Changelog

Changelog#

Version 0.1.0 (Current Development)#

🚀 New Features#

  • Core Embedding Engine: Complete implementation of learn_embedding() function

  • Five Embedding Modes: Neural (unsupervised), target-based, PCA, t-SNE, and UMAP

  • Intelligent Preprocessing: Automatic missing value imputation and categorical encoding

  • Neural Architecture Search: Automated optimal architecture discovery

  • Model Serialization: Save/load trained models with metadata

  • Comprehensive CLI: Three-command system for different workflows

  • sklearn Integration: Transformer and classifier interfaces

  • pandas Integration: DataFrame accessor methods

🧠 Advanced Features#

  • Multi-layer Networks: Support for deep architectures with 1-4+ hidden layers

  • Adaptive Imputation: Multiple strategies for missing value handling

  • Categorical Encoding: OneHot, target encoding, entity embeddings

  • Auto Dimension Selection: Find optimal embedding dimensions

  • Contrastive Learning: Advanced embedding technique

  • Configuration Objects: Type-safe configuration management

🔧 Technical Improvements#

  • Modern Build System: pyproject.toml with hatchling backend

  • Type Safety: Complete MyPy annotations

  • Comprehensive Testing: 111 tests with 92% coverage

  • Code Quality: ruff linting and formatting

  • Documentation: Jupyter Book-based executable documentation

  • Performance Monitoring: Built-in timing and memory tracking

📊 Data Handling#

  • Multiple Formats: CSV, Parquet, Excel, JSON, TSV support

  • Large Dataset Support: Sampling and batch processing

  • Schema Validation: Input data validation for models

  • Scaling Options: MinMax, standard, robust, L2 scaling

  • Error Handling: Comprehensive validation and error messages

🏗️ Architecture#

  • Modular Design: Clear separation of concerns

  • Plugin System: Optional dependencies (UMAP, CLI tools)

  • Pipeline Builder: Flexible preprocessing pipeline construction

  • Extensible: Easy to add new embedding methods

Planned Releases#

Version 0.2.0 (Target: Q2 2025)#

Planned Features:

  • Multi-task learning mode

  • Temporal embedding support

  • Visualization module (row2vec.viz)

  • Interactive documentation with Binder

  • Performance benchmarks

Version 0.3.0 (Target: Q3 2025)#

Planned Features:

  • Variational autoencoders (VAE)

  • Transformer-based architectures

  • Graph neural network support

  • Federated learning capabilities

  • Advanced visualization tools

Version 1.0.0 (Target: Q4 2025)#

Production Release:

  • Full API stability guarantee

  • Comprehensive performance optimization

  • Enterprise features

  • Professional support documentation

Development History#

Pre-release Development#

  • Core Implementation: Main embedding functionality with neural networks

  • Advanced Features: Neural Architecture Search, imputation, serialization

  • Documentation: Comprehensive Jupyter Book documentation

  • Production Readiness: Testing, CI/CD, packaging

Breaking Changes#

Future breaking changes will be minimized and clearly documented with advance notice.

Acknowledgments#

Contributors#

  • Tiago Tresoldi - Creator and primary maintainer

  • Community Contributors - Bug reports, feature requests, and feedback

Dependencies#

Row2Vec builds on excellent open-source libraries:

  • TensorFlow/Keras - Neural network backend

  • scikit-learn - Preprocessing and classical ML methods

  • pandas - Data manipulation

  • NumPy - Numerical computing

Inspiration#

  • Word2Vec - Original embedding concept

  • Node2Vec - Graph-based embeddings

  • FastText - Subword embeddings

  • t-SNE/UMAP - Dimensionality reduction techniques

License#

Row2Vec is released under the MIT License. See LICENSE file for details.

Support#

This changelog follows Keep a Changelog format.

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