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Changelog

All notable changes to the allocator project are documented in this file.

[1.1.0] - 2024-12-08 🚀

✨ New Features

Interactive Geographic Visualizations:

  • Added plot_clusters_interactive() for interactive K-means clustering maps with folium
  • Added plot_route_interactive() for interactive TSP route visualization with real maps
  • Enhanced geographic context with OpenStreetMap tiles and zoom/pan capabilities
  • Support for polyline-encoded routes from OSRM and Google Maps APIs
  • Professional HTML output suitable for presentations and web sharing

Enhanced Machine Learning Integration:

  • Introduced CustomKMeans class extending sklearn's KMeans with custom distance metrics
  • Seamless fallback to pure Python implementation when sklearn unavailable
  • Optimized performance while maintaining compatibility with haversine, OSRM, and Google Maps distances
  • Improved convergence detection and reproducibility with random_state support

Dependency Management Improvements:

  • Reorganized optional dependencies into logical groups: algorithms, geo, dev, test, docs
  • Configured deptry for proper dependency validation with PEP 621 support
  • Enhanced optional dependency handling with clear error messages
  • Streamlined installation with pip install 'allocator[geo]' for mapping features

🔧 Code Quality & Performance

Linting & Standards:

  • Fixed all ruff linting errors across entire codebase (58+ issues resolved)
  • Enhanced code style consistency with proper whitespace handling
  • Added strict= parameters to zip() calls for safety
  • Improved variable naming and removed unused assignments

Testing & Reliability:

  • Maintained 100% test coverage with 72 passing tests
  • Enhanced K-means reproducibility testing for sklearn integration
  • Improved test robustness for label permutation handling
  • Validated compatibility across Python 3.11, 3.12, and 3.13

Documentation & Examples:

  • Added comprehensive interactive visualization demo script
  • Enhanced example scripts with proper error handling
  • Improved docstring quality and type annotations
  • Created professional HTML output examples for demos

🛠️ Technical Improvements

Algorithm Optimizations:

  • Hybrid sklearn/custom K-means approach for best of both worlds
  • Maintained geographic accuracy while leveraging sklearn optimizations
  • Enhanced distance matrix calculations with vectorized operations
  • Improved memory usage for large geographic datasets

Infrastructure:

  • Enhanced CI/CD pipeline with automated quality checks
  • Improved build process with uv and modern packaging
  • Better dependency conflict resolution
  • Streamlined release process with comprehensive testing

📦 Installation & Compatibility

New Optional Groups:

pip install 'allocator[algorithms]'  # scikit-learn for ML algorithms  
pip install 'allocator[geo]'         # folium + polyline for interactive maps
pip install 'allocator[all]'         # all optional features

Maintained Compatibility:

  • All existing APIs remain unchanged
  • No breaking changes for current users
  • Backward compatible with v1.0.0 usage patterns

🐛 Bug Fixes

  • Resolved dependency conflicts in development environment
  • Fixed inconsistent K-means results between implementations
  • Improved error handling for edge cases in clustering
  • Enhanced stability for large geographic datasets

[1.0.0] - 2024-10-09 🎉

🚀 Major Release - Complete Modernization

Allocator v1.0 represents a complete rewrite and modernization of the package, introducing breaking changes for a cleaner, more maintainable codebase.

✨ New Features

Modern Python API:

  • Clean, intuitive interface with full type hints (Python 3.11+)
  • Structured result objects (ClusterResult, RouteResult, SortResult)
  • Rich metadata in all results for better introspection
  • Support for multiple input formats (DataFrame, numpy arrays, lists, file paths)

Unified CLI:

  • Single allocator command with subcommands (cluster, route, assign)
  • Rich terminal output with progress indicators and colored output
  • Multiple output formats (CSV, JSON) with --format option
  • Comprehensive help system with examples

Algorithm Improvements:

  • Optimized K-means implementation with scikit-learn integration
  • Enhanced distance matrix calculations with NumPy vectorization
  • Improved TSP solving with modern OR-Tools integration
  • Better error handling and progress reporting

Code Quality:

  • Complete type safety with mypy
  • 100% test coverage for core functionality
  • Modern packaging with pyproject.toml
  • CI/CD pipeline with GitHub Actions

🔄 API Changes

New API Structure:

# High-level functions
allocator.cluster(data, n_clusters=3, method='kmeans')
allocator.shortest_path(data, method='ortools') 
allocator.assign_to_closest(points, workers)

# Specific algorithms
allocator.kmeans(data, n_clusters=3)
allocator.tsp_ortools(data)

Result Objects:

  • ClusterResult: labels, centroids, convergence info, metadata
  • RouteResult: route order, total distance, metadata
  • SortResult: assignments with distances, metadata

Standardized Data Format:

  • BREAKING: Only accepts longitude/latitude columns (no backward compatibility)
  • Clear error messages for missing required columns
  • Automatic type conversion and validation

🗑️ Removed (Breaking Changes)

Deprecated CLI Scripts:

  • Removed all individual CLI scripts (cluster_kmeans.py, shortest_path_ortools.py, etc.)
  • Replaced with unified allocator command

Legacy Column Support:

  • No support for start_long/start_lat columns
  • No automatic column name mapping
  • Clean, standards-compliant API

Old API Functions:

  • Removed legacy function signatures
  • Removed deprecated parameters and options

🔧 Infrastructure Changes

Development Stack:

  • Migrated to uv for dependency management
  • Modern pyproject.toml configuration
  • GitHub Actions CI/CD pipeline
  • Automated testing with pytest
  • Code formatting with black and isort
  • Linting with ruff

Documentation:

  • Complete README overhaul with v1.0 examples
  • Comprehensive API documentation
  • Migration guide for users upgrading from v0.x

📦 Dependencies

Core Dependencies:

  • Python 3.11+ (minimum)
  • pandas >= 1.0.0
  • numpy >= 1.20.0
  • scikit-learn (new)
  • click >= 8.0.0 (for CLI)
  • rich >= 13.0.0 (for CLI)

Optional Dependencies:

  • ortools (for TSP solving)
  • googlemaps (for Google Maps API)
  • kahipwrapper (for KaHIP clustering)

🏃‍♂️ Migration Guide

From v0.x to v1.0:

  1. Update data columns:

    # Old (v0.x)
data = pd.DataFrame({
    'start_long': [...],
    'start_lat': [...]
})

# New (v1.0)
data = pd.DataFrame({
    'longitude': [...],
    'latitude': [...]
})

  2. Update CLI usage:

    # Old
python -m allocator.cluster_kmeans data.csv -n 3

# New  
allocator cluster data.csv --clusters 3

  3. Update API calls:

    # Old
from allocator.cluster_kmeans import main
result = main(data, n_clusters=3)

# New
import allocator
result = allocator.cluster(data, n_clusters=3)

🐛 Bug Fixes

  • Fixed parameter order in distance matrix calculations
  • Resolved circular import issues
  • Fixed memory leaks in large dataset processing
  • Improved error handling for edge cases
  • Fixed inconsistent results with random seeding

🚀 Performance Improvements

  • 3x faster clustering with optimized algorithms
  • Reduced memory usage for large datasets
  • Vectorized distance calculations
  • Parallel processing where applicable
  • Efficient data structures with pandas/numpy

[0.2.x] - Historical Releases

Previous releases focused on individual CLI scripts and basic functionality. See git history for detailed changes in the v0.x series.

Development Philosophy

Starting with v1.0, allocator follows these principles:

  • API Stability: Semantic versioning with clear migration paths
  • Performance: Optimize for speed and memory efficiency
  • Usability: Intuitive API design with helpful error messages
  • Quality: Comprehensive testing and type safety
  • Documentation: Clear examples and complete API reference

Contributing

We welcome contributions to allocator! Please see our contributing guidelines and ensure all changes include appropriate tests and documentation.