def staged_predict_proba(self, X): X = self.get_train_vars(X) for scores in zip(*[clf._uboost_staged_predict_score(X) for clf in self.classifiers]): yield commonutils.score_to_proba(sum(scores) / self.efficiency_steps)
def predict_proba(self, X): X = self.get_train_vars(X) score = sum(clf._uboost_predict_score(X) for clf in self.classifiers) return commonutils.score_to_proba(score / self.efficiency_steps)
def staged_predict_proba(self, X): for score in self.staged_predict_score(X): yield commonutils.score_to_proba(score)
def predict_proba(self, X): return commonutils.score_to_proba(self.predict_score(X))
def staged_predict_proba(self, X, step=100): for score in self.staged_decision_function(X, step=step): yield score_to_proba(score)
def predict_proba(self, X): return score_to_proba(self.decision_function(X))