GoldenMatch

v1.20.0 Feature

This release adds 2 notable features for engineering teams evaluating rollout.

Published 2mo Data Pipelines & ETL

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✓ No known CVEs patched

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✓ 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

Adds cluster-decision tuner to propose per-dataset auto-approve thresholds.

Full changelog

goldenmatch 1.20.0: cluster-decision tuner (PR #451). Third tuner in the Learning Memory family (alongside the pair-level MemoryLearner and field-level field-strategy tuner): consumes cluster-level approve/reject decisions and proposes a per-dataset auto-approve threshold via tune_decision_threshold (core/autoconfig_cluster_threshold_tuner.py), with a 90/10 train/heldout split + overfit guard. Adds Decision.CLUSTER_DECISION, Correction.cluster_score/cluster_outcome, and MemoryStore.record_cluster_decision. See packages/python/goldenmatch/CHANGELOG.md.

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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.