def __init__(self, config: Config):
super().__init__(config)
self.network = config.network # neural network / model
self.optimizer = config.optimizer_fn(self.network.parameters())
self.total_rewards = np.zeros(config.num_workers)
self.states = self.task.reset()
self.prediction = None
from conformer_rl.environments import Task
from conformer_rl.models import RTGN
from conformer_rl.molecule_generation import test_alkane
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
mol_config = test_alkane()
config = Config()
config.tag = 'example2'
config.network = RTGN(6, 128, edge_dim=6, node_dim=5).to(device)
# Batch Hyperparameters
config.num_workers = 20
config.rollout_length = 20
config.optimization_epochs = 4
config.max_steps = 10000000
config.save_interval = config.num_workers*200*5
config.eval_interval = config.num_workers*200*5
config.eval_episodes = 2
config.mini_batch_size = 50
# Coefficient Hyperparameters
lr = 5e-6 * np.sqrt(config.num_workers)
config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=lr, eps=1e-5)
from conformer_rl.molecule_generation import branched_alkane
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if __name__ == '__main__':
# Set up the environment.
alkane_env_config = branched_alkane(num_atoms=18)
training_env = Task('GibbsScorePruningEnv-v0',
concurrency=True,
num_envs=20,
mol_config=alkane_env_config,
max_steps=200)
# initialize Config
config = Config()
config.tag = 'tutorial'
config.train_env = training_env
# Set up neural network
config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
config.num_workers = 20
# Logging Parameters
config.save_interval = 20000
config.data_dir = 'data'
config.use_tensorboard = True
# Set up evaluation
config.eval_env = Task('GibbsScorePruningEnv-v0',
num_envs=1,
from conformer_rl.molecule_generation.generate_alkanes import generate_branched_alkane
from conformer_rl.molecule_generation.generate_molecule_config import config_from_rdkit
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
mol_config = config_from_rdkit(generate_branched_alkane(10), num_conformers=4, calc_normalizers=True, save_file='10_alkane')
with open('10_alkane.pkl', 'rb') as file:
mol_config = pickle.load(file)
config = Config()
config.tag = 'example1'
# config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
# Batch Hyperparameters
config.rollout_length = 2
config.recurrence = 1
config.optimization_epochs = 1
config.max_steps = 24
config.save_interval = 8
config.eval_interval = 8
config.eval_episodes = 1
config.mini_batch_size = 4
# Coefficient Hyperparameters
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
# Create config object
mol = generate_branched_alkane(14)
mol_config = config_from_rdkit(mol,
num_conformers=200,
calc_normalizers=True,
save_file='alkane')
# Create agent training config object
config = Config()
config.tag = 'example1'
# Configure Environment
config.train_env = Task('GibbsScorePruningEnv-v0',
concurrency=True,
num_envs=5,
seed=np.random.randint(0, 1e5),
mol_config=mol_config)
config.eval_env = Task('GibbsScorePruningEnv-v0',
seed=np.random.randint(0, 7e4),
mol_config=mol_config)
config.eval_episodes = 10000
agent = PPORecurrentAgent(config)
agent.run_steps()
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
# Create mol_configs for the curriculum
mol_configs = [
config_from_rdkit(generate_branched_alkane(i),
num_conformers=200,
calc_normalizers=True) for i in range(8, 16)
]
eval_mol_config = config_from_rdkit(generate_branched_alkane(16),
num_conformers=200,
calc_normalizers=True)
config = Config()
config.tag = 'curriculum_test'
config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
# Batch Hyperparameters
config.max_steps = 100000
# training Hyperparameters
lr = 5e-6 * np.sqrt(10)
config.optimizer_fn = lambda params: torch.optim.Adam(
params, lr=lr, eps=1e-5)
# Task Settings
config.train_env = Task('GibbsScorePruningCurriculumEnv-v0',
concurrency=True,
num_envs=10,
from conformer_rl.environments import Task
from conformer_rl.models import RTGNRecurrent, RTGN, RTGNGat
from conformer_rl.molecule_generation import xorgate
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
mol_config = xorgate(2, 3)
config = Config()
config.tag = 'example1'
# config.network = RTGN(6, 128, edge_dim=6, node_dim=5).to(device)
config.network = RTGNGat(6, 128, node_dim=5).to(device)
# Batch Hyperparameters
config.num_workers = 2
config.rollout_length = 2
config.recurrence = 1
config.max_steps = 16
config.save_interval = 8
config.eval_interval = 8
config.eval_episodes = 1
# Coefficient Hyperparameters
lr = 5e-6 * np.sqrt(config.num_workers)
config.optimizer_fn = lambda params: torch.optim.Adam(