jay-code

# Foundation Model Builder Agent for MLE-STAR ## Overview The Foundation Model Builder is a critical component of the MLE-STAR (Machine Learning Engineering - Search, Train, Ablate, Refine) automation workflow. This agent handles the foundation phase, focusing on: - Data preprocessing and feature engineering - Initial model building and baseline creation - Performance benchmarking - Model persistence and versioning ## Architecture The Foundation Agent consists of four main modules: ### 1. `foundation_agent_core.py` Core functionality for model building: - **FoundationModelBuilder**: Main class handling model training and evaluation - **ModelResult**: Data structure for storing model performance metrics - Dataset analysis and problem type detection - Preprocessing pipeline creation - Baseline model training with cross-validation - Ensemble model creation - Comprehensive reporting ### 2. `foundation_agent_features.py` Advanced feature engineering capabilities: - **FeatureEngineer**: Comprehensive feature engineering toolkit - Polynomial and interaction features - Statistical aggregate features - Ratio and difference features - Clustering-based features - Mathematical transformations - Binning and discretization - Feature selection (univariate, mutual information, RFE) - Dimensionality reduction (PCA, TruncatedSVD) ### 3. `foundation_agent_integration.py` Integration with MLE-STAR workflow: - **FoundationAgentIntegration**: Coordination layer - Claude-flow hooks integration - Memory system coordination - Workflow step processing - Cross-agent communication - Result sharing and persistence ### 4. `test_foundation_agent.py` Comprehensive test suite: - Unit tests for all major components - Integration tests for workflow scenarios - Feature engineering validation - Model training verification ## Usage ### Standalone Execution ```python from foundation_agent_core import FoundationModelBuilder # Initialize builder builder = FoundationModelBuilder( session_id="my_session", execution_id="my_execution" ) # Load and analyze data import pandas as pd data = pd.read_csv("my_data.csv") analysis = builder.analyze_dataset(data, target_column="target") # Train baseline models X = data.drop(columns=["target"]) y = data["target"] results = builder.train_baseline_models(X, y, cv_folds=5) # Create ensemble ensemble = builder.create_ensemble_baseline(X, y) # Save results report = builder.save_results() ``` ### Workflow Integration ```bash # Run as part of MLE-STAR workflow python foundation_agent_integration.py \ --session-id "automation-session-123" \ --execution-id "workflow-exec-456" \ --dataset "path/to/data.csv" \ --target "target_column" \ --step "full_pipeline" ``` ### Feature Engineering ```python from foundation_agent_features import FeatureEngineer # Initialize engineer engineer = FeatureEngineer(problem_type="classification") # Create features X_poly = engineer.create_polynomial_features(X, degree=2) X_stats = engineer.create_statistical_features(X) X_all = engineer.create_all_features(X, config={ 'polynomial': True, 'statistical': True, 'clustering': {'n_clusters': 5} }) # Select features X_selected, scores = engineer.select_features_univariate(X_all, y, k=20) ``` ## Baseline Models The agent automatically selects appropriate models based on problem type: ### Classification - Logistic Regression - Decision Tree - Random Forest - Support Vector Machine - K-Nearest Neighbors - Naive Bayes - Neural Network (MLP) ### Regression - Linear Regression - Ridge Regression - Lasso Regression - Decision Tree - Random Forest - Support Vector Regression - K-Nearest Neighbors - Neural Network (MLP) ## Coordination & Memory The agent uses Claude-flow hooks for coordination: ```bash # Pre-task coordination npx jay-code@alpha hooks pre-task --description "Foundation building" # Post-edit notifications npx jay-code@alpha hooks post-edit --file "model.pkl" # Memory storage npx jay-code@alpha memory store "agent/foundation/results" "{...}" # Result sharing npx jay-code@alpha hooks notify --message "Foundation complete" ``` ## Output Structure ``` models/foundation_{session_id}/ ├── LogisticRegression_baseline.pkl ├── RandomForest_baseline.pkl ├── ensemble_baseline.pkl ├── preprocessing_pipeline.pkl └── foundation_report.json ``` ### Foundation Report Structure ```json { "session_id": "...", "execution_id": "...", "timestamp": "2025-01-04T10:00:00Z", "problem_type": "classification", "preprocessing": { "features": ["feature_1", "feature_2", ...], "pipeline_steps": "..." }, "baseline_models": [ { "model_name": "RandomForest", "mean_cv_score": 0.85, "std_cv_score": 0.03, "training_time": 2.5 } ], "best_model": { "name": "RandomForest", "score": 0.85, "std": 0.03 }, "recommendations": [ "Consider feature engineering", "Try ensemble methods" ] } ``` ## Performance Optimization The agent includes several optimizations: 1. **Parallel Processing**: Cross-validation uses all available cores 2. **Memory Efficiency**: Streaming data processing for large datasets 3. **Caching**: Preprocessed data cached between model training 4. **Early Stopping**: Poor performing models stopped early 5. **Sparse Matrix Support**: Efficient handling of sparse features ## Error Handling The agent includes robust error handling: - Graceful degradation when models fail - Automatic recovery from memory errors - Validation of input data formats - Clear error messages and logging ## Testing Run the comprehensive test suite: ```bash # Run all tests python test_foundation_agent.py # Run specific test class python -m unittest test_foundation_agent.TestFoundationModelBuilder # Run with coverage coverage run test_foundation_agent.py coverage report ``` ## Future Enhancements Planned improvements include: 1. **AutoML Integration**: Automatic hyperparameter tuning 2. **GPU Support**: RAPIDS integration for faster processing 3. **Distributed Training**: Dask/Ray support for large datasets 4. **Advanced Ensembles**: Stacking and blending methods 5. **Explainability**: SHAP/LIME integration 6. **Online Learning**: Incremental model updates ## Dependencies ```python # Core dependencies pandas>=1.3.0 numpy>=1.21.0 scikit-learn>=1.0.0 joblib>=1.1.0 # Optional dependencies dask>=2022.1.0 # For distributed processing shap>=0.40.0 # For model explainability matplotlib>=3.4.0 # For visualizations ``` ## Contributing When contributing to the Foundation Agent: 1. Follow the existing code structure 2. Add comprehensive tests for new features 3. Update documentation 4. Ensure all tests pass 5. Follow PEP 8 style guidelines ## License This module is part of the Jay-Code project and follows the same licensing terms.