woe-iv 0.2.0

caculate woe(weight of evidence) of each feature and then iv(information value).

woe_iv

caculate woe(weight of evidence) of each feature and then iv(information value).

Features

• 1 Calculation of WOE and IV

  def WOE(cls, data, varList, type0=’Con’, target_id=’y’, resfile=’result.xlsx’):

  “”” 对分类变量直接进行分组统计并进行WOE、IV值 计算 对连续型变量进行分组（default:10）后进行WOE、IV值 计算 :param data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table) :param varList: variable list :param type0: Continuous or Discontinuous(Category), ‘con’ is the required input for Continuous :param target_id: y flag when gen the train data :param resfile: download path of the result file of WOE and IV :return: pandas DataFrame, result of woe and iv value according y flag “”” pass

• 2 Apply of WOE repalcement of ABT

  def applyWOE(cls, X_data, X_map, var_list, id_cols_list=None, flag_y=None):

  “”” 将最优分箱的结果WOE值对原始数据进行编码 :param X_data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table) :param X_map: pandas dataframe, map table, result of applying WOE, refer the func woe_iv.WOE :param var_list: variable list :param id_cols_list: some other features not been analysed but wanted like id, adress, etc. :param flag_y: y flag when gen the train data :return: pandas DataFrame, result of bining with y flag “”” pass

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2018-12-19) 0.2.0 (2018-12-2l) ——————

• First release on PyPI.