AlphaPy Pro Pipeline

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:

Configuration Loading - Read model parameters from YAML files
Data Ingestion - Load training and testing data
Feature Engineering - Transform and create features
Feature Selection - Select the most informative features
Model Training - Train multiple algorithms with hyperparameter tuning
Model Evaluation - Assess model performance with various metrics
Ensemble Creation - Blend predictions from multiple models
Visualization - Generate plots for model interpretation
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:

Data Splitting - Stratified train/validation splits
Cross-Validation - K-fold CV with configurable folds
Hyperparameter Tuning - Grid search or random search
Model Fitting - Parallel training of multiple algorithms
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

Confusion Matrix: Visualizes classification errors by class

Feature Importance Plot: Shows relative importance of features

Learning Curve: Displays training/validation scores vs. sample size

ROC Curve: Receiver Operating Characteristic curve for binary classification

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

Start Simple - Begin with a few algorithms and basic features
Iterate Quickly - Use small grid search iterations initially
Monitor Overfitting - Watch train/validation score gaps
Feature Engineering - Spend time on domain-specific features
Ensemble Wisely - Combine diverse models for best results
Track Experiments - Use the timestamped runs for comparison

For specific domain applications, see:

MarketFlow - Financial market analysis and trading strategies