy_optimizer._wc_counts)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
numpy_optimizer._move(word_id, new_class_id)
theano_optimizer._move(word_id, new_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertEqual(numpy.count_nonzero(numpy_optimizer._class_counts != orig_class_counts), 2)
self.assertEqual(numpy.sum(numpy_optimizer._class_counts), numpy.sum(orig_class_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cc_counts != orig_cc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cc_counts), numpy.sum(orig_cc_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cw_counts != orig_cw_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cw_counts), numpy.sum(orig_cw_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._wc_counts != orig_wc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._wc_counts), numpy.sum(orig_wc_counts))
numpy_optimizer._move(word_id, orig_class_id)
theano_optimizer._move(word_id, orig_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertTrue(numpy.array_equal(numpy_optimizer._class_counts, orig_class_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cc_counts, orig_cc_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cw_counts, orig_cw_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._wc_counts, orig_wc_counts))
def test_move_and_recompute(self):
optimizer1 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = optimizer1.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
optimizer1._word_to_class[word_id] = new_class_id
counts = optimizer1._compute_class_statistics(
optimizer1._word_counts, optimizer1._ww_counts,
optimizer1._word_to_class)
class_counts = numpy.zeros(optimizer1.num_classes, 'int32')
cc_counts = numpy.zeros(
(optimizer1.num_classes, optimizer1.num_classes), dtype='int32')
cw_counts = numpy.zeros(
(optimizer1.num_classes, optimizer1.vocabulary_size),
dtype='int32')
wc_counts = numpy.zeros(
(optimizer1.vocabulary_size, optimizer1.num_classes),
dtype='int32')
for wid, cid in enumerate(optimizer1._word_to_class):
class_counts[cid] += optimizer1._word_counts[wid]
for left_wid, right_wid in zip(*optimizer1._ww_counts.nonzero()):
count = optimizer1._ww_counts[left_wid, right_wid]
left_cid = optimizer1._word_to_class[left_wid]
right_cid = optimizer1._word_to_class[right_wid]
cc_counts[left_cid, right_cid] += count
cw_counts[left_cid, right_wid] += count
wc_counts[left_wid, right_cid] += count
self.assertTrue(numpy.array_equal(class_counts, counts[0]))
self.assertTrue(numpy.array_equal(cc_counts, counts[1]))
self.assertTrue(numpy.array_equal(cw_counts, counts[2]))
self.assertTrue(numpy.array_equal(wc_counts, counts[3]))
optimizer1._class_counts = counts[0]
optimizer1._cc_counts = counts[1]
optimizer1._cw_counts = counts[2]
optimizer1._wc_counts = counts[3]
optimizer2 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer2.get_word_class(word_id)
optimizer2._move(word_id, new_class_id)
self.assertEqual(
numpy.count_nonzero(
optimizer1._class_counts != optimizer2._class_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._cc_counts != optimizer2._cc_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._cw_counts != optimizer2._cw_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._wc_counts != optimizer2._wc_counts), 0)
optimizer3 = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer3.get_word_class(word_id)
optimizer3._move(word_id, new_class_id)
self.assert_optimizers_equal(optimizer2, optimizer3)
def test_move_and_recompute(self):
optimizer1 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = optimizer1.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
optimizer1._word_to_class[word_id] = new_class_id
counts = optimizer1._compute_class_statistics(optimizer1._word_counts,
optimizer1._ww_counts,
optimizer1._word_to_class)
class_counts = numpy.zeros(optimizer1.num_classes, 'int32')
cc_counts = numpy.zeros((optimizer1.num_classes, optimizer1.num_classes), dtype='int32')
cw_counts = numpy.zeros((optimizer1.num_classes, optimizer1.vocabulary_size), dtype='int32')
wc_counts = numpy.zeros((optimizer1.vocabulary_size, optimizer1.num_classes), dtype='int32')
for wid, cid in enumerate(optimizer1._word_to_class):
class_counts[cid] += optimizer1._word_counts[wid]
for left_wid, right_wid in zip(*optimizer1._ww_counts.nonzero()):
count = optimizer1._ww_counts[left_wid, right_wid]
left_cid = optimizer1._word_to_class[left_wid]
right_cid = optimizer1._word_to_class[right_wid]
cc_counts[left_cid,right_cid] += count
cw_counts[left_cid,right_wid] += count
wc_counts[left_wid,right_cid] += count
self.assertTrue(numpy.array_equal(class_counts, counts[0]))
self.assertTrue(numpy.array_equal(cc_counts, counts[1]))
self.assertTrue(numpy.array_equal(cw_counts, counts[2]))
self.assertTrue(numpy.array_equal(wc_counts, counts[3]))
optimizer1._class_counts = counts[0]
optimizer1._cc_counts = counts[1]
optimizer1._cw_counts = counts[2]
optimizer1._wc_counts = counts[3]
optimizer2 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer2.get_word_class(word_id)
optimizer2._move(word_id, new_class_id)
self.assertEqual(numpy.count_nonzero(optimizer1._class_counts != optimizer2._class_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._cc_counts != optimizer2._cc_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._cw_counts != optimizer2._cw_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._wc_counts != optimizer2._wc_counts), 0)
optimizer3 = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer3.get_word_class(word_id)
optimizer3._move(word_id, new_class_id)
self.assert_optimizers_equal(optimizer2, optimizer3)
def test_evaluate(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics,
self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics,
self.vocabulary)
word_id = numpy_optimizer.get_word_id('d')
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 1 if orig_class_id != 1 else 0
orig_ll = numpy_optimizer.log_likelihood()
self.assertTrue(
numpy.isclose(orig_ll, theano_optimizer.log_likelihood()))
ll_diff = numpy_optimizer._evaluate(word_id, new_class_id)
self.assertTrue(
numpy.isclose(ll_diff,
theano_optimizer._evaluate(word_id, new_class_id)))
numpy_optimizer._move(word_id, new_class_id)
new_ll = numpy_optimizer.log_likelihood()
self.assertFalse(numpy.isclose(orig_ll, new_ll))
self.assertTrue(numpy.isclose(orig_ll + ll_diff, new_ll))
theano_optimizer._move(word_id, new_class_id)
self.assertTrue(
numpy.isclose(new_ll, theano_optimizer.log_likelihood()))
def test_move_and_back(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_counts = numpy.copy(numpy_optimizer._class_counts)
orig_cc_counts = numpy.copy(numpy_optimizer._cc_counts)
orig_cw_counts = numpy.copy(numpy_optimizer._cw_counts)
orig_wc_counts = numpy.copy(numpy_optimizer._wc_counts)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
numpy_optimizer._move(word_id, new_class_id)
theano_optimizer._move(word_id, new_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertEqual(numpy.count_nonzero(numpy_optimizer._class_counts != orig_class_counts), 2)
self.assertEqual(numpy.sum(numpy_optimizer._class_counts), numpy.sum(orig_class_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cc_counts != orig_cc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cc_counts), numpy.sum(orig_cc_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cw_counts != orig_cw_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cw_counts), numpy.sum(orig_cw_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._wc_counts != orig_wc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._wc_counts), numpy.sum(orig_wc_counts))
numpy_optimizer._move(word_id, orig_class_id)
theano_optimizer._move(word_id, orig_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertTrue(numpy.array_equal(numpy_optimizer._class_counts, orig_class_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cc_counts, orig_cc_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cw_counts, orig_cw_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._wc_counts, orig_wc_counts))
def test_statistics(self):
num_words = 8
theano_optimizer = TheanoBigramOptimizer(self.statistics,
self.vocabulary)
numpy_optimizer = NumpyBigramOptimizer(self.statistics,
self.vocabulary)
self.assertEqual(theano_optimizer.vocabulary_size, num_words)
self.assertEqual(numpy_optimizer.vocabulary_size, num_words)
self.assertEqual(theano_optimizer.num_classes, self.num_classes + 3)
self.assertEqual(numpy_optimizer.num_classes, self.num_classes + 3)
self.assertEqual(len(theano_optimizer._word_to_class.get_value()),
num_words)
self.assertEqual(len(numpy_optimizer._word_to_class), num_words)
sos_word_id = self.vocabulary.word_to_id['<s>']
a_word_id = self.vocabulary.word_to_id['a']
b_word_id = self.vocabulary.word_to_id['b']
c_word_id = self.vocabulary.word_to_id['c']
d_word_id = self.vocabulary.word_to_id['d']
e_word_id = self.vocabulary.word_to_id['e']
unk_word_id = self.vocabulary.word_to_id['<unk>']
eos_word_id = self.vocabulary.word_to_id['</s>']
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertEqual(len(numpy_optimizer._word_counts), num_words)
self.assertEqual(numpy_optimizer._word_counts[sos_word_id], 11)
self.assertEqual(numpy_optimizer._word_counts[a_word_id], 13)
self.assertEqual(numpy_optimizer._word_counts[b_word_id], 8)
self.assertEqual(numpy_optimizer._word_counts[c_word_id], 8)
self.assertEqual(numpy_optimizer._word_counts[d_word_id], 11)
self.assertEqual(numpy_optimizer._word_counts[e_word_id], 15)
self.assertEqual(numpy_optimizer._word_counts[unk_word_id], 0)
self.assertEqual(numpy_optimizer._word_counts[eos_word_id], 11)
self.assertEqual(numpy_optimizer._ww_counts.shape[0], num_words)
self.assertEqual(numpy_optimizer._ww_counts.shape[1], num_words)
self.assertEqual(len(numpy_optimizer._class_counts),
self.num_classes + 3)
self.assertEqual(numpy_optimizer._cc_counts.shape[0],
self.num_classes + 3)
self.assertEqual(numpy_optimizer._cw_counts.shape[0],
self.num_classes + 3)
self.assertEqual(numpy_optimizer._cw_counts.shape[1], num_words)
self.assertEqual(numpy_optimizer._wc_counts.shape[0], num_words)
self.assertEqual(numpy_optimizer._wc_counts.shape[1],
self.num_classes + 3)
def test_evaluate(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
word_id = numpy_optimizer.get_word_id('d')
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 1 if orig_class_id != 1 else 0
orig_ll = numpy_optimizer.log_likelihood()
self.assertTrue(numpy.isclose(orig_ll, theano_optimizer.log_likelihood()))
ll_diff = numpy_optimizer._evaluate(word_id, new_class_id)
self.assertTrue(numpy.isclose(ll_diff, theano_optimizer._evaluate(word_id, new_class_id)))
numpy_optimizer._move(word_id, new_class_id)
new_ll = numpy_optimizer.log_likelihood()
self.assertFalse(numpy.isclose(orig_ll, new_ll))
self.assertTrue(numpy.isclose(orig_ll + ll_diff, new_ll))
theano_optimizer._move(word_id, new_class_id)
self.assertTrue(numpy.isclose(new_ll, theano_optimizer.log_likelihood()))
def main():
parser = argparse.ArgumentParser(prog='wctool')
argument_group = parser.add_argument_group("files")
argument_group.add_argument(
'--training-set',
metavar='FILE',
type=TextFileType('r'),
nargs='+',
required=True,
help='text or .gz files containing training data (one sentence per '
'line)')
argument_group.add_argument(
'--vocabulary',
metavar='FILE',
type=TextFileType('r'),
default=None,
help='text or .gz file containing a list of words to include in class '
'forming, and possibly their initial classes')
argument_group.add_argument(
'--vocabulary-format',
metavar='FORMAT',
type=str,
default='words',
help='vocabulary format, one of "words" (one word per line, default), '
'"classes" (word and class ID per line), "srilm-classes" (class '
'name, membership probability, and word per line)')
argument_group.add_argument(
'--output-file',
metavar='FILE',
type=TextFileType('w'),
default='-',
help='where to write the word classes (default stdout)')
argument_group.add_argument(
'--output-format',
metavar='FORMAT',
type=str,
default='srilm-classes',
help='format of the output file, one of "classes" (word and class ID '
'per line), "srilm-classes" (default; class name, membership '
'probability, and word per line)')
argument_group.add_argument(
'--output-frequency',
metavar='N',
type=int,
default='1',
help='save classes N times per optimization iteration (default 1)')
argument_group = parser.add_argument_group("optimization")
argument_group.add_argument(
'--num-classes',
metavar='N',
type=int,
default=2000,
help='number of classes to form, if vocabulary is not specified '
'(default 2000)')
argument_group.add_argument(
'--method',
metavar='NAME',
type=str,
default='bigram-theano',
help='method for creating word classes, one of "bigram-theano", '
'"bigram-numpy" (default "bigram-theano")')
argument_group = parser.add_argument_group("logging and debugging")
argument_group.add_argument(
'--log-file',
metavar='FILE',
type=str,
default='-',
help='path where to write log file (default is standard output)')
argument_group.add_argument(
'--log-level',
metavar='LEVEL',
type=str,
default='info',
help='minimum level of events to log, one of "debug", "info", "warn" '
'(default "info")')
argument_group.add_argument(
'--log-interval',
metavar='N',
type=int,
default=1000,
help='print statistics after every Nth word; quiet if less than one '
'(default 1000)')
args = parser.parse_args()
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
raise ValueError("Invalid logging level requested: " + args.log_level)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout,
format=log_format,
level=log_level)
else:
logging.basicConfig(filename=log_file,
format=log_format,
level=log_level)
if args.vocabulary is None:
vocabulary = Vocabulary.from_corpus(args.training_set,
args.num_classes)
for subset_file in args.training_set:
subset_file.seek(0)
else:
vocabulary = Vocabulary.from_file(args.vocabulary,
args.vocabulary_format)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
print("Number of normal word classes:", vocabulary.num_normal_classes)
logging.info("Reading word unigram and bigram statistics.")
statistics = WordStatistics(args.training_set, vocabulary)
if args.method == 'bigram-theano':
optimizer = TheanoBigramOptimizer(statistics, vocabulary)
elif args.method == 'bigram-numpy':
optimizer = NumpyBigramOptimizer(statistics, vocabulary)
else:
raise ValueError("Invalid method requested: " + args.method)
iteration = 1
while True:
logging.info("Starting iteration %d.", iteration)
num_words = 0
num_moves = 0
for word in vocabulary.words():
start_time = time()
num_words += 1
if optimizer.move_to_best_class(word):
num_moves += 1
duration = time() - start_time
if (args.log_interval >= 1) and \
(num_words % args.log_interval == 0):
logging.info(
"[%d] (%.1f %%) of iteration %d -- moves = %d, cost = %.2f, duration = %.1f ms",
num_words, num_words / vocabulary.num_words() * 100,
iteration, num_moves, optimizer.log_likelihood(),
duration * 100)
if is_scheduled(num_words, args.output_frequency,
vocabulary.num_words()):
save(optimizer, args.output_file, args.output_format)
if num_moves == 0:
break
iteration += 1
logging.info("Optimization finished.")
save(optimizer, args.output_file, args.output_format)
def main():
parser = argparse.ArgumentParser(prog='wctool')
argument_group = parser.add_argument_group("files")
argument_group.add_argument(
'--training-set', metavar='FILE', type=TextFileType('r'),
nargs='+', required=True,
help='text or .gz files containing training data (one sentence per '
'line)')
argument_group.add_argument(
'--vocabulary', metavar='FILE', type=TextFileType('r'), default=None,
help='text or .gz file containing a list of words to include in class '
'forming, and possibly their initial classes')
argument_group.add_argument(
'--vocabulary-format', metavar='FORMAT', type=str, default='words',
help='vocabulary format, one of "words" (one word per line, default), '
'"classes" (word and class ID per line), "srilm-classes" (class '
'name, membership probability, and word per line)')
argument_group.add_argument(
'--output-file', metavar='FILE', type=TextFileType('w'), default='-',
help='where to write the word classes (default stdout)')
argument_group.add_argument(
'--output-format', metavar='FORMAT', type=str, default='srilm-classes',
help='format of the output file, one of "classes" (word and class ID '
'per line), "srilm-classes" (default; class name, membership '
'probability, and word per line)')
argument_group.add_argument(
'--output-frequency', metavar='N', type=int, default='1',
help='save classes N times per optimization iteration (default 1)')
argument_group = parser.add_argument_group("optimization")
argument_group.add_argument(
'--num-classes', metavar='N', type=int, default=2000,
help='number of classes to form, if vocabulary is not specified '
'(default 2000)')
argument_group.add_argument(
'--method', metavar='NAME', type=str, default='bigram-theano',
help='method for creating word classes, one of "bigram-theano", '
'"bigram-numpy" (default "bigram-theano")')
argument_group = parser.add_argument_group("logging and debugging")
argument_group.add_argument(
'--log-file', metavar='FILE', type=str, default='-',
help='path where to write log file (default is standard output)')
argument_group.add_argument(
'--log-level', metavar='LEVEL', type=str, default='info',
help='minimum level of events to log, one of "debug", "info", "warn" '
'(default "info")')
argument_group.add_argument(
'--log-interval', metavar='N', type=int, default=1000,
help='print statistics after every Nth word; quiet if less than one '
'(default 1000)')
args = parser.parse_args()
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
raise ValueError("Invalid logging level requested: " + args.log_level)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level)
else:
logging.basicConfig(filename=log_file, format=log_format, level=log_level)
if args.vocabulary is None:
vocabulary = Vocabulary.from_corpus(args.training_set,
args.num_classes)
for subset_file in args.training_set:
subset_file.seek(0)
else:
vocabulary = Vocabulary.from_file(args.vocabulary,
args.vocabulary_format)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
print("Number of normal word classes:", vocabulary.num_normal_classes)
logging.info("Reading word unigram and bigram statistics.")
statistics = WordStatistics(args.training_set, vocabulary)
if args.method == 'bigram-theano':
optimizer = TheanoBigramOptimizer(statistics, vocabulary)
elif args.method == 'bigram-numpy':
optimizer = NumpyBigramOptimizer(statistics, vocabulary)
else:
raise ValueError("Invalid method requested: " + args.method)
iteration = 1
while True:
logging.info("Starting iteration %d.", iteration)
num_words = 0
num_moves = 0
for word in vocabulary.words():
start_time = time()
num_words += 1
if optimizer.move_to_best_class(word):
num_moves += 1
duration = time() - start_time
if (args.log_interval >= 1) and \
(num_words % args.log_interval == 0):
logging.info("[%d] (%.1f %%) of iteration %d -- moves = %d, cost = %.2f, duration = %.1f ms",
num_words,
num_words / vocabulary.num_words() * 100,
iteration,
num_moves,
optimizer.log_likelihood(),
duration * 100)
if is_scheduled(num_words,
args.output_frequency,
vocabulary.num_words()):
save(optimizer, args.output_file, args.output_format)
if num_moves == 0:
break
iteration += 1
logging.info("Optimization finished.")
save(optimizer, args.output_file, args.output_format)