简介&安装

简介

评分卡建模尽在于此

Scorecard-Bundle是一个基于Python的高级评分卡建模API，实施方便且符合Scikit-Learn的调用习惯，包含的类均遵守Scikit-Learn的fit-transform-predict习惯。Scorecard-Bundle包括基于ChiMerge的特征离散化、WOE编码、基于信息值（IV）和共线性的特征评估、基于逻辑回归的评分卡模型、针对二元分类任务的模型评估、以及评分卡模型的模型解释工具。

展示如何训练评分卡模型的完整示例见Example Notebooks

Scorecard-Bundle各模块的详细介绍见API Reference (Detailed documentation about the package)

Scorecard-Bundle中WOE和IV的计算、评分卡转化等的核心计算逻辑源自《信用风险评分卡研究 —基于SAS的开发与实施》一书，该书籍由王松奇和林治乾翻译自Mamdouh Refaat的"Credit Risk Scorecards: Development and Implementation Using SAS"；而ChiMerge算法则是复现了原作者Randy Kerber的论文"ChiMerge: Discretization of Numeric Attributes"。

安装

由于每次版本更新都在修复已知的bug或添加重要的新功能，强烈建议安装最新版本 。严重的bug原则上都会在被发现的第一时间修复，因此若在使用Scorecard-Bundle的过程中怀疑存在bug，欢迎到Github主页的issue中记录。

注意，Scorecard-Bundle依赖NumPy, Pandas, matplotlib, Scikit-Learn, SciPy，可单独安装或直接使用Anaconda安装。

• Pip: Scorecard-Bundle可使用pip安装: pip install --upgrade scorecardbundle

  注意！最新版本可能尚未被纳入一些镜像源网站 (e.g. https://mirrors.aliyun.com/pypi/simple/ )。因此为了更新到最新版本，可以使用下面的命令，指定 https://pypi.org/project 作为源

  pip install -i https://pypi.org/project --upgrade scorecardbundle
  

  导入模块：

  from scorecardbundle.feature_discretization import ChiMerge as cm
  from scorecardbundle.feature_discretization import FeatureIntervalAdjustment as fia
  from scorecardbundle.feature_encoding import WOE as woe
  from scorecardbundle.feature_selection import FeatureSelection as fs
  from scorecardbundle.model_training import LogisticRegressionScoreCard as lrsc
  from scorecardbundle.model_evaluation import ModelEvaluation as me
  from scorecardbundle.model_interpretation import ScorecardExplainer as mise
  

• 手动: 也可以从Github下载代码<https://github.com/Lantianzz/Scorecard-Bundle>， 直接导入:

  import sys
  sys.path.append('E:\Github\Scorecard-Bundle') # add path that contains the codes
  from scorecardbundle.feature_discretization import ChiMerge as cm
  from scorecardbundle.feature_discretization import FeatureIntervalAdjustment as fia
  from scorecardbundle.feature_encoding import WOE as woe
  from scorecardbundle.feature_selection import FeatureSelection as fs
  from scorecardbundle.model_training import LogisticRegressionScoreCard as lrsc
  from scorecardbundle.model_evaluation import ModelEvaluation as me
  from scorecardbundle.model_interpretation import ScorecardExplainer as mise