Tools for WoE Transformation mostly used in ScoreCard Model for credit rating
Project description
woe
==========
.. image:: https://travis-ci.org/justdoit0823/pywxclient.svg?branch=master
:target: https://travis-ci.org/justdoit0823/pywxclient
version: 0.1.1
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 aims to only support Python 2.7, so there is no guarantee for Python 3.**
**woe** module function tree
=======
.. code-block:: bash
|- __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()
| |-- 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()
| |-- search()
| |-- woe_trans()
|- GridSearch.py
| |-- grid_search_lr_c()
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 0.1.1 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,1002937942@qq.com.
==========
.. image:: https://travis-ci.org/justdoit0823/pywxclient.svg?branch=master
:target: https://travis-ci.org/justdoit0823/pywxclient
version: 0.1.1
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 aims to only support Python 2.7, so there is no guarantee for Python 3.**
**woe** module function tree
=======
.. code-block:: bash
|- __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()
| |-- 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()
| |-- search()
| |-- woe_trans()
|- GridSearch.py
| |-- grid_search_lr_c()
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 0.1.1 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,1002937942@qq.com.
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