ml-graphy 0.1.0

A Python package for plotting machine learning training metrics

ml-graphy

A Python package for plotting machine learning training metrics with automatic detection of validation data and clean, publication-ready visualizations.

Features

• 📊 Automatic detection of training and validation metrics
• 🎨 Clean, publication-ready plots using seaborn styling
• 📈 Support for loss and accuracy metrics
• 🔍 Automatic handling of missing data
• 📋 Training summary statistics

Installation

pip install ml-graphy

This will automatically install all required dependencies:

• numpy>=1.20.0
• matplotlib>=3.5.0
• seaborn>=0.11.0

Development Installation

git clone https://github.com/Prasoon-Rai/Graphy.git
cd Graphy
pip install -e ".[dev]"

Quick Start

from graphy import plot_metrics

# Assuming you have a trained model with history
# model.history = {
#     'loss': [0.5, 0.4, 0.3, 0.2],
#     'accuracy': [0.8, 0.85, 0.9, 0.95],
#     'val_loss': [0.6, 0.45, 0.35, 0.25],
#     'val_accuracy': [0.75, 0.8, 0.85, 0.9]
# }

plot_metrics(model)

This will automatically:

• Detect available metrics (loss, accuracy)
• Check for validation data
• Create side-by-side plots
• Display training summary statistics

Publishing to PyPI

This package uses GitHub releases for automated publishing. See RELEASE.md for instructions.

Quick Release Process

1. Update version in pyproject.toml
2. Commit and push changes
3. Create a GitHub release
4. GitHub Actions automatically publishes to PyPI

Requirements

• Python >= 3.8
• numpy >= 1.20.0
• matplotlib >= 3.5.0
• seaborn >= 0.11.0

All dependencies are automatically installed when you install ml-graphy.

Testing

Test the package after installation:

from graphy import plot_metrics

# Create a mock model with training history
class MockModel:
    def __init__(self):
        self.history = {
            'loss': [0.8, 0.6, 0.4, 0.3, 0.2],
            'accuracy': [0.6, 0.7, 0.8, 0.85, 0.9],
            'val_loss': [0.9, 0.7, 0.5, 0.4, 0.3],
            'val_accuracy': [0.55, 0.65, 0.75, 0.8, 0.85]
        }

model = MockModel()
plot_metrics(model)

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Development

For development, install with:

pip install -e ".[dev]"

This includes additional tools:

• pytest for testing
• black for code formatting
• flake8 for linting
• mypy for type checking