# Neuro-Symbolic AI Forecasting - Requirements

# ============================================
# Python Version
# ============================================
# Python 3.9+ required
# Tested on: Python 3.10, 3.11

# ============================================
# Core Dependencies
# ============================================

# Deep Learning Framework
torch>=2.0.0           # PyTorch for neural network and GPU support
torchvision>=0.15.0    # For data loading utilities

# Data Processing & Analysis
numpy>=1.21.0          # Numerical computing
pandas>=1.3.0          # Data manipulation and time series handling
scikit-learn>=1.0.0    # Machine learning utilities (MinMaxScaler)

# Financial Data
yfinance>=0.2.28       # Download stock data from Yahoo Finance

# Optimization (Symbolic Layer)
cvxpy>=1.3.0           # Convex optimization for symbolic seam projection
osqp>=0.6.2            # OSQP solver (backend for cvxpy)

# Visualization
matplotlib>=3.5.0      # Plotting and visualization

# ============================================
# Optional Dependencies
# ============================================

# Jupyter Notebooks
jupyter>=1.0.0         # Jupyter notebook support
ipython>=8.0.0         # Enhanced interactive Python

# Development & Testing
pytest>=7.0.0          # Unit testing framework
black>=22.0.0          # Code formatting
pylint>=2.12.0         # Code linting

# ============================================
# GPU/CPU Requirements
# ============================================

# GPU Support (Optional but Recommended)
# For NVIDIA GPU: Install CUDA 11.8 or 12.1
# Command: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# For CPU-only (slower training, 10-20x slower on large datasets):
# Already included in default torch installation

# ============================================
# Hardware Specifications
# ============================================

# Minimum (CPU-only):
# - Processor: 4 cores
# - RAM: 8 GB
# - Storage: 2 GB
# - Time to train: ~30 minutes per epoch

# Recommended (GPU):
# - GPU: NVIDIA with 4GB+ VRAM (tested on T4, V100)
# - Processor: 8 cores
# - RAM: 16 GB
# - Storage: 5 GB
# - Time to train: ~1-2 minutes per epoch

# ============================================
# Installation Instructions
# ============================================

# 1. Create virtual environment
# python -m venv neuro-symbolic-env
# .\neuro-symbolic-env\Scripts\activate  # Windows
# source neuro-symbolic-env/bin/activate # Linux/Mac

# 2. Install basic requirements
# pip install -r requirements.txt

# 3. For GPU support (NVIDIA only)
# pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# 4. Verify installation
# python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"

# ============================================
# Version Pinning (Exact Versions Used)
# ============================================

# Uncomment below to use exact versions that were tested:
# torch==2.1.0
# torchvision==0.16.0
# numpy==1.24.3
# pandas==2.0.3
# scikit-learn==1.3.0
# yfinance==0.2.32
# cvxpy==1.3.2
# matplotlib==3.7.2
# jupyter==1.0.0