Roadmap

The canonical, detailed roadmap lives in ROADMAP.md at the repository root. This page gives a higher-level tour of why each phase is ordered the way it is, and what it unlocks for users.

The destination

datarust aims to be the scikit-learn of Rust for classical, CPU-bound machine learning: the library you reach for when you need preprocessing, trees, clustering, SVM, cross-validation, and text features — without pulling in BLAS, a Python runtime, or a GPU stack.

The principles that shape every decision:

• Zero dependencies by default. Every algorithm ships as pure Rust.
• CPU-first. GPU and deep learning are served by candle and burn.
• scikit-learn API parity where Rust allows it; type-safe improvements where Rust enables them.
• No panics — public APIs return Result; missing_docs is enforced.

Where we are (v0.6.5)

The preprocessing, linear-model, and clustering foundations are solid:

• 18 transformers/encoders/imputers/selectors — the deepest preprocessing coverage of any Rust ML library.
• 4 linear models (Linear / Ridge / Lasso / Logistic regression) with binary IRLS and multiclass softmax support.
• KMeans clustering (Lloyd’s algorithm, k-means++ initialization) + silhouette score.
• 15 metrics: regression (MSE, MAE, R², …), classification (accuracy, precision, recall, F1, ROC-AUC, PR-AUC, kappa, Matthews, …), clustering (silhouette).
• Pipeline, ColumnTransformer, cross-validation, Params trait.
• Zero external dependencies by default; serde / rayon / matrixmultiply are opt-in.

What is conspicuously absent: trees and ensembles, hyperparameter search, text features, and SVM. The roadmap below addresses each, in order of impact.

The release track

v0.7 — Tree-based learning

Why this comes first. Trees and ensembles are the single most requested feature and the backbone of tabular ML. A shared, seedable RNG is a prerequisite for bootstrap sampling in ensembles — the current private xorshift64 needs to be promoted first.

Highlights: DecisionTree (CART), RandomForest, ExtraTrees, a Bagging meta-estimator, and feature_importances_ output. A new src/tree/ and src/ensemble/ module pair.

v0.8 — Model selection & text

Why this comes second. Once the algorithm catalog is broader, the next bottleneck is tuning and NLP. GridSearchCV builds directly on the Params trait (get_params/set_params) shipped in v0.6. Text vectorizers depend on sparse matrix arithmetic, which today does not exist — so this phase widens SparseMatrix from read-only storage into a real linear-algebra type.

Highlights: GridSearchCV/RandomizedSearchCV, validation_curve/ learning_curve, CountVectorizer/TfidfVectorizer, KNeighbors, and Naive Bayes — the last three together forming a complete document-classification stack.

v0.9 — Depth & breadth

Why this is late. These are high-value but lower-leverage than the foundational phases. Many users will be productive with v0.7–0.8 alone.

Highlights: GradientBoosting/AdaBoost/Voting/Stacking, LinearSVC/SVC (SMO solver, pure Rust), DBSCAN/AgglomerativeClustering, ElasticNet/ SGDClassifier, NMF/FactorAnalysis, an embedded datasets module, and a csv feature flag for data loading.

v1.0 — Stability

Why v1.0 is mostly cleanup. By this point the API surface has been exercised across many estimators and is ready to be frozen. v1.0 removes legacy #[doc(hidden)] APIs, refreshes ARCHITECTURE.md, runs a full public-API audit, and publishes the SemVer stability commitment.

What is deliberately out of scope

The roadmap is as much about what datarust will not become:

• GPU compute and deep learning — served by candle and burn.
• Distributed training — a networking project, not a classical-ML one.
• pickle/joblib compatibility — technically impossible across languages.
• SHAP/LIME — permutation importance (planned) covers the common need.

A few items are under consideration for post-1.0 but not committed: f32 generics, manifold learning (TSNE), HistGradientBoosting, ONNX export/import, NumPy .npy interop, and a minimal MLPClassifier. See ROADMAP.md for the full list and rationale.

Contributing

The checkboxes in ROADMAP.md double as a contribution guide. Pick an unchecked item, open an issue to align on API shape, and land it with doc comments and sklearn-parity tests. Progress is recorded in CHANGELOG.md under each release.