TOAD
Toad is dedicated to facilitating model development process, especially for a scorecard. It provides intuitive functions of the entire process, from EDA, feature engineering and selection etc. to results validation and scorecard transformation. Its key functionality streamlines the most critical and time-consuming process such as feature selection and fine binning.
Toad 是专为工业界模型开发设计的Python工具包，特别针对评分卡的开发。Toad 的功能覆盖了建模全流程，从 EDA、特征工程、特征筛选 到 模型验证和评分卡转化。Toad 的主要功能极大简化了建模中最重要最费时的流程，即特征筛选和分箱。
Install and Upgrade · 安装与升级
Pip
pip install toad # to install
pip install -U toad # to upgradeConda
conda install toad --channel conda-forge # to install
conda install -U toad --channel conda-forge # to upgradeSource code
python setup.py installKey features · 主要功能
The following showcases some of the most popular features of toad, for more detailed demonstrations and user guidance, please refer to the tutorials.
以下部分简单介绍了toad最受欢迎的一些功能，具体的使用方法和使用教程，请详见文档部分。
- Simple IV calculation for all features · 一键算IV:
toad.quality(data, 'target', indicators = ['iv'])- Preliminary selection based on criteria · 根据特定条件的初步变量筛选;
- and stepwise feature selection (with optimised algorithm) · 优化过的逐步回归:
selected_data = toad.selection.select(data,target = 'target', empty = 0.5, iv = 0.02, corr = 0.7, return_drop=True, exclude=['ID','month'])
final_data = toad.selection.stepwise(data_woe,target = 'target', estimator='ols', direction = 'both', criterion = 'aic', exclude = to_drop)- Reliable fine binning with visualisation · 分箱及可视化:
# Chi-squared fine binning
c = toad.transform.Combiner()
c.fit(data_selected.drop(to_drop, axis=1), y = 'target', method = 'chi', min_samples = 0.05)
print(c.export())
# Visualisation to check binning results
col = 'feature_name'
bin_plot(c.transform(data_selected[[col,'target']], labels=True), x=col, target='target')- Intuitive model results presentation · 模型结果展示:
toad.metrics.KS_bucket(pred_proba, final_data['target'], bucket=10, method = 'quantile')- One-click scorecard transformation · 评分卡转化:
card = toad.ScoreCard(
combiner = c,
transer = transer,
class_weight = 'balanced',
C=0.1,
base_score = 600,
base_odds = 35 ,
pdo = 60,
rate = 2
)
card.fit(final_data[col], final_data['target'])
print(card.export())Documents · 文档
Community · 社区
We welcome public feedback and new PRs. We hold a WeChat group for questions and suggestions.
欢迎各位提PR，同时我们有toad使用交流的微信群，欢迎询问加群。