"""
Packages container for DLinear model.
"""
from pytorch_forecasting.base._base_pkg import Base_pkg
[docs]
class DLinear_pkg_v2(Base_pkg):
"""DLinear package container."""
_tags = {
"info:name": "DLinear",
"info:compute": 2,
"authors": ["PranavBhatP"],
"capability:exogenous": True,
"capability:multivariate": True,
"capability:pred_int": True,
"capability:flexible_history_length": True,
"capability:cold_start": False,
}
[docs]
@classmethod
def get_cls(cls):
"""Get model class."""
from pytorch_forecasting.models.dlinear._dlinear_v2 import DLinear
return DLinear
[docs]
@classmethod
def get_datamodule_cls(cls):
"""Get the underlying DataModule class."""
from pytorch_forecasting.data._tslib_data_module import TslibDataModule
return TslibDataModule
@classmethod
def _get_test_datamodule_from(cls, trainer_kwargs):
"""Create test dataloaders from trainer_kwargs - following v1 pattern."""
from pytorch_forecasting.data._tslib_data_module import TslibDataModule
from pytorch_forecasting.tests._data_scenarios import (
data_with_covariates_v2,
make_datasets_v2,
)
data_with_covariates = data_with_covariates_v2()
data_loader_default_kwargs = dict(
target="target",
group_ids=["agency_encoded", "sku_encoded"],
add_relative_time_idx=True,
)
data_loader_kwargs = trainer_kwargs.get("data_loader_kwargs", {})
data_loader_default_kwargs.update(data_loader_kwargs)
datasets_info = make_datasets_v2(
data_with_covariates, **data_loader_default_kwargs
)
training_dataset = datasets_info["training_dataset"]
validation_dataset = datasets_info["validation_dataset"]
context_length = data_loader_kwargs.get("context_length", 8)
prediction_length = data_loader_kwargs.get("prediction_length", 2)
batch_size = data_loader_kwargs.get("batch_size", 2)
train_datamodule = TslibDataModule(
time_series_dataset=training_dataset,
context_length=context_length,
prediction_length=prediction_length,
add_relative_time_idx=data_loader_kwargs.get("add_relative_time_idx", True),
batch_size=batch_size,
train_val_test_split=(0.8, 0.2, 0.0),
)
val_datamodule = TslibDataModule(
time_series_dataset=validation_dataset,
context_length=context_length,
prediction_length=prediction_length,
add_relative_time_idx=data_loader_kwargs.get("add_relative_time_idx", True),
batch_size=batch_size,
train_val_test_split=(0.0, 1.0, 0.0),
)
test_datamodule = TslibDataModule(
time_series_dataset=validation_dataset,
context_length=context_length,
prediction_length=prediction_length,
add_relative_time_idx=data_loader_kwargs.get("add_relative_time_idx", True),
batch_size=batch_size,
train_val_test_split=(0.0, 0.0, 1.0),
)
train_datamodule.setup("fit")
val_datamodule.setup("fit")
test_datamodule.setup("test")
train_dataloader = train_datamodule.train_dataloader()
val_dataloader = val_datamodule.val_dataloader()
test_dataloader = test_datamodule.test_dataloader()
return {
"train": train_dataloader,
"val": val_dataloader,
"test": test_dataloader,
"data_module": train_datamodule,
}
[docs]
@classmethod
def get_test_train_params(cls):
"""
Return testing parameter settings for the trainer.
Parameters
----------
params : dict or list of dict, default = {}
Parameters to create testing instances of the class
"""
from pytorch_forecasting.metrics import MAE, MAPE, SMAPE, QuantileLoss
params = [
{},
dict(moving_avg=25, individual=False, logging_metrics=[SMAPE()]),
dict(
moving_avg=4,
individual=True,
),
dict(
moving_avg=5,
individual=False,
logging_metrics=[SMAPE()],
),
]
default_dm_cfg = {"context_length": 8, "prediction_length": 2}
for param in params:
current_dm_cfg = param.get("datamodule_cfg", {})
default_dm_cfg.update(current_dm_cfg)
param["datamodule_cfg"] = default_dm_cfg
return params
