No module named 'surprise.prediction_algorithms.asymmetric_algo'
时间: 2023-06-12 20:03:27 浏览: 76
这个错误可能是因为你使用了 `surprise` 库中的 `asymmetric_algo` 模块,但是该模块在最新版本的 `surprise` 库中已经被移除了。你可以尝试更新 `surprise` 库,或者改用其他的推荐算法模块。如果你需要使用 `asymmetric_algo` 模块,你可以安装旧版本的 `surprise` 库,如 `surprise==1.1.1`。你可以使用以下命令安装旧版本的 `surprise` 库:
```
pip install surprise==1.1.1
```
请注意,使用旧版本的库可能会带来一些安全和功能上的问题,因此请谨慎使用。
相关问题
多元回归中results.get_prediction怎么用
在多元回归中,`results.get_prediction()`函数用于根据模型预测给定的自变量值的因变量值。它返回一个包含预测值和置信区间的结果对象。
以下是使用`results.get_prediction()`进行预测的示例代码:
```python
import statsmodels.api as sm
# 加载数据
data = sm.datasets.get_rdataset("Guerry", "HistData").data
# 确定自变量和因变量
y = data['Lottery']
X = data[['Literacy', 'Wealth', 'Region']]
# 添加截距项
X = sm.add_constant(X)
# 拟合多元回归模型
model = sm.OLS(y, X).fit()
# 预测新的自变量值
new_X = [[1, 70, 20, 1]]
# 预测因变量值和置信区间
pred = model.get_prediction(new_X)
print(pred.summary_frame(alpha=0.05))
```
在上面的示例中,我们首先加载数据,确定自变量和因变量,然后拟合多元回归模型。接下来,我们创建一个包含新的自变量值的列表`new_X`,并使用`results.get_prediction()`函数来预测因变量值和置信区间。最后,我们使用`summary_frame()`函数将预测结果以表格形式输出。
pytorch with torch.no_grad() english
In PyTorch, `torch.no_grad()` is a context manager that temporarily disables gradient computation.
In deep learning, gradient computation is used to update the weights of a neural network during training. However, when we evaluate the performance of a model on a test set or use it for prediction, we don't need to compute gradients. Therefore, using `torch.no_grad()` can speed up the computation and reduce memory usage.
When we use `torch.no_grad()`, any computation performed inside the with block will not have gradients computed or stored. This means that we cannot update the weights of the model inside the block, but we can still use the model for evaluation or prediction.
For example, if we have a trained model and a test set, we can use `torch.no_grad()` to evaluate the model on the test set without computing gradients:
```
with torch.no_grad():
for input, target in test_set:
output = model(input)
loss = loss_fn(output, target)
total_loss += loss.item()
```
Note that `torch.no_grad()` is a context manager, so it should be used with the `with` statement. Once we exit the `with` block, gradient computation is enabled again.
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