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name: linear_calibration_ranking description: Fit non-negative layer calibration weights for LPIPS-style distances using bounded 2AFC ranking feedback.

Linear Calibration Ranking

Use this skill when a recovery or evaluation needs to apply or fit the LPIPS paper's shallow human-judgment calibration over per-layer distances.

Do not use it to train or fine-tune a trunk network. This skill updates only calibration weights.

Inputs

• Items with layers0, layers1, and judge.
• layers0 and layers1 are equal-length lists of per-layer distances for ref-p0 and ref-p1.
• judge = 0 means p0 should score closer; judge = 1 means p1 should score closer.
• Optional initial weights, step count, and learning rate.

Outputs

• Non-negative calibrated weights.
• Loss/accuracy before and after calibration.
• params_before and params_after for recovery validation.

Workflow

1. Validate layer vectors and labels.
2. Score each candidate by weighted layer-distance sum.
3. Compute logistic ranking loss over score0 - score1.
4. Run bounded gradient steps on calibration weights.
5. Project weights to non-negative values after each step.
6. Emit a trace with before/after loss, accuracy, and weights.

Validation

Run:

python scripts/calibrate_ranking.py --self-test
python tests/test_calibrate_ranking.py

Limitations

The script is intentionally small and deterministic. It is suitable for reduced recovery and unit tests, not full-scale LPIPS training.