def do_stat_srl(insts):
cc = Counter()
cc_narg = Counter()
voc = SimpleVocab.build_empty()
# set_ee_heads(insts)
voc_pred, voc_arg = SimpleVocab.build_empty(), SimpleVocab.build_empty()
voc_deplab = SimpleVocab.build_empty()
for sent in yield_sents(insts):
cc["sent"] += 1
cc["tok"] += len(sent)
cc["frame"] += len(sent.events)
# --
_tree = sent.tree_dep
if _tree is not None:
voc_deplab.feed_iter(_tree.seq_label.vals)
for evt in sent.events:
voc_pred.feed_one(evt.label)
evt_widx = evt.mention.shead_widx
cc_narg[f"NARG={len(evt.args)}"] += 1
for arg in evt.args:
voc_arg.feed_one(arg.label)
cc["arg"] += 1
# check arg overlap
for a2 in evt.args:
if a2 is arg: continue # not self
if not (arg.mention.widx >= a2.mention.wridx
or a2.mention.widx >= arg.mention.wridx):
cc["arg_overlap"] += 1
else:
cc["arg_overlap"] += 0
# --
voc.build_sort()
voc_pred.build_sort()
voc_arg.build_sort()
voc_deplab.build_sort()
# --
# get more stat
cc2 = dict(cc)
cc2.update({
"t/s": f"{cc['tok']/cc['sent']:.2f}",
"f/s": f"{cc['frame']/cc['sent']:.2f}",
"a/f": f"{cc['arg']/cc['frame']:.2f}"
})
zlog(f"CC: {cc2}")
zlog(cc_narg)
zlog(voc_arg.counts)
# --
MAX_PRINT_ITEMS = 20
d_pred = voc_pred.get_info_table()
print(d_pred[:MAX_PRINT_ITEMS].to_string())
d_arg = voc_arg.get_info_table()
print(d_arg[:MAX_PRINT_ITEMS].to_string())
d_deplab = voc_deplab.get_info_table()
print(d_deplab[:MAX_PRINT_ITEMS].to_string())
d = voc.get_info_table()
print(d[:MAX_PRINT_ITEMS].to_string())
def do_stat(insts):
cc = Counter()
voc = SimpleVocab.build_empty()
for sent in yield_sents(insts):
cc["sent"] += 1
cc["tok"] += len(sent)
cc["tok_pair"] += len(sent)**2
_tree = sent.tree_dep
_deplabs = _tree.seq_label.vals
_slen = len(sent)
for i0 in range(_slen):
for i1 in range(_slen):
if abs(i0 - i1) > 5:
continue
path1, path2 = _tree.get_path(i0, i1)
labs1, labs2 = sorted(
[[_deplabs[z].split(":")[0] for z in path1],
[_deplabs[z].split(":")[0] for z in path2]])
_len = len(labs1) + len(labs2)
# if _len<=0 or _len>2 or "punct" in labs1 or "punct" in labs2:
if _len != 2 or "punct" in labs1 or "punct" in labs2:
continue
_k = (tuple(labs1), tuple(labs2))
voc.feed_one(_k)
# --
zlog(cc)
voc.build_sort()
d = voc.get_info_table()
print(d[:100].to_string())
def build_vocab(self, datasets: List):
voc_upos = SimpleVocab.build_empty(self.name)
for dataset in datasets:
for sent in yield_sents(dataset.insts):
voc_upos.feed_iter(sent.seq_upos.vals)
# finnished
voc_upos.build_sort()
return (voc_upos, )
def build_vocab(self, datasets: List):
conf: ZTaskUdepConf = self.conf
# --
voc_udep = SimpleVocab.build_empty(self.name)
for dataset in datasets:
for sent in yield_sents(dataset.insts):
_vals = sent.tree_dep.seq_label.vals
if conf.use_l1:
_vals = [z.split(":")[0] for z in _vals]
voc_udep.feed_iter(_vals)
voc_udep.build_sort()
_, udep_direct_range = voc_udep.non_special_range() # range of direct labels
zlog(f"Finish building voc_udep: {voc_udep}")
return (voc_udep, udep_direct_range)
def build_from_stream(dconf: DConf, stream, extra_stream):
zlog("Build vocabs from streams.")
# here, collect them all
# -- basic inputs
voc_word = SimpleVocab.build_empty("word")
voc_lemma = SimpleVocab.build_empty("lemma")
voc_upos = SimpleVocab.build_empty("upos")
voc_char = SimpleVocab.build_empty("char")
voc_deplab = SimpleVocab.build_empty("deplab")
# -- frame ones
voc_evt, voc_ef, voc_arg = SimpleVocab.build_empty("evt"), SimpleVocab.build_empty("ef"), SimpleVocab.build_empty("arg")
voc_collections = {"word": voc_word, "lemma": voc_lemma, "upos": voc_upos, "char": voc_char, "deplab": voc_deplab,
"evt": voc_evt, "ef": voc_ef, "arg": voc_arg}
# read all and build
for sent in yield_sents(stream):
# -- basic inputs
if sent.seq_word is not None:
voc_word.feed_iter(sent.seq_word.vals)
for w in sent.seq_word.vals:
voc_char.feed_iter(w)
if sent.seq_lemma is not None:
voc_lemma.feed_iter(sent.seq_lemma.vals)
if sent.seq_upos is not None:
voc_upos.feed_iter(sent.seq_upos.vals)
if sent.tree_dep is not None and sent.tree_dep.seq_label is not None:
voc_deplab.feed_iter(sent.tree_dep.seq_label.vals)
# -- frames
if sent.entity_fillers is not None:
voc_ef.feed_iter((ef.type for ef in sent.entity_fillers))
if sent.events is not None:
voc_evt.feed_iter((evt.type for evt in sent.events))
for evt in sent.events:
if evt.args is not None:
voc_arg.feed_iter((arg.role for arg in evt.args))
# sort everyone!
for voc in voc_collections.values():
voc.build_sort()
# extra for evt/arg
if dconf.dict_frame_file:
frames = default_json_serializer.from_file(dconf.dict_frame_file)
for one_f in frames.values(): # no count, simply feed!!
if len(one_f["lexUnit"]) > 0: # todo(+W): currently ignore non-lex frames
voc_evt.feed_one(one_f["name"], c=0)
for one_fe in one_f["FE"]:
voc_arg.feed_one(one_fe["name"], c=0)
zlog(f"After adding frames from {dconf.dict_frame_file}, evt={voc_evt}, arg={voc_arg}")
# -----
# deal with pre-trained word embeddings
w2vec = None
if dconf.pretrain_wv_file:
# todo(warn): for convenience, extra vocab (usually dev&test) is only used for collecting pre-train vecs
# collect extra words and lemmas
extra_word_counts = {}
extra_lemma_counts = {}
for sent in yield_sents(extra_stream):
if sent.seq_word is not None:
for w in sent.seq_word.vals:
extra_word_counts[w] = extra_word_counts.get(w, 0) + 1
if sent.seq_lemma is not None:
for w in sent.seq_lemma.vals:
extra_lemma_counts[w] = extra_lemma_counts.get(w, 0) + 1
# must provide dconf.pretrain_file
w2vec = WordVectors.load(dconf.pretrain_wv_file)
# first filter according to thresholds
_filter_f = lambda ww, rank, val: (val >= dconf.word_fthres and rank <= dconf.word_rthres) or \
w2vec.find_key(ww) is not None
voc_word.build_filter(_filter_f)
voc_lemma.build_filter(_filter_f)
# then add extra ones
for w in sorted(extra_word_counts.keys(), key=lambda z: (-extra_word_counts[z], z)):
if w2vec.find_key(w) is not None and (w not in voc_word):
voc_word.feed_one(w)
for w in sorted(extra_lemma_counts.keys(), key=lambda z: (-extra_lemma_counts[z], z)):
if w2vec.find_key(w) is not None and (w not in voc_lemma):
voc_lemma.feed_one(w)
# by-product of filtered output pre-trained embeddings for later faster processing
if dconf.pretrain_hits_outf:
# find all keys again!!
w2vec.clear_hits()
for vv in [voc_word, voc_lemma]:
for _idx in range(*(vv.non_special_range())):
w2vec.find_key(vv.idx2word(_idx))
w2vec.save_hits(dconf.pretrain_hits_outf)
# embeds
word_embed1 = voc_word.filter_embed(w2vec, scale=dconf.pretrain_scale)
lemma_embed1 = voc_lemma.filter_embed(w2vec, scale=dconf.pretrain_scale)
else:
voc_word.build_filter_thresh(rthres=dconf.word_rthres, fthres=dconf.word_fthres)
voc_lemma.build_filter_thresh(rthres=dconf.word_rthres, fthres=dconf.word_fthres)
word_embed1 = lemma_embed1 = None
# return
ret = ZsfpVocabPackage(voc_collections, {"word": word_embed1, "lemma": lemma_embed1}, dconf)
return ret
