remove ensemble learning class

Author: aditya0by0

chebai/models/ensemble.py

@@ -498,81 +498,3 @@ def _process_for_loss(
         if labels is not None:
             labels = labels.float()
         return model_output["logits"], labels, kwargs_copy
-
-
-class ChebiEnsembleLearning(_EnsembleBase):
-    """
-    A specialized ensemble learning class for ChEBI classification.
-
-    This ensemble combines multiple models by concatenating their logits and
-    passing them through a feedforward neural network (FFN) for final predictions.
-    """
-
-    NAME = "ChebiEnsembleLearning"
-
-    def __init__(self, model_configs: Dict[str, Dict], **kwargs: Any):
-        """
-        Initializes the ChebiEnsembleLearning class.
-
-        Args:
-            model_configs (Dict[str, Dict]): Configuration dictionary for each model.
-            **kwargs (Any): Additional keyword arguments for configuring the FFN.
-        """
-        super().__init__(model_configs, **kwargs)
-
-        ffn_kwargs = kwargs.copy()
-        ffn_kwargs["input_size"] = len(self.model_configs) * int(kwargs["out_dim"])
-        self.ffn: FFN = FFN(**ffn_kwargs)
-
-    def forward(self, data: Dict[str, Tensor], **kwargs: Any) -> Dict[str, Any]:
-        """
-        Performs a forward pass through the ensemble model.
-
-        Args:
-            data (Dict[str, Tensor]): Input data dictionary for the models.
-            **kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            Dict[str, Any]: Output from the FFN model.
-        """
-        logits_list = [model(data)["logits"] for model in self.models.values()]
-        return self.ffn({"features": torch.cat(logits_list, dim=1)})
-
-    def _get_prediction_and_labels(
-        self, data: Dict[str, Any], labels: Tensor, output: Tensor
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Extracts predictions and labels for evaluation.
-
-        Args:
-            data (Dict[str, Any]): Input data dictionary.
-            labels (Tensor): Ground truth labels.
-            output (Tensor): Model output.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Processed predictions and labels.
-        """
-        return self.ffn._get_prediction_and_labels(data, labels, output)
-
-    def _process_for_loss(
-        self,
-        model_output: Dict[str, Tensor],
-        labels: Tensor,
-        loss_kwargs: Dict[str, Any],
-    ) -> Tuple[Tensor, Tensor, Dict[str, Any]]:
-        """
-        Processes model output and labels for computing the loss.
-
-        Args:
-            model_output (Dict[str, Tensor]): Output dictionary from the model.
-            labels (Tensor): Ground truth labels.
-            loss_kwargs (Dict[str, Any]): Additional arguments for loss computation.
-
-        Returns:
-            Tuple[Tensor, Tensor, Dict[str, Any]]: Loss, processed predictions, and additional info.
-        """
-        return self.ffn._process_for_loss(model_output, labels, loss_kwargs)
-
-
-if __name__ == "__main__":
-    pass