-
Notifications
You must be signed in to change notification settings - Fork 1
/
run_ner.py
171 lines (145 loc) · 7.13 KB
/
run_ner.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
# -*- coding: utf-8 -*-
from pathlib import Path
from NLPreprocessing.annotation2BIO import generate_BIO, pre_processing, BIOdata_to_file
from NLPreprocessing.text_process.sentence_tokenization import SentenceBoundaryDetection
from ClinicalTransformerNER.src.run_transformer_batch_prediction import multiprocessing_wrapper, argparser, main
from ClinicalTransformerNER.src.transformer_ner.transfomer_log import TransformerNERLogger
MIMICIII_PATTERN = "\[\*\*|\*\*\]"
import torch.multiprocessing as mp
import unicodedata, os
import argparse, torch
import cProfile, yaml, copy
from encode_text import preprocessing
# encode raw textdef
def add_subdir_to_path(p,subdir):
if subdir is not None:
return p.parent / subdir / p.name
else:
return p
def encode_raw_text(_source_path, _encoded_path):
for root, _, files in os.walk(_source_path):
for file in files:
if not file.endswith(".txt"):
continue
txt_fn = Path(root) / file
if os.stat(txt_fn).st_size == 0:
continue
with open(txt_fn,'r',encoding="utf-8") as f:
txt = unicodedata.normalize("NFKD", f.read()).strip()
with open (_encoded_path / file, 'w', encoding="utf-8") as f:
f.write(txt)
# generate bio
def encoded_txt_to_bio(encoded_path, bio_path, subdirs):
sent_tokenizer = SentenceBoundaryDetection()
for subdir in subdirs:
_encoded_path = add_subdir_to_path(encoded_path,subdir)
_bio_path = add_subdir_to_path(bio_path,subdir)
for root, _, files in os.walk(_encoded_path):
for file in files:
if not file.endswith(".txt"):
continue
txt_fn = Path(root) / file
bio_fn = _bio_path / (txt_fn.stem + ".bio.txt")
_, sents = pre_processing(txt_fn, deid_pattern=MIMICIII_PATTERN, sent_tokenizer=sent_tokenizer)
nsents, _ = generate_BIO(sents, [], file_id=txt_fn, no_overlap=False)
BIOdata_to_file(bio_fn, nsents)
# run ner prediction
def run_ner_pred(sys_args, subdirs):
sys_args = sum([([k, v] if not isinstance(v, list) else [k]+v) if (v is not None) else [k] for k,v in sys_args.items()],[])
args = argparser(sys_args)
args.subdirs = subdirs
if args.gpu_nodes is not None:
multiprocessing_wrapper(args)
else:
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
logger = TransformerNERLogger(args.log_file, args.log_lvl).get_logger()
args.logger = logger
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info("Task will use cuda device: GPU_{}.".format(torch.cuda.current_device())
if torch.cuda.device_count() else 'Task will use CPU.')
main(args)
def get_subdirs(p):
sub_ps = [(p / x) for x in os.listdir(p)]
return sum([get_subdirs(x) for x in sub_ps], []) if len(sub_ps) else [p]
def get_subdir(root_path, subdir_path):
subdir_recursive = []
while subdir_path != root_path:
subdir_recursive.insert(0, subdir_path.name)
subdir_path = subdir_path.parent
return Path('/'.join(subdir_recursive))
def run(experiment_info):
# pr = cProfile.Profile()
# pr.enable()
test_root = Path(experiment_info['root_dir'])
source_path = Path(experiment_info['raw_data_dir'])
generate_bio = experiment_info.get('generate_bio', False)
encoded_text = experiment_info.get('encoded_text', False)
ner_model = experiment_info['ner_model']
# Check if encoded text exist
test_roots = [x.parent for x in test_root.rglob("**/") if x.name == "encoded_text"]
if not(encoded_text and len(test_roots)):
# Generate encoded text if it doesn't exist
preprocessing(source_path, test_root)
test_roots = [x.parent for x in test_root.rglob("**/") if x.name == "encoded_text"]
if args.gpu_nodes is not None:
mp.set_start_method('spawn')
if not test_roots:
subdirs = [None]
else:
subdirs = [get_subdir(test_root, copy.deepcopy(x)) for x in test_roots]
encoded_path = test_root / "encoded_text"
# Create output/log path
suffix = experiment_info.get('suffix', '')
log_path = test_root / ("_".join(["logs", suffix]) if suffix else "logs")
pred_brat_path = test_root / ("_".join(["brat", suffix]) if suffix else "brat")
# Run NER prediction
sys_args_dict = {"--model_type":ner_model['type'],\
"--pretrained_model":ner_model['path'],\
"--raw_text_dir":str(encoded_path),\
"--output_dir_brat": str(pred_brat_path),\
"--max_seq_length":"128",\
"--do_lower_case":None,\
"--eval_batch_size":"8",\
"--log_file":str( log_path / "ner{}{}.log".format(("_" if suffix else ""), suffix)),\
"--do_format":"1",\
"--data_has_offset_information":None}
# Specify gpu nodes if defined
if experiment_info["gpu_nodes"] is not None:
sys_args_dict["--gpu_nodes"] = experiment_info["gpu_nodes"]
# Run NER prediction
if generate_bio:
bio_path = test_root / "bio_init"
encoded_txt_to_bio(encoded_path, bio_path, subdirs)
pred_bio_path = test_root / "bio"
sys_args = copy.deepcopy(sys_args_dict)
sys_args.update({"--preprocessed_text_dir":str(bio_path),\
"--output_dir":str(pred_bio_path),\
"--do_copy": None})
run_ner_pred(sys_args, subdirs)
else:
sys_args = copy.deepcopy(sys_args_dict)
sys_args.update({"--preprocessed_text_dir":str(encoded_path),\
"--output_dir":'',
"--no_bio": None})
run_ner_pred(sys_args, subdirs)
# pr.disable()
# pr.dump_stats(log_path / f'{suffix}.profile')
# Example:
# python run_ner.py --config ./config.yml --experiment SDoH_pipeline --gpu_nodes 0 1 2 3 4
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, required=True, help="configuration file")
parser.add_argument("--experiment", type=str, required=True, help="experiement to run")
parser.add_argument("--gpu_nodes", nargs="+", default=None, help="gpu_device_id")
# sys_args = ["--config", "/home/jameshuang/Projects/NLP_annotation/params/config.yml", "--experiment", "lungrads_ner_validation_baseline"]
# sys_args = ["--config", "/home/jameshuang/Projects/NLP_annotation/params/config.yml", "--experiment", "lungrads_pipeline", "--gpu_nodes", "0", "1", "2", "3"]
# sys_args = ["--config", "/home/jameshuang/Projects/NLP_annotation/params/config.yml", "--experiment", "SDoH_pipeline", "--gpu_nodes", "0", "1", "2", "3", "4"]
# args = parser.parse_args(sys_args)
args = parser.parse_args()
# Load configuration
with open(Path(args.config), 'r') as f:
experiment_info = yaml.safe_load(f)[args.experiment]
experiment_info['gpu_nodes'] = args.gpu_nodes
# Main function
run(experiment_info)