def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Train morphology generation model')
parser.add_argument('category', help='Russian word category to (R/V/A/N/M)')
parser.add_argument('model', help='output file for trained model')
parser.add_argument('--penalty', help='regularization penalty', type=float, default=0.001)
args = parser.parse_args()
assert len(args.category) == 1
with open(args.model, 'w') as f:
f.write('write test / training...')
logging.info('Extracting features for training data')
training_features = []
training_outputs = []
for source, target, alignment in read_sentences(sys.stdin):
for features, output in extract_instances(args.category, source, target, alignment):
training_features.append(features)
training_outputs.append(output)
vectorizer = DictVectorizer()
X = vectorizer.fit_transform(training_features)
y = training_outputs
logging.info('Training data size: %d instances x %d features', *X.shape)
logging.info('Training model for category: %s (%d tags)', args.category, len(set(y)))
model = LogisticRegression(C=args.penalty)
model.fit(X, y)
with open(args.model, 'w') as f:
cPickle.dump((args.category, vectorizer, model), f, protocol=-1)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(
description='Create synthetic phrases'
' using trained CRF models and lemma grammar')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('models',
nargs='+',
help='trained models (category:file)')
parser.add_argument('sgm', help='original sentences + grammar pointers')
parser.add_argument('sgm_lem',
help='original sentences + lemma grammar pointers')
parser.add_argument('out', help='grammar output directory')
args = parser.parse_args()
if not os.path.exists(args.out):
os.mkdir(args.out)
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Loading inflection prediction models')
models = load_models(args.models)
logging.info('Generating extended grammars')
data = izip(read_sentences(sys.stdin, skip_empty=False),
read_sgm(args.sgm), read_sgm(args.sgm_lem))
for (source, _, _), (grm_path, sid, left, right),\
(lem_grm_path, lem_sid, lem_left, lem_right) in data:
assert sid == lem_sid and left == lem_left and right == lem_right
# Create grammar file
out_path = os.path.join(args.out, 'grammar.{}.gz'.format(sid))
grammar_file = gzip.open(out_path, 'w')
# Copy original grammar
with gzip.open(grm_path) as f:
for line in f:
grammar_file.write(line)
# Generate synthetic phrases from lemma grammar
for rule in read_grammar(lem_grm_path):
assert not any(src.startswith('[X,')
for src in rule.lhs) # no gaps, please
for match in source_match(rule.lhs, source):
# create (at most) a synthetic rule
for new_rule in synthetic_rule(rev_map, models, rule, source,
match):
grammar_file.write(unicode(new_rule).encode('utf8') + '\n')
grammar_file.close()
# Write sgm
new_left = u'<seg grammar="{}" id="{}">{}</seg>'.format(
out_path, sid, left)
print(u' ||| '.join([new_left] + right).encode('utf8'))
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Create synthetic phrases'
' using trained CRF models and lemma grammar')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('models', nargs='+', help='trained models (category:file)')
parser.add_argument('sgm', help='original sentences + grammar pointers')
parser.add_argument('sgm_lem', help='original sentences + lemma grammar pointers')
parser.add_argument('out', help='grammar output directory')
args = parser.parse_args()
if not os.path.exists(args.out):
os.mkdir(args.out)
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Loading inflection prediction models')
models = load_models(args.models)
extracted_tags = ''.join([cat for cat in models.keys()])
logging.info('Inflecting categories: {}'.format(extracted_tags))
lemma_re = re.compile('^(.+)_(['+''+extracted_tags+'])$')
logging.info('Generating extended grammars')
data = izip(read_sentences(sys.stdin, skip_empty=False),
read_sgm(args.sgm), read_sgm(args.sgm_lem))
for (source, _, _), (grm_path, sid, left, right),\
(lem_grm_path, lem_sid, lem_left, lem_right) in data:
assert sid == lem_sid and left == lem_left and right == lem_right
# Create grammar file
out_path = os.path.join(args.out, 'grammar.{}.gz'.format(sid))
grammar_file = gzip.open(out_path, 'w')
# Copy original grammar
with gzip.open(grm_path) as f:
for line in f:
grammar_file.write(line)
# Generate synthetic phrases from lemma grammar
for rule in read_grammar(lem_grm_path):
assert not any(src.startswith('[X,') for src in rule.lhs) # no gaps, please
for match in source_match(rule.lhs, source):
# create (at most) a synthetic rule
for new_rule in synthetic_rule(rev_map, models, rule, source, match, lemma_re):
grammar_file.write(unicode(new_rule).encode('utf8')+'\n')
grammar_file.close()
# Write sgm
new_left = u'<seg grammar="{}" id="{}">{}</seg>'.format(out_path, sid, left)
print(u' ||| '.join([new_left] + right).encode('utf8'))
def main():
parser = argparse.ArgumentParser(description='Create source ||| lemma_tag corpus')
parser.add_argument('--partial', action='store_true',
help='exclude non-predicted categories from lemmatization')
args = parser.parse_args()
def lemmatize(tgt, lemma, tag):
if args.partial and tag[0] not in config.EXTRACTED_TAGS:
return tgt
return lemma+'_'+tag[0]
for source, target, _ in read_sentences(sys.stdin):
src = ' '.join(w.token for w in source)
tgt = ' '.join(lemmatize(tgt, lemma, tag) for tgt, lemma, tag in target)
print(u'{} ||| {}'.format(src, tgt).encode('utf8'))
def main():
parser = argparse.ArgumentParser(
description='Create source ||| lemma_tag corpus')
parser.add_argument(
'--partial',
action='store_true',
help='exclude non-predicted categories from lemmatization')
args = parser.parse_args()
def lemmatize(tgt, lemma, tag):
if args.partial and tag[0] not in config.EXTRACTED_TAGS:
return tgt
return lemma + '_' + tag[0]
for source, target, _ in read_sentences(sys.stdin):
src = ' '.join(w.token for w in source)
tgt = ' '.join(
lemmatize(tgt, lemma, tag) for tgt, lemma, tag in target)
print(u'{} ||| {}'.format(src, tgt).encode('utf8'))
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(
description='Train morphology generation model')
parser.add_argument('category',
help='Russian word category to (R/V/A/N/M)')
parser.add_argument('model', help='output file for trained model')
parser.add_argument('--penalty',
help='regularization penalty',
type=float,
default=0.001)
args = parser.parse_args()
assert len(args.category) == 1
with open(args.model, 'w') as f:
f.write('write test / training...')
logging.info('Extracting features for training data')
training_features = []
training_outputs = []
for source, target, alignment in read_sentences(sys.stdin):
for features, output in extract_instances(args.category, source,
target, alignment):
training_features.append(features)
training_outputs.append(output)
vectorizer = DictVectorizer()
X = vectorizer.fit_transform(training_features)
y = training_outputs
logging.info('Training data size: %d instances x %d features', *X.shape)
logging.info('Training model for category: %s (%d tags)', args.category,
len(set(y)))
model = LogisticRegression(C=args.penalty)
model.fit(X, y)
with open(args.model, 'w') as f:
cPickle.dump((args.category, vectorizer, model), f, protocol=-1)
def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(description='Create reverse inflection map')
parser.add_argument('rev_map', help='output file')
args = parser.parse_args()
lemma_map = defaultdict(lambda: defaultdict(Counter))
logging.info('Finding inflections and counting tag/form occurences...')
for _, target, _ in read_sentences(sys.stdin):
for (inflection, lemma, tag) in target:
if tag[0] not in config.EXTRACTED_TAGS: continue
lemma_map[lemma, tag[0]][tag[1:]][inflection] += 1
logging.info('Selecting most frequent form for each tag')
rev_map = {lt: set() for lt in lemma_map.iterkeys()}
for lt, inflections in lemma_map.iteritems():
for tag, forms in inflections.iteritems():
((best_form, _),) = forms.most_common(1)
rev_map[lt].add((tag, best_form))
logging.info('Saving inflection map')
with open(args.rev_map, 'w') as f:
cPickle.dump(rev_map, f, protocol=-1)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Create cdec CRF grammars and training data')
parser.add_argument('category', help='Russian word category to (R/V/A/N/M)')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('output', help='training output path')
args = parser.parse_args()
category = args.category
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
# Create training data paths
if not os.path.exists(args.output):
os.mkdir(args.output)
grammar_path = os.path.join(args.output, 'grammars')
if not os.path.exists(grammar_path):
os.mkdir(grammar_path)
sgm = io.open(os.path.join(args.output, 'train.sgm'), 'w', encoding='utf8')
fvoc = Vocabulary()
n_sentences = 0
logging.info('Generating the grammars')
for source, target, alignment in read_sentences(sys.stdin):
n_sentences += 1
if n_sentences % 1000 == 0:
if too_much_mem():
logging.info('Running out of memory')
break
for word, features in extract_instances(category, source, target, alignment):
inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
if (ref_attributes, inflection) not in possible_inflections:
logging.debug('Skip: %s (%s)', inflection, ref_attributes)
continue
# Write sentence grammar
grammar_name = os.path.join(grammar_path, uuid.uuid1().hex)
with io.open(grammar_name, 'w', encoding='utf8') as grammar:
for attributes, _ in possible_inflections:
rule = fvoc.make_rule(lemma, category, attributes, features)
grammar.write(rule)
# Write src / ref
src = lemma+'_'+category
ref = ' '.join(config.get_attributes(category, ref_attributes))
sgm.write(u'<seg grammar="{}"> {} ||| {} {} </seg>\n'.format(
os.path.abspath(grammar_name), src, category, ref))
logging.info('Processed %d sentences', n_sentences)
logging.info('Saving weights')
ff_path = os.path.join(args.output, 'weights.ini')
with io.open(ff_path, 'w', encoding='utf8') as f:
for fname, fid in fvoc.iteritems():
f.write(u'# {}\n'.format(fname))
f.write(u'F{} 0\n'.format(fid))
sgm.close()
def main():
logging.basicConfig(level=logging.INFO, format="%(message)s")
parser = argparse.ArgumentParser(description="Predict using trained models")
parser.add_argument("rev_map", help="reverse inflection map")
parser.add_argument("models", nargs="+", help="trained models (category:file)")
parser.add_argument("--ambiguous", action="store_true", help="evaluate only lemmas with multiple inflections")
args = parser.parse_args()
logging.info("Loading reverse inflection map")
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info("Loading inflection prediction models")
models = load_models(args.models)
logging.info("Loaded models for %d categories", len(models))
stats = {cat: [0, 0, 0, 0, 0] for cat in config.EXTRACTED_TAGS}
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(source, target, alignment):
gold_inflection, lemma, tag = word
category = tag[0]
gold_tag = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
if (gold_tag, gold_inflection) not in possible_inflections:
print(u"Expected: {} ({}) not found".format(gold_inflection, gold_tag).encode("utf8"))
continue
if args.ambiguous and len(possible_inflections) == 1:
continue
model = models[category]
scored_inflections = model.score_all(possible_inflections, features)
ranked_inflections = sorted(scored_inflections, reverse=True)
predicted_score, predicted_tag, predicted_inflection = ranked_inflections[0]
gold_rank = 1 + [tag for _, tag, _ in ranked_inflections].index(gold_tag)
gold_score = next((score for score, tag, _ in ranked_inflections if tag == gold_tag))
print(
u"Expected: {} ({}) r={} score={:.3f} |"
" Predicted: {} ({}) score={:.3f}".format(
gold_inflection,
gold_tag,
gold_rank,
gold_score,
predicted_inflection,
predicted_tag,
predicted_score,
).encode("utf8")
)
stats[category][0] += 1
stats[category][1] += 1 / float(gold_rank)
stats[category][2] += gold_inflection == predicted_inflection
stats[category][3] += gold_score
stats[category][4] += len(ranked_inflections)
for category, (n_instances, rrank_sum, n_correct, total_log_prob, n_inflections) in stats.items():
if n_instances == 0:
continue
mrr = rrank_sum / n_instances
accuracy = n_correct / float(n_instances)
ppl = math.exp(-total_log_prob / n_instances)
avg_inflections = n_inflections / float(n_instances)
print(
"Category {}: MRR={:.3f} acc={:.1%} ppl={:.2f} ({} instances; avg #infl={:.2f})".format(
category, mrr, accuracy, ppl, n_instances, avg_inflections
)
)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i', '--n_iter', type=int, help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
args = parser.parse_args()
category = args.category
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target, alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections: continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {attr: 1 for attr in config.get_attributes(category, attributes)}
Y_all.append(label) # attributes map
lims = (n, n+len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it+1)), 'w') as f:
cPickle.dump(model, f, protocol=-1)
model = StructuredModel(args.category)
model.train(X, Y_all, Y_star, Y_lim, n_iter=args.n_iter,
alpha_sgd=args.rate, every_iter=save_model)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(
description='Predict using trained models')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('models',
nargs='+',
help='trained models (category:file)')
parser.add_argument('--ambiguous',
action='store_true',
help='evaluate only lemmas with multiple inflections')
args = parser.parse_args()
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Loading inflection prediction models')
models = load_models(args.models)
logging.info('Loaded models for %d categories', len(models))
stats = {cat: [0, 0, 0, 0, 0] for cat in config.EXTRACTED_TAGS}
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(source, target, alignment):
gold_inflection, lemma, tag = word
category = tag[0]
gold_tag = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
if (gold_tag, gold_inflection) not in possible_inflections:
print(u'Expected: {} ({}) not found'.format(
gold_inflection, gold_tag).encode('utf8'))
continue
if args.ambiguous and len(possible_inflections) == 1: continue
model = models[category]
scored_inflections = model.score_all(possible_inflections,
features)
ranked_inflections = sorted(scored_inflections, reverse=True)
predicted_score, predicted_tag, predicted_inflection = ranked_inflections[
0]
gold_rank = 1 + [tag for _, tag, _ in ranked_inflections
].index(gold_tag)
gold_score = next((score for score, tag, _ in ranked_inflections
if tag == gold_tag))
print(u'Expected: {} ({}) r={} score={:.3f} |'
' Predicted: {} ({}) score={:.3f}'.format(
gold_inflection, gold_tag, gold_rank, gold_score,
predicted_inflection, predicted_tag,
predicted_score).encode('utf8'))
stats[category][0] += 1
stats[category][1] += 1 / float(gold_rank)
stats[category][2] += (gold_inflection == predicted_inflection)
stats[category][3] += gold_score
stats[category][4] += len(ranked_inflections)
for category, (n_instances, rrank_sum, n_correct, total_log_prob,
n_inflections) in stats.items():
if n_instances == 0: continue
mrr = rrank_sum / n_instances
accuracy = n_correct / float(n_instances)
ppl = math.exp(-total_log_prob / n_instances)
avg_inflections = n_inflections / float(n_instances)
print(
'Category {}: MRR={:.3f} acc={:.1%} ppl={:.2f} ({} instances; avg #infl={:.2f})'
.format(category, mrr, accuracy, ppl, n_instances,
avg_inflections))
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i',
'--n_iter',
type=int,
help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
args = parser.parse_args()
category = args.category
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target,
alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections:
continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {
attr: 1
for attr in config.get_attributes(
category, attributes)
}
Y_all.append(label) # attributes map
lims = (n, n + len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it + 1)),
'w') as f:
cPickle.dump(model, f, protocol=-1)
model = StructuredModel(args.category)
model.train(X,
Y_all,
Y_star,
Y_lim,
n_iter=args.n_iter,
alpha_sgd=args.rate,
every_iter=save_model)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i', '--n_iter', type=int, help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
parser.add_argument('-c', '--config', help='configuration module for supervised models (must be in config directory)')
parser.add_argument('-a','--adagrad', action='store_true', default=False, help='Use the AdaGrad adaptive gradient technique to adjust rate')
parser.add_argument('-l','--l1', type=float, default=0.0 , help='lambda value for l1 regualrization (l1 regularization currently only implemented for use with adagrad). If none given, regularization will not be used')
args = parser.parse_args()
category = args.category
logging.info('Training inflection model for category {}'.format(category))
if args.config:
logging.info('Loading external configuration module {}'.format(args.config))
sup_config = __import__('config_files.'+args.config,globals(),locals(),['get_attributes'])
attr_function = lambda cat, attr: sup_config.get_attributes(cat, attr)
else:
attr_function = lambda cat, attr: config.get_attributes(cat, attr)
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('length of reverse map: {}'.format(len(rev_map)))
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target, alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections: continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {attr: 1 for attr in attr_function(category, attributes)}
Y_all.append(label) # attributes map
lims = (n, n+len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it+1)), 'w') as f:
cPickle.dump(model, f, protocol=-1)
if args.config:
model = StructuredModel(args.category, functools.partial(sup_config.get_attributes))
else:
model = StructuredModel(args.category, config.get_attributes)
model.train(X, Y_all, Y_star, Y_lim, n_iter=args.n_iter,
alpha_sgd=args.rate, every_iter=save_model,
adagrad=args.adagrad, l1=args.l1)
