-
Notifications
You must be signed in to change notification settings - Fork 9
/
eval_score_classifier.py
executable file
·227 lines (175 loc) · 9.3 KB
/
eval_score_classifier.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
import json
import logging
import numpy as np
import itertools
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import accuracy_score
from sklearn.externals import joblib
from matcher_exactmatch import WhitespaceTokenizer # ???
from medner import MedNER
from medlinker import MedLinker
from medlinker import MedLinkerDoc
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%d-%b-%y %H:%M:%S')
logging.info('Loading MedLinker ...')
# st21pv
from umls import umls_kb_st21pv as umls_kb
cx_ner_path = 'models/ContextualNER/mm_st21pv_SCIBERT_uncased/'
em_ner_path = 'models/ExactMatchNER/umls.2017AA.active.st21pv.nerfed_nlp_and_matcher.max3.p'
ngram_db_path = 'models/SimString/umls.2017AA.active.st21pv.aliases.3gram.5toks.db'
ngram_map_path = 'models/SimString/umls.2017AA.active.st21pv.aliases.5toks.map'
st_vsm_path = 'models/VSMs/mm_st21pv.sts_anns.scibert_scivocab_uncased.vecs'
cui_vsm_path = 'models/VSMs/mm_st21pv.cuis.scibert_scivocab_uncased.vecs'
cui_idx_path = 'models/VSMs/umls.2017AA.active.st21pv.scibert_scivocab_uncased.cuis.index'
cui_lbs_path = 'models/VSMs/umls.2017AA.active.st21pv.scibert_scivocab_uncased.cuis.labels'
print('Loading MedNER ...')
# medner = MedNER(cx_ner_path, em_ner_path)
medner = MedNER(contextual_ner_path=cx_ner_path)
print('Loading MedLinker ...')
medlinker = MedLinker(medner, umls_kb)
medlinker.load_st_VSM(st_vsm_path)
medlinker.load_string_matcher(ngram_db_path, ngram_map_path)
medlinker.load_cui_VSM(cui_vsm_path)
def read_mm_converted(mm_set_path):
with open(mm_set_path, 'r') as json_f:
mm_set = json.load(json_f)
return list(mm_set['docs'])
if __name__ == '__main__':
logging.info('Loading MedMentions ...')
mm_docs = read_mm_converted('data/MedMentions/st21pv/custom/mm_converted.test.json')
logging.info('Loading Classifiers ...')
sty_lr_clf = joblib.load('models/Validators/mm_st21pv.lr_clf_sty.12346feats.joblib')
cui_lr_clf = joblib.load('models/Validators/mm_st21pv.lr_clf_cui.12346feats.joblib')
X_cui, X_sty, y_cui, y_sty = [], [], [], []
n_skipped = 0
logging.info('Processing Instances ...')
for doc_idx, doc in enumerate(mm_docs):
logging.info('At doc #%d - len(X)=%d, n_skipped=%d' % (doc_idx, len(X_cui), n_skipped))
for sent_idx, gold_sent in enumerate(doc['sentences']):
gold_spans = [(s['start'], s['end']) for s in gold_sent['spans']]
medlinker_doc = MedLinkerDoc(text=' '.join(gold_sent['tokens']),
tokens=gold_sent['tokens'],
spans=gold_spans)
medlinker_doc.set_contextual_vectors()
for span_start, span_end, span_vec in medlinker_doc.get_spans(include_vectors=True, normalize=True):
span_str = ' '.join(medlinker_doc.tokens[span_start:span_end])
# STY Matching
matches_sty_str = medlinker.string_matcher.match_sts(span_str.lower())
matches_sty_vsm = medlinker.st_vsm.most_similar(span_vec)
if len(matches_sty_str + matches_sty_vsm) == 0:
n_skipped += 1
continue
sty_matchers_agree = False
if len(matches_sty_str) > 0 and len(matches_sty_vsm) > 0:
if matches_sty_str[0][0] == matches_sty_vsm[0][0]:
sty_matchers_agree = True
scores_sty_str = dict(matches_sty_str)
scores_sty_vsm = dict(matches_sty_vsm)
sty_matches = {sty: max(scores_sty_str.get(sty, 0), scores_sty_vsm.get(sty, 0))
for sty in scores_sty_str.keys() | scores_sty_vsm.keys()}
sty_matches = sorted(sty_matches.items(), key=lambda x: x[1], reverse=True)
sty_top_match = sty_matches[0][0]
# CUI Matching
matches_cui_str = medlinker.string_matcher.match_cuis(span_str.lower())
matches_cui_vsm = medlinker.cui_vsm.most_similar(span_vec)
if len(matches_cui_str + matches_cui_vsm) == 0:
n_skipped += 1
continue
cui_matchers_agree = False
if len(matches_cui_str) > 0 and len(matches_cui_vsm) > 0:
if matches_cui_str[0][0] == matches_cui_vsm[0][0]:
cui_matchers_agree = True
scores_cui_str = dict(matches_cui_str)
scores_cui_vsm = dict(matches_cui_vsm)
cui_matches = {cui: max(scores_cui_str.get(cui, 0), scores_cui_vsm.get(cui, 0))
for cui in scores_cui_str.keys() | scores_cui_vsm.keys()}
cui_matches = sorted(cui_matches.items(), key=lambda x: x[1], reverse=True)
cui_top_match = cui_matches[0][0]
# STY Features
x_sty = []
if len(matches_sty_str) > 0:
x_sty.append(matches_sty_str[0][1])
else:
x_sty.append(0)
if len(matches_sty_vsm) > 0:
x_sty.append(matches_sty_vsm[0][1])
else:
x_sty.append(0)
x_sty.append(sty_matches[0][1])
x_sty.append((scores_sty_str.get(sty_top_match, 0) + scores_sty_vsm.get(sty_top_match, 0))/2)
x_sty.append(int(sty_matchers_agree))
# x_sty.append(int(cui_matchers_agree))
X_sty.append(x_sty)
sty_pred_correct = False
for gold_span in gold_sent['spans']:
if span_start == gold_span['start'] and span_end == gold_span['end']:
if sty_top_match == gold_span['st']:
sty_pred_correct = True
break
y_sty.append(int(sty_pred_correct))
# CUI Features
x_cui = []
if len(matches_cui_str) > 0:
x_cui.append(matches_cui_str[0][1])
else:
x_cui.append(0)
if len(matches_cui_vsm) > 0:
x_cui.append(matches_cui_vsm[0][1])
else:
x_cui.append(0)
x_cui.append(cui_matches[0][1])
x_cui.append((scores_cui_str.get(cui_top_match, 0) + scores_cui_vsm.get(cui_top_match, 0))/2)
# x_cui.append(int(sty_matchers_agree))
x_cui.append(int(cui_matchers_agree))
X_cui.append(x_cui)
#
cui_pred_correct = False
for gold_span in gold_sent['spans']:
if span_start == gold_span['start'] and span_end == gold_span['end']:
if cui_top_match == gold_span['cui'].lstrip('UMLS:'):
cui_pred_correct = True
break
y_cui.append(int(cui_pred_correct))
# matches_cui_str = medlinker.string_matcher.match_cuis(span_str.lower())
# matches_cui_vsm = medlinker.cui_vsm.most_similar(span_vec)
# matchers_agree = False
# if len(matches_cui_str) > 0 and len(matches_cui_vsm) > 0:
# if matches_cui_str[0][0] == matches_cui_vsm[0][0]:
# matchers_agree = True
# scores_cui_str = dict(matches_cui_str)
# scores_cui_vsm = dict(matches_cui_vsm)
# matches = {cui: max(scores_cui_str.get(cui, 0), scores_cui_vsm.get(cui, 0))
# for cui in scores_cui_str.keys() | scores_cui_vsm.keys()}
# matches = sorted(matches.items(), key=lambda x: x[1], reverse=True)
# top_match = matches[0][0]
# pred_correct = False
# for gold_span in gold_sent['spans']:
# if span_start == gold_span['start'] and span_end == gold_span['end']:
# if matches[0][0] == gold_span['cui'].lstrip('UMLS:'):
# pred_correct = True
# break
# x = []
# if len(matches_cui_str) > 0:
# x.append(matches_cui_str[0][1])
# else:
# x.append(0)
# if len(matches_cui_vsm) > 0:
# x.append(matches_cui_vsm[0][1])
# else:
# x.append(0)
# x.append(matches[0][1])
# x.append((scores_cui_str.get(top_match, 0) + scores_cui_vsm.get(top_match, 0))/2)
# x.append(int(matchers_agree))
# X.append(x)
# y.append(int(pred_correct))
logging.info('Getting Predictions ...')
y_preds_sty = sty_lr_clf.predict(X_sty)
y_preds_cui = cui_lr_clf.predict(X_cui)
p, r, f1, s = precision_recall_fscore_support(y_sty, y_preds_sty, average='binary')
acc = accuracy_score(y_sty, y_preds_sty)
print('[STY]', 'P:', p, 'R:', r, 'F1:', f1, 'ACC:', acc)
p, r, f1, s = precision_recall_fscore_support(y_cui, y_preds_cui, average='binary')
acc = accuracy_score(y_cui, y_preds_cui)
print('[CUI]', 'P:', p, 'R:', r, 'F1:', f1, 'ACC:', acc)