Spaces:
Build error
Build error
File size: 11,067 Bytes
6680682 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 |
import json
import logging
import os
from collections import defaultdict, namedtuple
from typing import *
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.instance import Instance
from .span_reader import SpanReader
from ..utils import Span
# logging.basicConfig(level=logging.DEBUG)
# for v in logging.Logger.manager.loggerDict.values():
# v.disabled = True
logger = logging.getLogger(__name__)
SpanTuple = namedtuple('Span', ['start', 'end'])
@DatasetReader.register('better')
class BetterDatasetReader(SpanReader):
def __init__(
self,
eval_type,
consolidation_strategy='first',
span_set_type='single',
max_argument_ss_size=1,
use_ref_events=False,
**extra
):
super().__init__(**extra)
self.eval_type = eval_type
assert self.eval_type in ['abstract', 'basic']
self.consolidation_strategy = consolidation_strategy
self.unitary_spans = span_set_type == 'single'
# event anchors are always singleton spans
self.max_arg_spans = max_argument_ss_size
self.use_ref_events = use_ref_events
self.n_overlap_arg = 0
self.n_overlap_trigger = 0
self.n_skip = 0
self.n_too_long = 0
@staticmethod
def post_process_basic_span(predicted_span, basic_entry):
# Convert token offsets back to characters, also get the text spans as a sanity check
# !!!!!
# SF outputs inclusive idxs
# char offsets are inc-exc
# token offsets are inc-inc
# !!!!!
start_idx = predicted_span['start_idx'] # inc
end_idx = predicted_span['end_idx'] # inc
char_start_idx = basic_entry['tok2char'][predicted_span['start_idx']][0] # inc
char_end_idx = basic_entry['tok2char'][predicted_span['end_idx']][-1] + 1 # exc
span_text = basic_entry['segment-text'][char_start_idx:char_end_idx] # inc exc
span_text_tok = basic_entry['segment-text-tok'][start_idx:end_idx + 1] # inc exc
span = {'string': span_text,
'start': char_start_idx,
'end': char_end_idx,
'start-token': start_idx,
'end-token': end_idx,
'string-tok': span_text_tok,
'label': predicted_span['label'],
'predicted': True}
return span
@staticmethod
def _get_shortest_span(spans):
# shortest_span_length = float('inf')
# shortest_span = None
# for span in spans:
# span_tokens = span['string-tok']
# span_length = len(span_tokens)
# if span_length < shortest_span_length:
# shortest_span_length = span_length
# shortest_span = span
# return shortest_span
return [s[-1] for s in sorted([(len(span['string']), ix, span) for ix, span in enumerate(spans)])]
@staticmethod
def _get_first_span(spans):
spans = [(span['start'], -len(span['string']), ix, span) for ix, span in enumerate(spans)]
try:
return [s[-1] for s in sorted(spans)]
except:
breakpoint()
@staticmethod
def _get_longest_span(spans):
return [s[-1] for s in sorted([(len(span['string']), ix, span) for ix, span in enumerate(spans)], reverse=True)]
@staticmethod
def _subfinder(text, pattern):
# https://stackoverflow.com/a/12576755
matches = []
pattern_length = len(pattern)
for i, token in enumerate(text):
try:
if token == pattern[0] and text[i:i + pattern_length] == pattern:
matches.append(SpanTuple(start=i, end=i + pattern_length - 1)) # inclusive boundaries
except:
continue
return matches
def consolidate_span_set(self, spans):
if self.consolidation_strategy == 'first':
spans = BetterDatasetReader._get_first_span(spans)
elif self.consolidation_strategy == 'shortest':
spans = BetterDatasetReader._get_shortest_span(spans)
elif self.consolidation_strategy == 'longest':
spans = BetterDatasetReader._get_longest_span(spans)
else:
raise NotImplementedError(f"{self.consolidation_strategy} does not exist")
if self.unitary_spans:
spans = [spans[0]]
else:
spans = spans[:self.max_arg_spans]
# TODO add some sanity checks here
return spans
def get_mention_spans(self, text: List[str], span_sets: Dict):
mention_spans = defaultdict(list)
for span_set_id in span_sets.keys():
spans = span_sets[span_set_id]['spans']
# span = BetterDatasetReader._get_shortest_span(spans)
# span = BetterDatasetReader._get_earliest_span(spans)
consolidated_spans = self.consolidate_span_set(spans)
# if len(spans) > 1:
# logging.info(f"Truncated a spanset from {len(spans)} spans to 1")
if self.eval_type == 'abstract':
span = consolidated_spans[0]
span_tokens = span['string-tok']
span_indices = BetterDatasetReader._subfinder(text=text, pattern=span_tokens)
if len(span_indices) > 1:
pass
if len(span_indices) == 0:
continue
mention_spans[span_set_id] = span_indices[0]
else:
# in basic, we already have token offsets in the right form
# if not span['string-tok'] == text[span['start-token']:span['end-token'] + 1]:
# print(span, text[span['start-token']:span['end-token'] + 1])
# we should use these token offsets only!
for span in consolidated_spans:
mention_spans[span_set_id].append(SpanTuple(start=span['start-token'], end=span['end-token']))
return mention_spans
def _read_single_file(self, file_path):
with open(file_path) as fp:
json_content = json.load(fp)
if 'entries' in json_content:
for doc_name, entry in json_content['entries'].items():
instance = self.text_to_instance(entry, 'train' in file_path)
yield instance
else: # TODO why is this split in 2 cases?
for doc_name, entry in json_content.items():
instance = self.text_to_instance(entry, True)
yield instance
logger.warning(f'{self.n_overlap_arg} overlapped args detected!')
logger.warning(f'{self.n_overlap_trigger} overlapped triggers detected!')
logger.warning(f'{self.n_skip} skipped detected!')
logger.warning(f'{self.n_too_long} were skipped because they are too long!')
self.n_overlap_arg = self.n_skip = self.n_too_long = self.n_overlap_trigger = 0
def _read(self, file_path: str) -> Iterable[Instance]:
if os.path.isdir(file_path):
for fn in os.listdir(file_path):
if not fn.endswith('.json'):
logger.info(f'Skipping {fn}')
continue
logger.info(f'Loading from {fn}')
yield from self._read_single_file(os.path.join(file_path, fn))
else:
yield from self._read_single_file(file_path)
def text_to_instance(self, entry, is_training=False):
word_tokens = entry['segment-text-tok']
# span sets have been trimmed to the earliest span mention
spans = self.get_mention_spans(
word_tokens, entry['annotation-sets'][f'{self.eval_type}-events']['span-sets']
)
# idx of every token that is a part of an event trigger/anchor span
all_trigger_idxs = set()
# actual inputs to the model
input_spans = []
self._local_child_overlap = 0
self._local_child_total = 0
better_events = entry['annotation-sets'][f'{self.eval_type}-events']['events']
skipped_events = set()
# check for events that overlap other event's anchors, skip them later
for event_id, event in better_events.items():
assert event['anchors'] in spans
# take the first consolidated span for anchors
anchor_start, anchor_end = spans[event['anchors']][0]
if any(ix in all_trigger_idxs for ix in range(anchor_start, anchor_end + 1)):
logger.warning(
f"Skipped {event_id} with anchor span {event['anchors']}, overlaps a previously found event trigger/anchor")
self.n_overlap_trigger += 1
skipped_events.add(event_id)
continue
all_trigger_idxs.update(range(anchor_start, anchor_end + 1)) # record the trigger
for event_id, event in better_events.items():
if event_id in skipped_events:
continue
# arguments for just this event
local_arg_idxs = set()
# take the first consolidated span for anchors
anchor_start, anchor_end = spans[event['anchors']][0]
event_span = Span(anchor_start, anchor_end, event['event-type'], True)
input_spans.append(event_span)
def add_a_child(span_id, label):
# TODO this is a bad way to do this
assert span_id in spans
for child_span in spans[span_id]:
self._local_child_total += 1
arg_start, arg_end = child_span
if any(ix in local_arg_idxs for ix in range(arg_start, arg_end + 1)):
# logger.warn(f"Skipped argument {span_id}, overlaps a previously found argument")
# print(entry['annotation-sets'][f'{self.eval_type}-events']['span-sets'][span_id])
self.n_overlap_arg += 1
self._local_child_overlap += 1
continue
local_arg_idxs.update(range(arg_start, arg_end + 1))
event_span.add_child(Span(arg_start, arg_end, label, False))
for agent in event['agents']:
add_a_child(agent, 'agent')
for patient in event['patients']:
add_a_child(patient, 'patient')
if self.use_ref_events:
for ref_event in event['ref-events']:
if ref_event in skipped_events:
continue
ref_event_anchor_id = better_events[ref_event]['anchors']
add_a_child(ref_event_anchor_id, 'ref-event')
# if len(event['ref-events']) > 0:
# breakpoint()
fields = self.prepare_inputs(word_tokens, spans=input_spans)
if self._local_child_overlap > 0:
logging.warning(
f"Skipped {self._local_child_overlap} / {self._local_child_total} argument spans due to overlaps")
return Instance(fields)
|