def handle(clientsocket):
# while 1:
cmd = clientsocket.recv(MAX_LENGTH)
print cmd
if args.mode == 'dataset':
try:
example_id = int(cmd)
example = [
e for e in test_data.examples if e.raw_id == example_id
][0]
except:
print 'something went wrong ...'
# continue
elif args.mode == 'new':
# we play with new examples!
query, str_map = canonicalize_query(cmd)
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example',
['query', 'data'])(query=query_tokens,
data=query_tokens_data)
if hasattr(example, 'parse_tree'):
print 'gold parse tree:'
print example.parse_tree
cand_list = model.decode(example,
train_data.grammar,
train_data.terminal_vocab,
beam_size=args.beam_size,
max_time_step=args.decode_max_time_step,
log=True)
has_grammar_error = any(
[c for c in cand_list if c.has_grammar_error])
print 'has_grammar_error: ', has_grammar_error
for cid, cand in enumerate(cand_list[:5]):
print '*' * 60
print 'cand #%d, score: %f' % (cid, cand.score)
try:
ast_tree = decode_tree_to_python_ast(cand.tree)
code = astor.to_source(ast_tree)
print 'code: ', code
print 'decode log: ', cand.log
except:
print "Exception in converting tree to code:"
print '-' * 60
print 'raw_id: %d, beam pos: %d' % (example.raw_id, cid)
traceback.print_exc(file=sys.stdout)
print '-' * 60
finally:
print '* parse tree *'
print cand.tree.__repr__()
print 'n_timestep: %d' % cand.n_timestep
print 'ast size: %d' % cand.tree.size
print '*' * 60
def decode_query(query):
"""decode a given natural language query, return a list of generated candidates"""
query, str_map = canonicalize_query(query)
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example', ['query', 'data'])(query=query_tokens, data=query_tokens_data)
cand_list = model.decode(example, train_data.grammar, train_data.terminal_vocab,
beam_size=args.beam_size, max_time_step=args.decode_max_time_step, log=True)
return cand_list
def decode_query(query):
"""decode a given natural language query, return a list of generated candidates"""
query, str_map = canonicalize_query(query)
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example', ['query', 'data'])(query=query_tokens,
data=query_tokens_data)
cand_list = model.decode(example,
train_data.grammar,
train_data.terminal_vocab,
beam_size=args.beam_size,
max_time_step=args.decode_max_time_step,
log=True)
return cand_list
assert model is not None
while True:
cmd = raw_input('example id or query: ')
if args.mode == 'dataset':
try:
example_id = int(cmd)
example = [
e for e in test_data.examples if e.raw_id == example_id
][0]
except:
print 'something went wrong ...'
continue
elif args.mode == 'new':
# we play with new examples!
query, str_map = canonicalize_query(cmd)
print 'Query :', query
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example',
['query', 'data'])(query=query_tokens,
data=query_tokens_data)
if hasattr(example, 'parse_tree'):
print 'gold parse tree:'
print example.parse_tree
cand_list = model.decode(example,
train_data.grammar,
train_data.terminal_vocab,
assert model is not None
while True:
cmd = raw_input('example id or query: ')
if args.mode == 'dataset':
try:
example_id = int(cmd)
example = [
e for e in test_data.examples if e.raw_id == example_id
][0]
except:
print 'something went wrong ...'
continue
elif args.mode == 'new':
# we play with new examples!
query, str_map = canonicalize_query(cmd)
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example',
['query', 'data'])(query=query_tokens,
data=query_tokens_data)
if hasattr(example, 'parse_tree'):
print 'gold parse tree:'
print example.parse_tree
cand_list = model.decode(example,
train_data.grammar,
train_data.terminal_vocab,
beam_size=args.beam_size,
from collections import namedtuple
from lang.py.parse import decode_tree_to_python_ast
assert model is not None
while True:
cmd = raw_input('example id or query: ')
if args.mode == 'dataset':
try:
example_id = int(cmd)
example = [e for e in test_data.examples if e.raw_id == example_id][0]
except:
print 'something went wrong ...'
continue
elif args.mode == 'new':
# we play with new examples!
query, str_map = canonicalize_query(cmd)
vocab = train_data.annot_vocab
query_tokens = query.split(' ')
query_tokens_data = [query_to_data(query, vocab)]
example = namedtuple('example', ['query', 'data'])(query=query_tokens, data=query_tokens_data)
if hasattr(example, 'parse_tree'):
print 'gold parse tree:'
print example.parse_tree
cand_list = model.decode(example, train_data.grammar, train_data.terminal_vocab,
beam_size=args.beam_size, max_time_step=args.decode_max_time_step, log=True)
has_grammar_error = any([c for c in cand_list if c.has_grammar_error])
print 'has_grammar_error: ', has_grammar_error