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name: c2st_metric_evaluation description: Compute a deterministic classifier two-sample accuracy proxy for comparing posterior sample distributions.

C2ST Metric Evaluation

Use this skill when a recovery experiment must compare reference posterior samples and approximate posterior samples with a C2ST-style distributional metric. The score is an accuracy where 0.5 indicates indistinguishable distributions.

Inputs

• Sample JSON containing reference_samples and approximate_samples.
• Optional threshold and seed.

Outputs

• Metric JSON with c2st_accuracy, mean-distance diagnostics, and pass/fail flag.

Workflow

1. Validate non-empty sample matrices with equal dimensions.
2. Z-score both groups using reference-sample statistics.
3. Build a deterministic linear-threshold classifier from the difference of group means.
4. Report balanced classification accuracy and whether it is near the reference target.

Validation

Run the included tests or validate the tree with validate_skill_tree.py --run-tests.

Limitations

This script is a lightweight deterministic proxy for bounded recovery; the paper used an MLP classifier with cross-validation for full benchmark results.