Metadata-Version: 2.1
Name: sapientml
Version: 0.1.3
Summary: Generative AutoML for Tabular Data
Home-page: https://github.com/sapientml/sapientml
License: Apache-2.0
Keywords: automl
Author: The SapientML Authors
Maintainer: Kosaku Kimura
Maintainer-email: kimura.kosaku@fujitsu.com
Requires-Python: >=3.9,<3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: catboost (>=1.2,<2.0)
Requires-Dist: fasttext-wheel (>=0.9.2,<0.10.0)
Requires-Dist: imbalanced-learn (>=0.11.0,<0.12.0)
Requires-Dist: jinja2 (>=3.1.2,<4.0.0)
Requires-Dist: lightgbm (>=4.0.0,<5.0.0)
Requires-Dist: mecab-python3 (>=1.0.6,<2.0.0)
Requires-Dist: nltk (>=3.8.1,<4.0.0)
Requires-Dist: numpy (>=1.19.5,<2.0.0)
Requires-Dist: optuna (>=3.2.0,<4.0.0)
Requires-Dist: pandas (>=2.0.3,<3.0.0)
Requires-Dist: pydantic (>=2.1.1,<3.0.0)
Requires-Dist: scikit-learn (==1.1.3)
Requires-Dist: scipy (>=1.11.1,<2.0.0)
Requires-Dist: shap (>=0.42.1,<0.43.0)
Requires-Dist: xgboost (>=1.7.6,<2.0.0)
Project-URL: Repository, https://github.com/sapientml/sapientml
Description-Content-Type: text/markdown

# SapientML


[![PyPI version](https://badge.fury.io/py/sapientml.svg)](https://badge.fury.io/py/sapientml) ![Static Badge](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11-blue) [![Release](https://github.com/sapientml/sapientml/actions/workflows/release.yml/badge.svg)](https://github.com/sapientml/sapientml/actions/workflows/release.yml) [![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-%23FE5196?logo=conventionalcommits&logoColor=white)](https://conventionalcommits.org)


# Getting Started

## Installation

```
pip install sapientml
```

### Generate Code in a notebook

Please download [housing-prices.csv](https://github.com/F-AutoML/sapientml/files/10430539/housing-prices.csv) to execute the following code.

```py
import pandas as pd

df = pd.read_csv("housing-prices.csv")

from sapientml import SapientML

sml = SapientML()

ret = sml.generate_code(
    training_data=df,
    task_type="regression",
    target_columns=["SalePrice"],
    ignore_columns=["Id"],
    adaptation_metric="RMSE",
    hyperparameter_tuning=False,
    id_columns_for_prediction=["Id"]
)
ret.save(
    "outputs",
    save_dev_scripts=True,
    save_user_scripts=True,
    save_datasets=True,
    save_running_arguments=True,
)
```

### Run Generated Code

```
cd outputs/
python final_script.py
```



