def create_envs(table_dict, data_set, en_vocab, embedding_model):
all_envs = []
t1 = time.time()
if FLAGS.executor == 'wtq':
score_fn = wtq_score
process_answer_fn = lambda x: x
executor_fn = executor_factory.WikiTableExecutor
elif FLAGS.executor == 'wikisql':
score_fn = wikisql_score
process_answer_fn = wikisql_process_answer
executor_fn = executor_factory.WikiSQLExecutor
else:
raise ValueError('Unknown executor {}'.format(FLAGS.executor))
for i, example in enumerate(data_set):
if i % 100 == 0:
tf.logging.info('creating environment #{}'.format(i))
kg_info = table_dict[example['context']]
executor = executor_fn(kg_info)
api = executor.get_api()
type_hierarchy = api['type_hierarchy']
func_dict = api['func_dict']
constant_dict = api['constant_dict']
interpreter = computer_factory.LispInterpreter(
type_hierarchy=type_hierarchy,
max_mem=FLAGS.max_n_mem,
max_n_exp=FLAGS.max_n_exp,
assisted=True)
for v in func_dict.values():
interpreter.add_function(**v)
interpreter.add_constant(value=kg_info['row_ents'],
type='entity_list',
name='all_rows')
de_vocab = interpreter.get_vocab()
constant_value_embedding_fn = lambda x: get_embedding_for_constant(
x, embedding_model, embedding_size=FLAGS.pretrained_embedding_size)
env = env_factory.QAProgrammingEnv(
en_vocab,
de_vocab,
question_annotation=example,
answer=process_answer_fn(example['answer']),
constants=constant_dict.values(),
interpreter=interpreter,
constant_value_embedding_fn=constant_value_embedding_fn,
score_fn=score_fn,
name=example['id'])
all_envs.append(env)
return all_envs
def run_random_exploration(shard_id):
experiment_dir = get_experiment_dir()
if not tf.gfile.Exists(experiment_dir):
tf.gfile.MkDir(experiment_dir)
if FLAGS.trigger_word_file:
with open(FLAGS.trigger_word_file, 'r') as f:
trigger_dict = json.load(f)
print 'use trigger words in {}'.format(FLAGS.trigger_word_file)
else:
trigger_dict = None
# Load dataset.
train_set = []
with open(FLAGS.train_file_tmpl.format(shard_id), 'r') as f:
for line in f:
example = json.loads(line)
train_set.append(example)
tf.logging.info('{} examples in training set.'.format(len(train_set)))
table_dict = {}
with open(FLAGS.table_file) as f:
for line in f:
table = json.loads(line)
table_dict[table['name']] = table
tf.logging.info('{} tables.'.format(len(table_dict)))
if FLAGS.executor == 'wtq':
score_fn = utils.wtq_score
process_answer_fn = lambda x: x
executor_fn = executor_factory.WikiTableExecutor
elif FLAGS.executor == 'wikisql':
score_fn = utils.wikisql_score
process_answer_fn = utils.wikisql_process_answer
executor_fn = executor_factory.WikiSQLExecutor
else:
raise ValueError('Unknown executor {}'.format(FLAGS.executor))
all_envs = []
t1 = time.time()
for i, example in enumerate(train_set):
if i % 100 == 0:
tf.logging.info('creating environment #{}'.format(i))
kg_info = table_dict[example['context']]
executor = executor_fn(kg_info)
api = executor.get_api()
type_hierarchy = api['type_hierarchy']
func_dict = api['func_dict']
constant_dict = api['constant_dict']
interpreter = computer_factory.LispInterpreter(
type_hierarchy=type_hierarchy,
max_mem=FLAGS.max_n_mem,
max_n_exp=FLAGS.max_n_exp,
assisted=True)
for v in func_dict.values():
interpreter.add_function(**v)
interpreter.add_constant(value=kg_info['row_ents'],
type='entity_list',
name='all_rows')
de_vocab = interpreter.get_vocab()
env = env_factory.QAProgrammingEnv(
data_utils.Vocab([]),
de_vocab,
question_annotation=example,
answer=process_answer_fn(example['answer']),
constants=constant_dict.values(),
interpreter=interpreter,
constant_value_embedding_fn=lambda x: None,
score_fn=score_fn,
max_cache_size=FLAGS.n_explore_samples * FLAGS.n_epoch * 10,
name=example['id'])
all_envs.append(env)
program_dict = dict([(env.name, []) for env in all_envs])
for i in xrange(1, FLAGS.n_epoch + 1):
tf.logging.info('iteration {}'.format(i))
t1 = time.time()
for env in all_envs:
for _ in xrange(FLAGS.n_explore_samples):
program = random_explore(env, trigger_dict=trigger_dict)
if program is not None:
program_dict[env.name].append(program)
t2 = time.time()
tf.logging.info('{} sec used in iteration {}'.format(t2 - t1, i))
if i % FLAGS.save_every_n == 0:
tf.logging.info(
'saving programs and cache in iteration {}'.format(i))
t1 = time.time()
with open(
os.path.join(
get_experiment_dir(),
'program_shard_{}-{}.json'.format(shard_id, i)),
'w') as f:
program_str_dict = dict([(k, [' '.join(p) for p in v])
for k, v in program_dict.iteritems()])
json.dump(program_str_dict, f, sort_keys=True, indent=2)
# cache_dict = dict([(env.name, list(env.cache._set)) for env in all_envs])
t2 = time.time()
tf.logging.info(
'{} sec used saving programs and cache in iteration {}'.format(
t2 - t1, i))
n = len(all_envs)
solution_ratio = len(
[env for env in all_envs if program_dict[env.name]]) * 1.0 / n
tf.logging.info(
'At least one solution found ratio: {}'.format(solution_ratio))
n_programs_per_env = np.array(
[len(program_dict[env.name]) for env in all_envs])
tf.logging.info(
'number of solutions found per example: max {}, min {}, avg {}, std {}'
.format(n_programs_per_env.max(), n_programs_per_env.min(),
n_programs_per_env.mean(), n_programs_per_env.std()))
# Macro average length.
mean_length = np.mean([
np.mean([len(p) for p in program_dict[env.name]])
for env in all_envs if program_dict[env.name]
])
tf.logging.info('macro average program length: {}'.format(mean_length))
