如何在循环中将字典形式的超参数传递给RandomForestRegressor

hyperparams = [{
    'n_estimators': 460,
    'bootstrap': False,
    'criterion': 'poisson',
    'max_depth': 60,
    'max_features': 2,
    'min_samples_leaf': 1,
    'min_samples_split': 2
},
{
    'n_estimators': 60,
    'bootstrap': False,
    'criterion': 'friedman_mse',
    'max_depth': 90,
    'max_features': 3,
    'min_samples_leaf': 1,
    'min_samples_split': 2
}]

for hparams in hyperparams:
    # 错误示例：直接传递字典
    # model_regressor = RandomForestRegressor(hparams)
    # ... 后续代码

sklearn.utils._param_validation.InvalidParameterError: The 'n_estimators' parameter of RandomForestRegressor must be an int in the range [1, inf). Got {'n_estimators': 460, 'bootstrap': False, 'criterion': 'poisson', 'max_depth': 60, 'max_features': 2, 'min_samples_leaf': 1, 'min_samples_split': 2} instead.

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

# 假设有X_train, y_train数据
# 为了示例完整性，创建一些虚拟数据
X = np.random.rand(100, 5)
y = np.random.rand(100) * 100
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

hyperparams = [{
    'n_estimators': 460,
    'bootstrap': False,
    'criterion': 'poisson',
    'max_depth': 60,
    'max_features': 2,
    'min_samples_leaf': 1,
    'min_samples_split': 2,
    'random_state': 42 # 添加random_state以确保结果可复现
},
{
    'n_estimators': 60,
    'bootstrap': False,
    'criterion': 'friedman_mse',
    'max_depth': 90,
    'max_features': 3,
    'min_samples_leaf': 1,
    'min_samples_split': 2,
    'random_state': 42
}]

print("开始模型训练和评估...")

for i, hparams in enumerate(hyperparams):
    print(f"\n--- 正在使用第 {i+1} 组超参数进行训练 ---")
    print(f"超参数: {hparams}")

    # 正确做法：使用字典解包运算符 **
    model_regressor = RandomForestRegressor(**hparams)

    # 验证模型参数是否正确设置
    print("模型初始化参数:", model_regressor.get_params())

    total_r2_score_value = 0
    total_mean_squared_error_value = 0 # 修正变量名

    total_tests = 5 # 减少循环次数以便快速运行示例
    for index in range(1, total_tests + 1):
        print(f"  - 训练轮次 {index}/{total_tests}")
        # 模型拟合
        model_regressor.fit(X_train, y_train)

        # 进行预测
        y_pred = model_regressor.predict(X_test)

        # 计算评估指标
        r2 = r2_score(y_test, y_pred)
        mse = mean_squared_error(y_test, y_pred)

        total_r2_score_value += r2
        total_mean_squared_error_value += mse

        print(f"    R2 Score: {r2:.4f}, Mean Squared Error: {mse:.4f}")

    # 计算平均评估指标
    avg_r2 = total_r2_score_value / total_tests
    avg_mse = total_mean_squared_error_value / total_tests

    print(f"\n第 {i+1} 组超参数平均结果:")
    print(f"  平均 R2 Score: {avg_r2:.4f}")
    print(f"  平均 Mean Squared Error: {avg_mse:.4f}")

print("\n所有超参数组合评估完成。")