OchreML (≧ω≦)
Classical Machine Learning Library in Rust with PyO3 Bindings
Welcome to OchreML! (＾▽＾)
OchreML is a high-performance classical Machine Learning library written in Rust with seamless Python bindings via PyO3. Designed with scikit-learn API compatibility, OchreML delivers pure native execution speed, zero unnecessary memory copies, and flexible multi-format data interoperability.
Key Features (^▽^)
-
Hardware Acceleration (CPU, GPU, Multi-GPU, & TPU) (≧ω≦)
- Native device engine with smart automatic hardware detection (
device="auto"): auto-prioritizes TPU -> Multi-GPU -> GPU -> CPU. - Data-parallel row partitioning across multiple GPUs ($\sum_k X_k^T X_k$) with all-reduce aggregation.
- Block-tiled systolic matrix accumulation designed for Tensor Processing Unit (TPU) MXUs.
- Diagnostics and device routing utilities (
ochreml.get_available_devices(),ochreml.get_device_info(),ochreml.set_default_device()).
- Native device engine with smart automatic hardware detection (
-
High-Performance Rust Kernels
- $O(D \cdot N \log N)$ CART split search with sliding histogram impurity evaluations.
- Closed-form Normal Equation solver with symmetric Gram matrix accumulation and automated Tikhonov (Ridge) regularization fallback.
- Outperforms standard Cython/C implementations on Decision Tree training and prediction.
-
Universal Data Interoperability (o´∀｀o)
- Accepts and processes data seamlessly across:
- Python Nested Lists (
list[list[float]]) - NumPy ndarrays (
numpy.ndarray) - Pandas DataFrames & Series (
pandas.DataFrame,pandas.Series) - Polars DataFrames & Series (
polars.DataFrame,polars.Series)
- Python Nested Lists (
- Accepts and processes data seamlessly across:
-
Scikit-Learn Compatible API & Multi-Output Support (＾▽＾)
- Standard estimator methods:
fit(X, y, sample_weight=None),predict(X), andscore(X, y, sample_weight=None). - Native support for Multi-Output Regression ($Y \in \mathbb{R}^{N \times K}$) with one-time Cholesky factorization.
- Individual sample weighting (
sample_weight) with zero-copy streaming accumulation. - Standard inspection attributes:
coef_,intercept_,classes_,device_, andn_features_in_.
- Standard estimator methods:
Model Catalog & Roadmap (＾▽＾)
Currently Available Models
| Model | Module | Primary Algorithm & Characteristics |
|---|---|---|
LinearRegression |
ochreml.LinearRegression |
Analytical Ordinary & Weighted Least Squares (OLS/WLS) via $(X^T W X)^{-1} X^T W Y$ with single-pass Cholesky factorization, Multi-Output ($N \times K$) support, and automated Tikhonov Ridge fallback. |
DecisionTreeClassifier |
ochreml.DecisionTreeClassifier |
Optimized CART classification tree supporting both Gini Impurity and Entropy splitting criteria with sliding histograms. |
DecisionTreeRegressor |
ochreml.DecisionTreeRegressor |
Fast non-linear regression tree minimizing Mean Squared Error (MSE / Variance Reduction) with $O(1)$ running sum variance updates. |
Upcoming Models (Roadmap) (^▽^)
- Logistic Regression: Binary and multinomial classification via L-BFGS / Newton-Raphson solvers.
- Random Forest: Ensemble bagging for both Classifier and Regressor with multi-threaded tree building.
- K-Nearest Neighbors (KNN): Fast KD-Tree / Ball-Tree spatial search for classification and regression.
- Support Vector Machines (SVM): Sequential Minimal Optimization (SMO) kernel classifiers.
- Naive Bayes: Gaussian and Multinomial probabilistic classifiers.
- Principal Component Analysis (PCA): Truncated SVD / covariance eigendecomposition for dimensionality reduction.
- K-Means Clustering: Lloyd's algorithm with K-Means++ initialization.
Performance Benchmarks (≧ω≦)
Comprehensive benchmark comparing OchreML's pure Rust engine (with Zero-Copy Python Buffer Protocol, SIMD, and Rayon multi-threading) against Scikit-Learn (Cython backend).
1. Linear Regression (OLS Normal Equation & Cholesky Solver)
Dataset: 20,000 samples (16,000 train, 4,000 test), 10 features
| Metric | OchreML (Rust) | Scikit-Learn | Ratio / Parity |
|---|---|---|---|
| Fit Time | 2.42 ms | 8.34 ms | 3.45x FASTER (≧ω≦) |
| Predict Time | 0.10 ms | 0.32 ms | 3.20x FASTER (o´∀｀o) |
| Test $R^2$ Score | 0.9980 | 0.9980 | Max coef diff: $7.11 \times 10^{-15}$ |
2. Multi-Output & Sample Weight Regression (OLS)
Dataset: 20,000 samples (16,000 train, 4,000 test), 10 features, 4 targets with sample_weight
| Metric | OchreML (Rust) | Scikit-Learn | Ratio / Parity |
|---|---|---|---|
| Fit Time | 3.46 ms | 8.20 ms | 2.37x FASTER (^▽^) |
| Predict Time | 0.20 ms | 0.38 ms | 1.90x FASTER (o´∀｀o) |
| Weighted Test $R^2$ | 0.9988 | 0.9988 | Max coef diff: $8.88 \times 10^{-15}$ |
3. Decision Tree Classifier (criterion="gini", max_depth=5)
Dataset: 2,500 samples (2,000 train, 500 test), 8 features
| Metric | OchreML (Rust) | Scikit-Learn | Ratio / Parity |
|---|---|---|---|
| Fit Time | 3.93 ms | 15.50 ms | 3.95x FASTER (^▽^) |
| Predict Time | 0.10 ms | 0.40 ms | 4.00x FASTER |
| Test Accuracy | 92.40 % | 92.40 % | Identical Parity |
4. Decision Tree Regressor (criterion="squared_error", max_depth=5)
Dataset: 2,500 samples (2,000 train, 500 test), 6 features
| Metric | OchreML (Rust) | Scikit-Learn | Ratio / Parity |
|---|---|---|---|
| Fit Time | 2.79 ms | 9.71 ms | 3.48x FASTER (o´∀｀o) |
| Predict Time | 0.09 ms | 0.34 ms | 3.77x FASTER |
| Test $R^2$ Score | 0.8700 | 0.8700 | Identical Parity |
Installation & Build (o´∀｀o)
From PyPI
pip install ochreml
From Source
# Clone the repository
git clone https://github.com/arufadesuwa/ochreml.git
cd ochreml
# Build and install locally with Maturin
maturin develop --release
Testing Suite (o´∀｀o)
# Run Rust unit tests
cargo test
# Run Python integration test suite (Lists, NumPy, Pandas, Polars)
pytest
1 maintainer
Metadata
1 maintainer
Release files for ochreml 0.2.6
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