# 导入所需库
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import matplotlib.pyplot as plt

# 1. 加载数据集（这里使用鸢尾花数据集）
iris = load_iris()
X = iris.data
y = iris.target

# 2. 划分训练集和测试集（80%训练，20%测试）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. 创建随机森林分类器
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)

# 4. 训练模型
rf_classifier.fit(X_train, y_train)

# 5. 在测试集上进行预测
y_pred = rf_classifier.predict(X_test)

# 6. 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
print(f"模型准确率：{accuracy:.4f}")

# 输出详细的分类报告（精确率、召回率、F1分数等）
print("分类报告：")
print(classification_report(y_test, y_pred))

# 7. 可视化特征重要性（可选）
feature_importances = rf_classifier.feature_importances_

plt.figure(figsize=(10, 6))
plt.barh(iris.feature_names, feature_importances)
plt.xlabel("feature importance")
plt.title("random forest model feature importance")
plt.savefig('./output/random_forest_c.png')