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))
