解释print('验证集AUC:{}'.format(roc_auc_score(y_test,y_pred1)))

这是一行 Python 代码，其中使用了字符串格式化函数 `format()` 和 Scikit-learn 库中的 `roc_auc_score` 函数。该行代码的作用是计算模型在测试集上的 AUC（Area Under the Curve）值，并将其打印出来。

具体来说，`roc_auc_score(y_test, y_pred1)` 函数计算了模型在测试集上的预测结果 `y_pred1` 对于真实标签 `y_test` 的 AUC 值。AUC 是一种衡量二分类模型性能的指标，其取值范围在 0.5 到 1 之间，越接近 1 表示模型性能越好。

`'验证集AUC:{}'` 是一个字符串，其中 `{}` 是一个占位符。`format()` 函数将 AUC 值填充到该占位符中，并打印出完整的字符串。

相关问题

Backtrace: ▆ 1. ├─pred_lm %>% roc_auc(truth = 是否发生, .pred_pass) 2. ├─yardstick::roc_auc(., truth = 是否发生, .pred_pass) 3. └─yardstick:::roc_auc.data.frame(., truth = 是否发生, .pred_pass) Run rlang::last_trace(drop = FALSE) to see 20 hidden frames.

这个Backtrace显示在运行`roc_auc()`函数时，代码中使用了`pred_lm`作为输入，但是在`roc_auc()`函数中没有正确指定`.pred_pass`列。这可能是因为在`pred_lm`中并没有包含`.pred_pass`列，或者是因为在调用`roc_auc()`函数时，没有正确指定`.pred_pass`列。

您可以尝试检查一下`pred_lm`数据框中是否包含`.pred_pass`列，并且检查一下是否正确指定了`.pred_pass`列。另外，您也可以尝试在`roc_auc()`函数中使用正确的列名。

修改这段代码，使得输出训练集结果是可重复的：# 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)

在每次训练时设置 NumPy 随机种子，这样可以保证每次训练的结果是可重复的。具体来说，可以在代码的开头加上以下两行：

import numpy as np
np.random.seed(42)

这样就可以保证每次训练时生成的随机数是一样的了。注意，这里设置的随机种子是 42，可以根据需要修改。修改后的代码如下：

import numpy as np
np.random.seed(42)

# 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)