toad 0.0.61

python utils for detect data

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.

Install

Pip

pip install toad

Conda

conda install toad --channel conda-forge

Source code

python setup.py install

Upgrade

Pip

pip install -U toad

Conda

conda install -U toad --channel conda-forge

Key features

• Simple IV calculation for all

toad.quality(data,'target',iv_only=True)

• Optimised stepwise feature selection algorithm, and selection by criteria

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

# 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

toad.metrics.KS_bucket(pred_proba, final_data['target'], bucket=10, method = 'quantile')

• One-click scorecard transformation

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

• Tutorial

• 中文指引

• docs

Community

We welcome public feedback and new PRs. We hold a WeChat group for questions and suggestions.

Dedicated by The ESC Team