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GoldenMatch

v1.3.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 3mo Data Pipelines & ETL
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution
+14 more
fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Updates 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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Related context

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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]