如何在sklearn中实施前行测试？ [英] how to implement walk forward testing in sklearn?
问题描述
在sklearn中，GridSearchCV可以将管道作为参数，以通过交叉验证找到最佳估计量。但是，通常的交叉验证是这样的：
In sklearn, GridSearchCV can take a pipeline as a parameter to find the best estimator through cross validation. However, the usual cross validation is like this:
要交叉验证时间序列数据，训练和测试数据通常像这：
to cross validate a time series data, the training and testing data are often splitted like this:
也就是说，测试数据应始终在训练数据之前。
That is to say, the testing data should be always ahead of training data.
我的想法是：
-
编写我自己的k-fold版本类并将其传递给GridSearchCV，这样我就可以享受管道带来的便利。问题在于，让GridSearchCV使用指定的训练和测试数据索引似乎很困难。
Write my own version class of k-fold and passing it to GridSearchCV so I can enjoy the convenience of pipeline. The problem is that it seems difficult to let GridSearchCV to use an specified indices of training and testing data.
编写一个新类GridSearchWalkForwardTest，它是类似于GridSearchCV，我正在研究源代码grid_search.py，发现它有点复杂。
Write a new class GridSearchWalkForwardTest which is similar to GridSearchCV, I am studying the source code grid_search.py and find it is a little complicated.
任何建议欢迎。
推荐答案
我认为您可以使用时间序列拆分而不是您自己的实现，或者作为实现CV方法的基础，
I think you could use a Time Series Split either instead of your own implementation or as a basis for implementing a CV method which is exactly as you describe it.
经过一番挖掘，似乎有人在此PR 似乎可以满足您的要求。
After digging around a bit, it seems like someone added a max_train_size to the TimeSeriesSplit in this PR which seems like it does what you want.
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