package modelkit-parallel

0.3.2 (2026-08-17)

• Add immutable dense dataset admission with explicit finiteness policies, aligned zero-copy row views, stable schema fingerprints, and observable copy/view reporting.
• Add immutable mean, median, and constant imputation, population standardization, and variance-based feature filtering while preserving feature identities.
• Add leakage-safe sequential pipelines that fit preprocessing only on training input, propagate schemas, derive stage-local random streams, and dispatch prediction, decision, and probability operations.
• Add portable weighted ordinary least squares, ridge regression, and binary logistic regression with stable numerical methods, fitted coefficients, and solver diagnostics.
• Add deterministic K-fold, stratified K-fold, group K-fold, and expanding-window time-series splitters with validated row views and explicit aligned dataset materialization.
• Add weighted regression and binary classification metrics, plotting-neutral residual, ROC, and precision-recall data, higher-is-better scorers, stable score aggregation, and explicit undefined-result policies.
• Add deterministic cross-validation with ordered train and test scores, CPU timings, optional fitted models and row indices, structured failures, and independent split and fit seeds.
• Add the separately installable modelkit-parallel package for bounded Domainslib fold execution with sequential fallback, stable logical results, and oversubscription diagnostics.
• Add typed finite grid search over immutable configurations with shared splits, failure-aware candidate reports, named-scorer ranking, deterministic tie handling, and best-model refitting on all training data.
• Add versioned data-only artifacts for fitted built-in regression and binary classification pipelines with feature-schema identity, bounded readers, accidental-corruption detection, file conveniences, and golden compatibility coverage.
• Add committed scikit-learn parity fixtures and deterministic comparative benchmark reports for preprocessing, linear models, splitters, metrics, cross-validation, parallel execution, and grid search without adding a Python runtime dependency or making performance claims.
• Expand executable documentation and the end-to-end evaluation example to cover the complete supervised workflow.
• Decompose the portable implementation into private cohesive source units while retaining the existing flat Modelkit.* public API and optional-package dependency boundaries.

0.2.1 (2026-08-11)

• Restore package builds on OCaml 5.5 by making typed target accessor specializations explicit.
• Test OCaml 5.5 on Linux, macOS, and Windows in addition to the existing OCaml 5.2 and 5.3 matrix.

0.2.0 (2026-08-10)

• Add immutable float64 vectors and matrices, row views, typed targets, feature schemas, sample weights, groups, and validation errors over portable Bigarray storage.
• Define common contracts for immutable specifications, fitted estimators, classifiers, regressors, transformers, scorers, splitters, execution, random-number generation, and numerical backends.
• Add deterministic seeds and random streams, stable sequential execution, and a portable reference backend with cancellation-safe reductions and matrix-vector kernels.
• Establish unit, property, metamorphic, mdx, public API compatibility, sklearn fixture, and reusable numerical-backend conformance tests.
• Add pinned development-only Python tooling and committed metadata for reproducible sklearn reference fixtures and comparative benchmark collection without introducing a runtime Python dependency.
• Verify build, tests, package installation targets, and odoc generation on Linux x86-64, macOS arm64, and Windows x86-64 with OCaml 5.2 and 5.3 in GitHub Actions.

0.1.0 (2026-08-04)

• Establish the initial ModelKit package with an OCaml 5.2 compiler floor and Apache-2.0 licensing.
• Add the portable library skeleton and reserved package boundaries for optional adapters and accelerated backends.
• Add the Dune workspace, generated odoc documentation, formatting checks, and platform-specific opam lock workflow.
• Document project governance, support, and maintenance policies.