cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(train_X.T, 3, 1.3, error=0.005, maxiter=1000, init=None) # 预测测试集 test_u, _, _, _, _, _= fuzz.cluster.cmeans_predict(test_X.T, cntr, 3, error=0.005, maxiter=1000) test_predictions = np.argmax(test_u, axis=0) # 计算 metrics test_accuracy = accuracy_score(test_y, test_predictions) test_auc = roc_auc_score(test_y, test_u.T,multi_class='ovo') # 打印结果 print('Accuracy:', test_accuracy) print('AUC:', test_auc)怎么用网格搜索法求得最优超参数
时间: 2023-12-13 16:03:05 浏览: 128
存储器映射和寄存器定义-详解pandas库pd.read_excel操作读取excel文件参数整理与实例
可以使用GridSearchCV来进行网格搜索法求得最优超参数。
首先,需要定义参数空间和模型对象。对于该代码段,可以将需要调整的参数和其对应的可能取值定义为一个字典,如下所示:
```
param_grid = {
'c': [1, 1.3, 1.6, 2],
'm': [1.2, 1.3, 1.5],
'error': [0.005, 0.01, 0.02],
'maxiter': [500, 1000, 1500]
}
model = fuzz.cluster.cmeans
```
然后,创建一个GridSearchCV对象,传入模型对象和参数空间,并指定评分指标(例如准确率)和交叉验证的折数(例如5折交叉验证):
```
from sklearn.model_selection import GridSearchCV
grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=5)
```
最后,使用训练数据拟合GridSearchCV对象,并输出最优参数和评分:
```
grid_search.fit(train_X.T)
print("Best parameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
```
完整代码如下所示:
```
import numpy as np
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import GridSearchCV
import skfuzzy as fuzz
# 数据准备
train_X = np.random.rand(10, 100)
train_y = np.random.randint(0, 3, 10)
test_X = np.random.rand(10, 100)
test_y = np.random.randint(0, 3, 10)
# 定义参数空间和模型对象
param_grid = {
'c': [1, 1.3, 1.6, 2],
'm': [1.2, 1.3, 1.5],
'error': [0.005, 0.01, 0.02],
'maxiter': [500, 1000, 1500]
}
model = fuzz.cluster.cmeans
# 创建GridSearchCV对象
grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=5)
# 拟合GridSearchCV对象
grid_search.fit(train_X.T)
# 输出最优参数和评分
print("Best parameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
```
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