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

__init__(pdo=60, rate=2, base_odds=35, base_score=750, card=None, combiner={}, transer=None, **kwargs)

Parameters:

• combiner (toad.Combiner) –
• transer (toad.WOETransformer) –

coef_

coef of LR model

fit(X, y)

Parameters:

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

predict(X, **kwargs)

predict score :param X: X to predict :type X: 2D-DataFrame|dict :param return_sub: if need to return sub score of each feature :type return_sub: Bool :param default: default sub score for unknown feature, min`(default), `max :type default: str|number

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

get_reason(X, base_effect=None, threshold_score=None, keep=3)

calculate top-effect-of-features as reasons

Parameters:

• X (2D DataFrame) – X to find reason
• base_effect (Series) – base effect score of each feature
• threshold_score (float) – threshold to find top k most important features, show the highest top k features when prediction score > threshold and show the lowest top k when prediction score <= threshold default is the sum of base_effect score
• keep (int) – top k most important reasons to keep, default 3

Returns:

top k most important reasons for each feature

Return type:

DataFrame

bin_to_score(bins, return_sub=False, default='min')

predict score from bins

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

woe_to_score(woe, weight=None)

calculate score by woe

after_load(rules)

after load card

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