Spaces:
Sleeping
Sleeping
File size: 1,189 Bytes
a777e34 c412087 a777e34 3cafd2c a777e34 a202b6d a777e34 c10a05f c412087 c10a05f a777e34 a202b6d a777e34 a202b6d a777e34 c10a05f c412087 |
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 |
import os
import torch
from .net import GreedyNet
from .play import play
from .utils import v_wrap
def evaluate_checkpoints(dir, env):
results = {}
for checkpoint in os.listdir(dir):
pretrained_model_path = os.path.join(dir, checkpoint)
if os.path.isfile(pretrained_model_path):
wins, guesses = evaluate(env, pretrained_model_path)
results[checkpoint] = wins, guesses
return dict(
sorted(results.items(), key=lambda x: (x[1][0], -x[1][1]), reverse=True)
)
def evaluate(env, pretrained_model_path):
n_wins = 0
n_guesses = 0
n_win_guesses = 0
env = env.unwrapped
N = env.allowable_words
for goal_word in env.words[:N]:
win, outcomes = play(env, pretrained_model_path, goal_word)
if win:
n_wins += 1
n_win_guesses += len(outcomes)
# else:
# print("Lost!", goal_word, outcomes)
n_guesses += len(outcomes)
print(
f"Evaluation complete, won {n_wins/N*100}% and \
took {n_win_guesses/n_wins} guesses per win, "
f"{n_guesses / N} including losses."
)
return n_wins / N * 100, n_win_guesses / n_wins
|