y_scores = y_pred # 将多分类标签转换为二分类标签 y_true = np.argmax(y_test, axis=0) # 计算FPR和TPR fpr, tpr, thresholds = roc_curve(y_true, y_scores) # 计算AUC roc_auc = auc(fpr, tpr)

这段代码中，`y_scores` 变量是用 `y_pred` 赋值的，`y_pred` 应该是模型的预测结果，这个结果可能是多分类标签，需要将其转换为二分类标签。`y_true` 变量是通过 `np.argmax(y_test, axis=0)` 计算得到的，用于计算 FPR 和 TPR。`roc_curve` 函数用于计算二分类问题的 ROC 曲线，需要传入真实标签和预测得分。在这段代码中，`y_true` 和 `y_scores` 应该都是一维的数组。

由于出现了错误信息中提到的未知数据格式的问题，可能是 `y_true` 或 `y_scores` 的数据格式不正确，可以先检查一下这两个变量的数据类型和形状是否正确。

相关问题

修改代码，使得输出结果是可重复的：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.01 dropout_rate = 0.7 # 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)

为了使输出结果可重复，可以在代码开头设置随机种子。修改后的代码如下：

import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score, f1_score, accuracy_score
from sklearn.model_selection import KFold
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# 设置随机种子
np.random.seed(42)

# 定义模型参数
input_dim = X_train.shape[1]
epochs = 100
batch_size = 32
learning_rate = 0.01
dropout_rate = 0.7

# 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() # 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)