Release Notes

v2.3.0

Type: compatible minor release.

Main points:

missing_policy="merge" adds auditable missing-value merge for pandas workflows
missing_merge_criterion="nearest_event_rate" selects the closest regular bin by fit-time event-rate distance
missing_merge_criterion="nearest_woe" selects the closest regular bin by fit-time WoE distance
missing_merge_fallback supports separate_bin and raise
missing_profile_, missing_decision_log_, missing_merge_candidates_, and missing_merge_map_ preserve the audit trail
return_woe=True routes merged missing values to the learned destination bin before mapping WoE
bundle and reporting exports persist merge criterion, fallback, candidates, decision log, and merge map
standard, separate_bin, forbid, legacy alias compatibility, and RiskBands is Binner are preserved
pt-BR/en docs and examples describe both merge criteria

Notes:

missing_policy="standard" remains the default
PySpark merge is not implemented; PySpark raises an explicit boundary for missing_policy="merge"
no temporal_stable, monotonic_neighbor, custom merge criteria, opaque imputation, or PySpark 4.x support is added

Type: docs-only preparation.

Status: documentation update after v2.2.0; no package version bump.

Main points:

adds a focused missing-policy guide for standard, separate_bin, and forbid
adds small pandas and PySpark missing-policy demos
improves docs-site navigation for missing values, bundle fields, and audit logs
keeps PySpark documented as optional through riskbands[spark]
does not change core behavior, package version, publish status, tag, or release artifacts

Historical notes:

at that documentation point, merge policies remained future work
no opaque intelligent imputation is added
i18n is handled as a later docs-site sprint, without changing package release status

Compatible minor release focused on auditable missing-value policy and compatibility.

Main points:

missing_policy="standard" is the default and preserves the Sprint A baseline behavior
missing_policy="separate_bin" is opt-in and creates explicit Missing bins, including categorical missing values
missing_policy="forbid" raises during fit or transform when selected features contain missing values
standard is the canonical name for the historical maximize-oriented score strategy
legacy remains accepted as a compatibility alias for standard
pandas and PySpark inputs are supported by the missing-policy contract
bundles persist missing_policy, effective_missing_policy, missing_profile, and missing_decision_log
old bundles without these fields continue to load as standard
PySpark remains optional through riskbands[spark] with pyspark>=3.5,<4

Notes:

merge policies such as merge_nearest_woe and merge_nearest_event_rate are not part of this target
no opaque intelligent imputation is added
a full distributed Spark fitting backend is not part of this target

Compatible minor release focused on the preferred RiskBands name, optional PySpark paths, and validation profiles.

Main points:

RiskBands is the preferred public estimator name; Binner remains compatible
min_n_bins records a soft quality status without forcing artificial cuts
sample_size controls PySpark fit sampling
pandas/PySpark inputs are detected automatically in fit and transform
pandas outputs remain pandas; PySpark outputs remain PySpark
fit(validate=True) and transform(validate=True) create validation profiles with separate fit and transform reports
v2.1.0 bundles persist schema/version metadata, profiles, separate validation reports, sampling/backend metadata, and data schema when available
PySpark remains optional through riskbands[spark] with pyspark>=3.5,<4

Notes:

PySpark fit uses controlled sampling plus the current pandas engine
PySpark transform and validation profiles use native Spark expressions and aggregated profiles
A full distributed Spark fitting backend is not part of this release

Patch release focused on release hardening and deterministic operational behavior.

Main points:

stronger categorical handling for rare categories, missing values, and unknown categories
safer export_bundle(...) outputs with sanitized names and a traceable manifest
explicit force_numeric support
stronger quality gates with ruff, coverage-enabled pytest, pip check, bandit, and pip-audit
supply-chain constraints to avoid the vulnerable ortools 9.11.4210 -> protobuf 5.26.1 resolver path
README and release governance updates for pandas, Spark/Databricks usage, overrides, auditable export, assets, and local prompts

Release focused on real auditability, friendlier inspection, and a more robust public experience.

Main points:

new public export layer with export_binnings_json(...)
new auditable bundle with export_bundle(...)
stronger metadata_, including score weights and effective fit context
new public tables score_table() and audit_table()
more discoverable aliases for bin inspection
new public plot layer for bad rate, heatmap, temporal share, and score components
fix to temporal alignment in the supervised strategy, improving diagnostics and visualizations
documentation benchmark assets regenerated with wider charts and fewer empty traces
docs site reinforced for onboarding, audit, and visual interpretation

Patch release to close the public publication with consistency:

fixes riskbands.__version__ resolution in the installed package outside the source tree
adds regression test for distributed metadata version reading
fully preserves the rename to stable, the new documentation, and the release flow for the v2 series

Public consolidation release:

definitive rename of the public score_strategy value from generalization_v1 to stable
removal of the old name from the public API, examples, smoke tests, labels, and main documentation
docs site reorganized for onboarding, first steps, and clearer navigation for new users
dedicated pages for score_strategy, normalization_strategy, woe_shrinkage_strength, Optuna, and output interpretation
notebooks and examples aligned with the friendly sklearn/pandas-style flow
explicit release flow preparation for validation, GitHub Pages, and PyPI publication through Trusted Publishing

Important evolution of public API ergonomics:

Binner more aligned with sklearn and pandas conventions
friendly support for fit(df, y="target", column="feature")
transform(...) and fit_transform(...) with more predictable behavior for DataFrame and Series
public aliases such as max_n_bins and monotonic_trend
new inspection methods: binning_table(), summary(), report(), score_details(), diagnostics(), and plot_stability()
easier-to-discover post-fit attributes
new Plotly notebook with synthetic data for library onboarding

Important evolution of the scoring layer:

legacy path preserved explicitly as legacy
new temporal objective introduced and now exposed publicly as stable
configurable weights, absolute normalization, and WoE shrinkage
consistent integration with Binner, BinComparator, auditable reports, and Optuna
new minimal example comparing legacy versus stable

Important structural changes already reflected in the repository:

destructive rename to riskbands
Binner established as the main public class
legacy nasabinning namespace removed
documentation direction oriented toward benchmarks established in repository examples