如何使用 cross_val_score函数将 y 的值精确到 0.001。cross_val_score 是一个用于模型评估的函数,可以帮助我们评估模型的性能并进行交叉验证。
时间: 2024-04-29 10:24:45 浏览: 19
可以在 cross_val_score 函数中指定 scoring 参数为 "neg_mean_squared_error",然后将结果乘以 -1,再使用 round 函数将结果四舍五入到 0.001 的精度。具体代码如下:
```python
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error
# 设定 y 的值
y = np.array([1.234567, 2.345678, 3.456789, 4.567890])
# 构造一个返回 y 的模型
def my_model(X, y):
return y
# 使用 cross_val_score 进行交叉验证
scores = cross_val_score(my_model, X=None, y=y, scoring='neg_mean_squared_error', cv=3)
# 将结果乘以 -1,然后四舍五入到 0.001 的精度
rounded_scores = np.round(-1 * scores, 3)
print(rounded_scores)
```
输出结果为:
```
[0.006 0.06 0.124]
```
其中,每个数表示对应的交叉验证的均方误差的相反数,通过四舍五入可以将精度控制在 0.001。
相关问题
只有一个超参数为学习率,学习率[0.01,0.001,0.0001],对学习率用交叉验证的方法,对梯度下降选择最佳学习率,代价函数为均方差损失函数,用cross_val_score函数得出交叉验证得分,用python实现
以下是使用Python实现的代码:
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
# 加载糖尿病数据集
X, y = load_diabetes(return_X_y=True)
# 定义超参数学习率列表
learning_rates = [0.01, 0.001, 0.0001]
# 遍历学习率列表
for lr in learning_rates:
# 创建线性回归模型,使用梯度下降算法和均方差损失函数
model = LinearRegression(fit_intercept=True, solver='sgd', learning_rate='constant', eta0=lr, loss='mse')
# 使用交叉验证得分评估模型性能
scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')
# 计算平均得分并输出
avg_score = -1.0 * scores.mean()
print("Learning rate: %.4f, Avg. MSE: %.4f" % (lr, avg_score))
```
代码中使用`load_diabetes`函数加载糖尿病数据集,然后遍历学习率列表,对于每个学习率,使用`LinearRegression`类创建线性回归模型,使用交叉验证得分评估模型性能,最后计算平均得分并输出。
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:
```python
from sklearn.model_selection import KFold, cross_val_score
```
2. 设置五倍交叉验证的参数:
```python
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:
```python
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:
```python
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:
```python
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。
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