This release adds 3 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
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Summary
AI summaryUpdates Details, What's New, and TWI across a mixed release.
Full changelog
What's New
CCMS Cluster Comparison
Compare two ER clustering outcomes without ground truth using the Case Count Metric System (Talburt et al., arXiv:2601.02824v1).
import goldenmatch as gm
result = gm.compare_clusters(clusters_a, clusters_b)
print(result.summary())
# {"unchanged": 42, "merged": 3, "partitioned": 5, "overlapping": 1, "twi": 0.92, ...}
CLI:
goldenmatch compare-clusters run_a.json run_b.json --details --case-type merged
Parameter Sensitivity Analysis
Sweep config parameters and see how clustering changes at each value:
from goldenmatch import run_sensitivity, SweepParam
results = run_sensitivity(
file_specs=[("data.csv", "src")],
config=cfg,
sweep_params=[SweepParam("threshold", 0.70, 0.95, 0.05)],
sample_size=5000,
)
for r in results:
print(r.stability_report())
CLI:
goldenmatch sensitivity data.csv -c config.yaml --sweep threshold:0.70:0.95:0.05 --sample 5000
Details
- 4 cluster transformation cases: unchanged, merged, partitioned, overlapping
- Talburt-Wang Index (TWI) for normalized similarity measure
- Per-point error handling — failed sweep points logged and skipped, partial results preserved
- 16 new tests, 1260 total passing
Full Changelog: https://github.com/benzsevern/goldenmatch/compare/v1.2.7...v1.3.0
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About GoldenMatch
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Earlier breaking changes
- v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
- vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
- vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
- vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
- v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.
Beta — feedback welcome: [email protected]