AlphaPy Pro Pipeline ==================== .. image:: model_pipeline.png :alt: AlphaPy Pro Model Pipeline :width: 100% :align: center Overview -------- The AlphaPy Pro pipeline is a comprehensive machine learning framework that automates the entire workflow from data ingestion to model deployment. The pipeline is designed to be flexible, supporting various algorithms and techniques while maintaining consistency and reproducibility. Pipeline Architecture --------------------- The AlphaPy Pro pipeline consists of several key stages: 1. **Configuration Loading** - Read model parameters from YAML files 2. **Data Ingestion** - Load training and testing data 3. **Feature Engineering** - Transform and create features 4. **Feature Selection** - Select the most informative features 5. **Model Training** - Train multiple algorithms with hyperparameter tuning 6. **Model Evaluation** - Assess model performance with various metrics 7. **Ensemble Creation** - Blend predictions from multiple models 8. **Visualization** - Generate plots for model interpretation 9. **Output Generation** - Save models, predictions, and artifacts Model Configuration ------------------- AlphaPy Pro uses YAML configuration files to control every aspect of the pipeline. The configuration is hierarchical and includes sections for: * **Project Settings** - Directory paths, file formats, submission options * **Model Parameters** - Algorithms, cross-validation, scoring metrics * **Data Processing** - Feature selection, sampling, transformations * **Feature Engineering** - Encoding, interactions, clustering, dimensionality reduction * **Pipeline Settings** - Parallelization, random seeds, verbosity Example configuration snippet:: model: algorithms: ['CATB', 'LGB', 'XGB', 'RF', 'LOGR'] cv_folds: 5 grid_search: option: True iterations: 50 random: True scoring_function: roc_auc type: classification Supported Algorithms -------------------- **Classification Algorithms:** * **CatBoost (CATB)** - Gradient boosting with categorical feature support * **LightGBM (LGB)** - Fast gradient boosting framework * **XGBoost (XGB)** - Extreme gradient boosting * **Random Forest (RF)** - Ensemble of decision trees * **Logistic Regression (LOGR)** - Linear classification model * **Extra Trees (EXT)** - Extremely randomized trees * **Support Vector Machine (SVM)** - Maximum margin classifier * **K-Nearest Neighbors (KNN)** - Instance-based learning **Regression Algorithms:** * **CatBoost Regressor** - Gradient boosting for regression * **LightGBM Regressor** - Light gradient boosting regressor * **XGBoost Regressor** - XGBoost for continuous targets * **Random Forest Regressor** - Ensemble regression * **Linear Regression** - Standard linear model * **Ridge/Lasso** - Regularized linear models Feature Engineering ------------------- AlphaPy Pro provides extensive feature engineering capabilities: **Automatic Feature Generation:** * **Clustering Features** - K-means clustering with configurable clusters * **Interaction Features** - Polynomial feature interactions * **Count Features** - Value counts and frequency encoding * **Date/Time Features** - Extract temporal components * **Text Features** - TF-IDF and count vectorization * **Statistical Features** - NumPy/SciPy transformations **Encoding Options:** * **Target Encoding** - Mean target encoding with smoothing * **One-Hot Encoding** - Binary columns for categories * **Ordinal Encoding** - Integer encoding for ordinal data * **Binary Encoding** - Efficient binary representation **Dimensionality Reduction:** * **PCA** - Principal Component Analysis * **ISOMAP** - Isometric mapping * **t-SNE** - t-distributed Stochastic Neighbor Embedding Feature Selection ----------------- Multiple feature selection methods are available: **Univariate Selection:** Statistical tests to select features based on univariate metrics (f_classif, mutual_info_classif, chi2, etc.) **LOFO Importance:** Leave One Feature Out importance analysis to identify features that contribute most to model performance **Recursive Feature Elimination (RFE):** Iteratively remove features based on model coefficients **Variance Threshold:** Remove low-variance features Model Training -------------- The training process includes: 1. **Data Splitting** - Stratified train/validation splits 2. **Cross-Validation** - K-fold CV with configurable folds 3. **Hyperparameter Tuning** - Grid search or random search 4. **Model Fitting** - Parallel training of multiple algorithms 5. **Calibration** - Probability calibration (Platt/Isotonic) Example training output:: [2025/06/18 20:45:45] INFO Training CATB [2025/06/18 20:45:46] INFO CV Score: 0.9158 [+/- 0.0234] [2025/06/18 20:45:47] INFO Training LGB [2025/06/18 20:45:48] INFO CV Score: 0.9944 [+/- 0.0012] [2025/06/18 20:45:49] INFO Training XGB [2025/06/18 20:45:50] INFO CV Score: 0.8171 [+/- 0.0156] Model Evaluation ---------------- Comprehensive evaluation metrics for different problem types: **Classification Metrics:** * Accuracy, Precision, Recall, F1-Score * ROC-AUC, Average Precision * Matthews Correlation Coefficient * Cohen's Kappa * Log Loss, Brier Score **Regression Metrics:** * Mean Absolute Error (MAE) * Mean Squared Error (MSE) * Root Mean Squared Error (RMSE) * R-squared (R²) * Mean Absolute Percentage Error (MAPE) Ensemble Methods ---------------- AlphaPy Pro automatically creates ensemble models: **Blending:** Combines predictions from multiple models using a meta-learner (Logistic Regression for classification, Ridge for regression) **Voting:** Simple or weighted voting across models **Stacking:** Multi-level stacking with cross-validated predictions Visualization ------------- The pipeline generates various plots for model interpretation: **Calibration Plot** Shows reliability of predicted probabilities .. image:: plot_calibration.png :alt: Calibration Plot :width: 80% :align: center **Confusion Matrix** Visualizes classification errors by class .. image:: plot_confusion_matrix.png :alt: Confusion Matrix :width: 80% :align: center **Feature Importance Plot** Shows relative importance of features .. image:: plot_feature_importances.png :alt: Feature Importances :width: 80% :align: center **Learning Curve** Displays training/validation scores vs. sample size .. image:: plot_learning_curve.png :alt: Learning Curve :width: 80% :align: center **ROC Curve** Receiver Operating Characteristic curve for binary classification .. image:: plot_roc_curve.png :alt: ROC Curve :width: 80% :align: center **LOFO Importance Plot** Leave One Feature Out importance analysis Output Structure ---------------- All outputs are saved in timestamped run directories:: runs/run_YYYYMMDD_HHMMSS/ ├── config/ │ └── model.yml # Configuration snapshot ├── input/ │ ├── train.csv # Training data snapshot │ └── test.csv # Test data snapshot ├── model/ │ ├── model.pkl # Trained model ensemble │ ├── feature_map.pkl # Feature transformation pipeline │ └── model_metrics.csv # Performance metrics ├── output/ │ ├── predictions.csv # Raw predictions │ ├── ranked_*.csv # Ranked predictions │ └── submission.csv # Competition submission file └── plots/ ├── *.png # All generated plots └── ... Advanced Features ----------------- **Meta-Labeling:** Triple Barrier Method for financial ML applications **Time Series Support:** Lag features, rolling statistics, and proper CV splits **GPU Acceleration:** Automatic GPU usage for XGBoost, LightGBM, and CatBoost **Distributed Training:** Support for distributed training on clusters **AutoML Integration:** Automated feature engineering and model selection Best Practices -------------- 1. **Start Simple** - Begin with a few algorithms and basic features 2. **Iterate Quickly** - Use small grid search iterations initially 3. **Monitor Overfitting** - Watch train/validation score gaps 4. **Feature Engineering** - Spend time on domain-specific features 5. **Ensemble Wisely** - Combine diverse models for best results 6. **Track Experiments** - Use the timestamped runs for comparison For specific domain applications, see: * :doc:`market_flow` - Financial market analysis and trading strategies