用pytorch写一个孤立森林代码

好的，以下是用PyTorch实现孤立森林的代码：

import torch
import torch.nn.functional as F

class IsolationTreeEnsemble:
    def __init__(self, num_trees=10, max_depth=10, subsampling_size=256):
        self.num_trees = num_trees
        self.max_depth = max_depth
        self.subsampling_size = subsampling_size
        self.trees = []

    def fit(self, X):
        for i in range(self.num_trees):
            X_subsampled = X[torch.randint(len(X), (self.subsampling_size,))]
            tree = IsolationTree(self.max_depth)
            tree.fit(X_subsampled)
            self.trees.append(tree)

    def path_length(self, X):
        S = torch.zeros(len(X))
        for tree in self.trees:
            S += tree.path_length(X).squeeze()
        return S / self.num_trees

    def anomaly_score(self, X):
        h = self.path_length(X)
        return torch.exp(-h / torch.mean(h))

class IsolationTree:
    def __init__(self, max_depth=10):
        self.max_depth = max_depth

    def fit(self, X, depth=0):
        if depth >= self.max_depth or len(X) <= 1:
            return IsolationNode(size=len(X))

        q = torch.randint(X.shape[1], (1,)).item()
        split_value = torch.FloatTensor([X[:, q].mean()]).item()

        left_idx = X[:, q] < split_value
        right_idx = X[:, q] >= split_value

        left_tree = self.fit(X[left_idx], depth + 1)
        right_tree = self.fit(X[right_idx], depth + 1)

        return IsolationNode(q=q, split_value=split_value, left_node=left_tree, right_node=right_tree)

    def path_length(self, X):
        H = torch.zeros(len(X))
        for i in range(len(X)):
            H[i] = self.traverse(X[i], 0)
        return H

    def traverse(self, x, depth):
        if isinstance(self, IsolationNode):
            if depth >= self.max_depth:
                return depth + c(self.size)
            elif x[self.q] < self.split_value:
                return self.left_node.traverse(x, depth + 1)
            else:
                return self.right_node.traverse(x, depth + 1)
        else:
            return depth + c(len(self.X))

class IsolationNode:
    def __init__(self, q=None, split_value=None, left_node=None, right_node=None, size=None):
        self.q = q
        self.split_value = split_value
        self.left_node = left_node
        self.right_node = right_node
        self.size = size

def c(size):
    if size > 2:
        return 2 * (np.log(size - 1) + 0.5772156649) - (2 * (size - 1) / size)
    elif size == 2:
        return 1
    else:
        return 0

使用方法：

ensemble = IsolationTreeEnsemble(num_trees=10, max_depth=10, subsampling_size=256)
ensemble.fit(X_train)

scores = ensemble.anomaly_score(X_test)

其中`X_train`是训练数据，`X_test`是测试数据，`scores`是测试数据的异常得分。