def sample_gen(args):
from pytreex.core.document import Document
opts, files = getopt(args, 'r:n:o:w:')
num_to_generate = 1
oracle_eval_file = None
fname_ttrees_out = None
for opt, arg in opts:
if opt == '-n':
num_to_generate = int(arg)
elif opt == '-o':
oracle_eval_file = arg
elif opt == '-w':
fname_ttrees_out = arg
if len(files) != 2:
sys.exit(__doc__)
fname_cand_model, fname_da_test = files
# load model
log_info('Initializing...')
candgen = RandomCandidateGenerator.load_from_file(fname_cand_model)
ranker = candgen
tgen = SamplingPlanner({'candgen': candgen, 'ranker': ranker})
# generate
log_info('Generating...')
gen_doc = Document()
das = read_das(fname_da_test)
for da in das:
for _ in xrange(num_to_generate): # repeat generation n times
tgen.generate_tree(da, gen_doc)
# evaluate if needed
if oracle_eval_file is not None:
log_info('Evaluating oracle F1...')
log_info('Loading gold data from ' + oracle_eval_file)
gold_trees = ttrees_from_doc(read_ttrees(oracle_eval_file), tgen.language, tgen.selector)
gen_trees = ttrees_from_doc(gen_doc, tgen.language, tgen.selector)
log_info('Gold data loaded.')
correct, predicted, gold = 0, 0, 0
for gold_tree, gen_trees in zip(gold_trees, chunk_list(gen_trees, num_to_generate)):
# find best of predicted trees (in terms of F1)
_, tc, tp, tg = max([(f1_from_counts(c, p, g), c, p, g) for c, p, g
in map(lambda gen_tree: corr_pred_gold(gold_tree, gen_tree),
gen_trees)],
key=lambda x: x[0])
correct += tc
predicted += tp
gold += tg
# evaluate oracle F1
log_info("Oracle Precision: %.6f, Recall: %.6f, F1: %.6f" % p_r_f1_from_counts(correct, predicted, gold))
# write output
if fname_ttrees_out is not None:
log_info('Writing output...')
write_ttrees(gen_doc, fname_ttrees_out)
def sample_gen(args):
from pytreex.core.document import Document
opts, files = getopt(args, 'r:n:o:w:')
num_to_generate = 1
oracle_eval_file = None
fname_ttrees_out = None
for opt, arg in opts:
if opt == '-n':
num_to_generate = int(arg)
elif opt == '-o':
oracle_eval_file = arg
elif opt == '-w':
fname_ttrees_out = arg
if len(files) != 2:
sys.exit(__doc__)
fname_cand_model, fname_da_test = files
# load model
log_info('Initializing...')
candgen = RandomCandidateGenerator.load_from_file(fname_cand_model)
ranker = candgen
tgen = SamplingPlanner({'candgen': candgen, 'ranker': ranker})
# generate
log_info('Generating...')
gen_doc = Document()
das = read_das(fname_da_test)
for da in das:
for _ in xrange(num_to_generate): # repeat generation n times
tgen.generate_tree(da, gen_doc)
# evaluate if needed
if oracle_eval_file is not None:
log_info('Evaluating oracle F1...')
log_info('Loading gold data from ' + oracle_eval_file)
gold_trees = ttrees_from_doc(read_ttrees(oracle_eval_file), tgen.language, tgen.selector)
gen_trees = ttrees_from_doc(gen_doc, tgen.language, tgen.selector)
log_info('Gold data loaded.')
correct, predicted, gold = 0, 0, 0
for gold_tree, gen_trees in zip(gold_trees, chunk_list(gen_trees, num_to_generate)):
# find best of predicted trees (in terms of F1)
_, tc, tp, tg = max([(f1_from_counts(c, p, g), c, p, g) for c, p, g
in map(lambda gen_tree: corr_pred_gold(gold_tree, gen_tree),
gen_trees)],
key=lambda x: x[0])
correct += tc
predicted += tp
gold += tg
# evaluate oracle F1
log_info("Oracle Precision: %.6f, Recall: %.6f, F1: %.6f" % p_r_f1_from_counts(correct, predicted, gold))
# write output
if fname_ttrees_out is not None:
log_info('Writing output...')
write_ttrees(gen_doc, fname_ttrees_out)