Metadata-Version: 2.4
Name: eZAutoML
Version: 0.1.0
Summary: A Democratized lightweight and transparent AutoML framework
Author-email: eZWALT <waltertv02@gmail.com>
License-Expression: BSD-3-Clause
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scikit-learn
Requires-Dist: numpy
Requires-Dist: pandas
Provides-Extra: sklearn
Requires-Dist: scikit-learn; extra == "sklearn"
Requires-Dist: threadpoolctl; extra == "sklearn"
Provides-Extra: optuna
Requires-Dist: optuna; extra == "optuna"
Provides-Extra: xgboost
Requires-Dist: xgboost; extra == "xgboost"
Provides-Extra: lightgbm
Requires-Dist: lightgbm; extra == "lightgbm"
Provides-Extra: path-loaders
Requires-Dist: openpyxl; extra == "path-loaders"
Requires-Dist: pyarrow; extra == "path-loaders"
Dynamic: license-file

# eZAutoML 

## Overview

## Installation 

Latest published version can be installed through PyPI using:

```bash 
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:

```bash
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:

```bash
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 
