Feature/synthetic data generation #298
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added 10 commits
June 22, 2025 00:40
- Implement Model-Agnostic Meta-Learning (MAML) classifier - Add Prototypical Networks for few-shot learning - Create domain adaptation utilities with fine-tuning and MAML methods - Add transfer learning capabilities with feature extraction and fine-tuning - Include utility functions for creating and evaluating few-shot tasks - Add CLI commands: few-shot-learn, domain-adapt, transfer-learn - Update models_dict to include few-shot learning algorithms - Add few_shot_learning as supported model type - Create comprehensive documentation and examples - Add complete test suite for all few-shot learning components - Update README with new features and model table This addresses GitHub issue nidhaloff#237 'Add Support for Few-Shot Learning'
…em (Issue nidhaloff#233) - Implement MLflow-like experiment tracking with ExperimentTracker class - Add model versioning with lineage tracking and metadata management - Create experiment visualization and analysis capabilities - Include SQLite database for experiment and model metadata storage - Add support for metric tracking, parameter logging, and model logging - Implement experiment comparison and visualization tools - Add model lineage visualization and deployment tracking - Include interactive Plotly dashboards for experiment analysis - Support for experiment export and model version management - Add comprehensive documentation and examples This addresses GitHub issue nidhaloff#233 'Create Model Versioning and Experiment Tracking'
- Add SyntheticDataGenerator class for creating test datasets - Support for classification and regression data generation - Quick function for generating sample datasets - Addresses GitHub issue nidhaloff#285 - Add Support for Synthetic Data Generation
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- Add AdvancedFeatureEngineer class with comprehensive feature engineering capabilities - Implement polynomial features, interaction terms, binning, and encoding - Add statistical features with rolling windows and cluster-based features - Include automated feature selection with multiple methods (mutual_info, f_score, pca) - Add comprehensive feature engineering pipeline with configurable options - Include example configuration file and comprehensive demo script - Add test suite for feature engineering functionality - Update package imports to include feature engineering modules - Addresses GitHub issue nidhaloff#298 - Implement Advanced Feature Engineering
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This PR adds a new module for generating synthetic datasets and a corresponding CLI command, addressing Issue #285.
Key Features
Tabular data generation for classification/regression tasks
Time series data generation with trend and seasonality options
Categorical data generation with configurable categories
Flexible CLI interface with multiple data type options
Usage
Closes #285