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import json |
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import os |
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import re |
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from copy import deepcopy |
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from typing import Any, Dict, List |
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import click |
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from db_free_reverse_engineering import run_reverse_by_pk_agent |
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from tqdm import tqdm |
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def get_attributed_data(data: List[Dict[str, Any]], utterance_pattern: str, do_normalize: bool) -> List[Dict[str, Any]]: |
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attributed_data = [] |
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print(f"Processing {len(data)} episodes") |
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error_count = 0 |
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for d in tqdm(data): |
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total_attributed_reward = 0.0 |
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all_utterances = [] |
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for uttr_key, attributed_uttr in d['attributed_utterances'].items(): |
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match = re.search(utterance_pattern, uttr_key) |
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if match: |
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turn_number = match.group(1) |
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agent_name = match.group(2) |
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else: |
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raise Exception(f"Utterance key not in correct format: {uttr_key}") |
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if agent_name != d['agent']: |
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continue |
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utterance_path = f"../../data/episode_utterances/{d['episode_id']}-{d['agent']}-{turn_number}.json" |
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if not os.path.exists(utterance_path): |
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raise Exception(f"Utterance not found: {utterance_path}") |
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with open(f"../../data/episode_utterances/{d['episode_id']}-{d['agent']}-{turn_number}.json", 'r') as f: |
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sotopia_utterance = json.load(f) |
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new_utterance = deepcopy(sotopia_utterance) |
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try: |
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new_utterance['attributed_reward'] = attributed_uttr[1]["reward"] |
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new_utterance['dimension'] = "goal" |
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new_utterance['scale'] = "3" |
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new_utterance['dim_score'] = 10 |
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except: |
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error_count += 1 |
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continue |
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new_utterance['turn_number'] = turn_number |
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new_utterance['goal_score'] = d['goal_score'] |
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total_attributed_reward += new_utterance['attributed_reward'] |
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all_utterances.append(new_utterance) |
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if do_normalize: |
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for utterance in all_utterances: |
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utterance['attributed_reward'] = utterance['attributed_reward'] / total_attributed_reward * d['goal_score'] |
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for utterance in all_utterances: |
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attributed_data.append(utterance) |
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print(f"Error Count: {error_count}") |
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return attributed_data |
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@click.command() |
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@click.option("--data_dir", type=str, required=True, help="Directory containing data files.") |
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@click.option("--input_file", type=str, required=True, help="Path to the raw JSONLINES file.") |
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@click.option("--reward_output_file", type=str, required=True, help="Path to the processed JSON file.") |
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@click.option("--do_normalize", type=bool, default=False, help="Whether to normalize the rewards.") |
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def main(data_dir: str, input_file: str, reward_output_file: str, do_normalize: bool = False) -> None: |
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with open(os.path.join(data_dir, input_file), 'r') as f: |
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data: List[Dict[str, Any]] = [json.loads(d) for d in f.readlines()] |
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cache_dir = os.path.join(data_dir, "episode_utterances") |
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if not os.path.exists(cache_dir): |
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os.makedirs(cache_dir) |
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for d in tqdm(data): |
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run_reverse_by_pk_agent(d['episode_id'], True, cache_dir) |
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run_reverse_by_pk_agent(d['episode_id'], False, cache_dir) |
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utterance_pattern = r'Utterance (\d+) by ([A-Za-z ]+)' |
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print("turning into attributed utterances") |
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attributed_data = get_attributed_data(data, utterance_pattern, do_normalize=do_normalize) |
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sotopia_pi_utterance_reward = [] |
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for d in tqdm(attributed_data): |
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sotopia_pi_utterance_reward.append( |
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{ |
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"input": d['prompt'], |
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"output": d['result'], |
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"value": d['attributed_reward'], |
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} |
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) |
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with open(os.path.join(data_dir, reward_output_file), 'w') as f: |
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json.dump(sotopia_pi_utterance_reward, f, indent=4) |
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if __name__ == "__main__": |
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main() |
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