"""TimeXer package container."""
from pytorch_forecasting.models.base._base_object import _BasePtForecaster
[docs]
class TimeXer_pkg(_BasePtForecaster):
"""TimeXer package container."""
_tags = {
"info:name": "TimeXer",
"info:compute": 3,
"info:pred_type": ["point", "quantile"],
"info:y_type": ["numeric"],
"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 import TimeXer
return TimeXer
[docs]
@classmethod
def get_base_test_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
"""
from pytorch_forecasting.data.encoders import GroupNormalizer
return [
{
# Basic test params
"hidden_size": 16,
"patch_length": 1,
"n_heads": 2,
"e_layers": 1,
"d_ff": 32,
"dropout": 0.1,
},
{
"hidden_size": 32,
"n_heads": 4,
"e_layers": 2,
"d_ff": 64,
"patch_length": 4,
"dropout": 0.2,
"activation": "gelu",
},
{
"hidden_size": 16,
"n_heads": 2,
"e_layers": 1,
"d_ff": 32,
"patch_length": 2,
"dropout": 0.1,
},
{
"hidden_size": 24,
"n_heads": 3,
"e_layers": 1,
"d_ff": 48,
"patch_length": 3,
"dropout": 0.15,
"data_loader_kwargs": dict(
target_normalizer=GroupNormalizer(
groups=["agency", "sku"], transformation="softplus"
),
),
},
{
"hidden_size": 32,
"patch_length": 1,
"n_heads": 4,
"e_layers": 1,
"d_ff": 32,
"dropout": 0.1,
"use_efficient_attention": True,
},
]
@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[str, DataLoader]
Dict of dataloaders created from the parameters.
Train, validation, and test dataloaders created from the parameters.
"""
loss = params.get("loss", None)
data_loader_kwargs = params.get("data_loader_kwargs", {})
from pytorch_forecasting.metrics import (
NegativeBinomialDistributionLoss,
PoissonLoss,
TweedieLoss,
)
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 isinstance(loss, TweedieLoss | PoissonLoss):
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
