rfx-ml 1.0.2

High-Performance Random Forests with GPU Acceleration and QLORA Compression

RFX: Random Forests X

RFX (Random Forests X) is a high-performance Python implementation of Breiman and Cutler's original Random Forest methodology with GPU acceleration and QLORA compression.

Key Features

• Complete classification: Out-of-bag error, confusion matrices, class probabilities
• Local importance: Per-sample feature importance (similar to SHAP, built-in)
• Proximity matrices: Pairwise sample similarities for outlier detection and visualization
• QLORA compression: 12,500× memory reduction (80GB → 6.4MB) for large-scale proximity analysis
• Full GPU acceleration: CUDA for trees, importance, and proximity matrices
• Interactive visualization: Python-native rfviz with 3D MDS and parallel coordinates

Result: Proximity-based workflows now scale to 200K–1M+ samples.

Installation

GPU-Enabled Version (supports both GPU and CPU fallback):

pip install rfx-ml

CPU-Only Version (lightweight, no CUDA dependencies):

pip install rfx-ml-cpu

Note: These packages are mutually exclusive. Both provide the rfx module. Choose based on your hardware:

• Have a GPU and want acceleration? → rfx-ml
• CPU-only system or want minimal dependencies? → rfx-ml-cpu

Prerequisites: CMake 3.12+, Python 3.7+, CUDA toolkit 11.0+ (required for building; GPU usage optional at runtime), C++ compiler with C++17 support.

The pip install command will automatically build from source. Make sure you have the prerequisites installed before running pip.

Quick Start

import numpy as np
import rfx as rf

# Load sample data
X, y = rf.load_wine()

# Train Random Forest
model = rf.RandomForestClassifier(
    ntree=100,
    compute_importance=True,
    compute_local_importance=True,
    compute_proximity=True,
    use_gpu=False  # Set to True for GPU acceleration
)

model.fit(X, y)

# Get predictions and metrics
oob_error = model.get_oob_error()
print(f"OOB Error: {oob_error:.4f}")

predictions = model.predict(X)
importance = model.feature_importances_()
local_imp = model.get_local_importance()

# Interactive visualization
rf.rfviz(
    rf_model=model,
    X=X,
    y=y,
    output_file="rfviz_example.html"
)

GPU Acceleration & QLORA

For large datasets, enable GPU acceleration and QLORA compression:

# Large-scale proximity analysis with QLORA
model = rf.RandomForestClassifier(
    ntree=500,
    use_gpu=True,
    compute_proximity=True,
    use_qlora=True,
    rank=32,  # Low-rank approximation
    quant_mode="int8"
)

model.fit(X, y)

# Get low-rank factors (memory efficient)
A, B, rank = model.get_lowrank_factors()

# Compute MDS directly from factors (no reconstruction!)
mds_coords = model.compute_mds_from_factors(k=3)

Memory savings: 100K samples: 74.5 GB (full matrix) → 19 MB (QLORA rank-100) = 4000× compression.

Documentation

For complete documentation, examples, and advanced usage, visit:

• GitHub: https://github.com/chriskuchar/RFX
• Full README: https://github.com/chriskuchar/RFX/blob/main/README.md

License

MIT License - see LICENSE file for details.

Links

• Source Code: https://github.com/chriskuchar/RFX
• Bug Reports: https://github.com/chriskuchar/RFX/issues
• Documentation: https://github.com/chriskuchar/RFX/blob/main/README.md