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name: evaluation description: "Evaluate SDGX real and synthetic tabular data with JSD, mutual-information similarity, and safe metric/benchmark adaptation guidance." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

SDGX Evaluation

Use this sub-skill when the task is about comparing real and synthetic SDGX tables, calculating JSD, mutual-information similarity, validating sampled outputs, or adapting benchmark-style checks without running heavyweight benchmark defaults.

Use ../single-table-synthesis/SKILL.md to generate the synthetic data first. Use ../data-preparation/SKILL.md if column metadata or data types are unclear.

Core metrics

• sdgx.metrics.column.jsd.JSD.calculate(real_data, synthetic_data, cols, discrete=True) returns a Jensen-Shannon divergence in [0, 1].
• sdgx.metrics.pair_column.mi_sim.MISim.calculate(src_col, tar_col, metadata) returns normalized mutual-information similarity for one pair of columns.

Read references/metrics-reference.md for signatures, data-type metadata expectations, and caveats.

Bundled helper

Run scripts/compute_metrics.py to calculate JSD and optional MI similarity from CSV files:

python sub-skills/evaluation/scripts/compute_metrics.py \
  --real real.csv \
  --synthetic synthetic.csv \
  --jsd-cols workclass \
  --discrete true

Benchmark caution

The source benchmark scripts generate large random datasets and run memory-profiler comparisons against SDV CTGAN. Treat them as evidence of performance intent, not as default validation. For normal repo-skill verification, use small metrics and smoke scripts.

Troubleshooting

Read references/troubleshooting.md for invalid columns, KDE cost on continuous JSD, metadata type names for MI, and interpreting metric values.