Metadata-Version: 2.1
Name: linora
Version: 0.10.0
Summary: Simple and efficient tools for data mining and data analysis.
Home-page: https://github.com/Hourout/linora
Author: JinQing Lee
Author-email: hourout@163.com
License: Apache License Version 2.0
Description: ![](https://github.com/Hourout/linora/blob/master/image/linora.png)
        
        
        ![PyPI version](https://img.shields.io/pypi/pyversions/linora.svg)
        ![Github license](https://img.shields.io/github/license/Hourout/linora.svg)
        [![PyPI](https://img.shields.io/pypi/v/linora.svg)](https://pypi.python.org/pypi/linora)
        ![PyPI format](https://img.shields.io/pypi/format/linora.svg)
        ![contributors](https://img.shields.io/github/contributors/Hourout/linora)
        ![downloads](https://img.shields.io/pypi/dm/linora.svg)
        
        Linora is a simple and efficient tools for data mining and data analysis.
         
        
        
        | [API Document](https://github.com/Hourout/linora/blob/master/document/api.md) | [中文介绍](https://github.com/Hourout/linora/blob/master/document/Chinese.md) |
        
        ## Installation
        
        To install [this verson from PyPI](https://pypi.org/project/linora/), type:
        
        ```
        pip3 install linora
        ```
        
        To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:
        
        ```
        pip3 install git+git://github.com/Hourout/linora.git
        ```
        
        ## Feature
        - metrics
        - metrics charts
        - feature columns
        - feature selection
        - image augmentation
        - text processing
        - model param search
        - sample splits
        
        ## Example
        [more example](https://github.com/Hourout/linora/blob/master/example/readme.md)
        
        ```python
        import linora as la
        
        # plot ks curve
        label = [1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1]
        label_prob = [0.8, 0.4, 0.2, 0.5, 0.9, 0.2, 0.8, 0.6, 0.1, 0.3, 0.8, 0.3, 0.9, 0.2, 0.84, 
                      0.2, 0.5, 0.23, 0.83, 0.71, 0.34, 0.3, 0.2, 0.7, 0.2, 0.8, 0.3, 0.59, 0.26, 0.16, 0.13, 0.8]
        la.chart.ks_curve(label, label_prob)
        ```
        ![](https://github.com/Hourout/linora/blob/master/image/ks_curve.png)
        
        ## Contact
        Please contact me if you have any related questions or improvements.
        
        [WeChat](https://github.com/Hourout/linora/blob/master/image/hourout_wechat.jpg)
        
Keywords: hyperparameter-optimization,XGBoost,LightGBM,data-mining,data-analysis,machine-learning,image,text,data-science
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
