Adaptable ML - Data Schemas

Project Description

This repository is part of a comprehensive tutorial series aimed at building adaptable machine learning models. It specifically corresponds to the "Using Data Schemas" tutorial. The primary goal of this tutorial, and by extension this repository, is to demonstrate how data schemas can be effectively leveraged to construct flexible machine learning implementations, thereby eliminating the need for hard-coding to specific datasets. By following the strategies outlined here, AI developers can create reusable, domain-agnostic algorithms and thereby enhance the versatility and applicability of their machine learning models.

Project Structure

binary_class_project/
├── examples/
│   ├── titanic_schema.json
│   ├── titanic_train.csv
│   └── titanic_test.csv
├── inputs/
│   ├── data/
│   │   ├── testing/
│   │   └── training/
│   └── schema/
├── model/
│   └── artifacts/
├── outputs/
│   ├── errors/
│   ├── hpt_outputs/
│   └── predictions/
├── src/
│   ├── config/
│   ├── data_models/
│   ├── hyperparameter_tuning/
│   ├── prediction/
│   ├── preprocessing/
│   ├── schema/
│   │   ├── __init__.py
│   │   └── data_schema.py
│   ├── xai/
│   ├── __init__.py
│   ├── check_schema.py
│   └── utils.py
├── tests/
│   ├── integration_tests/
│   ├── performance_tests/
│   └── unit_tests/
│       ├── <mirrors /src structure>
│       └── ...
├── tmp/
├── .gitignore
├── LICENSE
├── pytest.ini
├── README.md
├── requirements.txt
└── requirements-test.txt

• /examples: This directory contains example files for the titanic dataset. Three files are included: titanic_schema.json, titanic_train.csv and titanic_test.csv. You can place these files in the inputs/schema, inputs/data/training and inputs/data/testing folders, respectively.
• /inputs: This directory contains all the input files for your project, including the data and schema files. The data is further divided into testing and training subsets.
• /model/artifacts: This directory is used to store the model artifacts, such as trained models and their parameters.
• /outputs: The outputs directory contains sub-directories for error logs, and hyperparameter tuning outputs, and prediction results. Note that model artifacts should not be saved in this directory. Instead, they should be saved in the /model/artifacts directory.
• /src: This directory holds the source code for the project. It is further divided into various subdirectories such as config for configuration files, data_models for data models for input validation, hyperparameter_tuning for hyperparameter-tuning (HPT) related files, prediction for prediction model scripts, preprocessing for data preprocessing scripts, schema for schema scripts, and xai for explainable AI scripts.
  • Check the src/schema/data_schema.py file for the data schema implementation.
• /tests: This directory contains all the tests for the project. It contains sub-directories for specific types of tests such as unit tests, integration tests, and performance tests. For unit tests, the directory structure mirrors the /src directory structure.
• /tmp: This directory is used for storing temporary files which are not necessary to commit to the repository.
• .gitignore: This file specifies the files and folders that should be ignored by Git.
• LICENSE: This file contains the license for the project.
• README.md: This file contains the documentation for the project, explaining how to set it up and use it.
• requirements.txt: This file lists the dependencies for the project, making it easier to install all necessary packages.

Usage

• Create your virtual environment and install dependencies listed in requirements.txt.
• Move the three example files (titanic_schema.json, titanic_train.csv and titanic_test.csv) into the inputs/schema, inputs/data/training and inputs/data/testing folders, respectively.
• To run the code, simply run the script as follows.

python src/check_schema.py

Requirements

Dependencies are listed in the file requirements.txt. These packages can be installed by running the following command:

pip install -r requirements.txt

For testing, dependencies are listed in the file requirements-test.txt. You can install these packages by running the following command:

pip install -r requirements-test.txt