def main_sweep(argv):
parser = argparse.ArgumentParser(description='Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument('-e', '--engine', dest='engine', help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument('--path', dest='corpora_path', metavar='CORPORA', default=None,
help='the path to the test corpora (default is the automatically splitted sample)')
args = parser.parse_args(argv)
samples = [int(e) for e in '10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.split()]
injector = dependency.Injector()
#injector.read_args(args)
engine = MMTEngine(args.engine)
injector.inject(engine)
node = ClusterNode(engine, api_port=DEFAULT_MMT_API_PORT)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node, google_key='1234', use_sessions=True)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.engine.set_config_option('suffixarrays', 'sample', sample)
injector.read_config(node.engine.config) # to get engine.set() to affect MosesFeatures -> moses.ini
injector.inject(node.engine)
node.engine.write_configs()
node.restart()
scores = evaluator.evaluate(corpora=corpora, heval_output=None,
debug=False)
engine_scores = [r for r in scores if r.id == 'MMT'][0]
if engine_scores.error:
raise RuntimeError(engine_scores.error)
bleu = engine_scores.bleu
print(sample, '%.2f' % (bleu * 100))
def translate(self, corpora, output):
"""
Translate the given corpora in parallel processing fashion.
:param corpora: list of ParallelCorpus
:param output: path to output directory
:return: ([ParallelCorpus, ...], time_per_sentence, parallelism)
"""
pool = multithread.Pool(self._threads)
try:
translations = []
start_time = datetime.now()
for corpus in corpora:
self._before_translate(corpus)
with open(corpus.get_file(self.source_lang)) as source:
output_path = os.path.join(
output, corpus.name + '.' + self.target_lang)
for line in source:
translation = pool.apply_async(self._get_translation,
(line, corpus))
translations.append((translation, output_path))
self._after_translate(corpus)
elapsed_time = 0
translation_count = 0
path = None
stream = None
for translation_job, output_path in translations:
translation, elapsed = translation_job.get()
if output_path != path:
if stream is not None:
stream.close()
stream = open(output_path, 'wb')
path = output_path
stream.write(translation.encode('utf-8'))
stream.write('\n')
elapsed_time += elapsed
translation_count += 1
if stream is not None:
stream.close()
end_time = datetime.now()
total_time = end_time - start_time
return BilingualCorpus.list(output), (
elapsed_time / translation_count), (elapsed_time /
total_time.total_seconds())
finally:
pool.terminate()
def _step_preprocess(self,
bilingual_corpora,
monolingual_corpora,
_,
skip=False,
logger=None,
delete_on_exit=False):
preprocessed_folder = self._get_tempdir('preprocessed')
cleaned_folder = self._get_tempdir('clean_corpora')
# if skip is true, then we are in resume mode, so return the already existing results
if skip:
processed_bicorpora, processed_monocorpora = BilingualCorpus.splitlist(
self._engine.source_lang,
self._engine.target_lang,
roots=preprocessed_folder)
cleaned_bicorpora = BilingualCorpus.list(cleaned_folder)
else:
processed_bicorpora, processed_monocorpora = self._engine.training_preprocessor.process(
bilingual_corpora + monolingual_corpora,
preprocessed_folder,
(self._engine.data_path if self._split_trainingset else None),
log=logger.stream)
cleaned_bicorpora = self._engine.training_preprocessor.clean(
processed_bicorpora, cleaned_folder)
return processed_bicorpora, processed_monocorpora, cleaned_bicorpora
def translate(self, corpora, output):
"""
Translate the given corpora in parallel processing fashion.
:param corpora: list of ParallelCorpus
:param output: path to output directory
:return: ([ParallelCorpus, ...], time_per_sentence, parallelism)
"""
pool = multithread.Pool(self._threads)
try:
translations = []
start_time = datetime.now()
for corpus in corpora:
self._before_translate(corpus)
with open(corpus.get_file(self.source_lang)) as source:
output_path = os.path.join(output, corpus.name + '.' + self.target_lang)
for line in source:
translation = pool.apply_async(self._get_translation, (line, corpus))
translations.append((translation, output_path))
self._after_translate(corpus)
elapsed_time = 0
translation_count = 0
path = None
stream = None
for translation_job, output_path in translations:
translation, elapsed = translation_job.get()
if output_path != path:
if stream is not None:
stream.close()
stream = open(output_path, 'wb')
path = output_path
stream.write(translation.encode('utf-8'))
stream.write('\n')
elapsed_time += elapsed
translation_count += 1
if stream is not None:
stream.close()
end_time = datetime.now()
total_time = end_time - start_time
return BilingualCorpus.list(output), (elapsed_time / translation_count), (
elapsed_time / total_time.total_seconds())
finally:
pool.terminate()
def process(self, corpora, dest_folder, print_tags=True, print_placeholders=False, original_spacing=False):
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.__process_file(source, dest, lang, print_tags, print_placeholders, original_spacing)
return BilingualCorpus.list(dest_folder)
def process_corpora(self, corpora, dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.process_file(source, dest, lang)
return BilingualCorpus.list(dest_folder)
def process_corpora(self, corpora, dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.process_file(source, dest, lang)
return BilingualCorpus.list(dest_folder)
def main_sweep(argv):
parser = argparse.ArgumentParser(description='Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument('-e', '--engine', dest='engine', help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument('--path', dest='corpora_path', metavar='CORPORA', default=None,
help='the path to the test corpora (default is the automatically splitted sample)')
args = parser.parse_args(argv)
samples = [int(e) for e in '10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.split()]
node = ConfiguredClusterNode(args.engine)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node.engine, node)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.set('suffixarrays', 'sample', sample)
node.apply_configs()
scores = evaluator.evaluate(corpora=corpora, google_key='1234', heval_output=None,
use_sessions=True, debug=False)
engine_scores = scores['MMT']
if isinstance(engine_scores, str):
raise RuntimeError(engine_scores)
bleu = engine_scores['bleu']
print(sample, '%.2f' % (bleu * 100))
def encode(self, corpora, dest_folder):
if not os.path.isdir(dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest_file = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang]).get_file(lang)
self.encode_file(source, dest_file, delete_nl=True)
return BilingualCorpus.list(dest_folder)
def encode(self, corpora, dest_folder):
if not os.path.isdir(dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest_file = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang]).get_file(lang)
self.encode_file(source, dest_file, delete_nl=True)
return BilingualCorpus.list(dest_folder)
def clean(self, corpora, output_path):
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
return BilingualCorpus.list(output_path)
def clean(self, corpora, output_path, log=None):
if log is None:
log = shell.DEVNULL
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdout=log, stderr=log)
return BilingualCorpus.list(output_path)
def _step_tm_cleanup(self,
corpora,
skip=False,
logger=None,
delete_on_exit=True):
# the folder where tm_cleanup results are to be stored
folder = self._get_tempdir('clean_tms')
# if skip is true, then we are in resume mode, so use the already existing results
if skip:
clean_tms = BilingualCorpus.list(folder)
# else perform the cleaning on the corpora and use the clean corpora
else:
clean_tms = self._engine.cleaner.clean(corpora,
folder,
log=logger.stream)
return clean_tms
def clean(self, corpora, output_path, log=None):
if log is None:
log = shell.DEVNULL
# read memory size
mem_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES') # e.g. 4015976448
mem_mb = mem_bytes / (1024. ** 2) # e.g. 3.74
extended_heap_mb = int(mem_mb*90/100)
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args=args, max_heap_mb=extended_heap_mb)
shell.execute(command, stdout=log, stderr=log)
return BilingualCorpus.list(output_path)
def tune(self, corpora=None, debug=False, context_enabled=True, random_seeds=False, max_iterations=25):
if corpora is None:
corpora = BilingualCorpus.list(os.path.join(self.engine.data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
if len(corpora) == 0:
raise IllegalArgumentException('empty corpora')
if not self.is_running():
raise IllegalStateException('No MMT Server running, start the engine first')
tokenizer = Tokenizer()
target_lang = self.engine.target_lang
source_lang = self.engine.source_lang
source_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [source_lang])
for corpus in corpora]
reference_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [target_lang])
for corpus in corpora]
cmdlogger = _tuning_logger(4)
cmdlogger.start(self, corpora)
working_dir = self.engine.get_tempdir('tuning')
mert_wd = os.path.join(working_dir, 'mert')
try:
# Tokenization
tokenized_output = os.path.join(working_dir, 'reference_corpora')
fileutils.makedirs(tokenized_output, exist_ok=True)
with cmdlogger.step('Corpora tokenization') as _:
reference_corpora = tokenizer.process_corpora(reference_corpora, tokenized_output)
# Create merged corpus
with cmdlogger.step('Merging corpus') as _:
# source
source_merged_corpus = os.path.join(working_dir, 'corpus.' + source_lang)
with open(source_merged_corpus, 'wb') as out:
for corpus in source_corpora:
out.write(corpus.get_file(source_lang) + '\n')
# target
target_merged_corpus = os.path.join(working_dir, 'corpus.' + target_lang)
fileutils.merge([corpus.get_file(target_lang) for corpus in reference_corpora], target_merged_corpus)
# Run MERT algorithm
with cmdlogger.step('Tuning') as _:
# Start MERT
decoder_flags = ['--port', str(self.api.port)]
if not context_enabled:
decoder_flags.append('--skip-context-analysis')
decoder_flags.append('1')
fileutils.makedirs(mert_wd, exist_ok=True)
with tempfile.NamedTemporaryFile() as runtime_moses_ini:
command = [self._mert_script, source_merged_corpus, target_merged_corpus,
self._mert_i_script, runtime_moses_ini.name, '--threads',
str(multiprocessing.cpu_count()), '--mertdir', cli.BIN_DIR,
'--mertargs', '\'--binary --sctype BLEU\'', '--working-dir', mert_wd, '--nbest', '100',
'--decoder-flags', '"' + ' '.join(decoder_flags) + '"', '--nonorm', '--closest',
'--no-filter-phrase-table']
if not random_seeds:
command.append('--predictable-seeds')
if max_iterations > 0:
command.append('--maximum-iterations={num}'.format(num=max_iterations))
with open(self.engine.get_logfile('mert'), 'wb') as log:
shell.execute(' '.join(command), stdout=log, stderr=log)
# Read optimized configuration
with cmdlogger.step('Applying changes') as _:
bleu_score = 0
weights = {}
found_weights = False
with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
for line in moses_ini:
line = line.strip()
if len(line) == 0:
continue
elif found_weights:
tokens = line.split()
weights[tokens[0].rstrip('=')] = [float(val) for val in tokens[1:]]
elif line.startswith('# BLEU'):
bleu_score = float(line.split()[2])
elif line == '[weight]':
found_weights = True
_ = self.api.update_features(weights)
cmdlogger.completed(bleu_score)
finally:
if not debug:
self.engine.clear_tempdir()
def clean(self, corpora, dest_folder, langs=None):
if langs is None and len(corpora) > 0:
langs = (corpora[0].langs[0], corpora[0].langs[1])
self._pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
def tune(self,
corpora=None,
debug=False,
context_enabled=True,
random_seeds=False,
max_iterations=25,
early_stopping_value=None):
if corpora is None:
corpora = BilingualCorpus.list(
os.path.join(self.engine.data_path,
TrainingPreprocessor.DEV_FOLDER_NAME))
target_lang = self.engine.target_lang
source_lang = self.engine.source_lang
corpora = [
corpus for corpus in corpora
if source_lang in corpus.langs and target_lang in corpus.langs
]
if len(corpora) == 0:
raise IllegalArgumentException(
'No %s > %s corpora found into specified path' %
(source_lang, target_lang))
source_corpora = [
BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
[source_lang]) for corpus in corpora
]
reference_corpora = [
BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
[target_lang]) for corpus in corpora
]
cmdlogger = _tuning_logger(4)
cmdlogger.start(self, corpora)
working_dir = self.engine.get_tempdir('tuning')
mert_wd = os.path.join(working_dir, 'mert')
try:
# Tokenization
tokenizer = Tokenizer(target_lang)
tokenized_output = os.path.join(working_dir, 'reference_corpora')
fileutils.makedirs(tokenized_output, exist_ok=True)
with cmdlogger.step('Corpora tokenization') as _:
reference_corpora = tokenizer.process_corpora(
reference_corpora, tokenized_output)
# Create merged corpus
with cmdlogger.step('Merging corpus') as _:
# source
source_merged_corpus = os.path.join(working_dir,
'corpus.' + source_lang)
with open(source_merged_corpus, 'wb') as out:
for corpus in source_corpora:
out.write(corpus.get_file(source_lang) + '\n')
# target
target_merged_corpus = os.path.join(working_dir,
'corpus.' + target_lang)
fileutils.merge([
corpus.get_file(target_lang)
for corpus in reference_corpora
], target_merged_corpus)
# Run MERT algorithm
with cmdlogger.step('Tuning') as _:
# Start MERT
decoder_flags = ['--port', str(self.api.port)]
if self.api.root is not None:
decoder_flags += ['--root', self.api.root]
if not context_enabled:
decoder_flags.append('--skip-context-analysis')
decoder_flags.append('1')
fileutils.makedirs(mert_wd, exist_ok=True)
with tempfile.NamedTemporaryFile() as runtime_moses_ini:
command = [
self._mert_script, source_merged_corpus,
target_merged_corpus, self._mert_i_script,
runtime_moses_ini.name, '--threads',
str(multiprocessing.cpu_count()), '--mertdir',
cli.BIN_DIR, '--mertargs',
'\'--binary --sctype BLEU\'', '--working-dir', mert_wd,
'--nbest', '100', '--decoder-flags',
'"' + ' '.join(decoder_flags) + '"', '--nonorm',
'--closest', '--no-filter-phrase-table'
]
if early_stopping_value is not None:
command += [
'--bleuscorer', self._scorer_script,
'--bleuscorer-flags "-nt" --early-stopping-value %d'
% early_stopping_value
]
if not random_seeds:
command.append('--predictable-seeds')
if max_iterations > 0:
command.append('--maximum-iterations={num}'.format(
num=max_iterations))
with open(self.engine.get_logfile('mert'), 'wb') as log:
shell.execute(' '.join(command),
stdout=log,
stderr=log)
# Read optimized configuration
with cmdlogger.step('Applying changes') as _:
bleu_score = 0
weights = {}
found_weights = False
with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
for line in moses_ini:
line = line.strip()
if len(line) == 0:
continue
elif found_weights:
tokens = line.split()
weights[tokens[0].rstrip('=')] = [
float(val) for val in tokens[1:]
]
elif line.startswith('# BLEU'):
bleu_score = float(line.split()[2])
elif line == '[weight]':
found_weights = True
_ = self.api.update_features(weights)
cmdlogger.completed(bleu_score)
finally:
if not debug:
self.engine.clear_tempdir("tuning")
def main_sweep(argv):
parser = argparse.ArgumentParser(
description=
'Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument(
'-e',
'--engine',
dest='engine',
help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument(
'--path',
dest='corpora_path',
metavar='CORPORA',
default=None,
help=
'the path to the test corpora (default is the automatically splitted sample)'
)
args = parser.parse_args(argv)
samples = [
int(e) for e in
'10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.
split()
]
injector = dependency.DependencyInjector()
#injector.read_args(args)
engine = MMTEngine(args.engine)
injector.inject(engine)
node = ClusterNode(engine, api_port=DEFAULT_MMT_API_PORT)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node, google_key='1234', use_sessions=True)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.engine.set_config_option('suffixarrays', 'sample', sample)
injector.read_config(
node.engine.config
) # to get engine.set() to affect MosesFeatures -> moses.ini
injector.inject(node.engine)
node.engine.write_configs()
node.restart()
scores = evaluator.evaluate(corpora=corpora,
heval_output=None,
debug=False)
engine_scores = [r for r in scores if r.id == 'MMT'][0]
if engine_scores.error:
raise RuntimeError(engine_scores.error)
bleu = engine_scores.bleu
print(sample, '%.2f' % (bleu * 100))
def clean(self, corpora, dest_folder):
langs = (self._source_lang, self._target_lang)
_pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
def clean(self, corpora, dest_folder):
langs = (self._source_lang, self._target_lang)
_pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
