toad.scorecard module

class toad.scorecard.ScoreCard(pdo=60, rate=2, base_odds=35, base_score=750, card=None, combiner={}, transer=None, **kwargs)

Bases: sklearn.base.BaseEstimator, toad.utils.mixin.RulesMixin, toad.utils.mixin.BinsMixin

coef_

coef of LR model

intercept_

n_features_

features_

combiner

fit(X, y)

Parameters:

• X (2D DataFrame) –
• Y (array-like) –

predict(X, **kwargs)

predict score :param X: X to predict :type X: 2D array-like :param return_sub: if need to return sub score, default False :type return_sub: bool

Returns:predicted score DataFrame: sub score for each feature Return type:array-like

predict_proba(X)

predict probability

Parameters:X (2D array-like) – X to predict Returns:probability of all classes Return type:2d array

proba_to_score(prob)

covert probability to score

odds = (1 - prob) / prob score = factor * log(odds) * offset

score_to_proba(score)

covert score to probability

Returns:the probability of 1 Return type:array-like|float

bin_to_score(bins, return_sub=False)

predict score from bins

woe_to_score(woe, weight=None)

calculate score by woe

after_export(card, to_frame=False, to_json=None, to_csv=None, **kwargs)

generate a scorecard object

Parameters:

• to_frame (bool) – return DataFrame of card
• to_json (str|IOBase) – io to write json file
• to_csv (filepath|IOBase) – file to write csv

Returns:

dict

testing_frame(**kwargs)

get testing frame with score

Returns:testing frame with score Return type:DataFrame