def tsdataset(request):
return TimeSeriesDataSet(
data=to.from_numpy(request.param[0]),
ratio_train=request.param[1],
window_size=request.param[2],
standardize_data=request.param[3],
scale_min_max_data=request.param[4]
)
def dataset_ts(request):
return TimeSeriesDataSet(
data=to.from_numpy(request.param[0]).to(dtype=to.get_default_dtype()),
ratio_train=request.param[1],
window_size=request.param[2],
standardize_data=request.param[3],
scale_min_max_data=request.param[4],
)
dtype=to.get_default_dtype()).view(-1, 1)
dt = 0.02
else:
raise pyrado.ValueErr(
given=data_set_name,
eq_constraint="'daily_min_temperatures', 'monthly_sunspots', "
"'oscillation_50Hz_initpos-0.5', or 'oscillation_100Hz_initpos-0.4",
)
# Dataset
data_set_hparam = dict(name=data_set_name,
ratio_train=0.8,
window_size=50,
standardize_data=False,
scale_min_max_data=True)
dataset = TimeSeriesDataSet(data, **data_set_hparam)
# Policy
policy_hparam = dict(
dt=dt,
activation_nonlin=to.tanh,
potentials_dyn_fcn=pd_linear,
obs_layer=None,
tau_init=10.0 if "oscillation" in data_set_name else 1.0,
tau_learnable=True,
kappa_init=1e-3,
kappa_learnable=True,
capacity_learnable=True,
potential_init_learnable=True,
init_param_kwargs=None,
use_cuda=False,
def train_and_eval(trial: optuna.Trial, study_dir: str, seed: int):
"""
Objective function for the Optuna `Study` to maximize.
.. note::
Optuna expects only the `trial` argument, thus we use `functools.partial` to sneak in custom arguments.
:param trial: Optuna Trial object for hyper-parameter optimization
:param study_dir: the parent directory for all trials in this study
:param seed: seed value for the random number generators, pass `None` for no seeding
:return: objective function value
"""
# Synchronize seeds between Optuna trials
pyrado.set_seed(seed)
# Load the data
data_set_name = "oscillation_50Hz_initpos-0.5"
data = pd.read_csv(osp.join(pyrado.PERMA_DIR, "misc", f"{data_set_name}.csv"))
if data_set_name == "daily_min_temperatures":
data = to.tensor(data["Temp"].values, dtype=to.get_default_dtype()).view(-1, 1)
elif data_set_name == "monthly_sunspots":
data = to.tensor(data["Sunspots"].values, dtype=to.get_default_dtype()).view(-1, 1)
elif "oscillation" in data_set_name:
data = to.tensor(data["Positions"].values, dtype=to.get_default_dtype()).view(-1, 1)
else:
raise pyrado.ValueErr(
given=data_set_name,
eq_constraint="'daily_min_temperatures', 'monthly_sunspots', "
"'oscillation_50Hz_initpos-0.5', or 'oscillation_100Hz_initpos-0.4",
)
# Dataset
data_set_hparam = dict(
name=data_set_name,
ratio_train=0.7,
window_size=trial.suggest_int("dataset_window_size", 1, 100),
standardize_data=False,
scale_min_max_data=True,
)
dataset = TimeSeriesDataSet(data, **data_set_hparam)
# Policy
policy_hparam = dict(
dt=0.02 if "oscillation" in data_set_name else 1.0,
obs_layer=None,
activation_nonlin=to.tanh,
potentials_dyn_fcn=fcn_from_str(
trial.suggest_categorical("policy_potentials_dyn_fcn", ["pd_linear", "pd_cubic"])
),
tau_init=trial.suggest_loguniform("policy_tau_init", 1e-2, 1e3),
tau_learnable=True,
kappa_init=trial.suggest_categorical("policy_kappa_init", [0, 1e-4, 1e-2]),
kappa_learnable=True,
capacity_learnable=True,
potential_init_learnable=trial.suggest_categorical("policy_potential_init_learnable", [True, False]),
init_param_kwargs=trial.suggest_categorical("policy_init_param_kwargs", [None]),
use_cuda=False,
)
policy = ADNPolicy(spec=EnvSpec(act_space=InfBoxSpace(shape=1), obs_space=InfBoxSpace(shape=1)), **policy_hparam)
# Algorithm
algo_hparam = dict(
windowed=trial.suggest_categorical("algo_windowed", [True, False]),
max_iter=1000,
optim_class=optim.Adam,
optim_hparam=dict(
lr=trial.suggest_uniform("optim_lr", 5e-4, 5e-2),
eps=trial.suggest_uniform("optim_eps", 1e-8, 1e-5),
weight_decay=trial.suggest_uniform("optim_weight_decay", 5e-5, 5e-3),
),
loss_fcn=nn.MSELoss(),
)
csv_logger = create_csv_step_logger(osp.join(study_dir, f"trial_{trial.number}"))
algo = TSPred(study_dir, dataset, policy, **algo_hparam, logger=csv_logger)
# Train without saving the results
algo.train(snapshot_mode="latest", seed=seed)
# Evaluate
num_init_samples = dataset.window_size
_, loss_trn = TSPred.evaluate(
policy,
dataset.data_trn_inp,
dataset.data_trn_targ,
windowed=algo.windowed,
num_init_samples=num_init_samples,
cascaded=False,
)
_, loss_tst = TSPred.evaluate(
policy,
dataset.data_tst_inp,
dataset.data_tst_targ,
windowed=algo.windowed,
num_init_samples=num_init_samples,
cascaded=False,
)
return loss_trn
def train_and_eval(trial: optuna.Trial, study_dir: str, seed: int):
"""
Objective function for the Optuna `Study` to maximize.
.. note::
Optuna expects only the `trial` argument, thus we use `functools.partial` to sneak in custom arguments.
:param trial: Optuna Trial object for hyper-parameter optimization
:param study_dir: the parent directory for all trials in this study
:param seed: seed value for the random number generators, pass `None` for no seeding
:return: objective function value
"""
# Synchronize seeds between Optuna trials
pyrado.set_seed(seed)
# Load the data
data_set_name = 'oscillation_50Hz_initpos-0.5'
data = pd.read_csv(osp.join(pyrado.PERMA_DIR, 'time_series', f'{data_set_name}.csv'))
if data_set_name == 'daily_min_temperatures':
data = to.tensor(data['Temp'].values, dtype=to.get_default_dtype()).view(-1, 1)
elif data_set_name == 'monthly_sunspots':
data = to.tensor(data['Sunspots'].values, dtype=to.get_default_dtype()).view(-1, 1)
elif 'oscillation' in data_set_name:
data = to.tensor(data['Positions'].values, dtype=to.get_default_dtype()).view(-1, 1)
else:
raise pyrado.ValueErr(
given=data_set_name, eq_constraint="'daily_min_temperatures', 'monthly_sunspots', "
"'oscillation_50Hz_initpos-0.5', or 'oscillation_100Hz_initpos-0.4")
# Dataset
data_set_hparam = dict(
name=data_set_name,
ratio_train=0.7,
window_size=trial.suggest_int('dataset_window_size', 1, 100),
standardize_data=False,
scale_min_max_data=True
)
dataset = TimeSeriesDataSet(data, **data_set_hparam)
# Policy
policy_hparam = dict(
dt=0.02 if 'oscillation' in data_set_name else 1.,
hidden_size=trial.suggest_int('policy_hidden_size', 2, 51),
obs_layer=None,
activation_nonlin=fcn_from_str(
trial.suggest_categorical('policy_activation_nonlin', ['to_tanh', 'to_sigmoid'])),
mirrored_conv_weights=trial.suggest_categorical('policy_mirrored_conv_weights', [True, False]),
conv_out_channels=1,
conv_kernel_size=None,
conv_padding_mode=trial.suggest_categorical('policy_conv_padding_mode', ['zeros', 'circular']),
tau_init=trial.suggest_loguniform('policy_tau_init', 1e-2, 1e3),
tau_learnable=True,
kappa_init=trial.suggest_categorical('policy_kappa_init', [0, 1e-4, 1e-2]),
kappa_learnable=True,
potential_init_learnable=trial.suggest_categorical('policy_potential_init_learnable', [True, False]),
init_param_kwargs=trial.suggest_categorical('policy_init_param_kwargs', [None, dict(bell=True)]),
use_cuda=False
)
policy = NFPolicy(spec=EnvSpec(act_space=InfBoxSpace(shape=1), obs_space=InfBoxSpace(shape=1)), **policy_hparam)
# Algorithm
algo_hparam = dict(
windowed=trial.suggest_categorical('algo_windowed', [True, False]),
max_iter=1000,
optim_class=optim.Adam,
optim_hparam=dict(
lr=trial.suggest_uniform('optim_lr', 5e-4, 5e-2),
eps=trial.suggest_uniform('optim_eps', 1e-8, 1e-5),
weight_decay=trial.suggest_uniform('optim_weight_decay', 5e-5, 5e-3)
),
loss_fcn=nn.MSELoss(),
)
csv_logger = create_csv_step_logger(osp.join(study_dir, f'trial_{trial.number}'))
algo = TSPred(study_dir, dataset, policy, **algo_hparam, logger=csv_logger)
# Train without saving the results
algo.train(snapshot_mode='latest', seed=seed)
# Evaluate
num_init_samples = dataset.window_size
_, loss_trn = TSPred.evaluate(policy, dataset.data_trn_inp, dataset.data_trn_targ, windowed=algo.windowed,
num_init_samples=num_init_samples, cascaded=False)
_, loss_tst = TSPred.evaluate(policy, dataset.data_tst_inp, dataset.data_tst_targ, windowed=algo.windowed,
num_init_samples=num_init_samples, cascaded=False)
return loss_trn