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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

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