pycaret中setup(data=x_train, test_data=x_test, target='label', fold=7)的fold参数
时间: 2024-03-04 07:48:38 浏览: 22
在 PyCaret 中,setup 函数的 fold 参数用于指定交叉验证的折数。交叉验证是一种评估模型泛化性能的技术,它将数据集分成 K 个互不重叠的子集,其中 K 就是交叉验证的折数。在每一轮交叉验证中,其中的一个子集被用作测试集,其余的子集被用作训练集。通过多轮交叉验证,可以获得模型的平均性能指标,从而更好地评估模型的泛化能力。在 PyCaret 中,默认的交叉验证折数是 10,可以通过设置 setup 函数的 fold 参数来修改折数。在给定的代码中,fold 参数设置为 7,即进行 7 折交叉验证。
相关问题
pycaret中setup(data=x_train, test_data=x_test, target='label', fold=7)的target参数能设置多个吗
在 PyCaret 中,setup 函数的 target 参数用于指定数据集中的目标变量(即标签)。目标变量是需要进行预测的变量,因此在建模和评估中都会用到它。在默认情况下,target 参数只能指定一个目标变量。如果数据集中有多个目标变量需要预测,可以将其拆分成多个单独的数据集,并对每个数据集分别进行建模和评估。另外,如果需要同时预测多个目标变量,可以使用多目标学习或多输出预测等技术,这超出了 PyCaret 的范畴。因此,总的来说,PyCaret 的 target 参数只能设置一个目标变量。
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)
```