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wrappers.py
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wrappers.py
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import json
import os
import gym
import numpy as np
import re
import string
from collections import Counter
DATA_DIR = "data"
HOTPOTQA_SPLIT_FILE = {
"train": "hotpot_train_v1.1_simplified.json",
"dev": "hotpot_dev_v1_simplified.json",
"test": "hotpot_test_v1_simplified.json",
}
FEVER_SPLIT_FILE = {
"train": "train.jsonl",
"dev": "paper_dev.jsonl",
}
class HistoryWrapper(gym.ObservationWrapper):
def __init__(self, env, obs_format, prompt=None):
super().__init__(env)
assert obs_format in ["obs", "history"]
if obs_format == "history":
assert hasattr(self.env, "traj")
self.obs_format = obs_format
self.prompt = prompt if prompt is not None else ""
def observation(self, obs):
if self.obs_format == "obs":
return obs
elif self.obs_format == "history":
observation = self.env.traj["observations"][0] + "\n"
for i, (o, a) in enumerate(zip(self.env.traj["observations"][1:], self.env.traj["actions"]), 1):
observation += f"Action {i}: {a}\nObservation {i}: {o}\n\n"
return self.prompt + observation
def normalize_answer(s):
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
normalized_prediction = normalize_answer(prediction)
normalized_ground_truth = normalize_answer(ground_truth)
ZERO_METRIC = (0, 0, 0)
if normalized_prediction in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
return ZERO_METRIC
if normalized_ground_truth in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
return ZERO_METRIC
prediction_tokens = normalized_prediction.split()
ground_truth_tokens = normalized_ground_truth.split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return ZERO_METRIC
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1, precision, recall
class HotPotQAWrapper(gym.Wrapper):
def __init__(self, env, split):
super().__init__(env)
data_file = f"{DATA_DIR}/{HOTPOTQA_SPLIT_FILE[split]}"
self.data = json.load(open(data_file))
self.data = [(d['question'], d['answer']) for d in self.data]
self.data_idx = 0
self.split = split
def reset(self, seed=None, return_info=False, options=None, idx=None):
self.env.reset(seed=seed, return_info=return_info, options=options)
try:
self.env.step('')
except:
pass
self.env.reset(seed=seed, return_info=return_info, options=options)
self.data_idx = int(np.random.randint(len(self.data))) if idx is None else idx
observation = f"Question: {self.data[self.data_idx][0]}"
info = self._get_info()
return (observation, info) if return_info else observation
def _get_info(self):
return {
"steps": self.steps,
"answer": self.answer,
"question": self.data[self.data_idx][0],
"hotpot_split": self.split
}
def get_reward(self, info):
if info['answer'] is not None:
pred = normalize_answer(self.data[self.data_idx][1])
gt = normalize_answer(info['answer'])
score = (pred == gt)
return int(score)
return 0
def get_metrics(self, info):
if info['answer'] is not None:
pred = normalize_answer(self.data[self.data_idx][1])
gt = normalize_answer(info['answer'])
em = (pred == gt)
f1 = f1_score(pred, gt)[0]
return {'reward': em, 'em': em, 'f1': f1}
return {'reward': 0, 'em': 0, 'f1': 0}
def step(self, action):
# TODO: first step obs does not have question.
obs, _, done, info = self.env.step(action)
reward = self.get_reward(info)
if done:
obs = f"Episode finished, reward = {reward}\n"
info.update({"gt_answer": self.data[self.data_idx][1], "question_idx": self.data_idx})
info.update(self.get_metrics(info))
return obs, reward, done, info
def __len__(self):
return len(self.data)
class FeverWrapper(gym.Wrapper):
def __init__(self, env, split):
super().__init__(env)
data_path = f"./data/{FEVER_SPLIT_FILE[split]}"
with open(data_path, "r") as json_file:
json_list = list(json_file)
data = []
for json_str in json_list:
json_str = json.loads(json_str)
label = json_str["label"]
claim = json_str["claim"]
data.append((claim, label))
self.data = data
self.data_idx = 0
self.split = split
def reset(self, seed=None, return_info=False, options=None, idx=None):
self.env.reset(seed=seed, return_info=return_info, options=options)
try:
self.env.step('')
except:
pass
self.env.reset(seed=seed, return_info=return_info, options=options)
self.data_idx = int(np.random.randint(len(self.data))) if idx is None else idx
observation = f"Claim: {self.data[self.data_idx][0]}"
info = self._get_info()
return (observation, info) if return_info else observation
def _get_info(self):
return {
"steps": self.steps,
"answer": self.answer,
"question": self.data[self.data_idx][0],
"fever_split": self.split
}
def get_reward(self, info):
if info['answer'] is not None:
label = normalize_answer(self.data[self.data_idx][1])
pred = normalize_answer(info['answer'])
if label == pred:
return 1
return 0
def step(self, action):
# TODO: first step obs does not have question.
obs, _, done, info = self.env.step(action)
reward = self.get_reward(info)
if done:
obs = f"Episode finished, reward = {reward}\n"
info.update({"gt_answer": self.data[self.data_idx][1], "question_idx": self.data_idx})
info.update({'em': reward, 'reward': reward, 'f1': reward})
return obs, reward, done, info
def __len__(self):
return len(self.data)
class LoggingWrapper(gym.Wrapper):
def __init__(self, env, folder="trajs", file_id=None):
super().__init__(env)
self.trajs = []
self.traj = {"observations": [], "actions": []}
self.folder = folder
self.file_id = np.random.randint(0, 10000000) if file_id is None else file_id
self.file_path = f"{self.folder}/{self.file_id}.json"
os.makedirs("trajs", exist_ok=True)
def __len__(self):
return len(self.env.data)
def reset(self, seed=None, return_info=False, options=None, idx=None):
output = self.env.reset(seed=seed, return_info=return_info, options=options, idx=idx)
observation = output[0] if return_info else output
self.traj = {"observations": [observation], "actions": []}
return output
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.traj["observations"].append(obs)
self.traj["actions"].append(action)
if done:
self.traj.update(info)
return obs, reward, done, info
def update_record(self):
if len(self.traj) > 0:
self.trajs.append(self.traj)
self.traj = {"observations": [], "actions": []}
def write(self):
self.update_record()
with open(self.file_path, "w") as f:
json.dump(self.trajs, f)
print(f"Saved trajs to trajs/{self.file_id}.json")
def close(self):
self.write()