eZAutoML 0.1.0

A Democratized lightweight and transparent AutoML framework

eZAutoML

Overview

Installation

Latest published version can be installed through PyPI using:

pip install ezautoml
ezautoml --help

Install from source

To install from source, you can clone this repo and install with pip:

pip install -e .

Usage

Command Line Interface

Usage:

ezautoml --dataset <path_to_data> --target <target_name> --task <classification|regression> --models <model1,model2,...> --cv <folds> --output <path_to_output>

Options:

• dataset: Path to the dataset file (CSV, parquet...)
• target: The target column name for prediction
• task: Task type: classification or regression
• search: Black-box optimization algorithm to perform
• models: Comma-separated list of models to use (e.g., lr,rf,xgb). Use initials!
• cv: Number of cross-validation folds (if needed)
• output: Directory to save the output models/results
• trials: Maximum number of trials inside an optimiation algorithm
• preprocess: Whether to perform minimal preprocessing (Scaling, Encoding...) or not
• verbose: Increase logging verbosity
• version: Show the current version

For more detailed help, use:

ezautoml --help

There are future features that are still a work-in-progress and will be enabled in the future such as scheduling, metalearning, pipelines...

Python Script

Features & WIP

3 core components:

Contributing

License