AffineFlow-ML
A high-performance Machine Learning engine for the AffineFlow ecosystem.
Designed for rapid experimentation without the Python Global Interpreter Lock (GIL) overhead, AffineFlow-ML executes compute-intensive tasks strictly in native C++ using Eigen and OpenBLAS. It features a dynamically compiled multi-architecture backend (Generic, AVX2, AVX-512) for maximum hardware-accelerated throughput safely dispatched at runtime.
Highlights
- Dynamic Hardware Dispatch: Automatically detects your CPU at runtime to dispatch highly optimized AVX2 or AVX-512 instructions.
- Zero-Copy Buffer Protocol: Uses
Eigen::Refand RowMajor structures to natively ingest NumPy arrays without memory duplication. - Advanced KNN Classifier: OpenMP parallelized C++17 implementation with efficient top-k neighbor selection (
std::nth_element). - Distance Metrics: Native vectorized support for Euclidean, Manhattan, and Cosine distance algorithms.
- Weighted Voting: Choose between standard
uniformvoting ordistance-weighted predictions. - Adaptive PCA: Built-in dimensionality reduction with standard and thin-SVD modes for high-dimensional data.
Installation
Install the released wheel from PyPI:
pip install AffineFlow-ML
Quick Start
import numpy as np
import affineflow_ml as ml
# Example data
X_train = np.random.rand(1000, 50).astype(np.float64)
y_train = ["class_A" if x[0] > 0.5 else "class_B" for x in X_train]
# Configure the engine with new features
cfg = ml.KNNConfig()
cfg.k = 5
cfg.variance = 0.95
cfg.metric = ml.DistanceMetric.COSINE
cfg.weights = ml.Weights.DISTANCE
engine = ml.KNNEngine(cfg)
engine.train(X_train, y_train, scale=True)
X_test = np.random.rand(10, 50).astype(np.float64)
predictions = engine.predict_batch(X_test)
print(predictions)
Performance Benchmark
AffineFlow-ML achieves strict mathematical parity with Scikit-Learn while delivering up to 5.4x faster inference and transformation pipelines on high-dimensional datasets. Powered by a multithreaded C++17 Eigen backend with dynamic hardware dispatch (AVX2/AVX-512) and OpenMP parallelism, it eliminates Python loop and GIL bottlenecks.
| Dataset | Metric & Weights | Scikit-Learn Time | AffineFlow-ML Time | Speedup | Accuracy |
|---|---|---|---|---|---|
| Olivetti Faces (400 samples, 4096 dims $\rightarrow$ PCA 90%) | Cosine / Distance | 0.4134s | 0.0760s | 5.44x | 90.00% |
| Handwritten Digits (1797 samples, 64 dims $\rightarrow$ PCA 95%) | Manhattan / Uniform | 0.0808s | 0.0354s | 2.29x | 98.61% |
| Iris Flower (150 samples, 4 dims $\rightarrow$ PCA 99%) | Euclidean / Uniform | 0.0105s | 0.0055s | 1.91x | 100.00% |
1 maintainer
Metadata
1 maintainer
Release files for AffineFlow-ML 0.1.0
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