def percrank_train(args):
opts, files = getopt(args, 'c:d:s:j:w:e:r:')
candgen_model = None
train_size = 1.0
parallel = False
jobs_number = 0
work_dir = None
experiment_id = None
for opt, arg in opts:
if opt == '-d':
set_debug_stream(file_stream(arg, mode='w'))
elif opt == '-s':
train_size = float(arg)
elif opt == '-c':
candgen_model = arg
elif opt == '-j':
parallel = True
jobs_number = int(arg)
elif opt == '-w':
work_dir = arg
elif opt == '-e':
experiment_id = arg
elif opt == '-r' and arg:
rnd.seed(arg)
if len(files) != 4:
sys.exit(__doc__)
fname_rank_config, fname_train_das, fname_train_ttrees, fname_rank_model = files
log_info('Training perceptron ranker...')
rank_config = Config(fname_rank_config)
if candgen_model:
rank_config['candgen_model'] = candgen_model
if rank_config.get('nn'):
from tgen.rank_nn import SimpleNNRanker, EmbNNRanker
if rank_config['nn'] in ['emb', 'emb_trees', 'emb_prev']:
ranker_class = EmbNNRanker
else:
ranker_class = SimpleNNRanker
else:
ranker_class = PerceptronRanker
log_info('Using %s for ranking' % ranker_class.__name__)
if not parallel:
ranker = ranker_class(rank_config)
else:
rank_config['jobs_number'] = jobs_number
if work_dir is None:
work_dir, _ = os.path.split(fname_rank_config)
ranker = ParallelRanker(rank_config, work_dir, experiment_id,
ranker_class)
ranker.train(fname_train_das, fname_train_ttrees, data_portion=train_size)
# avoid the "maximum recursion depth exceeded" error
sys.setrecursionlimit(100000)
ranker.save_to_file(fname_rank_model)
def percrank_train(args):
opts, files = getopt(args, 'c:d:s:j:w:e:')
candgen_model = None
train_size = 1.0
parallel = False
jobs_number = 0
work_dir = None
experiment_id = None
for opt, arg in opts:
if opt == '-d':
set_debug_stream(file_stream(arg, mode='w'))
elif opt == '-s':
train_size = float(arg)
elif opt == '-c':
candgen_model = arg
elif opt == '-j':
parallel = True
jobs_number = int(arg)
elif opt == '-w':
work_dir = arg
elif opt == '-e':
experiment_id = arg
if len(files) != 4:
sys.exit(__doc__)
fname_rank_config, fname_train_das, fname_train_ttrees, fname_rank_model = files
log_info('Training perceptron ranker...')
rank_config = Config(fname_rank_config)
if candgen_model:
rank_config['candgen_model'] = candgen_model
if rank_config.get('nn'):
if rank_config['nn'] == 'emb':
ranker_class = EmbNNRanker
else:
ranker_class = SimpleNNRanker
else:
ranker_class = PerceptronRanker
if not parallel:
ranker = ranker_class(rank_config)
else:
rank_config['jobs_number'] = jobs_number
if work_dir is None:
work_dir, _ = os.path.split(fname_rank_config)
ranker = ParallelRanker(rank_config, work_dir, experiment_id, ranker_class)
ranker.train(fname_train_das, fname_train_ttrees, data_portion=train_size)
ranker.save_to_file(fname_rank_model)
def rerank_cl_train(args):
opts, files = getopt(args, 'a:')
load_seq2seq_model = None
for opt, arg in opts:
if opt == '-a':
load_seq2seq_model = arg
if len(files) != 4:
sys.exit("Invalid arguments.\n" + __doc__)
fname_config, fname_da_train, fname_trees_train, fname_cl_model = files
if load_seq2seq_model:
tgen = Seq2SeqBase.load_from_file(load_seq2seq_model)
config = Config(fname_config)
rerank_cl = RerankingClassifier(config)
rerank_cl.train(fname_da_train, fname_trees_train)
if load_seq2seq_model:
tgen.classif_filter = rerank_cl
tgen.save_to_file(fname_cl_model)
else:
rerank_cl.save_to_file(fname_cl_model)
def seq2seq_train(args):
ap = ArgumentParser()
ap.add_argument('-s', '--train-size', type=float,
help='Portion of the training data to use (default: 1.0)', default=1.0)
ap.add_argument('-d', '--debug-logfile', type=str, help='Debug output file name')
ap.add_argument('-j', '--jobs', type=int, help='Number of parallel jobs to use')
ap.add_argument('-w', '--work-dir', type=str, help='Main working for parallel jobs')
ap.add_argument('-e', '--experiment-id', type=str,
help='Experiment ID for parallel jobs (used as job name prefix)')
ap.add_argument('-r', '--random-seed', type=str,
help='Initial random seed (used as string).')
ap.add_argument('-c', '--context-file', type=str,
help='Input ttree/text file with context utterances')
ap.add_argument('-v', '--valid-data', type=str,
help='Validation data paths (2-3 comma-separated files: DAs, trees/sentences, contexts)')
ap.add_argument('-l', '--lexic-data', type=str,
help='Lexicalization data paths (1-2 comma-separated files: surface forms,' +
'training lexic. instructions)')
ap.add_argument('seq2seq_config_file', type=str, help='Seq2Seq generator configuration file')
ap.add_argument('da_train_file', type=str, help='Input training DAs')
ap.add_argument('tree_train_file', type=str, help='Input training trees/sentences')
ap.add_argument('seq2seq_model_file', type=str,
help='File name where to save the trained Seq2Seq generator model')
args = ap.parse_args(args)
if args.debug_logfile:
set_debug_stream(file_stream(args.debug_logfile, mode='w'))
if args.random_seed:
rnd.seed(rnd.seed(args.random_seed))
log_info('Training sequence-to-sequence generator...')
config = Config(args.seq2seq_config_file)
if args.jobs:
config['jobs_number'] = args.jobs
if not args.work_dir:
work_dir, _ = os.path.split(args.seq2seq_config_file)
generator = ParallelSeq2SeqTraining(config, args.work_dir or work_dir, args.experiment_id)
else:
generator = Seq2SeqGen(config)
generator.train(args.da_train_file, args.tree_train_file,
data_portion=args.train_size, context_file=args.context_file,
validation_files=args.valid_data, lexic_files=args.lexic_data)
sys.setrecursionlimit(100000)
generator.save_to_file(args.seq2seq_model_file)
def treecl_train(args):
from tgen.classif import TreeClassifier
opts, files = getopt(args, '')
if len(files) != 4:
sys.exit("Invalid arguments.\n" + __doc__)
fname_config, fname_da_train, fname_trees_train, fname_cl_model = files
config = Config(fname_config)
treecl = TreeClassifier(config)
treecl.train(fname_da_train, fname_trees_train)
treecl.save_to_file(fname_cl_model)
def main(argv):
opts, filenames = getopt(argv, 'c:o:')
config_filename = None
output_filename = None
for opt, arg in opts:
if opt == '-c':
config_filename = arg
elif opt == '-o':
output_filename = arg
if not config_filename or not output_filename or not filenames:
sys.exit(__doc__)
cfg = Config(config_filename)
m = ConcatModel.load_from_files(cfg, filenames)
m.save_to_file(output_filename)
def candgen_train(args):
opts, files = getopt(args, 'p:lnc:sd:t:')
prune_threshold = 1
parent_lemmas = False
node_limits = False
comp_type = None
comp_limit = None
comp_slots = False
tree_classif = False
for opt, arg in opts:
if opt == '-p':
prune_threshold = int(arg)
elif opt == '-d':
set_debug_stream(file_stream(arg, mode='w'))
elif opt == '-l':
parent_lemmas = True
elif opt == '-n':
node_limits = True
elif opt == '-c':
comp_type = arg
if ':' in comp_type:
comp_type, comp_limit = comp_type.split(':', 1)
comp_limit = int(comp_limit)
elif opt == '-t':
tree_classif = Config(arg)
elif opt == '-s':
comp_slots = True
if len(files) != 3:
sys.exit("Invalid arguments.\n" + __doc__)
fname_da_train, fname_ttrees_train, fname_cand_model = files
log_info('Training candidate generator...')
candgen = RandomCandidateGenerator({
'prune_threshold': prune_threshold,
'parent_lemmas': parent_lemmas,
'node_limits': node_limits,
'compatible_dais_type': comp_type,
'compatible_dais_limit': comp_limit,
'compatible_slots': comp_slots,
'tree_classif': tree_classif
})
candgen.train(fname_da_train, fname_ttrees_train)
candgen.save_to_file(fname_cand_model)
def run_training(work_dir, config_file, train_file, model_file,
test_file=None, classif_file=None, memory=MEMORY,
name='train'):
"""\
Run the model training.
"""
# initialization from the configuration file
_, ext = os.path.splitext(config_file)
# load configuration from a pickle (we're already in the working directory)
if ext == '.pickle':
fh = open(config_file, mode='rb')
cfg = pickle.load(fh)
fh.close()
demarshal_lambda(cfg, 'filter_attr')
demarshal_lambda(cfg, 'postprocess')
# load by running Python code (make paths relative to working directory)
else:
config_file = os.path.join(work_dir, config_file)
cfg = Config(config_file)
# training
if cfg.get('unfold_pattern'):
pattern = cfg['unfold_pattern']
del cfg['unfold_pattern']
unfold_key = cfg.get('unfold_key', 'unfold_key')
cfgs = cfg.unfold_lists(pattern, unfold_key)
for cfg in cfgs:
key = re.sub(r'[^A-Za-z0-9_]', '', cfg[unfold_key])
create_job(cfg, name + '-' + key, work_dir, train_file, model_file,
test_file, classif_file, memory)
return
if cfg.get('divide_func'):
model = SplitModel(cfg)
model.train(train_file, work_dir, memory)
else:
model = Model(cfg)
model.train(train_file)
# evaluation
if test_file is not None and classif_file is not None:
if ext != '.pickle': # this means we're not in the working directory
classif_file = os.path.join(work_dir, classif_file)
log_info('Evaluation on file: ' + test_file)
score = model.evaluate(test_file, classif_file=classif_file)
log_info('Score: ' + str(score))
# save the model
if ext != '.pickle': # we need to make the path relative to work_dir
model_file = os.path.join(work_dir, model_file)
model.save_to_file(model_file)
def asearch_gen(args):
"""A*search generation"""
from pytreex.core.document import Document
opts, files = getopt(args, 'e:d:w:c:s:')
eval_file = None
fname_ttrees_out = None
cfg_file = None
eval_selector = ''
for opt, arg in opts:
if opt == '-e':
eval_file = arg
elif opt == '-s':
eval_selector = arg
elif opt == '-d':
set_debug_stream(file_stream(arg, mode='w'))
elif opt == '-w':
fname_ttrees_out = arg
elif opt == '-c':
cfg_file = arg
if len(files) != 3:
sys.exit('Invalid arguments.\n' + __doc__)
fname_cand_model, fname_rank_model, fname_da_test = files
log_info('Initializing...')
candgen = RandomCandidateGenerator.load_from_file(fname_cand_model)
ranker = PerceptronRanker.load_from_file(fname_rank_model)
cfg = Config(cfg_file) if cfg_file else {}
cfg.update({'candgen': candgen, 'ranker': ranker})
tgen = ASearchPlanner(cfg)
log_info('Generating...')
das = read_das(fname_da_test)
if eval_file is None:
gen_doc = Document()
else:
eval_doc = read_ttrees(eval_file)
if eval_selector == tgen.selector:
gen_doc = Document()
else:
gen_doc = eval_doc
# generate and evaluate
if eval_file is not None:
# generate + analyze open&close lists
lists_analyzer = ASearchListsAnalyzer()
for num, (da, gold_tree) in enumerate(zip(
das, trees_from_doc(eval_doc, tgen.language, eval_selector)),
start=1):
log_debug("\n\nTREE No. %03d" % num)
gen_tree = tgen.generate_tree(da, gen_doc)
lists_analyzer.append(gold_tree, tgen.open_list, tgen.close_list)
if gen_tree != gold_tree:
log_debug("\nDIFFING TREES:\n" +
tgen.ranker.diffing_trees_with_scores(
da, gold_tree, gen_tree) + "\n")
log_info('Gold tree BEST: %.4f, on CLOSE: %.4f, on ANY list: %4f' %
lists_analyzer.stats())
# evaluate the generated trees against golden trees
eval_ttrees = ttrees_from_doc(eval_doc, tgen.language, eval_selector)
gen_ttrees = ttrees_from_doc(gen_doc, tgen.language, tgen.selector)
log_info('Evaluating...')
evaler = Evaluator()
for eval_bundle, eval_ttree, gen_ttree, da in zip(
eval_doc.bundles, eval_ttrees, gen_ttrees, das):
# add some stats about the tree directly into the output file
add_bundle_text(
eval_bundle, tgen.language, tgen.selector + 'Xscore',
"P: %.4f R: %.4f F1: %.4f" %
p_r_f1_from_counts(*corr_pred_gold(eval_ttree, gen_ttree)))
# collect overall stats
evaler.append(eval_ttree, gen_ttree,
ranker.score(TreeData.from_ttree(eval_ttree), da),
ranker.score(TreeData.from_ttree(gen_ttree), da))
# print overall stats
log_info("NODE precision: %.4f, Recall: %.4f, F1: %.4f" %
evaler.p_r_f1())
log_info("DEP precision: %.4f, Recall: %.4f, F1: %.4f" %
evaler.p_r_f1(EvalTypes.DEP))
log_info("Tree size stats:\n * GOLD %s\n * PRED %s\n * DIFF %s" %
evaler.size_stats())
log_info("Score stats:\n * GOLD %s\n * PRED %s\n * DIFF %s" %
evaler.score_stats())
log_info(
"Common subtree stats:\n -- SIZE: %s\n -- ΔGLD: %s\n -- ΔPRD: %s" %
evaler.common_substruct_stats())
# just generate
else:
for da in das:
tgen.generate_tree(da, gen_doc)
# write output
if fname_ttrees_out is not None:
log_info('Writing output...')
write_ttrees(gen_doc, fname_ttrees_out)
#!/usr/bin/env python
from flect.config import Config
from tgen.features import Features
from tgen.futil import trees_from_doc, read_ttrees, read_das
import sys
import timeit
import datetime
if len(sys.argv[1:]) != 3:
sys.exit('Usage: ./bench_feats.py features_cfg.py trees.yaml.gz das.txt')
print >> sys.stderr, 'Loading...'
cfg = Config(sys.argv[1])
trees = trees_from_doc(read_ttrees(sys.argv[2]), 'en', '')
das = read_das(sys.argv[3])
feats = Features(cfg['features'])
def test_func():
for tree, da in zip(trees, das):
feats.get_features(tree, {'da': da})
print >> sys.stderr, 'Running test...'
secs = timeit.timeit('test_func()',
setup='from __main__ import test_func',
number=10)
td = datetime.timedelta(seconds=secs)
def asearch_gen(args):
"""A*search generation"""
opts, files = getopt(args, 'e:d:w:c:s:')
eval_file = None
fname_ttrees_out = None
cfg_file = None
eval_selector = ''
for opt, arg in opts:
if opt == '-e':
eval_file = arg
elif opt == '-s':
eval_selector = arg
elif opt == '-d':
set_debug_stream(file_stream(arg, mode='w'))
elif opt == '-w':
fname_ttrees_out = arg
elif opt == '-c':
cfg_file = arg
if len(files) != 3:
sys.exit('Invalid arguments.\n' + __doc__)
fname_cand_model, fname_rank_model, fname_da_test = files
log_info('Initializing...')
candgen = RandomCandidateGenerator.load_from_file(fname_cand_model)
ranker = PerceptronRanker.load_from_file(fname_rank_model)
cfg = Config(cfg_file) if cfg_file else {}
cfg.update({'candgen': candgen, 'ranker': ranker})
tgen = ASearchPlanner(cfg)
log_info('Generating...')
das = read_das(fname_da_test)
if eval_file is None:
gen_doc = Document()
else:
eval_doc = read_ttrees(eval_file)
if eval_selector == tgen.selector:
gen_doc = Document()
else:
gen_doc = eval_doc
# generate and evaluate
if eval_file is not None:
# generate + analyze open&close lists
lists_analyzer = ASearchListsAnalyzer()
for num, (da, gold_tree) in enumerate(zip(das,
trees_from_doc(eval_doc, tgen.language, eval_selector)),
start=1):
log_debug("\n\nTREE No. %03d" % num)
open_list, close_list = tgen.generate_tree(da, gen_doc, return_lists=True)
lists_analyzer.append(gold_tree, open_list, close_list)
gen_tree = close_list.peek()[0]
if gen_tree != gold_tree:
log_debug("\nDIFFING TREES:\n" + tgen.ranker.diffing_trees_with_scores(da, gold_tree, gen_tree) + "\n")
log_info('Gold tree BEST: %.4f, on CLOSE: %.4f, on ANY list: %4f' % lists_analyzer.stats())
# evaluate the generated trees against golden trees
eval_ttrees = ttrees_from_doc(eval_doc, tgen.language, eval_selector)
gen_ttrees = ttrees_from_doc(gen_doc, tgen.language, tgen.selector)
log_info('Evaluating...')
evaler = Evaluator()
for eval_bundle, eval_ttree, gen_ttree, da in zip(eval_doc.bundles, eval_ttrees, gen_ttrees, das):
# add some stats about the tree directly into the output file
add_bundle_text(eval_bundle, tgen.language, tgen.selector + 'Xscore',
"P: %.4f R: %.4f F1: %.4f" % p_r_f1_from_counts(*corr_pred_gold(eval_ttree, gen_ttree)))
# collect overall stats
evaler.append(eval_ttree,
gen_ttree,
ranker.score(TreeData.from_ttree(eval_ttree), da),
ranker.score(TreeData.from_ttree(gen_ttree), da))
# print overall stats
log_info("NODE precision: %.4f, Recall: %.4f, F1: %.4f" % evaler.p_r_f1())
log_info("DEP precision: %.4f, Recall: %.4f, F1: %.4f" % evaler.p_r_f1(EvalTypes.DEP))
log_info("Tree size stats:\n * GOLD %s\n * PRED %s\n * DIFF %s" % evaler.tree_size_stats())
log_info("Score stats:\n * GOLD %s\n * PRED %s\n * DIFF %s" % evaler.score_stats())
log_info("Common subtree stats:\n -- SIZE: %s\n -- ΔGLD: %s\n -- ΔPRD: %s" %
evaler.common_subtree_stats())
# just generate
else:
for da in das:
tgen.generate_tree(da, gen_doc)
# write output
if fname_ttrees_out is not None:
log_info('Writing output...')
write_ttrees(gen_doc, fname_ttrees_out)
