Metadata-Version: 2.1
Name: mlvizlib
Version: 0.1.2
Summary: MLVizLib is a powerful package for generating quick, insightful, and stylish visualizations for machine learning.
Home-page: https://github.com/kristianbonnici/mlvizlib
Author: Kristian Bonnici
Author-email: kristiandaaniel@gmail.com
License: MIT license
Keywords: mlvizlib
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown

[![Fancy fonts](https://see.fontimg.com/api/renderfont4/YzqJL/eyJyIjoiZHciLCJoIjoxMTcsInciOjEwMDAsImZzIjoxMTcsImZnYyI6IiMyMUJDQzUiLCJiZ2MiOiIjRkZGRkZGIn0/W01MXSBWaXogTGli/broshk-plum.png)](https://www.fontspace.com/category/fancy)

--------------------------------------

![PyPI Version](https://img.shields.io/pypi/v/mlvizlib)
![License](https://img.shields.io/pypi/l/mlvizlib)

**MLVizLib (Machine Learning Visualization Library)** is a powerful
library for generating quick, insightful, and stylish visualizations for
machine learning (ML). Our goal is to enhance the ML workflow by
providing insightful visualizations with minimum effort.

-   Documentation: (COMING SOON) <https://mlvizlib.readthedocs.io>.

> **NOTE**
>
> This project is in early stage development, and can thus go trough major changes.

Install
-------

MLVizLib can be installed from
[PyPI](https://pypi.org/project/mlvizlib/):

``` {.sourceCode .python}
pip install mlvizlib
```

Features
--------

-   Confusion Matrix Visualization

> **note**
>
> More coming soon.

Confusion Matrix Visualization example
--------------------------------------

``` {.sourceCode .python}
import matplotlib.pyplot as plt
from mlvizlib.classification import confusion_matrix

# example data
eg_y_true = [2,0,1,0,2,0,1,2,0,0,2,0,1,1,0,1,1,0,0,0,0,2,2]
eg_y_pred = [2,0,0,0,2,0,1,2,1,0,2,2,1,1,0,2,1,0,1,0,0,1,2]

confusion_matrix(eg_y_true, eg_y_pred)
plt.show()
```
<p align="center">
  <img src="https://github.com/kristianbonnici/mlvizlib/blob/master/img/example-cm-viz.jpg?raw=true" width="800" />
</p>


