解释print('验证集AUC:{}'.format(roc_auc_score(y_test,y_pred1)))
时间: 2024-06-09 21:10:36 浏览: 8
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
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。
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:
```python
# 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)
```
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