def test_something(self):
sim_data = DataNode(label='node_label', metadata='test the metadata')
nodes_list = []
sim_data.data = nodes_list
data_node = DataNode(label="seed_0")
nodes_list.append(data_node)
# sub-nodes of sim data resource
loss_lst = []
train_loss_node = DataNode(label="training_loss", data=loss_lst)
metrics_dict = {}
metrics_node = DataNode(label="validation_metrics", data=metrics_dict)
scores_lst = []
scores_node = DataNode(label="validation_score", data=scores_lst)
# add sim data nodes to parent node
sim_data_node = data_node
if sim_data_node:
sim_data_node.data = [train_loss_node, metrics_node, scores_node]
sim_data.to_json(path="./")
self.assertIsNotNone(sim_data.to_json_str())
def main(flags):
irl_lbl = 'no_irl' if flags.use_true_reward else 'with_irl'
sim_label = flags.exp_name + '_min_IReLeaSE-REINFORCE_' + irl_lbl + (
'_no_vflag' if flags.no_smiles_validity_flag else '')
sim_data = DataNode(label=sim_label,
metadata={
'exp': flags.exp_name,
'date': date_label
})
nodes_list = []
sim_data.data = nodes_list
for seed in seeds:
summary_writer_creator = lambda: SummaryWriter(
log_dir="irelease_tb"
"/{}_{}_{}/".format(sim_label, seed,
dt.now().strftime("%Y_%m_%d__%H_%M_%S")))
# for data collection of this round of simulation.
data_node = DataNode(label="seed_%d" % seed)
nodes_list.append(data_node)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
print(
'--------------------------------------------------------------------------------'
)
print(f'{device}\n{sim_label}\tDemonstrations file: {flags.demo_file}')
print(
'--------------------------------------------------------------------------------'
)
irelease = IReLeaSE()
k = 1
if flags.hparam_search:
print(f'Hyperparameter search enabled: {flags.hparam_search_alg}')
# arguments to callables
extra_init_args = {}
extra_data_args = {'flags': flags}
extra_train_args = {
'agent_net_path': flags.model_dir,
'agent_net_name': flags.pretrained_model,
'learn_irl': not flags.use_true_reward,
'seed': seed,
'n_episodes': 600,
'is_hsearch': True,
'tb_writer': summary_writer_creator
}
hparams_conf = get_hparam_config(flags)
search_alg = {
'random_search': RandomSearch,
'bayopt_search': BayesianOptSearch
}.get(flags.hparam_search_alg, BayesianOptSearch)
search_args = GPMinArgs(n_calls=20, random_state=seed)
hparam_search = search_alg(
hparam_config=hparams_conf,
num_folds=1,
initializer=irelease.initialize,
data_provider=irelease.data_provider,
train_fn=irelease.train,
save_model_fn=irelease.save_model,
alg_args=search_args,
init_args=extra_init_args,
data_args=extra_data_args,
train_args=extra_train_args,
data_node=data_node,
split_label='reinforce-rl',
sim_label=sim_label,
dataset_label=None,
results_file=f'{flags.hparam_search_alg}_{sim_label}'
f'_{date_label}_seed_{seed}')
start = time.time()
stats = hparam_search.fit()
print(f'Duration = {time_since(start)}')
print(stats)
print("\nBest params = {}, duration={}".format(
stats.best(), time_since(start)))
else:
hyper_params = default_hparams(flags)
data_gens = irelease.data_provider(k, flags)
init_args = irelease.initialize(hyper_params,
data_gens['demo_data'],
data_gens['unbiased_data'],
data_gens['prior_data'])
results = irelease.train(init_args,
flags.model_dir,
flags.pretrained_model,
seed,
sim_data_node=data_node,
n_episodes=600,
bias_mode=flags.bias_mode,
learn_irl=not flags.use_true_reward,
tb_writer=summary_writer_creator)
irelease.save_model(
results['model'][0],
path=flags.model_dir,
name=
f'{flags.exp_name}_{irl_lbl}_irelease_stack-rnn_{hyper_params["agent_params"]["unit_type"]}'
f'_reinforce_agent_{date_label}_{results["score"]}_{results["epoch"]}'
)
irelease.save_model(
results['model'][1],
path=flags.model_dir,
name=
f'{flags.exp_name}_{irl_lbl}_irelease_stack-rnn_{hyper_params["agent_params"]["unit_type"]}'
f'_reinforce_reward_net_{date_label}_{results["score"]}_{results["epoch"]}'
)
# save simulation data resource tree to file.
sim_data.to_json(path="./analysis/")
def main(flags):
mode = 'eval' if flags.eval else 'train'
sim_label = f'expert_rnn_reg_model_{mode}'
print(
'--------------------------------------------------------------------------------'
)
print(f'{device}\n{sim_label}\tData file: {flags.data_file}')
print(
'--------------------------------------------------------------------------------'
)
hparam_search = None
sim_data = DataNode(label=sim_label,
metadata=json.dumps({
'date': date_label,
'seeds': seeds,
'mode': mode,
'sim_label': sim_label,
'num_folds': flags.folds
}))
nodes_list = []
sim_data.data = nodes_list
# Load the data
data_dict, transformer = load_smiles_data(flags.data_file,
flags.cv,
normalize_y=True,
k=flags.folds,
shuffle=5,
create_val=False,
train_size=.8)
for seed in seeds:
data_node = DataNode(label="seed_%d" % seed)
nodes_list.append(data_node)
# ensure reproducibility
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
trainer = ExpertTrainer()
folds = flags.folds if flags.cv else 1
if flags.hparam_search:
print(f'Hyperparameter search enabled: {flags.hparam_search_alg}')
# arguments to callables
extra_init_args = {}
extra_data_args = {'cv': flags.cv, 'data': data_dict}
extra_train_args = {
'n_iterations': 5000,
'transformer': transformer,
'is_hsearch': True,
'tb_writer': None
}
hparams_conf = hparams_config()
if hparam_search is None:
search_alg = {
'random_search': RandomSearch,
'bayopt_search': BayesianOptSearch
}.get(flags.hparam_search_alg, BayesianOptSearch)
search_args = GPMinArgs(n_calls=10, random_state=seed)
hparam_search = search_alg(
hparam_config=hparams_conf,
num_folds=folds,
initializer=trainer.initialize,
data_provider=trainer.data_provider,
train_fn=trainer.train,
save_model_fn=trainer.save_model,
alg_args=search_args,
init_args=extra_init_args,
data_args=extra_data_args,
train_args=extra_train_args,
data_node=data_node,
split_label='random',
sim_label=sim_label,
dataset_label=os.path.split(flags.data_file)[1],
results_file=
f'{flags.hparam_search_alg}_{sim_label}_{date_label}')
start = time.time()
stats = hparam_search.fit()
print(f'Duration = {time_since(start)}')
print(stats)
print("Best params = {}, duration={}".format(
stats.best(), time_since(start)))
else:
hyper_params = default_params(flags)
# Initialize the model and other related entities for training.
if flags.cv:
folds_data = []
data_node.data = folds_data
data_node.label = data_node.label + 'cv'
for k in range(folds):
k_node = DataNode(label="fold-%d" % k)
folds_data.append(k_node)
start_fold(k_node, data_dict, transformer, flags,
hyper_params, trainer, k, None)
else:
start_fold(data_node, data_dict, transformer, flags,
hyper_params, trainer, folds, None)
# save simulation data resource tree to file.
sim_data.to_json(path="./analysis/")
alg_args=search_args,
init_args=extra_init_args,
data_args=extra_data_args,
train_args=extra_train_args,
data_node=sim_data,
sim_label=sim_label,
results_file="{}_{}_poc_{}".format(flags.hparam_search_alg, sim_label, date_label))
stats = hparam_search.fit(model_dir="models", model_name=flags.model_name)
print(stats)
print("Best params = {}".format(stats.best()))
if isinstance(hparam_search, BayesianOptSearch):
res = hparam_search.results
if res is not None:
r = plot_evaluations(res, bins=10)
eval_fig = np.random.choice(r.ravel()).figure
eval_fig.savefig('hparam_evaluations.png')
r = plot_objective(res)
obj_func_eval_fig = np.random.choice(r.ravel()).figure
obj_func_eval_fig.savefig('hparam_obj_func_eval.png')
else:
models, agent, optimizer, batch_size, gamma, dvc = trainer.initialize(get_hparams(flags))
if flags.eval:
pass
else:
model, score, epoch = trainer.train(models, agent, optimizer, batch_size, gamma, dvc,
sim_data_node=sim_data)
trainer.save_model(model, flags.model_dir,
"DQN_{}_{}_poc_{}_{:.5f}".format(sim_label, flags.model_name, epoch, score))
sim_data.to_json('./')
def main(flags):
sim_label = flags.exp_name if flags.exp_name else 'Irelease-pretraining-Stack-RNN'
if flags.eval:
sim_label += '_eval'
sim_data = DataNode(label=sim_label,
metadata={
'exp': flags.exp_name,
'date': date_label
})
nodes_list = []
sim_data.data = nodes_list
# For searching over multiple seeds
hparam_search = None
for seed in seeds:
summary_writer_creator = lambda: SummaryWriter(
log_dir="tb_gpmt"
"/{}_{}_{}/".format(sim_label, seed,
dt.now().strftime("%Y_%m_%d__%H_%M_%S")))
# for data collection of this round of simulation.
data_node = DataNode(label="seed_%d" % seed)
nodes_list.append(data_node)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
print(
'-------------------------------------------------------------------------------------------------'
)
print(
f'Running on dataset: {flags.data_file}, experiment = {flags.exp_name}'
)
print(
'-------------------------------------------------------------------------------------------------'
)
trainer = IreleasePretrain()
k = 1
if flags["hparam_search"]:
print("Hyperparameter search enabled: {}".format(
flags["hparam_search_alg"]))
# arguments to callables
extra_init_args = {}
extra_data_args = {"flags": flags}
extra_train_args = {
"is_hsearch": True,
"n_iters": 50000,
"tb_writer": summary_writer_creator
}
hparams_conf = get_hparam_config(flags)
if hparam_search is None:
search_alg = {
"random_search": RandomSearch,
"bayopt_search": BayesianOptSearch
}.get(flags["hparam_search_alg"], BayesianOptSearch)
search_args = GPMinArgs(n_calls=20, random_state=seed)
hparam_search = search_alg(
hparam_config=hparams_conf,
num_folds=1,
initializer=trainer.initialize,
data_provider=trainer.data_provider,
train_fn=trainer.train,
save_model_fn=trainer.save_model,
alg_args=search_args,
init_args=extra_init_args,
data_args=extra_data_args,
train_args=extra_train_args,
data_node=data_node,
split_label='',
sim_label=sim_label,
dataset_label='ChEMBL_SMILES',
results_file="{}_{}_gpmt_{}.csv".format(
flags["hparam_search_alg"], sim_label, date_label))
stats = hparam_search.fit(model_dir="models",
model_name='irelease')
print(stats)
print("Best params = {}".format(stats.best()))
else:
hyper_params = default_hparams(flags)
model, optimizer, gen_data, rnn_args = trainer.initialize(
hyper_params,
gen_data=trainer.data_provider(k, flags)['train'])
if flags.eval:
load_model = trainer.load_model(flags.model_dir,
flags.eval_model_name)
model.load_state_dict(load_model)
trainer.evaluate_model(model,
gen_data,
rnn_args,
data_node,
num_smiles=flags.num_smiles)
else:
if flags.init_model:
load_model = trainer.load_model(flags.model_dir,
flags.init_model)
model.load_state_dict(load_model)
print(
f'Model weights {flags.init_model} loaded successfully!'
)
results = trainer.train(model=model,
optimizer=optimizer,
gen_data=gen_data,
rnn_args=rnn_args,
n_iters=1500000,
sim_data_node=data_node,
tb_writer=summary_writer_creator)
trainer.save_model(
results['model'],
flags.model_dir,
name=
f'irelease-pretrained_stack-rnn_{hyper_params["unit_type"]}_'
f'{date_label}_{results["score"]}_{results["epoch"]}')
# save simulation data resource tree to file.
sim_data.to_json(path="./analysis/")
def main(flags):
sim_label = f'RNN_XEnt_Generator_Baseline_{flags.exp_type}'
if flags.eval:
sim_label += '_eval'
sim_data = DataNode(label=sim_label,
metadata={
'exp': flags.exp_type,
'date': date_label
})
nodes_list = []
sim_data.data = nodes_list
# For searching over multiple seeds
hparam_search = None
pretraining = flags.exp_type == 'pretraining'
for seed in seeds:
summary_writer_creator = lambda: SummaryWriter(
log_dir="irelease_tb_rnn_xent"
"/{}_{}_{}/".format(sim_label, seed,
dt.now().strftime("%Y_%m_%d__%H_%M_%S")))
# for data collection of this round of simulation.
data_node = DataNode(label="seed_%d" % seed)
nodes_list.append(data_node)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
print(
'--------------------------------------------------------------------------------'
)
print(
f'{device}\n{sim_label}\tDemonstrations file: {flags.prior_data if pretraining else flags.demo_file}'
)
print(
'--------------------------------------------------------------------------------'
)
trainer = RNNBaseline()
k = 1
if flags["hparam_search"]:
print("Hyperparameter search enabled: {}".format(
flags["hparam_search_alg"]))
# arguments to callables
extra_init_args = {}
extra_data_args = {"flags": flags}
extra_train_args = {
"is_hsearch": True,
"n_iters": 50000,
"tb_writer": summary_writer_creator
}
hparams_conf = get_hparam_config(flags)
if hparam_search is None:
search_alg = {
"random_search": RandomSearch,
"bayopt_search": BayesianOptSearch
}.get(flags["hparam_search_alg"], BayesianOptSearch)
search_args = GPMinArgs(n_calls=20, random_state=seed)
hparam_search = search_alg(
hparam_config=hparams_conf,
num_folds=1,
initializer=trainer.initialize,
data_provider=trainer.data_provider,
train_fn=trainer.train,
save_model_fn=trainer.save_model,
alg_args=search_args,
init_args=extra_init_args,
data_args=extra_data_args,
train_args=extra_train_args,
data_node=data_node,
split_label='',
sim_label=sim_label,
dataset_label='ChEMBL_SMILES',
results_file="{}_{}_gpmt_{}.csv".format(
flags["hparam_search_alg"], sim_label, date_label))
stats = hparam_search.fit(model_dir="models",
model_name='irelease')
print(stats)
print("Best params = {}".format(stats.best()))
else:
hyper_params = default_hparams(flags)
data_gens = trainer.data_provider(k, flags)
model, optimizer, rnn_args = trainer.initialize(
hyper_params, data_gens['demo_data'],
data_gens['unbiased_data'], data_gens['prior_data'])
if flags.eval:
load_model = trainer.load_model(flags.model_dir,
flags.eval_model_name)
model.load_state_dict(load_model)
trainer.evaluate_model(model,
data_gens['demo_data'],
rnn_args,
data_node,
num_smiles=200)
else:
results = trainer.train(
generator=model,
optimizer=optimizer,
rnn_args=rnn_args,
n_iters=40000,
sim_data_node=data_node,
tb_writer=summary_writer_creator,
is_pretraining=pretraining,
pretrained_net_path=flags.model_dir,
pretrained_net_name=flags.pretrained_model)
trainer.save_model(
results['model'],
flags.model_dir,
name=
f'rnn_xent_gen_baseline_{flags.exp_type}_{hyper_params["unit_type"]}_'
f'{date_label}_{results["score"]}_{results["epoch"]}_seed_{seed}'
)
# save simulation data resource tree to file.
sim_data.to_json(path="./analysis/")