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#!/usr/bin/env python3
import argparse
import json
import os
from datasets import load_dataset
import openai
from openai import OpenAI
import pandas as pd
from tqdm import tqdm
# --- Functions ---
def evaluate_item(client, item, model_name, model_params):
system_prompt = item["system_prompt"]
user_prompt = item["prompt"]
answer = item["answer"]
question_points = item["question_points"]
uid = item["uid"]
temperature = model_params['temperature']
max_tokens = model_params['max_tokens']
top_p = model_params['top_p']
seed = model_params['seed']
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
]
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
seed=seed
)
model_output = response.choices[0].message.content.strip()
# Check if output matches the expected answer
eval_passed = model_output.strip() == answer.strip()
token_usage = response.usage if hasattr(response, "usage") else {}
finish_reason = response.choices[0].finish_reason
result = {
"data_source_id": uid,
"item": item,
"sample": {
"trajectory": messages,
"outputs": [{"role": "assistant", "content": model_output}],
"finish_reason": finish_reason,
"sampled_model_name": model_name,
"sampled_model_params": {"seed": seed, "temperature": temperature, "max_tokens": max_tokens, "top_p": top_p},
"token_usage": dict(token_usage),
},
"grades": {"String check": question_points if eval_passed else 0.0},
"grader_samples": {},
"passes": {"String check": eval_passed},
}
return result
def evaluate_data(client, data, results, model_name, model_params):
"""
Evaluates a list of items using the provided OpenAI client.
"""
results = {} if results is None else results
with tqdm(data) as pbar:
for row in pbar:
uid = row['uid']
if results.get(uid):
print(f"Skipping row with uid {uid} as it has already been evaluated.")
continue
try:
result = evaluate_item(client, row, model_name, model_params)
results[uid] = result
pbar.set_description(f"Evaluated row with uid {uid}")
except Exception as e:
print(f"Error evaluating row: {e}")
return results
def get_grades(results, model_name):
"""
Returns a dictionary of grades for each test_id.
"""
grades = {'sampled_model_name': model_name}
for key, item in results.items():
test_id = item['item']['test_id']
point = item['grades']['String check']
if test_id not in grades:
grades[test_id] = {'points': 0, 'total': 0}
grades[test_id]['points'] += int(point)
grades[test_id]['total'] += 1
return grades
def main():
parser = argparse.ArgumentParser(description="Evaluate dataset using an OpenAI client")
parser.add_argument("--model_name", type=str, required=True, help="Model name to use (e.g., mistralai/mistral-small-3.1-24b-instruct)")
parser.add_argument("--eval_subset", type=str, default="all", help="Evaluation subset (default: all)")
parser.add_argument("--output_path", type=str, required=True, help="Path for saving the results")
args = parser.parse_args()
# Read API key from environment variable
API_KEY = os.environ.get('OPEN_ROUTER_API_KEY')
if API_KEY is None:
print("Error: OPEN_ROUTER_API_KEY environment variable not set.")
exit(1)
EVAL_DATASET = "Ekgren/swedish_skolprov"
EVAL_SUBSET = args.eval_subset
MODEL_NAME = args.model_name
model_params = {'temperature': 1, 'max_tokens': 2048, 'top_p': 1, 'seed': 42}
# Load dataset
ds = load_dataset(EVAL_DATASET, EVAL_SUBSET)
ds = ds['train']
# Initialize client
client = OpenAI(
api_key=API_KEY,
base_url="https://openrouter.ai/api/v1"
)
results = evaluate_data(client, ds, None, MODEL_NAME, model_params)
# Build file names
file_name = EVAL_DATASET.replace("/", "-") + "_" + EVAL_SUBSET + "_" + MODEL_NAME.replace("/", "-") + ".jsonl"
file_name = file_name.lower()
grade_file_name = file_name.replace(".jsonl", "_grades.json")
# Ensure output directory exists
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
results_file_path = os.path.join(args.output_path, file_name)
grade_file_path = os.path.join(args.output_path, grade_file_name)
grades = get_grades(results, MODEL_NAME)
print(grades)
# Save results
with open(results_file_path, "w", encoding="utf-8") as f:
for key, item in results.items():
f.write(json.dumps(item) + "\n")
# Save grades
with open(grade_file_path, "w", encoding="utf-8") as f:
f.write(json.dumps(grades) + "\n")
if __name__ == "__main__":
main()
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