def setUp(self):
# Creates a task context with the correct testing paths.
initial_task_context = os.path.join(FLAGS.test_srcdir, "syntaxnet/" "testdata/context.pbtxt")
self._task_context = os.path.join(FLAGS.test_tmpdir, "context.pbtxt")
with open(initial_task_context, "r") as fin:
with open(self._task_context, "w") as fout:
fout.write(fin.read().replace("SRCDIR", FLAGS.test_srcdir).replace("OUTPATH", FLAGS.test_tmpdir))
# Creates necessary term maps.
with self.test_session() as sess:
gen_parser_ops.lexicon_builder(task_context=self._task_context, corpus_name="training-corpus").run()
self._num_features, self._num_feature_ids, _, self._num_actions = sess.run(
gen_parser_ops.feature_size(task_context=self._task_context, arg_prefix="brain_parser")
)
def setUp(self):
# Creates a task context with the correct testing paths.
initial_task_context = os.path.join(FLAGS.test_srcdir,
'syntaxnet/'
'testdata/context.pbtxt')
self._task_context = os.path.join(FLAGS.test_tmpdir, 'context.pbtxt')
with open(initial_task_context, 'r') as fin:
with open(self._task_context, 'w') as fout:
fout.write(fin.read().replace('SRCDIR', FLAGS.test_srcdir)
.replace('OUTPATH', FLAGS.test_tmpdir))
# Creates necessary term maps.
with self.test_session() as sess:
gen_parser_ops.lexicon_builder(task_context=self._task_context,
corpus_name='training-corpus').run()
self._num_features, self._num_feature_ids, _, self._num_actions = (
sess.run(gen_parser_ops.feature_size(task_context=self._task_context,
arg_prefix='brain_parser')))
def setUp(self):
# Creates a task context with the correct testing paths.
initial_task_context = os.path.join(FLAGS.test_srcdir,
'syntaxnet/'
'testdata/context.pbtxt')
self._task_context = os.path.join(FLAGS.test_tmpdir, 'context.pbtxt')
with open(initial_task_context, 'r') as fin:
with open(self._task_context, 'w') as fout:
fout.write(fin.read().replace('SRCDIR', FLAGS.test_srcdir)
.replace('OUTPATH', FLAGS.test_tmpdir))
# Creates necessary term maps.
with self.test_session() as sess:
gen_parser_ops.lexicon_builder(task_context=self._task_context,
corpus_name='training-corpus').run()
self._num_features, self._num_feature_ids, _, self._num_actions = (
sess.run(gen_parser_ops.feature_size(task_context=self._task_context,
arg_prefix='brain_parser')))
def main(unused_argv):
logging.set_verbosity(logging.INFO)
if not gfile.IsDirectory(OutputPath('')):
gfile.MakeDirs(OutputPath(''))
# Rewrite context.
RewriteContext()
# Creates necessary term maps.
if FLAGS.compute_lexicon:
logging.info('Computing lexicon...')
with tf.Session(FLAGS.tf_master) as sess:
gen_parser_ops.lexicon_builder(task_context=OutputPath('context'),
corpus_name=FLAGS.training_corpus).run()
with tf.Session(FLAGS.tf_master) as sess:
feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
gen_parser_ops.feature_size(task_context=OutputPath('context'),
arg_prefix=FLAGS.arg_prefix))
# Well formed and projectivize.
if FLAGS.projectivize_training_set:
logging.info('Preprocessing...')
with tf.Session(FLAGS.tf_master) as sess:
source, last = gen_parser_ops.document_source(
task_context=OutputPath('context'),
batch_size=FLAGS.batch_size,
corpus_name=FLAGS.training_corpus)
sink = gen_parser_ops.document_sink(
task_context=OutputPath('context'),
corpus_name='projectivized-training-corpus',
documents=gen_parser_ops.projectivize_filter(
gen_parser_ops.well_formed_filter(source,
task_context=OutputPath(
'context')),
task_context=OutputPath('context')))
while True:
tf_last, _ = sess.run([last, sink])
if tf_last:
break
logging.info('Training...')
with tf.Session(FLAGS.tf_master) as sess:
Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
def main(unused_argv):
logging.set_verbosity(logging.INFO)
if not gfile.IsDirectory(OutputPath('')):
gfile.MakeDirs(OutputPath(''))
# Rewrite context.
RewriteContext()
# Creates necessary term maps.
if FLAGS.compute_lexicon:
logging.info('Computing lexicon...')
with tf.Session(FLAGS.tf_master) as sess:
gen_parser_ops.lexicon_builder(
task_context=OutputPath('context'),
corpus_name=FLAGS.training_corpus).run()
with tf.Session(FLAGS.tf_master) as sess:
feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
gen_parser_ops.feature_size(task_context=OutputPath('context'),
arg_prefix=FLAGS.arg_prefix))
# Well formed and projectivize.
if FLAGS.projectivize_training_set:
logging.info('Preprocessing...')
with tf.Session(FLAGS.tf_master) as sess:
source, last = gen_parser_ops.document_source(
task_context=OutputPath('context'),
batch_size=FLAGS.batch_size,
corpus_name=FLAGS.training_corpus)
sink = gen_parser_ops.document_sink(
task_context=OutputPath('context'),
corpus_name='projectivized-training-corpus',
documents=gen_parser_ops.projectivize_filter(
gen_parser_ops.well_formed_filter(
source, task_context=OutputPath('context')),
task_context=OutputPath('context')))
while True:
tf_last, _ = sess.run([last, sink])
if tf_last:
break
logging.info('Training...')
with tf.Session(FLAGS.tf_master) as sess:
Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
def build_lexicon(output_path,
training_corpus_path,
tf_master='',
training_corpus_format='conll-sentence',
morph_to_pos=False,
**kwargs):
"""Constructs a SyntaxNet lexicon at the given path.
Args:
output_path: Location to construct the lexicon.
training_corpus_path: Path to CONLL formatted training data.
tf_master: TensorFlow master executor (string, defaults to '' to use the
local instance).
training_corpus_format: Format of the training corpus (defaults to CONLL;
search for REGISTER_SYNTAXNET_DOCUMENT_FORMAT for other formats).
morph_to_pos: Whether to serialize morph attributes to the tag field,
combined with category and fine POS tag.
**kwargs: Forwarded to the LexiconBuilder op.
"""
context = create_lexicon_context(output_path)
if morph_to_pos:
context.parameter.add(name='join_category_to_pos', value='true')
context.parameter.add(name='add_pos_as_attribute', value='true')
context.parameter.add(name='serialize_morph_to_pos', value='true')
# Add the training data to the context.
resource = context.input.add()
resource.name = 'corpus'
resource.record_format.extend([training_corpus_format])
part = resource.part.add()
part.file_pattern = training_corpus_path
# Run the lexicon builder op.
with tf.Session(tf_master) as sess:
sess.run(
gen_parser_ops.lexicon_builder(task_context_str=str(context),
corpus_name='corpus',
**kwargs))
def build_lexicon(output_path,
training_corpus_path,
tf_master='',
training_corpus_format='conll-sentence',
morph_to_pos=False,
**kwargs):
"""Constructs a SyntaxNet lexicon at the given path.
Args:
output_path: Location to construct the lexicon.
training_corpus_path: Path to CONLL formatted training data.
tf_master: TensorFlow master executor (string, defaults to '' to use the
local instance).
training_corpus_format: Format of the training corpus (defaults to CONLL;
search for REGISTER_SYNTAXNET_DOCUMENT_FORMAT for other formats).
morph_to_pos: Whether to serialize morph attributes to the tag field,
combined with category and fine POS tag.
**kwargs: Forwarded to the LexiconBuilder op.
"""
context = create_lexicon_context(output_path)
if morph_to_pos:
context.parameter.add(name='join_category_to_pos', value='true')
context.parameter.add(name='add_pos_as_attribute', value='true')
context.parameter.add(name='serialize_morph_to_pos', value='true')
# Add the training data to the context.
resource = context.input.add()
resource.name = 'corpus'
resource.record_format.extend([training_corpus_format])
part = resource.part.add()
part.file_pattern = training_corpus_path
# Run the lexicon builder op.
with tf.Session(tf_master) as sess:
sess.run(
gen_parser_ops.lexicon_builder(
task_context_str=str(context), corpus_name='corpus', **kwargs))
def BuildLexicon(self):
with self.test_session():
gen_parser_ops.lexicon_builder(
task_context=self.context_file,
lexicon_max_char_ngram_length=2,
lexicon_char_ngram_mark_boundaries=True).run()
def BuildLexicon(self):
with self.test_session():
gen_parser_ops.lexicon_builder(
task_context=self.context_file,
lexicon_max_char_ngram_length=2,
lexicon_char_ngram_mark_boundaries=True).run()
def BuildLexicon(self):
with self.test_session():
gen_parser_ops.lexicon_builder(task_context=self.context_file).run()
def BuildLexicon(self):
with self.test_session():
gen_parser_ops.lexicon_builder(
task_context=self.context_file).run()