"""
Metadata container for TimeXer v2.
"""
from pytorch_forecasting.base._base_pkg import Base_pkg
[docs]
class TimeXer_pkg_v2(Base_pkg):
"""TimeXer metadata container."""
_tags = {
"info:name": "TimeXer",
"authors": ["PranavBhatP"],
"capability:exogenous": True,
"capability:multivariate": True,
"capability:pred_int": True,
"capability:flexible_history_length": False,
}
[docs]
@classmethod
def get_cls(cls):
"""Get model class."""
from pytorch_forecasting.models.timexer._timexer_v2 import TimeXer
return TimeXer
[docs]
@classmethod
def get_datamodule_cls(cls):
"""Get the underlying DataModule class."""
from pytorch_forecasting.data._tslib_data_module import TslibDataModule
return TslibDataModule
[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.metrics import QuantileLoss
params = [
{},
dict(
hidden_size=64,
n_heads=4,
),
dict(datamodule_cfg=dict(context_length=12, prediction_length=3)),
dict(
hidden_size=32,
n_heads=2,
datamodule_cfg=dict(
context_length=12,
prediction_length=3,
add_relative_time_idx=False,
),
),
dict(
hidden_size=128,
patch_length=12,
datamodule_cfg=dict(context_length=16, prediction_length=4),
),
dict(
n_heads=2,
e_layers=1,
patch_length=6,
),
dict(
hidden_size=256,
n_heads=8,
e_layers=3,
d_ff=1024,
patch_length=8,
factor=3,
activation="gelu",
dropout=0.2,
),
dict(
hidden_size=32,
n_heads=2,
e_layers=1,
d_ff=64,
patch_length=4,
factor=2,
activation="relu",
dropout=0.05,
datamodule_cfg=dict(
context_length=16,
prediction_length=4,
),
loss=QuantileLoss(quantiles=[0.1, 0.5, 0.9]),
),
dict(
hidden_size=32,
patch_length=1,
n_heads=4,
e_layers=1,
d_ff=32,
dropout=0.1,
use_efficient_attention=True,
),
]
default_dm_cfg = {"context_length": 12, "prediction_length": 4}
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
