As mentioned in #3 we have 3 different scoring methods: decision_function, rules_vote, score_top_rules.
In sklearn API, we have decision_function and score_samples, which are related through decision_function = score_samples + offset, offset being defined in a way that predict = decision_function > 0.
Maybe we should add a class parameter to chose one of these 3 functions at initialization, and return the chosen function in a method called score_samples (from which we can define decision_function) ?
The 3 actual scoring functions could be renamed e.g. _score_mean, _score_vote, _score_max and kept private?
As mentioned in #3 we have 3 different scoring methods:
decision_function,rules_vote,score_top_rules.In sklearn API, we have
decision_functionandscore_samples, which are related throughdecision_function = score_samples + offset, offset being defined in a way thatpredict = decision_function > 0.Maybe we should add a class parameter to chose one of these 3 functions at initialization, and return the chosen function in a method called
score_samples(from which we can definedecision_function) ?The 3 actual scoring functions could be renamed e.g.
_score_mean,_score_vote,_score_maxand kept private?Any other suggestions?