def get_tree(n_inst):
trees = ConllCorpus(file_parsed, howbig=1000000, lemmas=False, eval_spec_rels=self.eval_spec_rel,
dirname=self.dirname, lr=self.lr)
trees.prepare_trees()
# extend instances with trees
assert len(trees.train) == n_inst, "Number of parses not equal to number of classification instances."
c_append = 0
for i in range(n_inst):
# we have a parse:
inst = self.normalize_tree(trees.train[c_append], trees.x_dict, c_append)
c_append += 1
# we don't have a parse:
yield inst
def get_tree(n_inst):
trees = ConllCorpus(file_parsed, howbig=1000000, lemmas=True, eval_spec_rels=self.eval_spec_rel,
dirname=self.dirname, lr=self.lr)
trees.prepare_trees()
# not every instance has a corresponding tree due to errors in parsing
conll_idx = ConllFilesIndex(files_parsed_path)
conll_idx.create_ids_set()
# extend instances with trees
c_append = 0
for i in range(n_inst):
# we have a parse:
if i + 1 in conll_idx.fileids:
inst = self.normalize_tree(trees.train[c_append], trees.x_dict, c_append)
c_append += 1
# we don't have a parse:
else:
inst = None
yield inst
def get_tree(n_inst):
trees = ConllCorpus(file_parsed,
howbig=1000000,
lemmas=False,
eval_spec_rels=self.eval_spec_rel,
dirname=self.dirname,
lr=self.lr)
trees.prepare_trees()
# extend instances with trees
assert len(
trees.train
) == n_inst, "Number of parses not equal to number of classification instances."
c_append = 0
for i in range(n_inst):
# we have a parse:
inst = self.normalize_tree(trees.train[c_append], trees.x_dict,
c_append)
c_append += 1
# we don't have a parse:
yield inst
def get_tree(n_inst):
trees = ConllCorpus(file_parsed,
howbig=1000000,
lemmas=True,
eval_spec_rels=self.eval_spec_rel,
dirname=self.dirname,
lr=self.lr)
trees.prepare_trees()
self.tree_vocab = trees.x_dict
# not every instance has a corresponding tree due to errors in parsing
conll_idx = ConllFilesIndex(files_parsed_path)
conll_idx.create_ids_set()
# extend instances with trees
c_append = 0
for i in range(n_inst):
# we have a parse:
if i + 1 in conll_idx.fileids:
inst = trees.train[c_append]
c_append += 1
# we don't have a parse:
else:
inst = None
yield inst
# 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()
h = HMRTM(n_states, n_obs, R=len(sents.r_dict), params=params_fixed, writeout=False, dirname=path,
omit_class_cond=omit_class_cond, omit_emis_cond=omit_emis_cond) if eval_spec_rel else \
HMTM(n_states, n_obs, params=params_fixed, writeout=False, dirname=path)
with open(args.outfile, "w") as out:
for tree in sents.train:
# obtain posteriors for all nodes
node_to_rep = h.posterior_decode(tree, cont=True)
# get words
for node in tree.get_nonroots():
out.write(
"{} {}\n".format(sents.x_dict.get_label_name(node.name), nparr_to_str(node_to_rep[node.index])))
out.write("\n")
