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Merge branch 'main' of github.com:sisl/InteractionImitation

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Johannes Fischer
2021-08-02 11:08:35 +02:00
parent 281f7773c4 fb91ee1a62
commit 4ea4d42df7
4 changed files with 45 additions and 30 deletions
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58
experiments/experiment.py
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@@ -3,6 +3,16 @@ from functools import partial
import os
opj = os.path.join
# set up ray tune
import ray
from ray import tune
from ray.tune import Analysis, ExperimentAnalysis
from ray.tune.schedulers import ASHAScheduler
from hyperopt import hp
from ray.tune.suggest.hyperopt import HyperOptSearch
from src.main import basestr, main
def parse_args():
@@ -72,18 +82,18 @@ def get_ray_config(method:str)->dict:
"""
if method == 'bc':
ray_config = {
"lr": tune.choice([1e-4, 1e-3, 1e-2, 1e-1]),
"weight_decay": tune.choice([0.001, 0.01, 0.1, 0.5, 0.9]),
"lr": tune.loguniform(1e-5, 1e-3),
"weight_decay": tune.choice([0, 0.1]),
"loss": tune.choice(['huber', 'mse']),
"train_batch_size": tune.choice([16,32,64]),
"deepsets_phi_hidden_n": tune.choice([1,2,3]),
"deepsets_phi_hidden_dim": tune.choice([16,32,64]),
"deepsets_latent_dim": tune.choice([16,32,64]),
"deepsets_rho_hidden_n": tune.choice([0,1,2]),
"deepsets_rho_hidden_dim": tune.choice([16,32,64]),
"deepsets_output_dim": tune.choice([8,16,32,64]),
"head_hidden_n": tune.choice([1,2,3]),
"head_hidden_dim": tune.choice([16,32,64]),
"deepsets_phi_hidden_n": tune.randint(1,5),
"deepsets_phi_hidden_dim": tune.lograndint(8,65),
"deepsets_latent_dim": tune.lograndint(8,129),
"deepsets_rho_hidden_n": tune.randint(0,3),
"deepsets_rho_hidden_dim": tune.lograndint(8,129),
"deepsets_output_dim": tune.lograndint(4,129),
"head_hidden_n": tune.randint(1,6),
"head_hidden_dim": tune.lograndint(16,257),
"head_final_activation": tune.choice(['sigmoid', None]),
}
else:
@@ -108,10 +118,6 @@ if __name__ == '__main__':
main(config, filestr=filestr, **kwargs)
elif kwargs['ray'] and kwargs['train']:
# set up ray tune
import ray
from ray import tune
from ray.tune.schedulers import ASHAScheduler
ray.shutdown()
ray.init(log_to_driver=False)
@@ -121,29 +127,31 @@ if __name__ == '__main__':
main(full_config, filestr='exp', datadir=datadir, **kwargs)
datadir = os.path.abspath('./expert_data')
ray_config = get_ray_config(kwargs['method'])
custom_scheduler = ASHAScheduler(
metric='cv_loss',
mode="min",
grace_period=25,
)
search = HyperOptSearch(ray_config, max_concurrent=8, metric='cv_loss',mode="min",)
custom_scheduler = ASHAScheduler(metric='cv_loss', mode="min", grace_period=15)
analysis = tune.run(
partial(ray_train, datadir=datadir),
config=ray_config,
#config=ray_config,
search_alg=search,
scheduler=custom_scheduler,
local_dir=outdir,
#resources_per_trial={"cpu": 2},
time_budget_s=45*60,
num_samples=2,
time_budget_s=120*60,
num_samples=100,
)
elif kwargs['ray'] and kwargs['test']:
import ray
from ray.tune import Analysis, ExperimentAnalysis
analysis = Analysis(outdir, default_metric="cv_loss", default_mode="min")
config = analysis.get_best_config()
filepath = analysis.get_best_logdir()
filestr = opj(filepath, 'exp')
config_path = filestr+'_config.json'
with open(config_path, 'r') as cfg:
config = json5.load(cfg)
print("Best ray experiment:", filepath)
main(None, filestr=opj(filepath, 'exp'), **kwargs)
main(config, filestr=filestr, **kwargs)
else:
raise Exception('No valid config found')