loss='mean_squared_error'如何改为RMSE

在Keras中，可以使用`Root Mean Squared Error (RMSE)`作为损失函数，可以通过将`mean_squared_error`替换为`mean_squared_error`的平方根来实现。

具体代码如下：

from keras.metrics import RootMeanSquaredError
model.compile(loss='mean_squared_error', optimizer='adam', metrics=[RootMeanSquaredError()])

在模型编译时，将损失函数设为`mean_squared_error`，并将指标(metrics)设为`RootMeanSquaredError()`，即可将损失函数改为RMSE。

相关问题

请调试这段代码以实现梯度上升模型进行波士顿房价预测，画出预测图，代码如下：import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error plt.style.use('ggplot') # 读入数据 data = pd.read_csv('D:\PycharmProjects\Boston_housing\housing.csv') X, y = data[data.columns.delete(-1)], data['MEDV'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=888) from sklearn.preprocessing import StandardScaler ss_x = StandardScaler() X_train = ss_x.fit_transform(X_train) X_test = ss_x.transform(X_test) ss_y = StandardScaler() y_train = ss_y.fit_transform(y_train.values.reshape(-1, 1)) y_test = ss_y.transform(y_test.values.reshape(-1, 1)) from sklearn import ensemble # params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,'learning_rate': 0.01, 'loss': 'ls'} # clf = ensemble.GradientBoostingRegressor(**params) clf = ensemble.GradientBoostingRegressor() clf.fit(X_train, y_train) clf_pre_values = clf.predict(X_test) # 预测值 print('SCORE:{:.4f}'.format(clf.score(X_test, y_test))) # 模型评分 print('RMSE:{:.4f}'.format(np.sqrt(mean_squared_error(y_test, clf_pre_values)))) # RMSE(标准误差) clf_pre = pd.DataFrame() clf_pre['Predict'] = clf_pre_values clf_pre['Truth'] = y_test.reset_index(drop=True) clf_pre.plot(figsize=(18, 8)) plt.figure() plt.show()

根据您提供的代码，我注意到您的代码中没有明确指定数据文件的路径。请确保`housing.csv`文件与您的代码在同一个文件夹中，并将文件的路径正确指定为相对路径或绝对路径。

另外，您的代码中没有导入`matplotlib.pyplot`模块。请在代码的开头添加以下导入语句：

import matplotlib.pyplot as plt

这样才能正确使用`plt.figure()`和`plt.show()`函数。

请尝试调试代码并确保数据文件路径正确，导入所需的模块，并重新运行代码。希望能帮到您！如果还有其他问题，请随时提问。

以下这段代码是关于CatBoost模型的超参数调整，但里面好像不是在五倍交叉验证下做的分析，请问应该怎么加上五倍交叉验证呢？import os import time import pandas as pd from catboost import CatBoostRegressor from hyperopt import fmin, hp, partial, Trials, tpe,rand from sklearn.metrics import r2_score, mean_squared_error from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold, cross_val_score as CVS, train_test_split as TTS 自定义hyperopt的参数空间 space = {"iterations": hp.choice("iterations", range(1, 30)), "depth": hp.randint("depth", 16), "l2_leaf_reg": hp.randint("l2_leaf_reg", 222), "border_count": hp.randint("border_count", 222), 'learning_rate': hp.uniform('learning_rate', 0.001, 0.9), } data = pd.read_csv(r"E:\exercise\synthesis\synthesis_dummy_2.csv") #验证随机森林填补缺失值方法是否有效 X = data.iloc[:,1:] y = data.iloc[:,0] Xtrain,Xtest,Ytrain,Ytest = TTS(X_wrapper,y,test_size=0.2,random_state=100) def epoch_time(start_time, end_time): elapsed_secs = end_time - start_time elapsed_mins = elapsed_secs / 60 return elapsed_mins, elapsed_secs 自动化调参并训练 def cat_factory(argsDict): estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22, learning_rate=argsDict['learning_rate'], iterations=argsDict['iterations'], l2_leaf_reg=argsDict['l2_leaf_reg'], border_count=argsDict['border_count'], depth=argsDict['depth'], verbose=0) estimator.fit(Xtrain, Ytrain) val_pred = estimator.predict(Xtest) mse = mean_squared_error(Ytest, val_pred) return mse

要在这段代码中添加五倍交叉验证，你可以按照以下步骤进行修改：

1. 导入所需的包：

from sklearn.model_selection import KFold, cross_val_score

2. 设置五倍交叉验证的参数：

n_folds = 5

3. 修改数据划分部分，将数据划分为训练集和测试集：

Xtrain, Xtest, Ytrain, Ytest = train_test_split(X_wrapper, y, test_size=0.2, random_state=100)

4. 修改自动化调参并训练的函数，使用五倍交叉验证进行模型评估：

def cat_factory(argsDict):
    estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22,
                                  learning_rate=argsDict['learning_rate'],
                                  iterations=argsDict['iterations'],
                                  l2_leaf_reg=argsDict['l2_leaf_reg'],
                                  border_count=argsDict['border_count'],
                                  depth=argsDict['depth'], verbose=0)
    
    # 设置五倍交叉验证
    kf = KFold(n_splits=n_folds)
    mse_scores = []
    
    for train_index, val_index in kf.split(Xtrain):
        X_train, X_val = Xtrain.iloc[train_index], Xtrain.iloc[val_index]
        Y_train, Y_val = Ytrain.iloc[train_index], Ytrain.iloc[val_index]
        
        estimator.fit(X_train, Y_train)
        val_pred = estimator.predict(X_val)
        mse = mean_squared_error(Y_val, val_pred)
        mse_scores.append(mse)
    
    mse_mean = np.mean(mse_scores)
    return mse_mean

5. 修改自定义hyperopt的参数空间部分，将模型评估函数替换为修改后的函数：

space = {"iterations": hp.choice("iterations", range(1, 30)),
         "depth": hp.randint("depth", 16),
         "l2_leaf_reg": hp.randint("l2_leaf_reg", 222),
         "border_count": hp.randint("border_count", 222),
         'learning_rate': hp.uniform('learning_rate', 0.001, 0.9),
         }

trials = Trials()
best = fmin(fn=cat_factory, space=space, algo=tpe.suggest, max_evals=100, trials=trials)

这样，你就在代码中添加了五倍交叉验证来评估CatBoost模型的超参数调整效果。