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import json |
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import os |
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import re |
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from collections import defaultdict |
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from copy import deepcopy |
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from typing import Any, Dict, List |
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from tqdm import tqdm |
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os.environ["REDIS_OM_URL"] = "redis://:QzmCUD3C3RdsR@localhost:6381" |
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from reverse_engineering import run_reverse_by_pk_agent |
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with open("../../data/sotopia_pi_openai_log_key_utterance.jsonl", 'r') as f: |
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data: List[Dict[str, Any]] = [json.loads(d) for d in f.readlines()] |
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if not os.path.exists("../../data/episode_utterances"): |
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os.makedirs("../../data/episode_utterances") |
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for d in tqdm(data): |
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run_reverse_by_pk_agent(d['episode_id'], True, "../../data/episode_utterances") |
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run_reverse_by_pk_agent(d['episode_id'], False, "../../data/episode_utterances") |
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utterance_pattern = r'Utterance (\d+) by ([A-Za-z ]+)' |
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print(len(data)) |
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print("turning into attributed utterances") |
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max_turn_dict: Dict[str, int] = defaultdict(int) |
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episode_id_goal_score: Dict[str, float] = defaultdict(float) |
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for d in tqdm(data): |
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for uttr_key, attributed_uttr in d['key_utterance_judgement'].items(): |
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episode_id_goal_score[d['episode_id']] = d['goal_score'] |
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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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max_turn_dict[f"{d['episode_id']}-{agent_name}"] = max(max_turn_dict[f"{d['episode_id']}-{agent_name}"], int(turn_number)) |
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max_turn_dict = dict(max_turn_dict) |
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discounting_factor = 0.9 |
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attribution_dict: Dict[str, Dict[Any, Any]] = defaultdict(dict) |
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def get_attribution_dict(hash_key: str) -> None: |
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episode_id, agent_name = hash_key.split("-") |
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attribution_list = [0.0] * (max_turn_dict[f"{episode_id}-{agent_name}"] + 1) |
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curr_reward = 1.0 |
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for i in range(max_turn_dict[f"{episode_id}-{agent_name}"], -1, -1): |
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attribution_list[i] += curr_reward |
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curr_reward *= discounting_factor |
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max_attribution = max(attribution_list) |
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attribution_list = [a / max_attribution for a in attribution_list] |
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turn_reward_dict = {} |
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for i in range(0, len(attribution_list)): |
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turn_reward_dict[i] = attribution_list[i] |
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attribution_dict[f"{episode_id}-{agent_name}"] = turn_reward_dict |
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for hash_key in max_turn_dict.keys(): |
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get_attribution_dict(hash_key) |
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attribution_dict = dict(attribution_dict) |
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print("turning into attributed utterances") |
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print(len(attribution_dict)) |
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attributed_data = [] |
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for hash_key in attribution_dict: |
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episode_id, agent_name = hash_key.split("-") |
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for turn_number in attribution_dict[hash_key]: |
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utterance_path = f"../../data/episode_utterances/{episode_id}-{agent_name}-{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/{episode_id}-{agent_name}-{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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new_utterance['attribution'] = attribution_dict[hash_key][turn_number] |
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new_utterance['turn_number'] = turn_number |
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new_utterance['goal_score'] = episode_id_goal_score[episode_id] |
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attributed_data.append(new_utterance) |
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def calc_reward(utter_attrib: float, goal_score: float) -> float: |
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return utter_attrib * goal_score |
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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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"instruction": d['prompt'], |
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"input": "", |
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"output": d['result'], |
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"value": calc_reward(d['attribution'], d['goal_score']), |
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"system": "", |
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"history": [] |
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} |
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) |
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with open("../../data/sotopia_pi_reward_baseline.json", 'w') as f: |
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json.dump(sotopia_pi_utterance_reward, f, indent=4) |
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