knn-engine-core 0.1.2

A high-performance C++ KNN Engine with Adaptive PCA

KNN Engine Core

High-performance K-Nearest Neighbors (KNN) classification engine implemented in C++17, with adaptive Principal Component Analysis (PCA) for dimensionality reduction, and exposed to Python via pybind11.

This project is designed for fast experimentation from Python while keeping the compute-intensive path in native C++.

Highlights

• C++17 implementation of KNN with efficient top-k neighbor selection (std::nth_element).
• Adaptive PCA that supports:
  • variance-threshold mode (for example 0.95 keeps enough components to preserve 95% variance),
  • fixed-component mode (for example 28 keeps exactly 28 components).
• Automatic thin PCA path for high-dimensional datasets where features > samples.
• Python bindings (knn_core) with a minimal, clean API.
• Build system based on CMake + scikit-build-core + pybind11.

Repository Structure

.
├── CMakeLists.txt
├── CMakePresets.json
├── pyproject.toml
├── include/
│   ├── KNN.hpp
│   ├── KNNEngine.hpp
│   └── PCA.hpp
├── src/
│   ├── KNN.cpp
│   ├── KNNEngine.cpp
│   ├── PCA.cpp
│   └── binding.cpp
└── examples/
    └── script.py

How It Works

1. KNNEngine.train(X, y, scale) fits PCA on X and projects training data.
2. The projected training data is passed to the internal KNN classifier.
3. During inference, incoming samples are projected using the same PCA transform.
4. KNN predicts labels by majority vote among the nearest neighbors.

PCA Modes

• Standard mode: used when number of features <= number of samples.
• Thin mode: used when number of features > number of samples (memory- and compute-friendly formulation).

Requirements

• Python 3.8+
• CMake 3.18+
• C++17 compiler
• Ninja (recommended generator)

Python dependencies are declared in pyproject.toml:

• numpy
• scikit-learn

Installation

Option 1: Install from PyPI (recommended)

pip install knn-engine-core

This installs the published package and makes knn_core importable.

Quick verification after install:

python -c "import knn_core; cfg = knn_core.KNNConfig(); print('knn_core OK | k =', cfg.k, '| variance =', cfg.variance)"

Option 2: Install from source (editable)

From the project root:

python -m pip install -U pip
python -m pip install -e .

Option 3: Build with CMake presets

cmake --preset Release
cmake --build --preset Release

Artifacts are generated under out/build/Release.

Quick Start

import numpy as np
import knn_core

# Example data
X = np.array([
    [5.1, 3.5, 1.4, 0.2],
    [4.9, 3.0, 1.4, 0.2],
    [6.2, 3.4, 5.4, 2.3],
    [5.9, 3.0, 5.1, 1.8],
], dtype=np.float64)

y = ["setosa", "setosa", "virginica", "virginica"]

cfg = knn_core.KNNConfig()
cfg.k = 3
cfg.variance = 0.95

engine = knn_core.KNNEngine(cfg)
engine.train(X, y, scale=False)

sample = np.array([6.0, 3.1, 5.0, 1.9], dtype=np.float64)
pred = engine.predict(sample)
print(pred)

Python API

KNNConfig

• k: int (default: 3)
  • Number of neighbors used for voting.
• variance: float (default: 0.95)
  • If < 1.0: treated as explained-variance threshold.
  • If >= 1.0: treated as fixed number of principal components.

KNNEngine(config: KNNConfig)

• train(X, y, scale=False)
  • X: 2D numpy.ndarray (float64 recommended), shape (n_samples, n_features)
  • y: list of labels (strings)
  • scale: whether to standardize features before PCA
• predict(sample)
  • sample: 1D feature vector
  • returns predicted label (str)
• predict_batch(samples)
  • samples: 2D feature matrix
  • returns list of predicted labels (list[str])

Benchmark Results

The following results were produced using the provided evaluation script pattern (examples/script.py) with train/test split and fixed random seed. Timings compare KNNEngine against an equivalent scikit-learn pipeline (PCA + KNeighborsClassifier).

Dataset	Accuracy	KNNEngine Time	Sklearn Time	Speedup
Olivetti Faces	92.50%	0.238s	0.386s	1.62×
Handwritten Digits	98.06%	0.020s	0.117s	5.82×
Iris Flower	100.00%	0.0002s	0.003s	14.01×

Console logs:

--- Testing Olivetti Faces ---
[PCA] Fit complete. Mode: Thin | Components: 62
[Engine] Trained successfully. Reduced to 62 dimensions.
KNNEngine  — Accuracy: 92.50%  |  Time: 0.2381s
Sklearn    — Accuracy: 92.50%  |  Time: 0.3864s
Speedup: 1.62x

--- Testing Iris Flower ---
[PCA] Fit complete. Mode: Standard | Components: 3
[Engine] Trained successfully. Reduced to 3 dimensions.
KNNEngine  — Accuracy: 100.00%  |  Time: 0.0002s
Sklearn    — Accuracy: 100.00%  |  Time: 0.0031s
Speedup: 14.01x

--- Testing Handwritten Digits ---
[PCA] Fit complete. Mode: Standard | Components: 28
[Engine] Trained successfully. Reduced to 28 dimensions.
KNNEngine  — Accuracy: 98.06%  |  Time: 0.0202s
Sklearn    — Accuracy: 98.06%  |  Time: 0.1174s
Speedup: 5.82x

Running the Example Script

python examples/script.py

Notes and Limitations

• Labels are currently represented as strings in the C++ core API.
• Distance metric is Euclidean (squared Euclidean for ranking).
• Tie-breaking follows std::map key ordering when vote counts are equal.

Development

Typical local workflow:

python -m pip install -e .
python examples/script.py

For release-style native builds:

cmake --preset Release
cmake --build --preset Release