使用python计算DT，SVM，GBDT和stacking模型的综合评分并画出诺莫图

以下是使用Python计算DT，SVM，GBDT和stacking模型的综合评分和绘制诺莫图的示例代码：

import numpy as np
import matplotlib.pyplot as plt

# DT、SVM、GBDT和stacking模型的评分
dt_score = 0.8
svm_score = 0.9
gbdt_score = 0.75
stacking_score = 0.95

# 计算评分矩阵
score_matrix = np.array([[0, (dt_score - svm_score) / 2, (dt_score - gbdt_score) / 2, (dt_score - stacking_score) / 2],
                         [(svm_score - dt_score) / 2, 0, (svm_score - gbdt_score) / 2, (svm_score - stacking_score) / 2],
                         [(gbdt_score - dt_score) / 2, (gbdt_score - svm_score) / 2, 0, (gbdt_score - stacking_score) / 2],
                         [(stacking_score - dt_score) / 2, (stacking_score - svm_score) / 2, (stacking_score - gbdt_score) / 2, 0]])

# 计算每个模型的得分
model_ranks = np.sum(score_matrix, axis=1)

# 计算每个模型的位置
model_positions = np.zeros((4, 2))
for i in range(4):
    angle = i * 2 * np.pi / 4
    model_positions[i][0] = np.cos(angle) * model_ranks[i]
    model_positions[i][1] = np.sin(angle) * model_ranks[i]

# 绘制诺莫图
fig, ax = plt.subplots()
ax.plot(model_positions[:, 0], model_positions[:, 1], 'o-')
ax.plot([model_positions[0][0], model_positions[1][0]], [model_positions[0][1], model_positions[1][1]], 'r-')
ax.plot([model_positions[0][0], model_positions[2][0]], [model_positions[0][1], model_positions[2][1]], 'r-')
ax.plot([model_positions[0][0], model_positions[3][0]], [model_positions[0][1], model_positions[3][1]], 'r-')
ax.plot([model_positions[1][0], model_positions[2][0]], [model_positions[1][1], model_positions[2][1]], 'r-')
ax.plot([model_positions[1][0], model_positions[3][0]], [model_positions[1][1], model_positions[3][1]], 'r-')
ax.plot([model_positions[2][0], model_positions[3][0]], [model_positions[2][1], model_positions[3][1]], 'r-')
for i, model in enumerate(['DT', 'SVM', 'GBDT', 'stacking']):
    ax.text(model_positions[i][0], model_positions[i][1], model)
plt.show()

该代码将生成一个诺莫图，其中每个模型都被表示为一个点，并按其综合评分进行排序和排列。点之间的线表示模型之间的差异，点的位置表示模型的综合评分和排名。