def testBestLogdir(self):
analysis = Analysis(self.test_dir)
logdir = analysis.get_best_logdir(self.metric)
self.assertTrue(logdir.startswith(self.test_dir))
logdir2 = analysis.get_best_logdir(self.metric, mode="min")
self.assertTrue(logdir2.startswith(self.test_dir))
self.assertNotEquals(logdir, logdir2)
def main():
analysis = Analysis(TUNE_RESULTS_FOLDER)
print("Best hyperparameter {}".format(
analysis.get_best_config(metric="mean_reward", mode="max")))
best_model_path = analysis.get_best_logdir(metric="mean_reward",
mode="max")
print(
"Best model found in {}, start rendering .gif".format(best_model_path))
best_model = SomeModelToTrain({
'learning_rate': 0.1,
'batch_size': 1,
'target_update': 1
})
checkpoint_path = f'{best_model_path}/checkpoint_{MAX_TRAINING_ITERATION}'
best_model.load(checkpoint_path + '/' + MODEL_FILENAME)
# we got this part from https://stable-baselines.readthedocs.io/en/master/guide/examples.html and modified it
env = gym.make('LunarLander-v2')
images = []
state = env.reset()
for j in range(210):
action = best_model.agent.act(state)
img = env.render(mode='rgb_array')
images.append(img)
state, reward, done, _ = env.step(action)
if done:
break
env.close()
imageio.mimsave(
'best_model.gif',
[np.array(img) for i, img in enumerate(images) if i % 2 == 0],
fps=29)
optimize('best_model.gif')
def testBestConfigIsLogdir(self):
analysis = Analysis(self.test_dir)
for metric, mode in [(self.metric, "min"), (self.metric, "max")]:
logdir = analysis.get_best_logdir(metric, mode=mode)
best_config = analysis.get_best_config(metric, mode=mode)
self.assertEquals(analysis.get_all_configs()[logdir], best_config)