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from collections import Counter
from copy import copy
import json
import numpy as np
import re
import logging
from stanza.models.common.utils import ud_scores, harmonic_mean
from stanza.utils.conll import CoNLL
from stanza.models.common.doc import *
logger = logging.getLogger('stanza')
def load_mwt_dict(filename):
if filename is not None:
with open(filename, 'r') as f:
mwt_dict0 = json.load(f)
mwt_dict = dict()
for item in mwt_dict0:
(key, expansion), count = item
if key not in mwt_dict or mwt_dict[key][1] < count:
mwt_dict[key] = (expansion, count)
return mwt_dict
else:
return
def process_sentence(sentence, mwt_dict=None):
sent = []
i = 0
for tok, p, position_info in sentence:
expansion = None
if (p == 3 or p == 4) and mwt_dict is not None:
# MWT found, (attempt to) expand it!
if tok in mwt_dict:
expansion = mwt_dict[tok][0]
elif tok.lower() in mwt_dict:
expansion = mwt_dict[tok.lower()][0]
if expansion is not None:
sent.append({ID: (i+1, i+len(expansion)), TEXT: tok})
if position_info is not None:
sent[-1][START_CHAR] = position_info[0]
sent[-1][END_CHAR] = position_info[1]
for etok in expansion:
sent.append({ID: (i+1, ), TEXT: etok})
i += 1
else:
if len(tok) <= 0:
continue
sent.append({ID: (i+1, ), TEXT: tok})
if position_info is not None:
sent[-1][START_CHAR] = position_info[0]
sent[-1][END_CHAR] = position_info[1]
if p == 3 or p == 4:# MARK
sent[-1][MISC] = 'MWT=Yes'
i += 1
return sent
# https://stackoverflow.com/questions/201323/how-to-validate-an-email-address-using-a-regular-expression
EMAIL_RAW_RE = r"""(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:(?:2(?:5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9]))\.){3}(?:(?:2(?:5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9])|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])"""
# https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
# modification: disallow " as opposed to all ^\s
URL_RAW_RE = r"""(?:https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s"]{2,}|www\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\.[^\s"]{2,}|https?:\/\/(?:www\.|(?!www))[a-zA-Z0-9]+\.[^\s"]{2,}|www\.[a-zA-Z0-9]+\.[^\s"]{2,})"""
MASK_RE = re.compile(f"(?:{EMAIL_RAW_RE}|{URL_RAW_RE})")
def find_spans(raw):
"""
Return spans of text which don't contain <PAD> and are split by <PAD>
"""
pads = [idx for idx, char in enumerate(raw) if char == '<PAD>']
if len(pads) == 0:
spans = [(0, len(raw))]
else:
prev = 0
spans = []
for pad in pads:
if pad != prev:
spans.append( (prev, pad) )
prev = pad + 1
if prev < len(raw):
spans.append( (prev, len(raw)) )
return spans
def update_pred_regex(raw, pred):
"""
Update the results of a tokenization batch by checking the raw text against a couple regular expressions
Currently, emails and urls are handled
TODO: this might work better as a constraint on the inference
for efficiency pred is modified in place
"""
spans = find_spans(raw)
for span_begin, span_end in spans:
text = "".join(raw[span_begin:span_end])
for match in MASK_RE.finditer(text):
match_begin, match_end = match.span()
# first, update all characters touched by the regex to not split
# with the exception of the last character...
for char in range(match_begin+span_begin, match_end+span_begin-1):
pred[char] = 0
# if the last character is not currently a split, make it a word split
if pred[match_end+span_begin-1] == 0:
pred[match_end+span_begin-1] = 1
return pred
SPACE_RE = re.compile(r'\s')
SPACE_SPLIT_RE = re.compile(r'( *[^ ]+)')
def output_predictions(output_file, trainer, data_generator, vocab, mwt_dict, max_seqlen=1000, orig_text=None, no_ssplit=False, use_regex_tokens=True):
paragraphs = []
for i, p in enumerate(data_generator.sentences):
start = 0 if i == 0 else paragraphs[-1][2]
length = sum([len(x[0]) for x in p])
paragraphs += [(i, start, start+length, length)] # para idx, start idx, end idx, length
paragraphs = list(sorted(paragraphs, key=lambda x: x[3], reverse=True))
all_preds = [None] * len(paragraphs)
all_raw = [None] * len(paragraphs)
eval_limit = max(3000, max_seqlen)
batch_size = trainer.args['batch_size']
skip_newline = trainer.args['skip_newline']
batches = int((len(paragraphs) + batch_size - 1) / batch_size)
for i in range(batches):
# At evaluation time, each paragraph is treated as a single "sentence", and a batch of `batch_size` paragraphs
# are tokenized together. `offsets` here are used by the data generator to identify which paragraphs to use
# for the next batch of evaluation.
batchparas = paragraphs[i * batch_size : (i + 1) * batch_size]
offsets = [x[1] for x in batchparas]
batch = data_generator.next(eval_offsets=offsets)
raw = batch[3]
N = len(batch[3][0])
if N <= eval_limit:
pred = np.argmax(trainer.predict(batch), axis=2)
else:
idx = [0] * len(batchparas)
adv = [0] * len(batchparas)
Ns = [p[3] for p in batchparas]
pred = [[] for _ in batchparas]
while True:
ens = [min(N - idx1, eval_limit) for idx1, N in zip(idx, Ns)]
en = max(ens)
batch1 = batch[0][:, :en], batch[1][:, :en], batch[2][:, :en], [x[:en] for x in batch[3]]
pred1 = np.argmax(trainer.predict(batch1), axis=2)
for j in range(len(batchparas)):
sentbreaks = np.where((pred1[j] == 2) + (pred1[j] == 4))[0]
if len(sentbreaks) <= 0 or idx[j] >= Ns[j] - eval_limit:
advance = ens[j]
else:
advance = np.max(sentbreaks) + 1
pred[j] += [pred1[j, :advance]]
idx[j] += advance
adv[j] = advance
if all([idx1 >= N for idx1, N in zip(idx, Ns)]):
break
# once we've made predictions on a certain number of characters for each paragraph (recorded in `adv`),
# we skip the first `adv` characters to make the updated batch
batch = data_generator.next(eval_offsets=adv, old_batch=batch)
pred = [np.concatenate(p, 0) for p in pred]
for j, p in enumerate(batchparas):
len1 = len([1 for x in raw[j] if x != '<PAD>'])
if pred[j][len1-1] < 2:
pred[j][len1-1] = 2
elif pred[j][len1-1] > 2:
pred[j][len1-1] = 4
if use_regex_tokens:
all_preds[p[0]] = update_pred_regex(raw[j], pred[j][:len1])
else:
all_preds[p[0]] = pred[j][:len1]
all_raw[p[0]] = raw[j]
offset = 0
oov_count = 0
doc = []
text = SPACE_RE.sub(' ', orig_text) if orig_text is not None else None
char_offset = 0
use_la_ittb_shorthand = trainer.args['shorthand'] == 'la_ittb'
UNK_ID = vocab.unit2id('<UNK>')
# Once everything is fed through the tokenizer model, it's time to decode the predictions
# into actual tokens and sentences that the rest of the pipeline uses
for j in range(len(paragraphs)):
raw = all_raw[j]
pred = all_preds[j]
current_tok = ''
current_sent = []
for t, p in zip(raw, pred):
if t == '<PAD>':
break
# hack la_ittb
if use_la_ittb_shorthand and t in (":", ";"):
p = 2
offset += 1
if vocab.unit2id(t) == UNK_ID:
oov_count += 1
current_tok += t
if p >= 1:
tok = vocab.normalize_token(current_tok)
assert '\t' not in tok, tok
if len(tok) <= 0:
current_tok = ''
continue
if orig_text is not None:
st = -1
tok_len = 0
for part in SPACE_SPLIT_RE.split(current_tok):
if len(part) == 0: continue
if skip_newline:
part_pattern = re.compile(r'\s*'.join(re.escape(c) for c in part))
match = part_pattern.search(text, char_offset)
st0 = match.start(0) - char_offset
partlen = match.end(0) - match.start(0)
else:
st0 = text.index(part, char_offset) - char_offset
partlen = len(part)
lstripped = part.lstrip()
if st < 0:
st = char_offset + st0 + (len(part) - len(lstripped))
char_offset += st0 + partlen
position_info = (st, char_offset)
else:
position_info = None
current_sent.append((tok, p, position_info))
current_tok = ''
if (p == 2 or p == 4) and not no_ssplit:
doc.append(process_sentence(current_sent, mwt_dict))
current_sent = []
assert(len(current_tok) == 0)
if len(current_sent):
doc.append(process_sentence(current_sent, mwt_dict))
if output_file: CoNLL.dict2conll(doc, output_file)
return oov_count, offset, all_preds, doc
def eval_model(args, trainer, batches, vocab, mwt_dict):
oov_count, N, all_preds, doc = output_predictions(args['conll_file'], trainer, batches, vocab, mwt_dict, args['max_seqlen'])
all_preds = np.concatenate(all_preds, 0)
labels = [y[1] for x in batches.data for y in x]
counter = Counter(zip(all_preds, labels))
def f1(pred, gold, mapping):
pred = [mapping[p] for p in pred]
gold = [mapping[g] for g in gold]
lastp = -1; lastg = -1
tp = 0; fp = 0; fn = 0
for i, (p, g) in enumerate(zip(pred, gold)):
if p == g > 0 and lastp == lastg:
lastp = i
lastg = i
tp += 1
elif p > 0 and g > 0:
lastp = i
lastg = i
fp += 1
fn += 1
elif p > 0:
# and g == 0
lastp = i
fp += 1
elif g > 0:
lastg = i
fn += 1
if tp == 0:
return 0
else:
return 2 * tp / (2 * tp + fp + fn)
f1tok = f1(all_preds, labels, {0:0, 1:1, 2:1, 3:1, 4:1})
f1sent = f1(all_preds, labels, {0:0, 1:0, 2:1, 3:0, 4:1})
f1mwt = f1(all_preds, labels, {0:0, 1:1, 2:1, 3:2, 4:2})
logger.info(f"{args['shorthand']}: token F1 = {f1tok*100:.2f}, sentence F1 = {f1sent*100:.2f}, mwt F1 = {f1mwt*100:.2f}")
return harmonic_mean([f1tok, f1sent, f1mwt], [1, 1, .01])
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