add custom loss, optim, metrics for model_sweep

[Atharva9621]

Add support for custom loss and metrics in model_sweep

Fixes #544

• Custom loss, metrics, and optimizers can now be passed to model_sweep in the same way as tabular_model.fit() through custom_fit_params.
• custom_fit_params expects a dictionary specifying the custom loss, metrics, or optimizer.
• Minimal code changes; fully backward compatible.
• Updated corresponding tests.

Example usage

class CustomLoss(nn.Module):
      def __init__(self):
          super(CustomLoss, self).__init__()
  
      def forward(self, inputs, targets):
          loss = torch.mean((inputs - targets) ** 4)
          return 100*loss.mean()

def custom_metric(y_hat, y):
    return (y_hat - y).mean()

sweep_df, best_model = model_sweep(
    task="regression",
    train=train,
    test=val,
    data_config=data_config,
    optimizer_config=optimizer_config,
    trainer_config=trainer_config,
    model_list="lite",
    custom_fit_params = {
        "loss": CustomLoss(),
        "metrics": [custom_metric],
        "metrics_prob_inputs": [True],
        "optimizer": torch.optim.Adagrad,
    }
)

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📚 Documentation preview 📚: https://pytorch-tabular--587.org.readthedocs.build/en/587/

[Copilot]

Pull Request Overview

This PR adds support for custom loss functions, metrics, and optimizers to the model_sweep function, making it consistent with the TabularModel.fit() API.

Key Changes

• Added custom_fit_params parameter to model_sweep function that accepts custom loss, metrics, and optimizer specifications
• Updated validation logic to ensure rank_metric is "loss" when custom metrics are provided
• Enhanced test coverage with a new test case (test_model_compare_custom) demonstrating custom fit parameters

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
src/pytorch_tabular/tabular_model_sweep.py	Added custom_fit_params parameter to model_sweep and _validate_args, with validation logic and documentation; unpacks params when calling prepare_model
tests/test_common.py	Updated _run_model_compare to accept and forward custom_fit_params; added new test case with custom loss, metrics, and optimizer

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