"""DecoderMLP package container."""
from pytorch_forecasting.models.base._base_object import _BasePtForecaster
[docs]
class DecoderMLP_pkg(_BasePtForecaster):
"""DecoderMLP package container."""
_tags = {
"info:name": "DecoderMLP",
"info:compute": 1,
"authors": ["jdb78"],
"capability:exogenous": True,
"capability:multivariate": True,
"capability:pred_int": True,
"capability:flexible_history_length": True,
"capability:cold_start": True,
}
[docs]
@classmethod
def get_model_cls(cls):
"""Get model class."""
from pytorch_forecasting.models import DecoderMLP
return DecoderMLP
[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 torchmetrics import MeanSquaredError
from pytorch_forecasting.metrics import (
MAE,
CrossEntropy,
MultiLoss,
QuantileLoss,
)
return [
{},
dict(
loss=MultiLoss([QuantileLoss(), MAE()]),
data_loader_kwargs=dict(
time_varying_unknown_reals=["volume", "discount"],
target=["volume", "discount"],
),
),
dict(
loss=CrossEntropy(),
data_loader_kwargs=dict(
target="agency",
),
),
dict(loss=MeanSquaredError()),
dict(
loss=MeanSquaredError(),
data_loader_kwargs=dict(min_prediction_length=1, min_encoder_length=1),
),
]
@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, in this order.
"""
data_loader_kwargs = params.get("data_loader_kwargs", {})
from pytorch_forecasting.tests._data_scenarios import (
data_with_covariates,
make_dataloaders,
)
dwc = data_with_covariates()
dwc.assign(target=lambda x: x.volume)
dl_default_kwargs = dict(
target="target",
time_varying_known_reals=["price_actual"],
time_varying_unknown_reals=["target"],
static_categoricals=["agency"],
add_relative_time_idx=True,
)
dl_default_kwargs.update(data_loader_kwargs)
dataloaders_with_covariates = make_dataloaders(dwc, **dl_default_kwargs)
return dataloaders_with_covariates
