ml-engine-core 0.1.1

A unified, high-performance Machine Learning Framework with native C++ backends

ML Engine Core

MLEngine is a unified, high-performance Machine Learning framework tailored for speed and mathematical transparency.

Under the hood, MLEngine acts as a pure Python API routing to highly optimized, custom-built C++ native engines (knn-engine-core and nn-engine-core). By pushing computational bottlenecks—including the entire training loop—down into C++ via pybind11 and Eigen, MLEngine entirely bypasses the Python Global Interpreter Lock (GIL), resulting in execution speeds up to 30x faster than traditional Python-based ML libraries.

Installation

Install the complete suite via PyPI (this automatically installs both C++ backend engines):

pip install mlengine

Modules Overview

MLEngine is divided into two primary sub-modules: classical and neural.

1. mlengine.classical

Powered by knn-engine-core. Features a highly optimized K-Nearest Neighbors classifier with built-in native Principal Component Analysis (PCA) for dimensionality reduction.

import numpy as np
from mlengine.classical import KNNEngine, KNNConfig

X_train = np.random.rand(100, 64)
y_train = [str(i % 5) for i in range(100)]

# Configure Engine
cfg = KNNConfig()
cfg.k = 3
cfg.variance = 0.95 # Retain 95% variance via native PCA

engine = KNNEngine(cfg)
engine.train(X_train, y_train, scale=True)

predictions = engine.predict_batch(X_train[:5])
print("KNN Predictions:", predictions)

2. mlengine.neural

Powered by nn-engine-core. A multi-layer perceptron framework that executes its full mini-batch gradient descent loop entirely in C++, preventing the Python GIL from interrupting training epochs.

import numpy as np
from mlengine.neural import Model, DenseLayer, ReLULayer, SoftmaxLayer, CategoricalCrossEntropyLoss

X_train = np.random.rand(100, 4).astype(np.float64)
y_train = np.eye(3)[np.random.choice(3, 100)].astype(np.float64) 

# Build Architecture
model = Model()
model.add(DenseLayer(4, 16))
model.add(ReLULayer())
model.add(DenseLayer(16, 3))
model.add(SoftmaxLayer())

model.compile(CategoricalCrossEntropyLoss())

# C++ Native Training Loop
model.fit(X_train, y_train, epochs=150, learning_rate=0.05, batch_size=16)

probs = model.predict(X_train[:1])
print("Neural Net Probs:", probs)

Architecture & Benchmarks

MLEngine's core philosophy is Native Loop Hoisting. Rather than returning to Python after every matrix operation or epoch step, MLEngine passes pointers to C++ once, executes the entire mathematical pipeline using Eigen's noalias() buffers, and returns only when finished.

In standard academic benchmarks (Iris, MNIST Digits, Olivetti Faces), MLEngine consistently achieves 2x to 30x speedups over Scikit-Learn while maintaining equal or superior accuracy. See the individual core repositories for detailed benchmark logs.