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 meta_setup(self):
from ray.tune import Analysis
out_meta = ContextCompositeNode.meta_setup(self)
if 'tune' in self.conf:
if 'local_dir' in self.conf['tune']:
path = self.conf['tune']['local_dir']
if 'name' in self.conf['tune']:
exp = self.conf['tune']['name']
try:
analysis = Analysis(path + '/' + exp)
if 'best' in self.conf:
best = analysis.get_best_config(
**self.conf['best'])
for key in best.keys():
self.conf['context'][key]['value'] = best[key]
print('get best', best)
out_meta.outports[self.OUTPUT_CONFIG] = self.conf
except Exception:
pass
return out_meta
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)
from ray.tune import Analysis
import pandas as pd
import os
import numpy as np
if __name__ == "__main__":
analysis = Analysis(
"/Users/shaobohu/Documents/我的坚果云/project/circles_experiment/TRY_ALL/Train")
print(sorted(analysis.dataframe()['acc'].tolist()))
print(analysis.get_best_config('acc', 'max'))
import torch
import matplotlib.pyplot as plt
from core.dataloader import *
from core.model import *
from core.training import *
from core.evaluating import *
from plots.plots import *
from core.create_folder import *
from core.pred_sequence import *
folders = []
folders.append(
"Run_w_11_timesteps_3_hiddenDim_1_layers_0.00021723989839730966_LR")
configs = []
analysis = Analysis("~/ray_results/Jan21")
configs.append(analysis.get_best_config(metric="error", mode="max"))
for i in range(2, 17):
analysis = Analysis("~/ray_results/" + str(i) + "forward")
config = analysis.get_best_config(metric="error", mode="max")
configs.append(config)
print(config)
folder_name = 'Predicting' + str(config["num_forward"]) + '_w_' + str(
config["timesteps"]) + '_timesteps_' + str(
config["hidden_dim"]) + '_hiddenDim_' + str(
config["num_layers"]) + '_layers_' + str(config["lr"]) + "_LR"
folders.append(folder_name)
#print(folder_name)
print(len(folders))
print(len(configs))
model_keys = []
config_multiple_models = {}