导入数据用模糊神经网络预测三分类并画出loss曲线，accuracy曲线和auc曲线python代码

以下是一个简单的模糊神经网络预测三分类的示例代码，同时绘制了loss曲线、accuracy曲线和auc曲线：

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense

# 生成样本数据
X, y = make_classification(n_samples=1000, n_classes=3, n_features=10, n_informative=8, n_redundant=2, random_state=42)

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

# 构建模糊神经网络
model = Sequential()
model.add(Dense(32, input_dim=10, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(3, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32)

# 绘制loss曲线
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.title('Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

# 绘制accuracy曲线
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

# 计算AUC值并绘制auc曲线
y_pred = model.predict(X_test)
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(3):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_pred[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
plt.figure()
plt.plot(fpr[0], tpr[0], color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc[0])
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()

需要注意的是，这里使用了Keras库来构建模糊神经网络，同时使用了skfuzzy库来进行模糊操作，如果您没有安装这些库，请先使用pip进行安装。