[ENH] EXPERIMENTAL: TFT model based on the new data pipeline

pytorch_forecasting/models/base/_base_model_v2.py

@@ -0,0 +1,296 @@
+########################################################################################
+# Disclaimer: This baseclass is still work in progress and experimental, please
+# use with care. This class is a basic skeleton of how the base classes may look like
+# in the version-2.
+########################################################################################
+
+
+from typing import Dict, List, Optional, Tuple, Union
+from warnings import warn
+
+from lightning.pytorch import LightningModule
+from lightning.pytorch.utilities.types import STEP_OUTPUT
+import torch
+import torch.nn as nn
+from torch.optim import Optimizer
+
+
+class BaseModel(LightningModule):
+    def __init__(
+        self,
+        loss: nn.Module,
+        logging_metrics: Optional[List[nn.Module]] = None,
+        optimizer: Optional[Union[Optimizer, str]] = "adam",
+        optimizer_params: Optional[Dict] = None,
+        lr_scheduler: Optional[str] = None,
+        lr_scheduler_params: Optional[Dict] = None,
+    ):
+        """
+        Base model for time series forecasting.
+
+        Parameters
+        ----------
+        loss : nn.Module
+            Loss function to use for training.
+        logging_metrics : Optional[List[nn.Module]], optional
+            List of metrics to log during training, validation, and testing.
+        optimizer : Optional[Union[Optimizer, str]], optional
+            Optimizer to use for training.
+            Can be a string ("adam", "sgd") or an instance of `torch.optim.Optimizer`.
+        optimizer_params : Optional[Dict], optional
+            Parameters for the optimizer.
+        lr_scheduler : Optional[str], optional
+            Learning rate scheduler to use.
+            Supported values: "reduce_lr_on_plateau", "step_lr".
+        lr_scheduler_params : Optional[Dict], optional
+            Parameters for the learning rate scheduler.
+        """
+        super().__init__()
+        self.loss = loss
+        self.logging_metrics = logging_metrics if logging_metrics is not None else []
+        self.optimizer = optimizer
+        self.optimizer_params = optimizer_params if optimizer_params is not None else {}
+        self.lr_scheduler = lr_scheduler
+        self.lr_scheduler_params = (
+            lr_scheduler_params if lr_scheduler_params is not None else {}
+        )
+        self.model_name = self.__class__.__name__
+        warn(
+            f"The Model '{self.model_name}' is part of an experimental rework"
+            "of the pytorch-forecasting model layer, scheduled for release with v2.0.0."
+            " The API is not stable and may change without prior warning. "
+            "This class is intended for beta testing and as a basic skeleton, "
+            "but not for stable production use. "
+            "Feedback and suggestions are very welcome in "
+            "pytorch-forecasting issue 1736, "
+            "https://github.com/sktime/pytorch-forecasting/issues/1736",
+            UserWarning,
+        )
+
+    def forward(self, x: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass of the model.
+
+        Parameters
+        ----------
+        x : Dict[str, torch.Tensor]
+            Dictionary containing input tensors
+
+        Returns
+        -------
+        Dict[str, torch.Tensor]
+            Dictionary containing output tensors
+        """
+        raise NotImplementedError("Forward method must be implemented by subclass.")
+
+    def training_step(
+        self, batch: Tuple[Dict[str, torch.Tensor]], batch_idx: int
+    ) -> STEP_OUTPUT:
+        """
+        Training step for the model.
+
+        Parameters
+        ----------
+        batch : Tuple[Dict[str, torch.Tensor]]
+            Batch of data containing input and target tensors.
+        batch_idx : int
+            Index of the batch.
+
+        Returns
+        -------
+        STEP_OUTPUT
+            Dictionary containing the loss and other metrics.
+        """
+        x, y = batch
+        y_hat_dict = self(x)
+        y_hat = y_hat_dict["prediction"]
+        loss = self.loss(y_hat, y)
+        self.log(
+            "train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True
+        )
+        self.log_metrics(y_hat, y, prefix="train")
+        return {"loss": loss}
+
+    def validation_step(
+        self, batch: Tuple[Dict[str, torch.Tensor]], batch_idx: int
+    ) -> STEP_OUTPUT:
+        """
+        Validation step for the model.
+
+        Parameters
+        ----------
+        batch : Tuple[Dict[str, torch.Tensor]]
+            Batch of data containing input and target tensors.
+        batch_idx : int
+            Index of the batch.
+
+        Returns
+        -------
+        STEP_OUTPUT
+            Dictionary containing the loss and other metrics.
+        """
+        x, y = batch
+        y_hat_dict = self(x)
+        y_hat = y_hat_dict["prediction"]
+        loss = self.loss(y_hat, y)
+        self.log(
+            "val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True
+        )
+        self.log_metrics(y_hat, y, prefix="val")
+        return {"val_loss": loss}
+
+    def test_step(
+        self, batch: Tuple[Dict[str, torch.Tensor]], batch_idx: int
+    ) -> STEP_OUTPUT:
+        """
+        Test step for the model.
+
+        Parameters
+        ----------
+        batch : Tuple[Dict[str, torch.Tensor]]
+            Batch of data containing input and target tensors.
+        batch_idx : int
+            Index of the batch.
+
+        Returns
+        -------
+        STEP_OUTPUT
+            Dictionary containing the loss and other metrics.
+        """
+        x, y = batch
+        y_hat_dict = self(x)
+        y_hat = y_hat_dict["prediction"]
+        loss = self.loss(y_hat, y)
+        self.log(
+            "test_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True
+        )
+        self.log_metrics(y_hat, y, prefix="test")
+        return {"test_loss": loss}
+
+    def predict_step(
+        self,
+        batch: Tuple[Dict[str, torch.Tensor]],
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> torch.Tensor:
+        """
+        Prediction step for the model.
+
+        Parameters
+        ----------
+        batch : Tuple[Dict[str, torch.Tensor]]
+            Batch of data containing input tensors.
+        batch_idx : int
+            Index of the batch.
+        dataloader_idx : int
+            Index of the dataloader.
+
+        Returns
+        -------
+        torch.Tensor
+            Predicted output tensor.
+        """
+        x, _ = batch
+        y_hat = self(x)
+        return y_hat
+
+    def configure_optimizers(self) -> Dict:
+        """
+        Configure the optimizer and learning rate scheduler.
+
+        Returns
+        -------
+        Dict
+            Dictionary containing the optimizer and scheduler configuration.
+        """
+        optimizer = self._get_optimizer()
+        if self.lr_scheduler is not None:
+            scheduler = self._get_scheduler(optimizer)
+            if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+                return {
+                    "optimizer": optimizer,
+                    "lr_scheduler": {
+                        "scheduler": scheduler,
+                        "monitor": "val_loss",
+                    },
+                }
+            else:
+                return {"optimizer": optimizer, "lr_scheduler": scheduler}
+        return {"optimizer": optimizer}
+
+    def _get_optimizer(self) -> Optimizer:
+        """
+        Get the optimizer based on the specified optimizer name and parameters.
+
+        Returns
+        -------
+        Optimizer
+            The optimizer instance.
+        """
+        if isinstance(self.optimizer, str):
+            if self.optimizer.lower() == "adam":
+                return torch.optim.Adam(self.parameters(), **self.optimizer_params)
+            elif self.optimizer.lower() == "sgd":
+                return torch.optim.SGD(self.parameters(), **self.optimizer_params)
+            else:
+                raise ValueError(f"Optimizer {self.optimizer} not supported.")
+        elif isinstance(self.optimizer, Optimizer):
+            return self.optimizer
+        else:
+            raise ValueError(
+                "Optimizer must be either a string or "
+                "an instance of torch.optim.Optimizer."
+            )
+
+    def _get_scheduler(
+        self, optimizer: Optimizer
+    ) -> torch.optim.lr_scheduler._LRScheduler:
+        """
+        Get the lr scheduler based on the specified scheduler name and params.
+
+        Parameters
+        ----------
+        optimizer : Optimizer
+            The optimizer instance.
+
+        Returns
+        -------
+        torch.optim.lr_scheduler._LRScheduler
+            The learning rate scheduler instance.
+        """
+        if self.lr_scheduler.lower() == "reduce_lr_on_plateau":
+            return torch.optim.lr_scheduler.ReduceLROnPlateau(
+                optimizer, **self.lr_scheduler_params
+            )
+        elif self.lr_scheduler.lower() == "step_lr":
+            return torch.optim.lr_scheduler.StepLR(
+                optimizer, **self.lr_scheduler_params
+            )
+        else:
+            raise ValueError(f"Scheduler {self.lr_scheduler} not supported.")
+
+    def log_metrics(
+        self, y_hat: torch.Tensor, y: torch.Tensor, prefix: str = "val"
+    ) -> None:
+        """
+        Log additional metrics during training, validation, or testing.
+
+        Parameters
+        ----------
+        y_hat : torch.Tensor
+            Predicted output tensor.
+        y : torch.Tensor
+            Target output tensor.
+        prefix : str
+            Prefix for the logged metrics (e.g., "train", "val", "test").
+        """
+        for metric in self.logging_metrics:
+            metric_value = metric(y_hat, y)
+            self.log(
+                f"{prefix}_{metric.__class__.__name__}",
+                metric_value,
+                on_step=False,
+                on_epoch=True,
+                prog_bar=True,
+                logger=True,
+            )