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name: sbi_neural_training description: Train or proxy-train SBI density estimator families on simulator-generated parameter observation pairs.

SBI Neural Training

Use this skill when simulation records need to be converted into a trained estimator for an sbi-style workflow. It covers the paper's SNPE, SNLE, and SNRE family distinction and provides a deterministic reduced SNPE-like proxy for environments where the full sbi and PyTorch stack is unavailable.

Do not report the reduced proxy as a full sbi run. The proxy is only valid when recovery mode permits reduced evidence and the runtime handoff records why the full stack is blocked.

Inputs

• Valid simulation records with theta and x vectors.
• Algorithm family: SNPE, SNLE, or SNRE.
• Training parameters such as learning rate and optimizer steps.
• A reduced-runtime allowance flag when the full stack is unavailable.

Outputs

• A trained estimator dictionary with scalar conditional-posterior parameters for reduced SNPE-style recovery.
• A training trace with loss_before, loss_after, params_before, params_after, and optimizer_state_changed.
• Family metadata identifying which paper algorithm family was represented.

Workflow

1. Verify that records contain paired numeric theta and x values.
2. Select the SBI family. Full recovery should call the corresponding package trainer when available.
3. In reduced mode, train a scalar conditional Gaussian posterior mean theta_hat = a * x + b.
4. Record the parameter update and loss movement before and after optimization.
5. Pass the estimator to the posterior API skill for conditioning, sampling, and log-probability checks.

Validation

Run:

python /share/project/yuyang/workspace/Paper2Skills/Distiller/skills/module-to-skill/scripts/validate_skill_tree.py /share/project/yuyang/workspace/Paperbench/record/case15/extracted_skills_attempt_001/sbi_toolkit/sbi_neural_training --run-tests

For a standalone smoke run:

python scripts/neural_training.py --demo

Limitations

The reduced trainer is intentionally tiny and standard-library only. It preserves the simulator-to-estimator training mechanism but does not implement normalizing flows, MCMC, classifier ratio estimation, PyTorch autograd, or multi-round proposal adaptation.