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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://docs.byteplus.com/en/docs/ModelArk/1099455
model list
https://docs.byteplus.com/en/docs/ModelArk/1330310
https://docs.byteplus.com/en/docs/ModelArk/Chat
"""
import argparse
from datetime import datetime
import json
import os
from pathlib import Path
import re
import sys
import time
from zoneinfo import ZoneInfo # Python 3.9+ 自带，无需安装
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../"))
from openai import OpenAI
from project_settings import environment, project_path
def get_args():
"""
model list:
https://docs.byteplus.com/en/docs/ModelArk/1330310
bytedance-seed-1.6
seed-1-6-250615
bytedance-seed-1.6-flash
seed-1-6-flash-250615
deepseek-v3
deepseek-v3-250324
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
# default="seed-1-6-250615",
default="seed-1-6-flash-250615",
# default="deepseek-v3-250324",
type=str
)
parser.add_argument(
"--eval_dataset_name",
default="agent-bingoplus-ph-25-summary.jsonl",
type=str
)
parser.add_argument(
"--eval_dataset_dir",
default=(project_path / "data/dataset").as_posix(),
type=str
)
parser.add_argument(
"--eval_data_dir",
default=(project_path / "data/eval_data").as_posix(),
type=str
)
parser.add_argument(
"--client",
default="shenzhen_sase",
type=str
)
parser.add_argument(
"--service",
default="byteplus_api_key",
type=str
)
parser.add_argument(
"--create_time_str",
default="null",
# default="20250728_113641",
type=str
)
parser.add_argument(
"--interval",
default=1,
type=int
)
args = parser.parse_args()
return args
def main():
args = get_args()
eval_dataset_dir = Path(args.eval_dataset_dir)
eval_dataset_dir.mkdir(parents=True, exist_ok=True)
eval_data_dir = Path(args.eval_data_dir)
eval_data_dir.mkdir(parents=True, exist_ok=True)
if args.create_time_str == "null":
tz = ZoneInfo("Asia/Shanghai")
now = datetime.now(tz)
create_time_str = now.strftime("%Y%m%d_%H%M%S")
# create_time_str = "20250724_090615"
else:
create_time_str = args.create_time_str
eval_dataset = eval_dataset_dir / args.eval_dataset_name
output_file = eval_data_dir / f"byteplus/byteplus/{args.model_name}/{args.client}/{args.service}/{create_time_str}/{args.eval_dataset_name}"
output_file.parent.mkdir(parents=True, exist_ok=True)
api_key = environment.get(args.service, dtype=str)
client = OpenAI(
base_url="https://ark.ap-southeast.bytepluses.com/api/v3/",
# Read your Ark API Key from the environment variable.
api_key=api_key
)
total = 0
total_score = 0
# finished
finished_idx_set = set()
if os.path.exists(output_file.as_posix()):
with open(output_file.as_posix(), "r", encoding="utf-8") as f:
for row in f:
row = json.loads(row)
idx = row["idx"]
total = row["total"]
total_score = row["total_score"]
finished_idx_set.add(idx)
print(f"finished count: {len(finished_idx_set)}")
with open(eval_dataset.as_posix(), "r", encoding="utf-8") as fin, open(output_file.as_posix(), "a+", encoding="utf-8") as fout:
for row in fin:
row = json.loads(row)
idx = row["idx"]
system_prompt: str = row["system_prompt"]
user_prompt: str = row["user_prompt"]
response = row["response"]
if idx in finished_idx_set:
continue
finished_idx_set.add(idx)
try:
time.sleep(args.interval)
print(f"sleep: {args.interval}")
time_begin = time.time()
# https://docs.byteplus.com/en/docs/ModelArk/1449737
llm_response = client.chat.completions.create(
model=args.model_name,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
stream=False,
max_tokens=4096,
extra_body={
"thinking": {
"type": "disabled",
# "type": "enabled",
}
}
)
time_cost = time.time() - time_begin
print(f"time_cost: {time_cost}")
except Exception as e:
print(f"request failed, error type: {type(e)}, error text: {str(e)}")
continue
prediction = llm_response.choices[0].message.content
response_ = json.loads(response)
response_tag_name_list = response_["tag_name_list"]
# print(response_tag_name_list)
if prediction.startswith("```json") and prediction.endswith("```"):
prediction_ = prediction[7:-3]
else:
prediction_ = prediction
prediction_tag_name_list = list()
try:
prediction_ = json.loads(prediction_)
prediction_tag_name_list = prediction_["tag_name_list"]
except json.JSONDecodeError:
pass
# print(prediction_tag_name_list)
# recall
recall_count = 0
for tag in response_tag_name_list:
if tag in prediction_tag_name_list:
recall_count += 1
recall = recall_count / (len(response_tag_name_list) + 1e-7)
# precision
precision_count = 0
for tag in prediction_tag_name_list:
if tag in response_tag_name_list:
precision_count += 1
precision = precision_count / (len(prediction_tag_name_list) + 1e-7)
# f1
f1 = 2 * (recall * precision) / (recall + precision + 1e-7)
total += 1
total_score += f1
score = total_score / total
row_ = {
"idx": idx,
"system_prompt": system_prompt,
"user_prompt": user_prompt,
"response": response,
"prediction": prediction,
"recall": recall,
"precision": precision,
"f1": f1,
"total": total,
"total_score": total_score,
"score": score,
"time_cost": time_cost,
}
row_ = json.dumps(row_, ensure_ascii=False)
fout.write(f"{row_}\n")
fout.flush()
return
if __name__ == "__main__":
main()
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