File size: 4,694 Bytes
8567b2b | 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 | import argparse
import json
import os.path
import random
from glob import glob
from tqdm import tqdm
from clemcore.clemgame.resources import load_json
def create_conversational_dataset_for(top_dir):
"""NOTE: This script requires interactions generated with clemcore >=2.4.0 !"""
interactions_files = glob(f"{top_dir}/**/interactions.json", recursive=True)
dataset_file = "results.jsonl"
dataset_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), dataset_file)
print(f"Writing dataset file to {dataset_path} interactions")
exceptions = set()
with open(dataset_path, "w", encoding="utf-8") as f:
print(f"Collecting {len(interactions_files)} interactions")
for interactions_file in tqdm(interactions_files):
interactions = load_json(interactions_file)
# read from meta info (since clemcore 2.4)
game_name = interactions["meta"]["game_name"]
experiment_name = interactions["meta"]["experiment_name"]
game_id = interactions["meta"]["game_id"]
outcome = None # this should also become part of the meta in later clemcore versions
try:
scores = load_json(os.path.join(os.path.dirname(interactions_file), "scores.json"))
episodes_scores = scores["episode scores"]
if episodes_scores["Aborted"]:
outcome = "aborted"
if episodes_scores["Success"]:
outcome = "success"
if episodes_scores["Lose"]:
outcome = "failure"
except Exception as e: # cannot determine outcome
pass
# We collect each episode from the perspective of all players individually
for player_name, player_details in interactions["players"].items():
try: # since clemcore 2.4
player_name = player_details["player_name"]
game_role = player_details["game_role"]
model_name = player_details["model_name"]
except Exception as e:
exceptions.add((game_name, player_details))
continue
if player_name == "GM":
continue # ignore game master perspective (we dont want to learn that here)
if model_name == "programmatic":
continue # do not train on programmatic behaviors
# print(f"Going through {len(interactions['turns'])} rounds")
messages = []
for events in interactions["turns"]:
# print(f"Scanning {len(events)} round events")
for event in events:
if event["to"] == player_name: # a message to the player (assistant)
messages.append(dict(role="user", content=event["action"]["content"]))
if event["from"] == player_name: # a message from the player (assistant)
messages.append(dict(role="assistant", content=event["action"]["content"]))
if messages: # ignore episodes where player had no turn because of initial failures of the other
f.write(json.dumps({
"messages": messages,
"meta": {
"game": game_name,
"experiment": experiment_name,
"task_id": game_id,
"player_name": player_name,
"game_role": game_role,
"model": model_name,
"outcome": outcome
}
}) + '\n')
for ex in exceptions:
print(ex)
counter = 0
random_examples = []
with open(dataset_path, "r", encoding="utf-8") as file:
for line in file:
counter += 1
if random.randint(0, 100) < 1:
random_examples.append(json.loads(line))
print(f"Written {counter} examples to {dataset_path}")
print()
print(f"See {len(random_examples)} examples:")
for example in random_examples:
print("Meta:", example["meta"])
print("Messages:")
for message in example["messages"]:
print(f" {message}")
print()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("top_dir",
help="The directory containing benchmark results for one or more models")
args = parser.parse_args()
create_conversational_dataset_for(args.top_dir)
if __name__ == "__main__":
main()
|