def predictProb(self, message):
        if (self._localClassifier is None):
            return self._globalClassifier.predictProba(message)
        else:
            #--- compute interpolated decision value
            globalDecisionFunction = self._globalClassifier.predictScore(
                message)['pos']
            localDecisionFunction = self._localClassifier.predictScore(
                message)['pos']
            df = (1 - self._alpha) * globalDecisionFunction + \
                self._alpha * localDecisionFunction
            prb = platt.SigmoidPredict(df, self._plattModel)
            probaDic = {}
            probaDic['pos'] = prb
            probaDic['neg'] = 1 - prb
            return probaDic
dataset/ETHPy150Open dssg/ushine-learning/dssg/classifier.py/DssgBinaryClassifierAdaptiveInterpolation.predictProb/original