-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate.py
479 lines (442 loc) · 21.8 KB
/
evaluate.py
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
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
#!/usr/bin/env python
# Scoring program for the AutoML challenge
# Isabelle Guyon and Arthur Pesah, ChaLearn, August 2014-November 2016
# ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS".
# ISABELLE GUYON, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM
# ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE
# WARRANTY OF NON-INFRINGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS.
# IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL,
# INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN
# CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS,
# PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE.
# Some libraries and options
import os
from sys import argv
import json
import yaml
import re
import copy
import json
import string
import numpy as np
from collections import Counter, defaultdict
from scipy.optimize import linear_sum_assignment
def get_normalized_answer(input_string) -> str:
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text: str):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text: str):
return " ".join(text.split())
def remove_punc(text: str):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text: str):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(input_string))))
def get_tokens(input_string):
return get_normalized_answer(input_string).split()
def compute_exact_match(label_str, pred_str):
return get_normalized_answer(pred_str) == get_normalized_answer(label_str)
def compute_bow_f1(label_str, pred_str, return_pr=False):
prediction_tokens = get_tokens(pred_str)
ground_truth_tokens = get_tokens(label_str)
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
if return_pr: return 0, 0, 0
else: return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
if return_pr:
return precision, recall, f1
else:
return f1
def get_pred_label_spans(pred_path, test_file, ignore_non_entity=False):
'''Compute metrics for each role of each trigger mention.
1. Count all mentions;
2. Average the metric across all mentions
'''
# convert to ``id to label per role type``
# {
# `doc id-event id-trigger id-label role type`: {`event type`: ``label type``, `spans`: [[`text`]]} # each item in spans is an entity
# }
maven_data = []
with open(test_file) as f:
for line in f.readlines():
maven_data.append(json.loads(line.strip()))
label_id2spans = defaultdict(list)
all_triggers = {} # {id: event_type}
docid2text = {}
tid2eid = {}
for item in maven_data:
docid2text[item["id"]] = item["text"]
for event in item["events"]:
for trigger in event["triggers"]:
all_triggers[f"{item['id']}-{event['id']}-{trigger['id']}"] = event["type"]
assert trigger['id'] not in tid2eid
tid2eid[trigger['id']] = event['id']
for argument in trigger["arguments"]:
id = f"{item['id']}-{event['id']}-{trigger['id']}-{argument['role']}"
if id not in label_id2spans: # Maybe multiple arguments have the same role
label_id2spans[id] = {
"event_type": event["type"],
"spans": []
}
spans = []
if "non-entity" in argument["id"] and ignore_non_entity:
for mention in argument["mentions"]:
spans.append("<NA>")
# continue
else:
for mention in argument["mentions"]:
spans.append(item["text"][mention["position"][0]:mention["position"][1]])
label_id2spans[id]["spans"].append(spans)
for mention in item['negative_triggers']:
tid2eid[mention['id']]="NA"
# (doc id, event id, trigger id, pred event type, position, pred role type)
# convert to ``id to prediction per role type``
# {
# `doc id-event id-trigger id-pred role type`: {`event type`: ``pred type``, `spans`: [`text`]}
# }
pred_id2spans = {}
with open(pred_path, "r") as fin:
lines=fin.readlines()
for line in lines:
doc=json.loads(line)
if doc['id'] not in docid2text:
continue
for tid in doc['preds']:
if tid not in tid2eid:
continue
event_id=tid2eid[tid]
event_type=doc['preds'][tid]['event_type']
for role in doc['preds'][tid]:
if role=='event_type':
continue
id = f"{doc['id']}-{event_id}-{tid}-{event_type}.{role}"
pred_id2spans[id]={
"event_type": event_type,
"spans": doc['preds'][tid][role]
}
return label_id2spans, pred_id2spans, all_triggers
def find_optimal_match(gold_spans, pred_spans):
scores = np.zeros([len(gold_spans), len(pred_spans)])
for gold_index, gold_item in enumerate(gold_spans):
for pred_index, pred_item in enumerate(pred_spans):
scores[gold_index, pred_index] = compute_bow_f1(gold_item, pred_item)
row_ind, col_ind = linear_sum_assignment(-scores)
return row_ind, [(gold_spans[i], pred_spans[j]) for i, j in zip(row_ind, col_ind)]
def compute_mention_level_F1(label_id2spans, pred_id2spans, all_triggers, schema):
mention_exact_match = []
mention_bow = {
"precision": [],
"recall": [],
"f1": []
}
# for all triggers
for trigger in all_triggers:
event_type = all_triggers[trigger]
all_roles = schema[event_type]
for role in all_roles:
role = f"{event_type}.{role}"
id = f"{trigger}-{role}"
if id not in pred_id2spans:
if id not in label_id2spans:
continue
else: # false negative roles
# gold_spans = [span for spans in label_id2spans[id]["spans"] for span in spans]
# mention_exact_match.extend([0]*len(gold_spans))
# for key in mention_bow:
# mention_bow[key].extend([0]*len(gold_spans))
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if id not in label_id2spans: # 1. false positive roles
# pred_spans = pred_id2spans[id]["spans"]
# mention_exact_match.extend([0]*len(pred_spans))
# for key in mention_bow:
# mention_bow[key].extend([0]*len(pred_spans))
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if pred_id2spans[id]["event_type"] != label_id2spans[id]["event_type"]:
# gold_spans = [span for spans in label_id2spans[id]["spans"] for span in spans]
# mention_exact_match.extend([0]*len(gold_spans))
# for key in mention_bow:
# mention_bow[key].extend([0]*len(gold_spans))
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
gold_spans = [span for spans in label_id2spans[id]["spans"] for span in spans]
pred_spans = pred_id2spans[id]["spans"]
gold_span_idx, pairs = find_optimal_match(gold_spans, pred_spans)
penalty = min(len(gold_spans), len(pred_spans)) / max(len(gold_spans), len(pred_spans))
# import pdb; pdb.set_trace()
for pair in pairs:
em = int(compute_exact_match(pair[0], pair[1]))
p, r, f1 = compute_bow_f1(pair[0], pair[1], True)
mention_exact_match.append(em * penalty)
mention_bow["precision"].append(p * penalty)
mention_bow["recall"].append(r * penalty)
mention_bow["f1"].append(f1 * penalty)
# # penalty
# diff = abs(len(gold_spans) - len(pred_spans))
# mention_exact_match.extend([0]*diff)
# for key in mention_bow:
# mention_bow[key].extend([0]*diff)
# for false positive trigger predictions
for id in pred_id2spans:
event_id = id.split("-")[1]
if event_id == "NA":
# pred_spans = pred_id2spans[id]["spans"]
# mention_exact_match.extend([0]*len(pred_spans))
# for key in mention_bow:
# mention_bow[key].extend([0]*len(pred_spans))
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
# average across all mentions
global_res = {
"EM": np.mean(mention_exact_match) * 100,
"Precision": np.mean(mention_bow["precision"]) * 100,
"Recall": np.mean(mention_bow["recall"]) * 100,
"F1": np.mean(mention_bow["f1"]) * 100
}
# print("Mention Level: Exact Match: {}, Bag-of-Words F1: {}".format(global_em, global_bow))
return global_res
def merge_entity_score(gold_spans):
mention_idx_to_entity_idx = {}
flat_spans = []
for entity_idx, spans in enumerate(gold_spans):
for span in spans:
mention_idx_to_entity_idx[len(flat_spans)] = entity_idx
flat_spans.append(span)
return mention_idx_to_entity_idx
def compute_entity_coref_level_F1(label_id2spans, pred_id2spans, all_triggers, schema):
mention_exact_match = []
mention_bow = {
"precision": [],
"recall": [],
"f1": []
}
# for all triggers
for trigger in all_triggers:
event_type = all_triggers[trigger]
all_roles = schema[event_type]
for role in all_roles:
role = f"{event_type}.{role}"
id = f"{trigger}-{role}"
if id not in pred_id2spans:
if id not in label_id2spans:
continue
else: # false negative roles
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if id not in label_id2spans: # 1. false positive roles
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if pred_id2spans[id]["event_type"] != label_id2spans[id]["event_type"]:
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
gold_spans = [span for spans in label_id2spans[id]["spans"] for span in spans]
pred_spans = pred_id2spans[id]["spans"]
gold_span_idx, pairs = find_optimal_match(gold_spans, pred_spans)
mention_idx_to_entity_idx = merge_entity_score(label_id2spans[id]["spans"])
max_entity_score = []
for i in range(len(label_id2spans[id]["spans"])):
max_entity_score.append({
"bow_f1": 0.0,
"gold_span": None,
"pred_span": None
})
for idx, pair in zip(gold_span_idx, pairs):
p, r, f1 = compute_bow_f1(pair[0], pair[1], True)
if f1 > max_entity_score[mention_idx_to_entity_idx[idx]]["bow_f1"]:
max_entity_score[mention_idx_to_entity_idx[idx]] = {
"bow_f1": f1,
"gold_span": pair[0],
"pred_span": pair[1]
}
for entity in max_entity_score:
if entity["gold_span"] is not None:
em = int(compute_exact_match(entity["gold_span"], entity["pred_span"]))
p, r, f1 = compute_bow_f1(entity["gold_span"], entity["pred_span"], True)
else:
em = 0
p, r, f1 = 0, 0, 0
mention_exact_match.append(em)
mention_bow["precision"].append(p)
mention_bow["recall"].append(r)
mention_bow["f1"].append(f1)
# for false positive trigger predictions
for id in pred_id2spans:
event_id = id.split("-")[1]
if event_id == "NA":
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
# average across all mentions
global_res = {
"EM": np.mean(mention_exact_match) * 100,
"Precision": np.mean(mention_bow["precision"]) * 100,
"Recall": np.mean(mention_bow["recall"]) * 100,
"F1": np.mean(mention_bow["f1"]) * 100
}
# print("Entity Coref Level: Exact Match: {}, Bag-of-Words F1: {}".format(global_em, global_bow))
return global_res
def compute_event_entity_coref_level_F1(label_id2spans, pred_id2spans, all_triggers, schema):
mention_exact_match = []
mention_bow = {
"precision": [],
"recall": [],
"f1": []
}
# for all triggers
scores_per_event = dict()
for trigger in all_triggers:
event_id = "-".join(trigger.split("-")[:-1])
event_type = all_triggers[trigger]
all_roles = schema[event_type]
for role in all_roles:
role = f"{event_type}.{role}"
id = f"{trigger}-{role}"
if id not in pred_id2spans:
if id not in label_id2spans:
continue
else: # false negative roles
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if id not in label_id2spans: # 1. false positive roles
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
if pred_id2spans[id]["event_type"] != label_id2spans[id]["event_type"]:
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
else:
event_role_id = f"{event_id}-{role}"
if event_role_id not in scores_per_event:
scores_per_event[event_role_id] = []
gold_spans = [span for spans in label_id2spans[id]["spans"] for span in spans]
pred_spans = pred_id2spans[id]["spans"]
gold_span_idx, pairs = find_optimal_match(gold_spans, pred_spans)
mention_idx_to_entity_idx = merge_entity_score(label_id2spans[id]["spans"])
max_entity_score = []
for i in range(len(label_id2spans[id]["spans"])):
max_entity_score.append({
"bow_f1": 0.0,
"gold_span": None,
"pred_span": None
})
for idx, pair in zip(gold_span_idx, pairs):
p, r, f1 = compute_bow_f1(pair[0], pair[1], True)
if f1 > max_entity_score[mention_idx_to_entity_idx[idx]]["bow_f1"]:
max_entity_score[mention_idx_to_entity_idx[idx]] = {
"bow_f1": f1,
"gold_span": pair[0],
"pred_span": pair[1]
}
entity_scores_per_trigger = []
for entity in max_entity_score:
if entity["gold_span"] is not None:
em = int(compute_exact_match(entity["gold_span"], entity["pred_span"]))
p, r, f1 = compute_bow_f1(entity["gold_span"], entity["pred_span"], True)
else:
em = 0
p, r, f1 = 0, 0, 0
score = {
"em": em,
"bow": {
"precision": p,
"recall": r,
"f1": f1
}
}
entity_scores_per_trigger.append(score)
scores_per_event[event_role_id].append(entity_scores_per_trigger)
# merge event-entity predictions
for event_role in scores_per_event:
all_entity_scores_for_all_triggers = scores_per_event[event_role]
max_all_entity_scores_per_event = copy.deepcopy(all_entity_scores_for_all_triggers[0])
for all_entity_scores_per_trigger in all_entity_scores_for_all_triggers:
for entity_idx, per_entity_scores_per_trigger in enumerate(all_entity_scores_per_trigger):
if per_entity_scores_per_trigger["em"] > max_all_entity_scores_per_event[entity_idx]["em"]:
max_all_entity_scores_per_event[entity_idx]["em"] = per_entity_scores_per_trigger["em"]
if per_entity_scores_per_trigger["bow"]["f1"] > max_all_entity_scores_per_event[entity_idx]["bow"]["f1"]:
max_all_entity_scores_per_event[entity_idx]["bow"]["precision"] = per_entity_scores_per_trigger["bow"]["precision"]
max_all_entity_scores_per_event[entity_idx]["bow"]["recall"] = per_entity_scores_per_trigger["bow"]["recall"]
max_all_entity_scores_per_event[entity_idx]["bow"]["f1"] = per_entity_scores_per_trigger["bow"]["f1"]
for max_per_entity_scores_per_event in max_all_entity_scores_per_event:
mention_exact_match.append(max_per_entity_scores_per_event["em"])
for key in mention_bow.keys():
mention_bow[key].append(max_per_entity_scores_per_event["bow"][key])
# for false positive trigger predictions
for id in pred_id2spans:
event_id = id.split("-")[1]
if event_id == "NA":
mention_exact_match.append(0)
for key in mention_bow:
mention_bow[key].append(0)
# average across all mentions
# global_em = np.mean(mention_exact_match) * 100
global_res = {
"EM": np.mean(mention_exact_match) * 100,
"Precision": np.mean(mention_bow["precision"]) * 100,
"Recall": np.mean(mention_bow["recall"]) * 100,
"F1": np.mean(mention_bow["f1"]) * 100
}
# print("Event-Entity Coref Leval: Exact Match: {}, Bag-of-Words F1: {}".format(global_em, global_bow))
return global_res
if __name__ == "__main__":
input_dir = argv[1]
output_dir = argv[2]
submit_dir = os.path.join(input_dir, 'res')
truth_dir = os.path.join(input_dir, 'ref')
# Create the output directory, if it does not already exist and open output files
if not os.path.exists(output_dir):
os.mkdir(output_dir)
score_file = open(os.path.join(output_dir, 'scores.txt'), 'w')
html_file = open(os.path.join(output_dir, 'scores.html'), 'w')
# read ground truth
pred_path = os.path.join(submit_dir,"test_prediction.jsonl")
test_file = os.path.join(truth_dir,"test.unified.jsonl")
schema = json.load(open(os.path.join(truth_dir,"label2role.json")))
label_id2spans, pred_id2spans, all_triggers = get_pred_label_spans(pred_path, test_file, ignore_non_entity=False)
m_global_res = compute_mention_level_F1(label_id2spans, pred_id2spans, all_triggers, schema)
ec_global_res = compute_entity_coref_level_F1(label_id2spans, pred_id2spans, all_triggers, schema)
eec_global_res = compute_event_entity_coref_level_F1(label_id2spans, pred_id2spans, all_triggers, schema)
metrics = {
"Mention_Level": m_global_res,
"Entity_Coref_Level": ec_global_res,
"Event_Coref_Level": eec_global_res
}
def output_score(key, score):
print(key + ": %0.2f\n" % score)
html_file.write("======= score (" + key + ")=%0.2f =======\n" % score)
score_file.write(key + ": %0.2f\n" % score)
for l in metrics:
for m in metrics[l]:
output_score(l+"_"+m, metrics[l][m])
# Read the execution time and add it to the scores:
try:
metadata = yaml.load(open(os.path.join(input_dir, 'res', 'metadata'), 'r'))
score_file.write("Duration: %0.2f\n" % metadata['elapsedTime'])
except:
score_file.write("Duration: 0\n")
html_file.close()
score_file.close()