File size: 9,758 Bytes
da3b15a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
#!/usr/bin/env python
import os
import csv
import json
import time
import pickle
import openai
import pandas as pd
from pathlib import Path
from tqdm import tqdm
from dotenv import load_dotenv
from mech.packages.valory.customs.prediction_request import prediction_request
from benchmark.utils import get_logger, TokenCounterCallback
load_dotenv()
logger = get_logger(__name__)
this_dir = Path(__file__).parent
def tool_map(tool):
"""Map the tool name to the tool class."""
tool_dict = {
"prediction-online": prediction_request,
"prediction-offline": prediction_request,
}
tool = tool_dict.get(tool, None)
if tool is None:
raise Exception(f"Tool {tool} not found.")
else:
return tool
def prepare_questions(kwargs):
test_questions = json.load(
open(this_dir / "olas-predict-benchmark/benchmark/data/autocast/autocast_questions_filtered.json")
)
with open(
this_dir / "olas-predict-benchmark/benchmark/data/autocast/autocast_questions_filtered.pkl", "rb"
) as f:
url_to_content = pickle.load(f)
num_questions = kwargs.pop("num_questions", len(test_questions))
questions = []
for q in test_questions:
if q["qtype"] == "t/f" and q["answer"] is not None:
questions.append(q)
if len(questions) >= num_questions:
break
return questions, url_to_content
def parse_response(response, test_q):
try:
result = json.loads(response[0])
except Exception as e:
print("The response is not json-format compatible")
print(f"################### response[0] = {response[0]}")
test_q["Correct"] = False
test_q["prediction"] = None
return test_q
if "p_yes" in result.keys():
test_q["p_yes"] = float(result["p_yes"])
else:
test_q["p_yes"] = None
if "p_no" in result.keys():
test_q["p_no"] = float(result["p_no"])
else:
test_q["p_no"] = None
if "confidence" in result.keys():
test_q["confidence"] = float(result["confidence"])
else:
test_q["confidence"] = None
if "info_utility" in result.keys():
test_q["info_utility"] = float(result["info_utility"])
else:
test_q["info_utility"] = None
if response[3] is not None:
test_q["input_tokens"] = response[3].cost_dict["input_tokens"]
test_q["output_tokens"] = response[3].cost_dict["output_tokens"]
test_q["total_tokens"] = response[3].cost_dict["total_tokens"]
test_q["input_cost"] = response[3].cost_dict["input_cost"]
test_q["output_cost"] = response[3].cost_dict["output_cost"]
test_q["total_cost"] = response[3].cost_dict["total_cost"]
test_q["prompt_response"] = response[1].replace(os.linesep, "")
if (test_q["p_yes"] is None) or (float(result["p_yes"]) == float(result["p_no"])):
test_q["prediction"] = None
else:
test_q["prediction"] = "yes" if test_q["p_yes"] > test_q["p_no"] else "no"
test_q["Correct"] = test_q["prediction"] == test_q["answer"]
return test_q
def write_results(csv_file_path):
results_path = Path(csv_file_path.parent)
time_string = csv_file_path.stem.split("_", 1)[-1]
results_df = pd.read_csv(csv_file_path)
num_errors = results_df["error"].count()
logger.info(f"Num errors: {str(num_errors)}")
results_df = results_df.dropna(subset=["prediction"])
grouped_df = results_df.groupby(["tool", "model"]).agg(
{
"Correct": ["mean", "sum", "count"],
"crowd_correct": ["mean"],
"input_tokens": ["mean"],
"output_tokens": ["mean"],
"total_tokens": ["mean"],
"input_cost": ["mean"],
"output_cost": ["mean"],
"total_cost": ["mean"],
}
)
grouped_df.columns = ["_".join(col).strip() for col in grouped_df.columns.values]
summary_df = grouped_df.reset_index().rename(
columns={
"Correct_mean": "accuracy",
"Correct_sum": "correct",
"Correct_count": "total",
"crowd_correct_mean": "crowd_accuracy",
}
)
logger.info(f"Results:\n\n {results_df}")
summary_df.to_csv(results_path / f"summary_{time_string}.csv", index=False)
def run_benchmark(kwargs):
"""Start the benchmark tests. If a category flag is provided, run the categories with that mark."""
logger.info("Running benchmark tests...")
tools = kwargs.pop("tools")
model = kwargs.pop("model")[0]
MAX_RETRIES = kwargs.pop("max_retries", 3)
questions, url_to_content = prepare_questions(kwargs)
logger.info(f"Running {len(questions)} questions for each tool: {tools}")
results_path = Path("results")
if not results_path.exists():
results_path.mkdir(exist_ok=True)
start_time = time.time()
time_string = time.strftime("%y%m%d%H%M%S", time.localtime(start_time))
csv_file_path = results_path / f"results_{time_string}.csv"
logger.info("Creating csv files...")
with open(csv_file_path, mode="a", newline="") as file:
fieldnames = [
"prompt",
"answer",
"tool",
"model",
"p_yes",
"p_no",
"confidence",
"info_utility",
"prediction",
"Correct",
"input_tokens",
"output_tokens",
"total_tokens",
"input_cost",
"output_cost",
"total_cost",
"prompt_response",
"error",
"crowd_prediction",
"crowd_correct",
]
writer = csv.DictWriter(file, fieldnames=fieldnames)
if file.tell() == 0:
writer.writeheader()
for t in tools:
logger.info("Loading the tool...")
try:
tool = tool_map(t)
except Exception as e:
logger.error(f"Error while loading the tool={tool}")
continue
correct_answers = 0
total_answers = 0
for test_question in tqdm(
questions, desc=f"Running tool {t}", total=len(questions)
):
test_q = {
"prompt": test_question["question"],
"answer": test_question["answer"],
"crowd_prediction": test_question["crowd"][-1]["forecast"],
"tool": t,
"model": model,
"counter_callback": TokenCounterCallback(),
"prompt_response": None,
}
if kwargs["provide_source_links"]:
test_q["source_links"] = test_question["source_links"]
test_q["source_links"] = {
source_link: url_to_content[source_link]
for source_link in test_q["source_links"]
}
crowd_forecast = test_question["crowd"][-1]["forecast"]
test_q["crowd_prediction"] = (
"yes"
if crowd_forecast > 0.5
else "no" if crowd_forecast < 0.5 else None
)
test_q["crowd_correct"] = test_q["crowd_prediction"] == test_q["answer"]
CURRENT_RETRIES = 0
while True:
try:
response = tool.run(**{**test_q, **kwargs})
test_q = parse_response(response, test_q)
if test_q["Correct"] == True:
correct_answers += 1
if test_q["prediction"] is not None:
total_answers += 1
print(
f"===========ACCURACY============== {correct_answers/total_answers*100}%"
)
break
except openai.APIError as e:
logger.error(f"Error running benchmark for tool {t}: {e}")
CURRENT_RETRIES += 1
if CURRENT_RETRIES > MAX_RETRIES:
logger.error(
f"Max retries reached for tool {t}. Skipping question."
)
test_q["error"] = e
break
else:
logger.info(
f"Retrying tool {t} for question {test_q['prompt']}"
)
continue
except Exception as e:
logger.error(f"Error running benchmark for tool {t}: {e}")
test_q["error"] = e
break
if kwargs["provide_source_links"]:
del test_q["source_links"]
del test_q["counter_callback"]
writer.writerow(test_q)
write_results(csv_file_path)
end_time = time.time()
total_time = end_time - start_time
logger.info(f"Total Time: {total_time} seconds")
if __name__ == "__main__":
kwargs = {}
kwargs["num_questions"] = 10
kwargs["tools"] = [
"prediction-online",
]
kwargs["model"] = [
"gpt-3.5-turbo-0125",
]
kwargs["api_keys"] = {}
kwargs["api_keys"]["openai"] = os.getenv("OPENAI_API_KEY")
kwargs["api_keys"]["anthropic"] = os.getenv("ANTHROPIC_API_KEY")
kwargs["api_keys"]["openrouter"] = os.getenv("OPENROUTER_API_KEY")
kwargs["num_urls"] = 3
kwargs["num_words"] = 300
kwargs["provide_source_links"] = True
run_benchmark(kwargs) |