Changelog

All notable changes to datarust are documented in the project’s CHANGELOG.md on GitHub.

This page provides a summary. For the full, detailed changelog (including internal refactors and performance tables), see the canonical source.

Unreleased

Restored enforceable Rust 1.70 compatibility by pinning MSRV-compatible Rayon and serde_json dependency lines.
Hardened deserialized categorical encoder state against mismatched category lists, index maps, and output widths.
Rejected fractional ordinal codes during inverse transformation.
Completed fitted-state checks for KNN/Simple imputers, MaxAbsScaler, and QuantileTransformer.
Aligned PCA solver and component-count documentation with the implemented behavior.

Silhouette scoring compacts arbitrary cluster IDs, handles singleton clusters correctly, and rejects invalid one-cluster-per-sample input.
Numerical estimators, decompositions, scalers, selectors, and regression metrics reject non-finite observations; imputers retain NaN missing-value support while rejecting infinity.
Non-finite scaler, selector, encoder, and SVD configuration values fail before fitting.
Inconsistent fitted state loaded from JSON returns a recoverable error instead of panicking during transform or prediction.
CI now builds and validates the docs/blog site and denies rustdoc warnings explicitly.

Ridge, Lasso, logistic regression, and KMeans now reject non-finite or otherwise invalid solver hyperparameters before optimization.
Params::set_params validation no longer mutates KMeans or logistic regression when a candidate is invalid.
CI now executes the intended OS × feature matrix, including datasets and an all-features build, and verifies the crates.io package.
The published crate excludes the blog, book, website, npm, and Cloudflare deployment sources, reducing the compressed archive from about 3.0 MiB to 289 KiB.

Safe dense access now checks bounds in release builds, while dense allocations reject dimension overflow.
Raw and deserialized CSR matrices validate pointer ranges and sorted column indices; duplicate triplets are summed.
Train/test and cross-validation reject undersized or mismatched inputs, and fractional test sizes now round up consistently.
StratifiedKFold supports validated, non-contiguous multiclass labels.
Binary log loss, ROC-AUC, and average precision validate labels and numeric inputs. New *_with_positive_label functions support label spaces such as {2, 5}, and average-precision ties are threshold-grouped.

Classification labels are now compacted safely, so metrics and LogisticRegression support non-contiguous labels such as {2, 5, 9} while preserving original predictions and probability-column mappings.
Precision, recall, and F1 now expose explicit binary, macro, weighted, and micro averaging, configurable zero-division handling, and per-class reports.
The optional datasets feature adds embedded Iris, Breast Cancer, Wine, and Diabetes datasets with no runtime file or network dependency.

Estimator hierarchy — Estimator, Predictor, Classifier, and PredictProba now define common supervised flows. Regressor is reserved for regression semantics.
SupervisedPipeline — Pipeline::with_estimator combines preprocessing, target-aware feature selection, and a final predictor in a cloneable, serde-serializable pipeline.
BREAKING: LogisticRegression classification semantics — predict returns hard labels; predict_proba returns a two-column probability matrix; predict_positive_proba exposes the positive-class vector.
BREAKING: Predictor imports — import Predictor to call shared fit/predict methods on supervised models.
BREAKING: Custom trait implementors — custom transformers and encoders must implement Estimator; custom regressors must implement Estimator and Predictor before Regressor.

model_selection module — train_test_split, KFold, StratifiedKFold, cross_val_score. Shared deterministic xorshift64 PRNG, now used by both model_selection and decomposition::randomized_svd.
LogisticRegression — binary classification via IRLS (Cholesky/SVD). The crate’s first classifier.
metrics::classification — accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, log_loss.
Ridge — L2-regularized regression (Cholesky/SVD). Succeeds on collinear inputs.
Lasso — L1-regularized regression via coordinate descent. Produces sparse models (feature selection).
Regressor trait — supervised counterpart of Transformer (fit(X, y) + predict).
linalg::cholesky module — shared SPD solver foundation.
LinearRegression — OLS regression, the crate’s first estimator.
metrics::regression — MSE/RMSE, MAE, R², max_error, explained_variance.
PCASolver enum (Auto / Full / Randomized) on PCA.
jacobi::eigh_topk_flat — power-iteration + deflation for top-k eigenpairs.
Flat-storage Jacobi eigensolver, flat matmul helpers, flat covariance.

PCA 50 000 × 200 dropped from ~320 ms to ~104 ms with matrixmultiply.
LinearRegression fit at 50 000 × 200: 258 ms → 84 ms with matrixmultiply.
Scaler transform rayon threshold: scalar loop below 4 096 rows, parallel above.

matrixmultiply feature: optional tuned pure-Rust GEMM (no system BLAS).
BREAKING: Matrix internal storage switched from Vec<Vec<f64>> to a single contiguous Vec<f64>. ~13× on RobustScaler, ~5× on StandardScaler at 50 000 × 200.
Flat-storage scalers, fused Welford statistics, NaN validation fused into transform loops.

See the full changelog on GitHub for 0.1.x and 0.2.x history.