def main(args):
conf, model, vpack, test_iter = prepare_test(args)
dconf = conf.dconf
# todo(note): here is the main change
# make sure the model is order 1 graph model, otherwise cannot run through
all_results = []
all_insts = []
with utils.Timer(tag="Run-score", info="", print_date=True):
for cur_insts in test_iter:
all_insts.extend(cur_insts)
batched_arc_scores, batched_label_scores = model.score_on_batch(
cur_insts)
batched_arc_scores, batched_label_scores = BK.get_value(
batched_arc_scores), BK.get_value(batched_label_scores)
for cur_idx in range(len(cur_insts)):
cur_len = len(cur_insts[cur_idx]) + 1
# discarding paddings
cur_res = (batched_arc_scores[cur_idx, :cur_len, :cur_len],
batched_label_scores[cur_idx, :cur_len, :cur_len])
all_results.append(cur_res)
# reorder to the original order
orig_indexes = [z.inst_idx for z in all_insts]
orig_results = [None] * len(orig_indexes)
for new_idx, orig_idx in enumerate(orig_indexes):
assert orig_results[orig_idx] is None
orig_results[orig_idx] = all_results[new_idx]
# saving
with utils.Timer(tag="Run-write",
info=f"Writing to {dconf.output_file}",
print_date=True):
import pickle
with utils.zopen(dconf.output_file, "wb") as fd:
for one in orig_results:
pickle.dump(one, fd)
utils.printing("The end.")
def main(args):
conf, model, vpack, test_iter = prepare_test(args)
dconf = conf.dconf
# go
rr = ParserTestingRunner(model, vpack, dconf.output_file, dconf.test, dconf.output_format)
x = rr.run(test_iter)
utils.printing("The end.")
def save_hits(self, fname):
num_hits = len(self.hits)
printing(f"Saving hit w2v num_words={num_hits:d}, embed_size={self.embed_size:d} to {fname}.")
with zopen(fname, "w") as fd:
tmp_words = sorted(self.hits.keys(), key=lambda k: self.wmap[k]) # use original ordering
tmp_vecs = [self.vecs[self.wmap[k]] for k in tmp_words]
WordVectors.save_txt(fd, tmp_words, tmp_vecs, self.sep)
def main(args):
conf, model, vpack, test_iter = prepare_test(args)
dconf = conf.dconf
# go
rr = MyIETestingRunner(model,
vpack,
dconf.output_file,
dconf.test,
dconf.output_format,
dconf.eval_conf,
release_resources=True)
x = rr.run(test_iter)
utils.printing("The end.")
def _load_bin(fname):
printing("Going to load pre-trained (binary) w2v from %s ..." % fname)
one = WordVectors()
#
from gensim.models import KeyedVectors
#
kv = KeyedVectors.load_word2vec_format(fname, binary=True)
# KeyedVectors.save_word2vec_format()
one.num_words, one.embed_size = len(kv.vectors), len(kv.vectors[0])
for w, z in kv.vocab.items():
one.vecs.append(kv.vectors[z.index])
one.wmap[w] = len(one.words)
one.words.append(w)
printing("Read ok: w2v num_words=%d, embed_size=%d." % (one.num_words, one.embed_size))
return one
def finish(self, word_filter=(lambda ww, rank, val: True), sort_by_count=True, target_range=DEFAULT_TARGET_RANGE):
v = self.v
if sort_by_count:
v.v, v.final_vals = VocabBuilder.ranking_vals(
self.counts_, v.pre_list, v.post_list, self.default_val_, True, word_filter=word_filter)
else:
tmp_counts_ = OrderedDict([(k, self.counts_[k]) for k in self.keys_])
v.v, v.final_vals = VocabBuilder.ranking_vals(
tmp_counts_, v.pre_list, v.post_list, self.default_val_, False, word_filter=word_filter)
v.final_words = Helper.reverse_idx(v.v)
printing("Build Vocab %s ok, from %d to %d, as %s." % (v.name, len(self.counts_), len(v), str(v)))
#
VocabBuilder._build_check(v)
VocabBuilder._build_target_range(v, target_range[0], target_range[1])
VocabBuilder._build_prop(v)
return v
def filter_embed(self, wv: 'WordVectors', init_nohit=0., scale=1.0, assert_all_hit=False):
if init_nohit <= 0.:
get_nohit = lambda s: np.zeros((s,), dtype=np.float32)
else:
get_nohit = lambda s: (Random.random_sample((s,)).astype(np.float32)-0.5) * (2*init_nohit)
#
ret = []
res = defaultdict(int)
for w in self.final_words:
hit, norm_name, norm_w = wv.norm_until_hit(w)
if hit:
value = np.asarray(wv.get_vec(norm_w, norm=False), dtype=np.float32)
res[norm_name] += 1
else:
value = get_nohit(wv.embed_size)
# value = np.zeros((wv.embed_size,), dtype=np.float32)
res["no-hit"] += 1
ret.append(value)
#
if assert_all_hit:
zcheck(res["no-hit"]==0, f"Filter-embed error: assert all-hit but get no-hit of {res['no-hit']}")
printing("Filter pre-trained embed: %s, no-hit is inited with %s." % (res, init_nohit))
return np.asarray(ret, dtype=np.float32) * scale
def _load_txt(fname, sep=" "):
printing("Going to load pre-trained (txt) w2v from %s ..." % fname)
one = WordVectors(sep=sep)
repeated_count = 0
with zopen(fname) as fd:
# first line
line = fd.readline()
try:
one.num_words, one.embed_size = [int(x) for x in line.split(sep)]
printing("Reading w2v num_words=%d, embed_size=%d." % (one.num_words, one.embed_size))
line = fd.readline()
except:
printing("Reading w2v.")
# the rest
while len(line) > 0:
line = line.rstrip()
fields = line.split(sep)
word, vec = fields[0], [float(x) for x in fields[1:]]
# zcheck(word not in one.wmap, "Repeated key.")
# keep the old one
if word in one.wmap:
repeated_count += 1
zwarn(f"Repeat key {word}")
line = fd.readline()
continue
#
if one.embed_size is None:
one.embed_size = len(vec)
else:
zcheck(len(vec) == one.embed_size, "Unmatched embed dimension.")
one.vecs.append(vec)
one.wmap[word] = len(one.words)
one.words.append(word)
line = fd.readline()
# final
if one.num_words is not None:
zcheck(one.num_words == len(one.vecs)+repeated_count, "Unmatched num of words.")
one.num_words = len(one.vecs)
printing(f"Read ok: w2v num_words={one.num_words:d}, embed_size={one.embed_size:d}, repeat={repeated_count:d}")
return one
def save(self, fname):
printing(f"Saving w2v num_words={self.num_words:d}, embed_size={self.embed_size:d} to {fname}.")
zcheck(self.num_words == len(self.vecs), "Internal error: unmatched number!")
with zopen(fname, "w") as fd:
WordVectors.save_txt(fd, self.words, self.vecs, self.sep)
def filter(self, word_filter=(lambda ww, rank, val: True)):
new_counts = VocabBuilder.filter_vals(self.counts_, word_filter)
printing("Filter in VocabBuilder %s ok, from %d to %d." % (self.v.name, len(self.counts_), len(new_counts)))
self.counts_ = new_counts