ad5beccf2b74dc39314843e8a970a891cbd52267,pyod/models/base.py,BaseDetector,predict_proba,#BaseDetector#Any#Any#,103

Before Change

            pre_erf_score = (test_scores - self._mu) / (
                    self._sigma * np.sqrt(2))
            erf_score = erf(pre_erf_score)
            proba = erf_score.clip(0)
            return proba
        else:
            raise ValueError(method,
                             "is not a valid probability conversion method")

After Change

        test_scores = self.decision_function(X)

        probs = np.zeros([X.shape[0], int(self.classes_)])
        if method == "linear":
            scaler = MinMaxScaler().fit(train_scores.reshape(-1, 1))
            probs[:, 1] = scaler.transform(
                test_scores.reshape(-1, 1)).ravel().clip(0, 1)
            probs[:, 0] = 1 - probs[:, 1]
            return probs

        elif method == "unify":
            // turn output into probability
            pre_erf_score = (test_scores - self._mu) / (
                    self._sigma * np.sqrt(2))
            erf_score = erf(pre_erf_score)
            probs[:, 1] = erf_score.clip(0, 1).ravel()
            probs[:, 0] = 1 - probs[:, 1]
            return probs
        else:
            raise ValueError(method,
                             "is not a valid probability conversion method")

Instances