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name: denoising_score_training description: Train and validate conditional denoising score models for F-NPSE or PF-NPSE simulation-based inference recoveries.

Denoising Score Training

Use this skill when a recovery or implementation needs the score-matching part of Compositional Score Modeling for Simulation-Based Inference. It is appropriate for NPSE, F-NPSE, and PF-NPSE score learners that receive simulator-generated (theta, condition) pairs and predict the Gaussian denoising score at a selected diffusion level.

Do not use this skill to combine multiple observations or to sample from the posterior. Score composition belongs to factorized_score_composition; posterior sampling belongs to annealed_langevin_sampler.

Inputs

• Clean simulator parameter vectors theta_clean.
• Conditioning values: one observation for F-NPSE, or a small observation group for PF-NPSE.
• Diffusion coefficient gamma with 0 < gamma < 1.
• A parameterized score predictor. The bundled script provides a small linear conditional predictor for bounded recovery experiments.

Outputs

• Gaussian denoising target scores.
• Mean squared denoising score loss.
• Optimizer trace with loss_before, loss_after, params_before, and params_after.
• A predictor callable or serialized parameter dictionary usable by composition and recovery scripts.

Workflow

1. Generate noisy parameters with theta_t = sqrt(gamma) * theta_clean + sqrt(1 - gamma) * epsilon.
2. Compute the target score (sqrt(gamma) * theta_clean - theta_t) / (1 - gamma).
3. Evaluate the conditional score predictor on theta_t, gamma, and the condition.
4. Minimize the squared error against the target.
5. Record parameter movement and before/after loss whenever this is used as reduced recovery evidence.

Validation

Run the deterministic test suite:

python /share/project/yuyang/workspace/Paper2Skills/Distiller/skills/module-to-skill/scripts/validate_skill_tree.py <skill_dir> --run-tests

The tests use a Gaussian fixture and verify finite denoising targets, decreasing or non-increasing loss after a small gradient step, and changed trainable parameters.

Limitations

The bundled linear predictor is a recovery aid, not the paper's full neural architecture. A full reproduction should replace it with a neural score network while preserving the same denoising target and trace contract.