Source code for flash.tabular.forecasting.model
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Dict, List, Optional, Union
import torchmetrics
from pytorch_lightning import LightningModule
from flash.core.adapter import AdapterTask
from flash.core.integrations.pytorch_forecasting.adapter import PyTorchForecastingAdapter
from flash.core.integrations.pytorch_forecasting.backbones import PYTORCH_FORECASTING_BACKBONES
from flash.core.registry import FlashRegistry
from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE
[docs]class TabularForecaster(AdapterTask):
backbones: FlashRegistry = FlashRegistry("backbones") + PYTORCH_FORECASTING_BACKBONES
required_extras: str = "tabular"
def __init__(
self,
parameters: Dict[str, Any],
backbone: str,
backbone_kwargs: Optional[Dict[str, Any]] = None,
loss_fn: Optional[Callable] = None,
optimizer: OPTIMIZER_TYPE = "Adam",
lr_scheduler: LR_SCHEDULER_TYPE = None,
metrics: Union[torchmetrics.Metric, List[torchmetrics.Metric]] = None,
learning_rate: Optional[float] = None,
):
self.save_hyperparameters()
if backbone_kwargs is None:
backbone_kwargs = {}
metadata = self.backbones.get(backbone, with_metadata=True)
adapter = metadata["metadata"]["adapter"].from_task(
self,
parameters=parameters,
backbone=backbone,
backbone_kwargs=backbone_kwargs,
loss_fn=loss_fn,
metrics=metrics,
)
super().__init__(
adapter,
learning_rate=learning_rate,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
)
@property
def pytorch_forecasting_model(self) -> LightningModule:
"""This property provides access to the ``LightningModule`` object that is wrapped by Flash for backbones
provided by PyTorch Forecasting.
This can be used with
:func:`~flash.core.integrations.pytorch_forecasting.transforms.convert_predictions` to access the visualization
features built in to PyTorch Forecasting.
"""
if not isinstance(self.adapter, PyTorchForecastingAdapter):
raise AttributeError(
"The `pytorch_forecasting_model` attribute can only be accessed for backbones provided by PyTorch "
"Forecasting."
)
return self.adapter.backbone