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search_content_for_answer_HIPAA.py
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142 lines (112 loc) · 4.76 KB
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import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import argparse
import copy
import json
import pandas as pd
from tqdm import tqdm
from parse_string import LlamaParser
from agents import AgentContentSearch, HuggingfaceChatbot
from utils import *
import random
import numpy as np
import torch
def set_seeds(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
def main(args):
set_seeds(args)
log(str(args)+"\n",args.log_path)
events = read_events(args.events_path)
kb = read_kb(args.kb_path)
args.kb = kb
#events = events[:5]
### if use api, replace chatbot with empty string
if args.api_name:
chatbot = ''
else:
chatbot = HuggingfaceChatbot(args.model)
agents = AgentContentSearch(chatbot, args, LlamaParser())
predictions = []
results = []
### new appened for continuing eval from errors
ids,accs = parse_log(args.log_path)
last_id = ids[-1] if ids else -1
if(last_id != -1):
acc = accs[-1]
correct = round(acc*(last_id+1))
results = [0] * last_id
results.append(correct)
print(f'last_id: {last_id} with acc {acc}, total correct: {correct}')
else:
print('start from index 0')
for i in tqdm(range(len(events))):
# if i < 110: continue
if i <= last_id:
continue
event = events.loc[i]
decision = agents.action(event.context)
decision["id"] = i
if not "decision" in decision:
results.append(0)
continue
log(str(decision)+"\n", args.log_path)
results.append(decision["decision"] in event.norm_type)
print(sum(results) / len(results))
log(str(sum(results) / len(results)) + "\n", args.log_path)
acc = (sum(results) / len(results))
log(str(f"accuracy:{acc}"), args.log_path)
def parse_log(log_path):
import ast
try:
with open(log_path, "r") as f:
lines = f.readlines()
except:
return []
results = []
acc = []
for line in lines:
if "{" == line[0]:
cur_dict = ast.literal_eval(line.strip())
id = cur_dict["id"]
results.append(id)
if line.startswith("0."):
acc.append(float(line.strip()))
return results, acc
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
parser.add_argument("--events_path", type=str, default="chatgpt_answer_case/real.csv")
parser.add_argument("--kb_path", type=str, default="KB_annotated.json")
parser.add_argument("--log_path", type=str, default=os.path.join(BASE_DIR,'logs','log.txt'))
parser.add_argument("--law_template", type=str, default="prompts/cot-knowledge-lookup-prompt.txt")
parser.add_argument("--law_filter_template", type=str, default="prompts/3-beam-law-filter-prompt.txt")
parser.add_argument("--law_judge_template", type=str, default="prompts/3-cot-judge-regulation-prompt.txt")
parser.add_argument("--decision_making_template", type=str, default="prompts/4-cot-decision-making-merge.txt")
parser.add_argument("--lawyer_tokens", type=int, default=512)
parser.add_argument("--law_filter_tokens", type=int, default=512)
parser.add_argument("--decision_tokens", type=int, default=512)
parser.add_argument("--law_judge_tokens", type=int, default=512)
parser.add_argument("--law_generation_round", type=int, default=3)
parser.add_argument("--law_filtering_round", type=int, default=3)
parser.add_argument("--generation_round", type=int, default=10)
parser.add_argument("--max_law_items", type=int, default=3)
parser.add_argument("--look_up_items", type=int, default=3)
parser.add_argument("--seed", type=int, default=42)
#parser.add_argument("--use_content", type=str, default='yes')
parser.add_argument("--api_name", type=str, default='Qwen/Qwen2-7B-Instruct')
# api_bearer_token
parser.add_argument("--api_bearer_token", type=str, default='')
args = parser.parse_args()
###lhr new add for debug
args.events_path = os.path.join(BASE_DIR, args.events_path)
args.kb_path = os.path.join(BASE_DIR, args.kb_path)
args.law_template = os.path.join(BASE_DIR, args.law_template)
args.law_filter_template = os.path.join(BASE_DIR, args.law_filter_template)
args.law_judge_template = os.path.join(BASE_DIR, args.law_judge_template)
args.decision_making_template = os.path.join(BASE_DIR, args.decision_making_template)
main(args)