Phase 13: Classical ML Superset — Making scikit‑learn Obsolete

Executive Summary

While RustyTorch++ already excels at deep learning, distributed training, inference, and agentic optimization, scikit-learn still dominates classical ML workflows with its breadth, usability, and ecosystem integration. Phase 13 bridges that gap—adding full coverage of classical algorithms, preprocessing, validation, and Python-native APIs. The goal: make scikit-learn obsolete by offering GPU-native, Rust-safe, high-performance, agent-augmented classical ML within the RustyTorch++ stack.

What We Already Offer (Aligned with scikit-learn)

Classical algorithms like linear/logistic regression, k-means, SVMs, and clustering through existing modules—already GPU-capable and faster than Python implementations.

Pipeline construction via rtx-graph.

Distributed AutoML support through rtx-evolve.

Model serialization, governance, and deployment pipelines.

What’s Missing (scikit-learn still wins on these)

Full algorithm coverage:

Implement: Decision Trees, Random Forests, Gradient Boosted Trees, Naive Bayes, KNN, ElasticNet, Gaussian Processes, HMMs, Association Rule Mining.

Complete preprocessing suite:

Add: Scalers (Standard, MinMax, Robust), Encoders (OneHot, Label), Transformers (PolynomialFeatures, Imputation, Feature Selection).

Model selection utilities:

Add: Grid/Random Search, Bayesian Optimization, Cross-validation strategies (KFold, StratifiedKFold, Time Series), standard metrics (precision, recall, F1, R²).

Python ecosystem interop:

Provide drop-in APIs and seamless NumPy/pandas compatibility.

Documentation API parity:

Replicate scikit-learn’s style and usability for easy migration.

Phase 13 Deliverables

Crates & Modules

rtx-ml-classic: Implements full algorithm suite (trees, ensembles, Bayesian methods, KNN, etc.), GPU or CPU enabled.

rtx-preprocessing: Parity with scikit-learn’s transformers.

rtx-validation: CV strategies, hyperparameter search, standard metrics.

rtx-sklearn-py: Python wrappers with scikit-learn-like API and DLPack/NumPy bridging.

rtx-docs-sklearn: Documentation modeled after scikit-learn’s structure and clarity.

rtx-automeasure: Agentic model selection and AutoML on classical side.

Exit Tests / KPIs

Algorithm coverage ≥95% of scikit-learn v1.x APIs.

Preprocessing transformers match scikit-learn behavior.

GridSearchCV parity with vectorization and GPU speedup (10–50× faster).

API tests: Python migration example works unchanged (e.g., same syntax).

Agent AutoML yields models within 5% of hand-tuned scikit-learn baselines using less compute.

Phase 13 Roadmap

Build rtx-ml-classic with core to advanced algorithms.

Add rtx-preprocessing and validation modules.

Create Python binding crate rtx-sklearn-py.

Draft docs with usage examples & scikit-like style.

Integrate AutoML agent support for classical ML.

Match coverage, then measure performance improvements & developer feedback.

Migration Goal

Enable drop-in replacement for scikit-learn code:

Before (scikit-learn):

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100).fit(X, y)

After (RustyTorch++ Phase 13):

from rustytorch_ml import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100).fit(X, y)

Outcome:

Same syntax.

Instant GPU acceleration.

Memory safety and Rust-native performance.