Mask boundary nodes in spatial loss maps during test_step

[RajdeepKushwaha5]

Describe your changes

The spatial_loss call in test_step did not pass mask=self.interior_mask_bool, unlike every other self.loss() call in training_step, validation_step, and test_step itself. This caused boundary grid nodes to be included in the spatial loss maps and the saved mean_spatial_loss.pt, inconsistent with the loss the model optimises.

Set boundary node values to NaN after computing the full-grid loss (to keep the num_grid_nodes shape needed by plot_spatial_error) and switch to torch.nanmean when averaging over samples in on_test_epoch_end.

Issue Link

closes #569

Type of change

• Bug fix (non-breaking change that fixes an issue)
• New feature (non-breaking change that adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation (Addition or improvements to documentation)

Checklist before requesting a review

• My branch is up-to-date with the target branch
• I have performed a self-review of my code
• For any new/modified functions/classes I have added docstrings that clearly describe its purpose, expected inputs and returned values
• I have placed in-line comments to clarify the intent of any hard-to-understand passages of my code
• I have updated the README to cover introduced code changes
• I have added tests that prove my fix is effective or that my feature works
• I have given the PR a name that clearly describes the change, written in imperative form
• I have requested a reviewer and an assignee

Checklist for reviewers

• the code is readable
• the code is well tested
• the code is documented (including return types and parameters)
• the code is easy to maintain

Author checklist after completed review

• I have added a line to the CHANGELOG describing this change, in a section reflecting type of change

Checklist for assignee

• PR is up to date with the base branch

[Copilot]

Pull request overview

This PR fixes an inconsistency in ARModel test-time spatial loss map generation by ensuring boundary grid nodes are excluded (as they already are in the optimized training/validation losses), so saved/visualized spatial loss maps reflect the same interior-only objective.

Changes:

• Mask boundary nodes in test_step spatial loss maps by setting boundary entries to NaN while preserving the full-grid shape for plotting.
• Switch aggregation in on_test_epoch_end from torch.mean to torch.nanmean so boundary-node NaNs are ignored when computing the mean spatial loss map.
• Add a corresponding entry to the unreleased CHANGELOG.md.

Reviewed changes

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

File	Description
neural_lam/models/ar_model.py	Masks boundary nodes in test spatial loss maps and uses nanmean when aggregating, aligning evaluation maps with interior-masked losses.
CHANGELOG.md	Documents the bug fix in the unreleased “Fixed” section.

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[Copilot]

The CHANGELOG entry still contains a placeholder PR reference ("#TBD" and a "/pull/TBD" URL). Please replace this with the actual PR number (and/or the real issue link) before merging so the link is valid in release notes.

Suggested change

	- Mask boundary nodes in spatial loss maps computed during `test_step`, consistent with all other loss calls that use `interior_mask_bool` [\#TBD](https://github.com/mllam/neural-lam/pull/TBD) @RajdeepKushwaha5
	- Mask boundary nodes in spatial loss maps computed during `test_step`, consistent with all other loss calls that use `interior_mask_bool` @RajdeepKushwaha5

[Copilot]

Test coverage: this change introduces NaN-masking for boundary nodes and switches the aggregation to nanmean, but there doesn’t appear to be a unit/integration test asserting that boundary nodes are excluded from the saved/plot-ready spatial loss maps. Adding a small test (e.g., with a minimal module/mocked masks similar to existing ARModel tests) would prevent regressions in evaluation outputs.

[sadamov]

ar_model.py was removed by #208, needs a rebase onto current main. Fix moves to models/module.py. Also I'd drop the standalone test, see inline.
If you are willing to bring this over the finish line over two months later, you can clear the conflicts and I'll merge.

[sadamov]

File is gone after the #208 refactor, this block should land in models/module.py:442 right after spatial_loss = self.loss(...):

        # Exclude boundary nodes, consistent with the loss used in
        # training/validation. NaN-mask in place so the tensor keeps its
        # (B, pred_steps, num_grid_nodes) shape for downstream plotting,
        # and is reduced with `torch.nanmean` in `on_test_epoch_end`.
        spatial_loss[..., ~self.interior_mask_bool] = float("nan")

[sadamov]

Same, apply at models/module.py:722:

            mean_spatial_loss = torch.nanmean(spatial_loss_tensor, dim=0)

[sadamov]

I'd drop this one. It only verifies that torch.nanmean ignores NaN, which is testing PyTorch behavior rather than this integration. Could you replace it with an integration test that builds a ForecasterModule with a known interior_mask, runs trainer.test() end-to-end, and asserts boundary nodes are NaN in the saved mean_spatial_loss.pt? That would also be the first real coverage for test_step / on_test_epoch_end in this repo.