if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-rep", "--rep_path", help="directory containing (hmm) word representations files")
parser.add_argument("-infile", help="file with sentences for decoding. Conll format for parsed sentences.")
parser.add_argument("-outfile", help="file to write posteriors to")
parser.add_argument("--use_lemmas", action='store_true', default=False, help="")
parser.add_argument("--synfunc", action='store_true', default=False,
help="Word representations model is sensitive to syntactic functions. Use flag when using model with names like \"hmm_en_rel_...\".")
args = parser.parse_args()
path = args.rep_path
infile = args.infile
# obtain model parameters
n_states, n_obs, _, _, _, omit_class_cond, omit_emis_cond = read_params_from_path(path)
lemmas = args.use_lemmas
eval_spec_rel = args.synfunc
lr = False
# load model
params_fixed = (np.load("{}ip.npy".format(path)),
np.load("{}tp.npy".format(path)),
np.load("{}fp.npy".format(path)),
np.load("{}ep.npy".format(path)))
# prepare sents for decoding
sents = ConllCorpus(infile, howbig=1000000, lemmas=lemmas, eval_spec_rels=eval_spec_rel, dirname=path, lr=lr)
sents.prepare_trees()
import numpy as np
from eval.ner.PrepareHmmRep import read_params_from_path
from hmrtm import HMRTM
from readers.conll_corpus import ConllCorpus
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-rep", "--rep_path", help="directory containing (hmm) word representations files")
parser.add_argument("--use_lemmas", action='store_true', default=False, help="")
args = parser.parse_args()
path = args.rep_path
posttype_f = "{}posttype_cumul.npy".format(path)
n_states, n_obs, n_sent, n_toks, corpus_file, omit_class_cond, omit_emis_cond = read_params_from_path(path)
lemmas = args.use_lemmas
eval_spec_rel = True
lr = False
params_fixed = (np.load("{}ip.npy".format(path)),
np.load("{}tp.npy".format(path)),
np.load("{}fp.npy".format(path)),
np.load("{}ep.npy".format(path)))
dataset = ConllCorpus("{}".format(corpus_file), howbig=n_sent, lemmas=lemmas, eval_spec_rels=eval_spec_rel,
dirname=path, lr=lr)
dataset.train = dataset.prepare_trees_gen() # generator
h = HMRTM(n_states, n_obs, R=len(dataset.r_dict), params=params_fixed, writeout=False, dirname=path,
omit_class_cond=omit_class_cond, omit_emis_cond=omit_emis_cond)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-rep",
"--rep_path",
help="directory containing (hmm) word representations files")
parser.add_argument("--use_lemmas",
action='store_true',
default=False,
help="")
args = parser.parse_args()
path = args.rep_path
posttype_f = "{}posttype_cumul.npy".format(path)
n_states, n_obs, n_sent, n_toks, corpus_file, omit_class_cond, omit_emis_cond = read_params_from_path(
path)
lemmas = args.use_lemmas
eval_spec_rel = True
lr = False
params_fixed = (np.load("{}ip.npy".format(path)),
np.load("{}tp.npy".format(path)),
np.load("{}fp.npy".format(path)),
np.load("{}ep.npy".format(path)))
dataset = ConllCorpus("{}".format(corpus_file),
howbig=n_sent,
lemmas=lemmas,
eval_spec_rels=eval_spec_rel,
dirname=path,
lr=lr)