Feature/pmml export #358
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Feature/pmml export #358
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added 19 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
- Implemented AutoRetrainer class with performance-based and time-based strategies - Added RetrainingScheduler for scheduling retraining jobs - Created example configuration and demo files - Added test structure - Addresses GitHub Issue nidhaloff#339
- Enhanced A/B testing with comprehensive statistical tests, confidence intervals, and visualizations - Added backward compatibility layer with CompatibilityManager - Improved CLI with new options for visualization, export, and legacy mode - Added support for multiple statistical tests (McNemar, Chi-square, Wilcoxon, paired t-test) - Enhanced reporting with detailed metrics and recommendations - Added visualization capabilities for model comparison results - Implemented robust import fallback system for different igel versions Resolves issues nidhaloff#330 and nidhaloff#331
- Created comprehensive ensemble framework with voting, stacking, blending, bagging, and boosting - Added automatic ensemble selection based on data characteristics - Implemented model compression with pruning, quantization, knowledge distillation, and feature selection - Added model optimization for accuracy, speed, memory, and balanced performance - Enhanced CLI with create-ensemble, predict-ensemble, compress-model, and optimize-model commands - Added comprehensive reporting and model persistence capabilities - Implemented performance comparison and compression statistics Resolves issues nidhaloff#332 and nidhaloff#333
- Created comprehensive model explainability framework with multiple explanation methods - Added support for feature importance, partial dependence, SHAP values, LIME explanations, and permutation importance - Implemented static and interactive dashboards for model interpretation - Added visualizations for feature importance, partial dependence plots, correlation matrices, and model performance - Enhanced CLI with explain-model command supporting multiple explanation types - Added interactive dashboard with Dash and Plotly for real-time model exploration - Implemented comprehensive reporting and explanation persistence - Added support for both static and interactive dashboard generation Resolves issue nidhaloff#334
- Implemented comprehensive federated learning with FedAvg and FedProx aggregation - Added client-server architecture with parallel training capabilities - Created federated training and prediction CLI commands - Added support for multiple model types and problem types - Implemented weighted aggregation based on client data size - Added comprehensive training reporting and model persistence - Enhanced privacy-preserving distributed machine learning Resolves issue nidhaloff#335
- Implemented comprehensive model leaderboard system for ranking and comparing models - Added support for multiple evaluation metrics (accuracy, F1, MSE, R², etc.) - Created composite scoring system for fair model comparison - Added model ranking and comparison functionality - Implemented leaderboard persistence and loading - Enhanced CLI with create-leaderboard and show-leaderboard commands - Added support for both classification and regression problems Resolves issue nidhaloff#293
- Implemented comprehensive time series anomaly detection framework - Added support for multiple detection methods: Isolation Forest, DBSCAN, Statistical, LSTM Autoencoder - Created statistical anomaly detection with rolling window analysis - Added LSTM autoencoder for deep learning-based anomaly detection - Implemented comprehensive evaluation metrics and reporting - Enhanced CLI with detect-anomalies command - Added support for both univariate and multivariate time series - Implemented anomaly scoring and threshold-based detection Resolves issue nidhaloff#292
- Added basic quantum ML framework with Qiskit integration - Implemented quantum circuit initialization and data encoding - Added quantum feature mapping capabilities - Enhanced CLI with quantum-ml command - Added support for quantum backends (simulator/hardware) Resolves issue nidhaloff#218
- Added PMML export functionality for sklearn models - Implemented support for common model types (RandomForest, LogisticRegression, etc.) - Added model compatibility checking - Enhanced CLI with export-pmml command - Added PMML pipeline creation and export capabilities Resolves issue nidhaloff#217
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