A lightweight quantum-enhanced regression model based on LightGBM
Project description
MDQuantumLite
A lightweight machine learning library for regression tasks, enhanced with optional quantum features using Pennylane. Based on LightGBM with GOSS sampling and target encoding.
Installation
pip install mdquantumlite
MDQuantumLite/ # فولدر اصلی پروژه (میتونی این رو به MD_library تغییر بدی اگر بخشی از ریپو بزرگتر باشه)
├── mdquantumlite/ # فولدر پکیج (اینجا کد اصلی میره)
│ ├── __init__.py # برای ایمپورت: from mdquantumlite import MDQuantumLite
│ ├── mdquantum_lite.py # کد اصلی کلاسها (از APP1.PY منتقل شده، بدون تابع تست)
│ └── quantum_feature.py # کلاس QuantumFeature رو جدا کردم برای تمیزتر شدن
├── tests/ # فولدر تستها
│ └── test_mdquantum_lite.py # تابع تست از APP1.PY
├── setup.py # فایل نصب (مهمترین فایل برای PyPI)
├── README.md # توضیح پروژه
├── LICENSE # لایسنس (مثلاً MIT)
├── MANIFEST.in # برای شامل کردن فایلهای غیرپایتون (اختیاری)
├── requirements.txt # لیست dependencies برای توسعه (اختیاری، اما خوب است)
└── .gitignore # برای نادیده گرفتن فایلهای موقتی مثل __pycache__
from mdquantumlite import MDQuantumLite
model = MDQuantumLite(use_quantum_features=True)
model.fit(X, y)
predictions = model.predict(X_test)
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