Opproplot v0.1.0 - Initial Release

Opproplot v0.1.0 - Initial Release

The first release of Opproplot - a Python library for visualizing binary classifier performance with operating profile plots.

🌟 Features

Core Functionality

• Vectorized computation engine (compute_operating_profile) that efficiently calculates TPR, FPR, and accuracy metrics across all decision thresholds
• Matplotlib plotting interface (operating_profile_plot) with stacked histograms showing score distributions for positive and negative classes alongside performance curves

Visualization Options

• Configurable bin counts for histogram resolution
• Optional legend/key placement control
• Grid styling options for clean presentation
• PNG export with transparency support

Package Infrastructure

• 📚 Documentation site at rollingstorms.github.io/opproplot
  • Getting started guide
  • API reference
  • Theory and examples
  • Breast cancer dataset demo
• 🧪 Test suite with CI/CD via GitHub Actions
• 🎨 Code quality enforced with ruff linting
• 📦 PyPI ready with proper project structure

📦 Installation

pip install opproplot

🚀 Quick Start

import numpy as np
from opproplot import operating_profile_plot

# Generate sample data
rng = np.random.default_rng(0)
y_true = rng.integers(0, 2, size=5000)
scores = rng.random(size=5000)

# Create the plot
operating_profile_plot(y_true, scores, bins=30)

📖 Documentation

Visit https://rollingstorms.github.io/opproplot/ (https://rollingstorms.github.io/opproplot/) for complete documentation.

🙏 Feedback

This is the initial release - feedback and contributions are welcome! 
Please open an issue or PR on GitHub.