Metadata-Version: 2.1
Name: flashML
Version: 0.0.1
Summary: AutoML tool
Home-page: https://github.com/omchaithanyav/flashML
Author: Om Chaithanya V
 Sai Prakash Y
Author-email: vomchaithanya@gmail.com
License: MIT
Keywords: AutoML
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENCE.txt
Requires-Dist: NumPy (>=1.16.2)
Requires-Dist: lightgbm (>=2.3.1)
Requires-Dist: scipy (>=1.4.1)
Requires-Dist: pandas (>=1.1.4)
Requires-Dist: scikit-learn (>=0.24)
Requires-Dist: catboost (>=0.26)
Requires-Dist: optuna

# flashML - AutoML tool

![flashml](https://user-images.githubusercontent.com/45726271/141427996-262fdb57-59e5-4969-b973-2b0807e212be.png)


flashML is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install flashML
```
## Quickstart

```python
from flashML import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release

