def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i',
'--n_iter',
type=int,
help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
args = parser.parse_args()
category = args.category
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target,
alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections:
continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {
attr: 1
for attr in config.get_attributes(
category, attributes)
}
Y_all.append(label) # attributes map
lims = (n, n + len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it + 1)),
'w') as f:
cPickle.dump(model, f, protocol=-1)
model = StructuredModel(args.category)
model.train(X,
Y_all,
Y_star,
Y_lim,
n_iter=args.n_iter,
alpha_sgd=args.rate,
every_iter=save_model)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i', '--n_iter', type=int, help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
args = parser.parse_args()
category = args.category
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target, alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections: continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {attr: 1 for attr in config.get_attributes(category, attributes)}
Y_all.append(label) # attributes map
lims = (n, n+len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it+1)), 'w') as f:
cPickle.dump(model, f, protocol=-1)
model = StructuredModel(args.category)
model.train(X, Y_all, Y_star, Y_lim, n_iter=args.n_iter,
alpha_sgd=args.rate, every_iter=save_model)
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Trained stuctured model')
parser.add_argument('category', help='target word category')
parser.add_argument('rev_map', help='reverse inflection map')
parser.add_argument('model', help='output directory for models')
parser.add_argument('-i', '--n_iter', type=int, help='number of SGD iterations')
parser.add_argument('-r', '--rate', type=float, help='SGD udpate rate')
parser.add_argument('-c', '--config', help='configuration module for supervised models (must be in config directory)')
parser.add_argument('-a','--adagrad', action='store_true', default=False, help='Use the AdaGrad adaptive gradient technique to adjust rate')
parser.add_argument('-l','--l1', type=float, default=0.0 , help='lambda value for l1 regualrization (l1 regularization currently only implemented for use with adagrad). If none given, regularization will not be used')
args = parser.parse_args()
category = args.category
logging.info('Training inflection model for category {}'.format(category))
if args.config:
logging.info('Loading external configuration module {}'.format(args.config))
sup_config = __import__('config_files.'+args.config,globals(),locals(),['get_attributes'])
attr_function = lambda cat, attr: sup_config.get_attributes(cat, attr)
else:
attr_function = lambda cat, attr: config.get_attributes(cat, attr)
logging.info('Loading reverse inflection map')
with open(args.rev_map) as f:
rev_map = cPickle.load(f)
logging.info('length of reverse map: {}'.format(len(rev_map)))
logging.info('Generating the training data')
X = []
Y_all = []
Y_star = []
Y_lim = []
n = 0
inflection_lims = {} # inflection set cache (ranges for y in Y_all)
for source, target, alignment in read_sentences(sys.stdin):
for word, features in extract_instances(category, source, target, alignment):
ref_inflection, lemma, tag = word
category = tag[0]
ref_attributes = tag[1:]
possible_inflections = rev_map.get((lemma, category), [])
# Skip if |inflections| = 1 [p(infl | lemma) = 1]
if len(possible_inflections) == 1: continue
if (ref_attributes, ref_inflection) not in possible_inflections: continue
X.append(features)
# Y_all / Y_lim
lims = inflection_lims.get((lemma, category), None)
if lims is None: # new set of inflections
for i, (attributes, _) in enumerate(possible_inflections):
label = {attr: 1 for attr in attr_function(category, attributes)}
Y_all.append(label) # attributes map
lims = (n, n+len(possible_inflections))
inflection_lims[lemma, category] = lims
n += len(possible_inflections)
Y_lim.append(lims)
# Y_star
for i, (attributes, _) in enumerate(possible_inflections):
if attributes == ref_attributes:
Y_star.append(i)
# free some memory
del rev_map
if not os.path.exists(args.model):
os.mkdir(args.model)
def save_model(it, model):
with open(os.path.join(args.model, 'model.{}.pickle'.format(it+1)), 'w') as f:
cPickle.dump(model, f, protocol=-1)
if args.config:
model = StructuredModel(args.category, functools.partial(sup_config.get_attributes))
else:
model = StructuredModel(args.category, config.get_attributes)
model.train(X, Y_all, Y_star, Y_lim, n_iter=args.n_iter,
alpha_sgd=args.rate, every_iter=save_model,
adagrad=args.adagrad, l1=args.l1)
