"""DeepAR package container."""
from pytorch_forecasting.models.base._base_object import _BasePtForecaster
[docs]
class DeepAR_pkg(_BasePtForecaster):
"""DeepAR package container."""
_tags = {
"info:name": "DeepAR",
"info:compute": 3,
"authors": ["jdb78"],
"capability:exogenous": True,
"capability:multivariate": True,
"capability:pred_int": True,
"capability:flexible_history_length": True,
"capability:cold_start": False,
}
[docs]
@classmethod
def get_model_cls(cls):
"""Get model class."""
from pytorch_forecasting.models import DeepAR
return DeepAR
[docs]
@classmethod
def get_test_train_params(cls):
"""Return testing parameter settings for the trainer.
Returns
-------
params : dict or list of dict, default = {}
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`
"""
from pytorch_forecasting.data.encoders import GroupNormalizer
from pytorch_forecasting.metrics import (
BetaDistributionLoss,
ImplicitQuantileNetworkDistributionLoss,
LogNormalDistributionLoss,
MultivariateNormalDistributionLoss,
NegativeBinomialDistributionLoss,
)
params = [
{},
{"cell_type": "GRU"},
dict(
loss=LogNormalDistributionLoss(),
clip_target=True,
data_loader_kwargs=dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], transformation="log"
)
),
),
dict(
loss=NegativeBinomialDistributionLoss(),
clip_target=False,
data_loader_kwargs=dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], center=False
)
),
),
dict(
loss=BetaDistributionLoss(),
clip_target=True,
data_loader_kwargs=dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], transformation="logit"
)
),
),
dict(
data_loader_kwargs=dict(
lags={"volume": [2, 5]},
target="volume",
time_varying_unknown_reals=["volume"],
min_encoder_length=2,
),
),
dict(
data_loader_kwargs=dict(
time_varying_unknown_reals=["volume", "discount"],
target=["volume", "discount"],
lags={"volume": [2], "discount": [2]},
),
),
dict(
loss=ImplicitQuantileNetworkDistributionLoss(hidden_size=8),
),
dict(
loss=MultivariateNormalDistributionLoss(),
trainer_kwargs=dict(accelerator="cpu"),
),
dict(
loss=MultivariateNormalDistributionLoss(),
data_loader_kwargs=dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], transformation="log1p"
)
),
trainer_kwargs=dict(accelerator="cpu"),
),
]
defaults = {
"hidden_size": 5,
"cell_type": "LSTM",
"n_plotting_samples": 100,
}
for param in params:
param.update(defaults)
return params
@classmethod
def _get_test_dataloaders_from(cls, params):
"""Get dataloaders from parameters.
Parameters
----------
params : dict
Parameters to create dataloaders.
One of the elements in the list returned by ``get_test_train_params``.
Returns
-------
dataloaders : dict with keys "train", "val", "test", values torch DataLoader
Dict of dataloaders created from the parameters.
Train, validation, and test dataloaders.
"""
loss = params.get("loss", None)
clip_target = params.get("clip_target", False)
data_loader_kwargs = params.get("data_loader_kwargs", {})
from pytorch_forecasting.metrics import NegativeBinomialDistributionLoss
from pytorch_forecasting.tests._conftest import make_dataloaders
from pytorch_forecasting.tests._data_scenarios import data_with_covariates
dwc = data_with_covariates()
if isinstance(loss, NegativeBinomialDistributionLoss):
dwc = dwc.assign(volume=lambda x: x.volume.round())
dwc = dwc.copy()
if clip_target:
dwc["target"] = dwc["volume"].clip(1e-3, 1.0)
else:
dwc["target"] = dwc["volume"]
data_loader_default_kwargs = dict(
target="target",
time_varying_known_reals=["price_actual"],
time_varying_unknown_reals=["target"],
static_categoricals=["agency"],
add_relative_time_idx=True,
)
data_loader_default_kwargs.update(data_loader_kwargs)
dataloaders_w_covariates = make_dataloaders(dwc, **data_loader_default_kwargs)
return dataloaders_w_covariates
