from sklearn.ensemble import IsolationForest

IsolationForest().fit()
IsolationForest().predict()
IsolationForest().decision_function()

def sigmoid(x):
    return 1.0/(1+np.exp(-x))

print(sigmoid(-3))
print(sigmoid(3))

我们来看predict文档：

    def predict(self, X):
        """
        Predict if a particular sample is an outlier or not.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csr_matrix``.

        Returns
        -------
        is_inlier : ndarray of shape (n_samples,)
            For each observation, tells whether or not (+1 or -1) it should
            be considered as an inlier according to the fitted model.
        """
        check_is_fitted(self)
        decision_func = self.decision_function(X)
        is_inlier = np.ones_like(decision_func, dtype=int)
        is_inlier[decision_func < 0] = -1
        return is_inlier

返回的是-1、1，显然-1位异常值，定位到源码is_inlier[decision_func < 0] = -1，结果很明显，分数越低，异常的概率越大，decision_function即返回异常分数的函数，sigmoid一下即可。