Metadata-Version: 2.1
Name: linora
Version: 1.0.0
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 data mining and data analysis tool that allows you to do related data mining tasks without using sklearn to the maximum extent. It is perfectly compatible with pandas and runs faster and saves memory compared to sklearn.
        
        
        | [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:
        
        ```
        pip 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:
        
        ```
        pip install git+git://github.com/Hourout/linora.git
        ```
        
        ## Feature
        - metrics
        - metrics charts
        - feature columns module
        - feature selection module
        - image augmentation
        - text processing
        - model param search
        - sample splits
        - sample
        - parallel
        - logger
        - config
        - progbar
        - schedulers
        
        ## 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,logging,parallel,feature-engineering,metrics,schedulers,datasets
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
