fix: use result.max_log_likelihood_instance for best_fit summary#34
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The previous accessor (result.samples.max_log_likelihood_sample.instance) was wrong — Sample has no .instance attribute. Surfaced as the "(unavailable: AttributeError ...)" string in every metric JSON, including the first clean A100 baseline (job 322560). result.max_log_likelihood_instance is the Result-level property that returns the model instance for the best-fit sample directly. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
The previous accessor used in `_runner.py` —
```python
best_instance = primary_result.samples.max_log_likelihood_sample.instance
```
is wrong: `Sample` has no `.instance` attribute. Every metric JSON written so far has serialised `best_fit` as the swallowed exception string:
```json
"best_fit": "(unavailable: AttributeError("'Sample' object has no attribute 'instance'"))"
```
This is visible in the A100 `imaging/mge × hst` baseline (job 322560) as well as both PR #29's smoke runs.
The correct accessor is the Result-level property:
```python
best_instance = primary_result.max_log_likelihood_instance
```
This matches the SLaM scripts (`result_list[0].max_log_likelihood_instance` in `autolens_workspace/scripts/multi/features/slam/independent.py`).
🤖 Generated with Claude Code