https://github.com/boredbird/woe

Tools for WoE Transformation mostly used in ScoreCard Model for credit rating
https://github.com/boredbird/woe

credit-scoring iv machine-learning scorecard woe

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Tools for WoE Transformation mostly used in ScoreCard Model for credit rating

• Host: GitHub
• URL: https://github.com/boredbird/woe
• Owner: boredbird
• License: mit
• Created: 2017-09-11T07:15:04.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-29T03:39:46.000Z (over 5 years ago)
• Last Synced: 2023-09-15T06:10:01.691Z (over 1 year ago)
• Topics: credit-scoring, iv, machine-learning, scorecard, woe
• Language: Python
• Size: 6.72 MB
• Stars: 249
• Watchers: 26
• Forks: 104
• Open Issues: 11
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-production-machine-learning - woe - Tools for WoE Transformation mostly used in ScoreCard Model for credit rating (Explaining Black Box Models and Datasets)
• Awesome-AIML-Data-Ops - woe - Tools for WoE Transformation mostly used in ScoreCard Model for credit rating (Explaining Black Box Models and Datasets)
• awesome-production-machine-learning - woe - Tools for WoE Transformation mostly used in ScoreCard Model for credit rating (Explainability and Fairness)

README

        
woe

===

.. image:: https://travis-ci.org/justdoit0823/pywxclient.svg?branch=master

    :target: https://travis-ci.org/justdoit0823/pywxclient

version: 0.1.4

Tools for WoE Transformation mostly used in ScoreCard Model for credit rating

Installation

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

We can simply use pip to install, as the following:

.. code-block:: bash

   $ pip install woe

or installing from git

.. code-block:: bash

   $ pip install git+https://github.com/boredbird/woe

Features

========

  * Split tree with IV criterion

  * Rich and plentiful model eval methods

  * Unified format and easy for output

  * Storage of IV tree for follow-up use

**woe** module function tree

============================

:: 

	|- __init__

	|- config.py 

	|   |-- config

	|   	|-- __init__

	|		|-- change_config_var_dtype()

	|		|-- load_file()

	|- eval.py 

	|   |-- compute_ks()

	|   |-- eval_data_summary()

	|   |-- eval_feature_detail()

	|   |-- eval_feature_stability()

	|   |-- eval_feature_summary()

	|   |-- eval_model_stability()

	|   |-- eval_model_summary()

	|   |-- eval_segment_metrics()

	|   |-- plot_ks()

	|   |-- proc_cor_eval()

	|   |-- proc_validation()

	|   |-- wald_test()

	|- feature_process.py 

	|   |-- binning_data_split()

	|   |-- calculate_iv_split()

	|   |-- calulate_iv()

	|   |-- change_feature_dtype()

	|   |-- check_point()

	|   |-- fillna()

	|   |-- format_iv_split()

	|   |-- proc_woe_continuous()

	|   |-- proc_woe_discrete()

	|   |-- process_train_woe()

	|   |-- process_woe_trans()

	|   |-- search()

	|   |-- woe_trans()

	|- ftrl.py 

	|   |-- FTRL()

	|   |-- LR()

	|- GridSearch.py 

	|   |-- fit_single_lr()

	|   |-- grid_search_lr_c()

	|   |-- grid_search_lr_c_main()

	|   |-- grid_search_lr_validation()

Examples

========

In the examples directory, there is a simple woe transformation program as tutorials.

Or you can write a more complex program with this `woe` package.

Version Records

================

woe 0.1.4 2018-03-01

	* support py3

woe 0.1.3 2018-02-09

	* woe.feature_process.proc_woe_discrete(): fix bug when deal with discrete varibales

	* woe.eval.eval_feature_detail(): fix bug : utf-8 output file format

	* woe.GridSearch.grid_search_lr_c_main(): add function warper for convenience and high efficiency

	* woe.GridSearch.grid_search_lr_c_validation(): monitor the ks performance of training sets and test sets on different 'c'

	* supplement examples test scripts

woe 0.1.2 2017-12-05

	* woe.ftrl.FTRL(): add online learning module

woe 0.1.1 2017-11-28

	* woe.config.load_file(): change param data_path to be optional

	* woe.eval.eval_feature_stability(): fix bug : psi_dict['stability_index'] computation error

	* woe.feature_process.change_feature_dtype(): add friendly tips when encounter a error

	* woe.feature_process.calulate_iv(): refactor the code

	* woe.feature_process.calculate_iv_split(): refactor the code

	* woe.feature_process.binning_data_split(): reduce the number of len() function calls with __len__() and shape attributes;replace namedtuple with dict

	* woe.feature_process.fillna(): new added function to fill null value

	* woe.GridSearch.grid_search_lr_c(): list of regularization parameter c specified inside the function is changed to the user specified

	

woe 0.0.9 2017-11-21

	* Add module : GridSearch for the search of optimal hyper parametric C in LogisticRegression

	* Code refactoring: function compute_ks and plot_ks

woe 0.0.8 2017-09-28

	* More flexible: cancel conditional restriction in function feature_process.change_feature_dtype() 

	* Fix bug: the wrong use of deepcopy in function feature_process.woe_trans()

	

woe 0.0.7 2017-09-19

	* Fix bug: eval.eval_feature_detail raises ValueError('arrays must all be same length')

	* Add parameter interface: alpha specified step learning rate ,default 0.01

How to Contribute

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

Email me,[email protected].