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import argparse
import numpy as np
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
from dataset import load_dataset
from config import get_config
from featurizer import get_frequency, get_self_eval, get_bool_eval
from gpt import GPTClient
def parse_args():
parser = argparse.ArgumentParser(
prog="conformal-safety",
description="Auto-filter claims from LLM to meet accuracy and safety guarantees.",
)
parser.add_argument('-config_path', '-c', default='configs/default.toml', help="Config for construction.")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
config = get_config(args.config_path)
dataset = load_dataset(config)
# client = GPTClient(f'.cache/{config.dataset.name}_frequency.pkl')
# with ThreadPoolExecutor(max_workers=8) as executor:
# frequencies = list(
# tqdm(
# executor.map(
# lambda x: get_frequency(client, [af['atom'] for af in x['atomic_facts']], x['prompt'], config.model.prob.frequency.model),
# dataset
# ),
# total=len(dataset)
# )
# )
# client.save_cache()
# eval_client = GPTClient(f'.cache/{config.dataset.name}_self_evals.pkl')
# with ThreadPoolExecutor(max_workers=25) as executor:
# self_evals = list(
# tqdm(
# executor.map(
# lambda x: get_self_eval(x['prompt'], [af['atom'] for af in x['atomic_facts']], eval_client),
# dataset
# ),
# total=len(dataset)
# )
# )
# eval_client.save_cache()
# bool_client = GPTClient(f'.cache/{config.dataset.name}_bool_evals.pkl')
# with ThreadPoolExecutor(max_workers=25) as executor:
# self_bools = list(
# tqdm(
# executor.map(
# lambda x: get_bool_eval(x['prompt'], [af['atom'] for af in x['atomic_facts']], bool_client),
# dataset
# ),
# total=len(dataset)
# )
# )
# bool_client.save_cache()
# features = np.concatenate(
# [
# np.concatenate(frequencies).reshape(-1,1),
# np.concatenate(self_evals).reshape(-1,1)
# ],
# axis=1
# )
import IPython; IPython.embed()
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