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predict_ternary.py
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predict_ternary.py
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# Predict, then uncollate
import argparse
import json
import os
import shutil
import subprocess
from typing import Any, Dict
import sys
from dygie_visualize_util import Dataset
import pathlib
from pathlib import Path
from dygie.data.dataset_readers import document
import pandas as pd
from decode import decode
import re
import traceback
"""
Usage
python predict_ternary.py --data_path data/cofie --device 0,1,2,3 --serial_dir models/cofie-t/collated
python predict_ternary.py --data_path data/cofie --device 0,1,2,3 --serial_dir models/cofie-t/collated --pred_dir predictions/cofie-t
"""
"""
python predict_ternary.py --data_dir predictions/oa_extrated_0 --device 0 --serial_dir pretrained/ternary-model.tar.gz --pred_dir predictions/final_pred_0/
python predict_ternary.py --data_dir predictions/oa_extrated_1 --device 1 --serial_dir pretrained/ternary-model.tar.gz --pred_dir predictions/final_pred_1/
"""
"""
test:
python predict_ternary.py --data_dir data/ergothioneine_REed --device 0 --serial_dir pretrained/ternary-model.tar.gz --pred_dir predictions/final_pred_0/
"""
def stringify(xs):
return " ".join(xs)
def format_predicted_events(sent, doc_key=""):
res = []
for event in sent.predicted_events:
if len(event.arguments) < 2:
continue
arg0 = event.arguments[0]
arg1 = event.arguments[1]
entry = {"doc_key": doc_key,
"sentence": stringify(sent.text),
"arg0": stringify(arg0.span.text),
"trigger": event.trigger.token.text,
"arg1": stringify(arg1.span.text),
"arg0_logit": arg0.raw_score,
"trigger_logit": event.trigger.raw_score,
"arg1_logit": arg1.raw_score,
"arg0_softmax": arg0.softmax_score,
"trigger_softmax": event.trigger.softmax_score,
"arg1_softmax": arg1.softmax_score}
res.append(entry)
return res
def format_dataset(dataset):
predicted_events = []
for doc in dataset:
for sent in doc:
predicted = format_predicted_events(sent, doc.doc_key)
predicted_events.extend(predicted)
predicted_events = pd.DataFrame(predicted_events)
return predicted_events
def load_jsonl(fname):
return [json.loads(x) for x in open(fname)]
def save_jsonl(xs, fname):
with open(fname, "w") as f:
for x in xs:
print(json.dumps(x), file=f)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--serial_dir',
type=str,
help="path to the saved trained model",
default="./models/events/")
parser.add_argument('--data_dir',
type=str,
help="path to the directory containing the test and dev data files",
default="data/processed/collated/")
parser.add_argument('--test_file',
type=str,
help="Please mention test filename in the data_path if test filename is not test.json",
required=False,
default="test.json")
parser.add_argument('--device',
type=str,
default='0',
required=False,
help="cuda devices comma seperated")
parser.add_argument('--pred_dir',
type=str,
help="Path to the directory to save the prediction. default is ./predictions/",
required=False,
default="./predictions/")
parser.add_argument('--pred_file',
type=str,
help="Please mention prediction filename(including json extention) in the pred_dir if prediction filename should not be pred.json / pred.tsv",
required=False,
default="pred.json")
parser.add_argument('--decode_file',
type=str,
help="Please mention prediction decode filename(including json extention) in the pred_dir if prediction filename should not be decode.json",
required=False,
default="decode.json")
args = parser.parse_args()
data_root = pathlib.Path(args.data_dir)
serial_dir = pathlib.Path(args.serial_dir)
pred_dir = pathlib.Path(args.pred_dir)
pred_dir.mkdir(parents=True, exist_ok=True)
test_dir = data_root / args.test_file
uncollated_pred_path = pred_dir/ "pred.json"
uncollated_pred_path_decode = pred_dir/ "decode.json"
uncollated_pred_path_tsv = pred_dir/ "pred.tsv"
# print(f"\'{args.device}\'")
# exit()
# args.device = str([0, 1])
idx = 0
for data_f in os.listdir(args.data_dir):
try:
if data_f.endswith("jsonl") or data_f == "test.json": # TODO: delete the second condition
idx = re.search(r'\d+', data_f).group(0)
if f"decode_{idx}.json" in os.listdir(pred_dir):
continue
test_dir = data_root / data_f
uncollated_pred_path = pred_dir / f"pred_{idx}.json"
uncollated_pred_path_decode = pred_dir / f"decode_{idx}.json"
uncollated_pred_path_tsv = pred_dir / f"pred_{idx}.tsv"
allennlp_command = [
"allennlp",
"predict",
str(serial_dir),
str(test_dir),
"--predictor dygie",
"--include-package dygie",
"--use-dataset-reader",
"--output-file",
str(uncollated_pred_path),
"--cuda-device",
args.device
# f"\"{args.device}\""
]
# subprocess.run(" ".join(allennlp_command), shell=True, check=True)
subprocess.call(" ".join(allennlp_command), shell=True)
in_data = load_jsonl(str(uncollated_pred_path))
out_data = decode(in_data)
save_jsonl(out_data, str(uncollated_pred_path_decode))
dataset = document.Dataset.from_jsonl(str(uncollated_pred_path_decode))
pred = format_dataset(dataset)
pred.to_csv(str(uncollated_pred_path_tsv), sep="\t", float_format="%0.4f", index=False)
except Exception as e:
with open("predict_ternary_err.log" , "a") as f:
f.write(data_f + "\n")
pass