应用随机森林构建层次分析法的判断矩阵，请给出python代码

以下是使用Python实现随机森林构建层次分析法的判断矩阵的代码：

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import pairwise_distances
import numpy as np

# 构建判断矩阵
criteria_matrix = np.array([[1, 2, 3], [1/2, 1, 2], [1/3, 1/2, 1]])

# 计算权重向量
n = criteria_matrix.shape[0]
eig_val, eig_vec = np.linalg.eig(criteria_matrix)
max_eig_val = max(eig_val)
weight_vector = eig_vec[:, np.argmax(eig_val)].real
weight_vector /= np.sum(weight_vector)

# 构建随机森林
X = np.random.rand(100, n)
y = np.random.randint(0, 2, 100)
rf = RandomForestClassifier(n_estimators=100, max_depth=5)
rf.fit(X, y)

# 计算判断矩阵的相对重要性
distances = pairwise_distances(rf.apply(X), metric='hamming')
relative_importance = np.zeros((n, n))
for i in range(n):
    for j in range(i+1, n):
        relative_importance[i, j] = np.mean(y[distances[:, i] != distances[:, j]])
        relative_importance[j, i] = 1 / relative_importance[i, j]

# 计算最终权重向量
final_weight_vector = np.dot(weight_vector, relative_importance) / np.sum(np.dot(weight_vector, relative_importance))

print(final_weight_vector)

注意：这只是一个简单的示例代码，实际应用中需要根据具体情况进行调整和优化。