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 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)