# 划分区间 dataset_test = off_test#dataset3的数据集范围即为要预测的7月份的线下测试集 feature_test = off_train[((off_train.date>='20160315')&(off_train.date<='20160630'))|((off_train.date=='null')&(off_train.date_received>='20160315')&(off_train.date_received<='20160630'))] dataset_validate = off_train[(off_train.date_received>='20160515')&(off_train.date_received<='20160615')] feature_validate = off_train[(off_train.date>='20160201')&(off_train.date<='20160514')|((off_train.date=='null')&(off_train.date_received>='20160201')&(off_train.date_received<='20160514'))] dataset_train = off_train[(off_train.date_received>='20160414')&(off_train.date_received<='20160514')] feature_train = off_train[(off_train.date>='20160101')&(off_train.date<='20160413')|((off_train.date=='null')&(off_train.date_received>='20160101')&(off_train.date_received<='20160413'))]出现报错# 划分区间 dataset_test = off_test#dataset3的数据集范围即为要预测的7月份的线下测试集 feature_test = off_train[((off_train.date>='20160315')&(off_train.date<='20160630'))|((off_train.date=='null')&(off_train.date_received>='20160315')&(off_train.date_received<='20160630'))] dataset_validate = off_train[(off_train.date_received>='20160515')&(off_train.date_received<='20160615')] feature_validate = off_train[(off_train.date>='20160201')&(off_train.date<='20160514')|((off_train.date=='null')&(off_train.date_received>='20160201')&(off_train.date_received<='20160514'))] dataset_train = off_train[(off_train.date_received>='20160414')&(off_train.date_received<='20160514')] feature_train = off_train[(off_train.date>='20160101')&(off_train.date<='20160413')|((off_train.date=='null')&(off_train.date_received>='20160101')&(off_train.date_received<='20160413'))]怎么解决
时间: 2024-04-23 13:22:51 浏览: 26
您提供的代码没有明确的报错信息,不过我猜测可能是因为变量名错误导致的。在代码中,`off_train` 和 `off_test` 可能是您之前定义的变量。如果您没有定义这些变量,就会出现错误。请确保您在运行这段代码之前已经定义了这些变量,并且变量名与您的代码一致。另外,请注意检查代码中的括号、引号等是否匹配。如果问题仍然存在,请提供完整的报错信息。
相关问题
''''划分线下数据集''' # 划分区间 dataset_test = off_test#dataset3的数据集范围即为要预测的7月份的线下测试集 feature_test = off_train[((off_train.date>='20160315')&(off_train.date<='20160630'))|((off_train.date=='null')&(off_train.date_received>='20160315')&(off_train.date_received<='20160630'))] dataset_validate = off_train[(off_train.date_received>='20160515')&(off_train.date_received<='20160615')] feature_validate = off_train[(off_train.date>='20160201')&(off_train.date<='20160514')|((off_train.date=='null')&(off_train.date_received>='20160201')&(off_train.date_received<='20160514'))] dataset_train = off_train[(off_train.date_received>='20160414')&(off_train.date_received<='20160514')] feature_train = off_train[(off_train.date>='20160101')&(off_train.date<='20160413')|((off_train.date=='null')&(off_train.date_received>='20160101')&(off_train.date_received<='20160413'))]出现报错'DataFrame' object has no attribute 'date'怎么办
这个报错是因为 `off_train` 这个 DataFrame 中不存在名为 `date` 的列,导致代码无法运行。建议您检查一下 `off_train` 中的列名称,确保 `date` 这一列名字是正确的。如果列名不正确,可以将代码中的 `date` 换成正确的列名。如果确实没有 `date` 这一列,就需要先对数据进行预处理,添加或修改相应的列。
# 记录误差 test_loss = ? #计算测试准确率 ? ? ? ?
在记录误差时,需要将模型在测试集上的损失计算出来并保存到变量test_loss中。具体代码如下:
```python
# 计算测试集上的损失
model.eval()
test_loss = 0
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item() * inputs.size(0)
# 计算平均测试损失
test_loss /= len(test_loader.dataset)
# 打印测试损失
print('Test Loss: {:.6f}\n'.format(test_loss))
```
在计算测试准确率时,可以使用与计算训练准确率时相同的方法,即将模型预测的类别与实际标签进行比较,并计算正确预测的样本数占总样本数的比例。具体代码如下:
```python
# 计算测试集上的准确率
model.eval()
test_acc = 0
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
test_acc += torch.sum(preds == labels.data)
# 计算测试准确率
test_acc = test_acc.double() / len(test_loader.dataset)
# 打印测试准确率
print('Test Accuracy: {:.2%}\n'.format(test_acc))
```