gafime 0.3.1

GPU Accelerated Feature Interaction Mining Engine

GAFIME: GPU-Accelerated Feature Interaction Mining Engine 🚀

GAFIME is a high-performance computing engine engineered to eliminate the biggest bottleneck in modern machine learning workflows: Feature Interaction Discovery.

While most data science tools prioritize ease-of-use over execution efficiency, GAFIME treats feature engineering as a low-level systems problem. By combining C++ optimization, Rust memory-safety pipelines, and cross-platform native bindings (CUDA/Metal), GAFIME bridges the gap between high-level data science and the raw power of modern hardware architectures.

📦 Installation

GAFIME ships natively compiled wheel binaries for Windows, macOS (Apple Silicon), and Linux heavily optimized for performance out-of-the-box.

Basic Install (Engine Only):

pip install gafime

Data Science Install (Includes Scikit-Learn Wrapper):

pip install gafime[sklearn]

⚡ Quickstart: The Interactive Tutorial

The fastest way to understand GAFIME's speed is to try our built-in interactive tutorial generator. Running this command will generate a pre-configured gafime_tutorial.ipynb Jupyter Notebook in your current directory with dummy feature data to instantly evaluate against:

gafime --init

🧩 Scikit-Learn Pipeline Integration

You don't need to rewrite your data pipelines to use GAFIME. By importing the GafimeSelector, you can inject GPU-accelerated feature discovery natively into sklearn.pipeline.Pipeline or GridSearchCV:

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from gafime.sklearn import GafimeSelector

# Define dummy data
X_train = np.random.randn(1000, 50).astype(np.float32)
y_train = np.random.randint(0, 2, size=1000).astype(np.float32)

# Create a pipeline that automatically discovers the Top 5 best Feature Interactions
# Evaluated instantly against the GPU logic and appends them to your training dataset
pipe = Pipeline([
    ('interaction_miner', GafimeSelector(k=5, backend='auto', operator='multiply')),
    ('classifier', LogisticRegression())
])

pipe.fit(X_train, y_train)

🌌 Why GAFIME? The Performance Ceiling

In the current data science landscape, mining interaction data (like checking Feature X * Feature Y against the target) is painfully slow on CPUs or inefficiently memory-managed on GPUs. GAFIME achieves:

1. Hardware-Bound Execution: GAFIME targets physical memory bandwidth limits, minimizing the overhead of standard GPU python workflows. You hit the system's ceiling.
2. Zero-Overhead Scaling: Utilizing Rust's FFI capabilities on top of optimized CUDA C++, GAFIME bypasses the Python Global Interpreter Lock (GIL) ensuring every clock cycle executes pure feature logic.
3. Cross-Platform Scalability: Whether you're on a MacBook executing Metal fallback logic via Rust, or an RTX workstation targeting CUDA registers, GAFIME auto-discovers and optimizes for your hardware at runtime.

Caching and Branch-less Operations

GAFIME's specialized memory management layout ensures tabular feature data is cached and aligned, dramatically minimizing GPU cycle stalls regardless of noisy dataset inputs.

🛠️ Technology Stack

• Core Engine: C++ / CUDA (Performance-critical computation paths) and Metal (Apple Silicon native acceleration)
• Safety Pipeline & Schedulers: Rust (Memory safe FFI interface scheduling)
• Data Science Interfacing: Python (Polars / Numpy bindings seamlessly communicating across boundaries)

✅ For being honest

-> Current state of the project is its first v0.2 open beta release.

-> The project is developed with the help of current frontier SOTA models such as Gemini 3.1 Pro (high reasoning effort) and Claude Opus 4.6 (high).

🤝 If you want

You could collaborate with me via using email to communicate 🥰

Email: [e-mail]

---

Contributing and Advanced Usage

Looking to expand the engine metrics or compile natively yourself? Please see our detailed references:

• USAGE.md - Advanced EngineConfig features and API logic.
• CONTRIBUTING.md - Local compilation instructions for OS developers.

GAFIME was conceptualized and engineered for extreme high-frequency feature permutations in complex categorical environments like Banking models.