XGBoost Inference Pipeline for Heart Disease UCI

Project Overview

This project implements an inference pipeline for heart disease prediction using a pre-trained XGBoost model. It provides functionality for loading the model, preprocessing new data, and making predictions.

Project Structure

gillopy-Deployment_XGBoost_Inference_Heart_Disease_UCI/
├── README.md                    # Project documentation
├── Dockerfile                   # Docker container configuration
├── LICENSE                      # Project license
├── pyproject.toml              # Poetry dependency management
├── .dockerignore               # Docker build exclusions
├── models/                     # Pre-trained model files
│   ├── trained_model_2025-01-06.joblib
│   └── trained_model_2025-01-08.joblib
├── src/                        # Source code
│   ├── data_preprocessor.py    # Data preprocessing functionality
│   ├── inference.py           # Main inference pipeline
│   └── model_loader.py        # Model loading utilities
└── tests/                      # Test suite
    ├── __init__.py
    ├── test_data_preprocessor.py
    ├── test_inference.py
    └── test_model_loader.py

Requirements

Python Version

• Python 3.10 (specific version requirement)

Dependencies

All dependencies are managed through Poetry and specified in pyproject.toml:

• pandas (^2.2.3)
• scikit-learn (^1.6.0)
• xgboost (^2.1.3)
• joblib (^1.4.2)

Setup and Installation

1. Clone the Repository:

   git clone https://github.com/gillopy/Deployment_XGBoost_Inference_Heart_Disease_UCI
   cd Deployment_XGBoost_Inference_Heart_Disease_UCI

2. Install Dependencies:

   poetry install

3. Docker Setup (optional):

   docker build -t heart-disease-inference .

Usage

Running Inference

Execute the main inference script:

poetry run python src/inference.py

Input Data Format

The model expects input data in the following format:

{
    "age": int,
    "sex": int,
    "cp": int,
    "trestbps": int,
    "chol": int,
    "fbs": int,
    "restecg": int,
    "thalach": int,
    "exang": int,
    "oldpeak": float,
    "slope": int,
    "ca": float,
    "thal": float
}

Testing

Run the test suite using pytest:

poetry run pytest

The test suite includes:

• Data preprocessing validation
• Inference pipeline testing
• Model loading verification

Project Components

Data Preprocessor

• Handles missing value imputation
• Converts input dictionary to DataFrame format
• Implements data validation checks

Model Loader

• Loads the pre-trained XGBoost model
• Includes error handling for missing model files
• Validates model compatibility

Inference Pipeline

• Orchestrates the complete inference process
• Supports batch predictions
• Provides formatted output

Docker Support

The project includes Docker support for containerized deployment:

• Base Python 3.10 image
• Automatic dependency installation
• Environment isolation

License

Apache License 2.0

Author

Guillermo (guillermocabrera9710@gmail.com)