pred_score = roc_auc_score(y_test,y_pred)

这段代码使用了roc_auc_score函数来计算模型的AUC值。其中y_test是测试集的真实标签，y_pred是模型对测试集的预测结果。roc_auc_score是用来计算ROC曲线下的面积，即AUC值。AUC值越大，说明模型的分类效果越好。因此，通过计算AUC值，我们可以对模型的性能进行评估。通常情况下，我们会使用多个指标来评估模型的性能，以便更全面地了解模型的表现。

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解释代码fpr, tpr, thresholds = roc_curve(y_test, y_pred) auc = roc_auc_score(y_test, y_pred)

这行代码是在 Python 中利用 scikit-learn 库中的 roc_curve() 和 roc_auc_score() 函数计算二分类模型的 ROC 曲线和 AUC 值。ROC 曲线是一种用于展示分类器性能的图形，横轴是假正例率（false positive rate，FPR），纵轴是真正例率（true positive rate，TPR），AUC（Area Under Curve）则是 ROC 曲线下的面积，是一个衡量分类器性能的指标。

在这个例子中，roc_curve() 函数的参数设置如下：
- y_test：测试集的真实标签值。
- y_pred：测试集上的预测概率值或者决策函数值。

roc_curve() 函数会计算出不同阈值下的 FPR 和 TPR 值，并返回一个包含三个数组的元组 (fpr, tpr, thresholds)，分别表示 FPR、TPR 和阈值的值。接着，roc_auc_score() 函数的参数也是 y_test 和 y_pred，用于计算 ROC 曲线下的面积 AUC。

通过计算 ROC 曲线和 AUC 值，我们可以评估二分类模型的性能，AUC 值越大，模型的分类性能越好。同时，ROC 曲线可以帮助我们选择合适的分类阈值，从而平衡分类器的精度和召回率。

将这段代码改为输出的AUC、f1_score、Accuracy是可重复的：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.001 dropout_rate = 0.1 # 定义模型结构 def create_model(): model = Sequential() model.add(Dense(64, input_dim=input_dim, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(32, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(1, activation='sigmoid')) optimizer = Adam(learning_rate=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # 5折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) cv_scores = [] for train_index, test_index in kf.split(X_train): # 划分训练集和验证集 X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index] y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index] # 创建模型 model = create_model() # 定义早停策略 #early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # 训练模型 model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size,verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc = roc_auc_score(y_val_fold, y_pred) cv_scores.append(auc) # 输出交叉验证结果 print('CV AUC:', np.mean(cv_scores)) # 在全量数据上重新训练模型 model = create_model() model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1) #测试集结果 test_pred = model.predict(X_test) test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred) test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred)) test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred)) print('Test AUC:', test_auc) print('Test F1 Score:', test_f1_score) print('Test Accuracy:', test_accuracy) #训练集结果 train_pred = model.predict(X_train) train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred) train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred)) train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred)) print('Train AUC:', train_auc) print('Train F1 Score:', train_f1_score) print('Train Accuracy:', train_accuracy)

将这段代码改为输出的AUC、f1_score、Accuracy是可重复的：

# 定义模型参数
input_dim = X_train.shape[1]
epochs = 100
batch_size = 32
learning_rate = 0.001
dropout_rate = 0.1

# 定义模型结构
def create_model():
    model = Sequential()
    model.add(Dense(64, input_dim=input_dim, activation='relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(1, activation='sigmoid'))
    optimizer = Adam(learning_rate=learning_rate)
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

# 5折交叉验证
kf = KFold(n_splits=5, shuffle=True, random_state=42)
cv_scores = []

for train_index, test_index in kf.split(X_train):
    # 划分训练集和验证集
    X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index]
    y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index]

    # 创建模型
    model = create_model()

    # 训练模型
    model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size, verbose=1)

    # 预测验证集
    y_pred = model.predict(X_val_fold)

    # 计算AUC指标
    auc = roc_auc_score(y_val_fold, y_pred)
    cv_scores.append(auc)

# 输出交叉验证结果
print('CV AUC:', np.mean(cv_scores))

# 在全量数据上重新训练模型
model = create_model()
model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1)

# 测试集结果
test_pred = model.predict(X_test)
test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred)
test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred))
test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred))

# 输出测试集结果
print('Test AUC:', test_auc)
print('Test F1 Score:', test_f1_score)
print('Test Accuracy:', test_accuracy)

# 训练集结果
train_pred = model.predict(X_train)
train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred)
train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred))
train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred))

# 输出训练集结果
print('Train AUC:', train_auc)
print('Train F1 Score:', train_f1_score)
print('Train Accuracy:', train_accuracy)