训练集和测试集是已经划分好了的，随机森林分类模型特征重要性程度排名前十的ice图怎么画python代码

在Python中，可以使用`sklearn`库中的`RandomForestClassifier`进行随机森林分类模型训练，并利用`eli5`库以及`matplotlib`库来绘制特征的重要性（例如通过`feature_importances_`属性）的Ice plot（也称为Permutation Importance）。以下是简单的步骤：

# 首先，确保安装了所需的库
!pip install eli5 scikit-learn matplotlib

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris  # 使用示例数据集
from sklearn.model_selection import train_test_split
from eli5.sklearn import PermutationImportance
import matplotlib.pyplot as plt

# 加载数据
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建随机森林分类器
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# 计算特征重要性
perm_importance = PermutationImportance(rf_model, scoring='accuracy', n_iter=5).fit(X_test, y_test)

# 获取排序后的特征重要性
sorted_idx = perm_importance.feature_importances_..argsort()

# 构建Ice Plot
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(sorted_idx, perm_importance.importances_mean[sorted_idx], 'o-', label="Mean importance")
ax.fill_between(
    sorted_idx,
    perm_importance.importances_mean[sorted_idx] - perm_importance.importances_std[sorted_idx],
    perm_importance.importances_mean[sorted_idx] + perm_importance.importances_std[sorted_idx],
    alpha=0.2,
    color="skyblue",
    edgecolor="none"
)
ax.set_ylabel("Feature Importance Score")
ax.set_xlabel("Feature Index")
ax.set_title("Top 10 Feature Importance in Random Forest Model (Iris Dataset)")
ax.set_xticks(sorted_idx)
ax.set_xticklabels(iris.feature_names[sorted_idx][:10])  # 只显示前10个特征名称
ax.legend(loc="best")
plt.show()