是否可以在 sklearn 管道中切换某个步骤? [英] Is it possible to toggle a certain step in sklearn pipeline?

问题描述

我想知道我们是否可以在 sklearn.pipeline 中设置一个可选"步骤.例如，对于分类问题，我可能想尝试使用 ExtraTreesClassifier 和 AND 而没有在它之前进行 PCA 转换.在实践中，它可能是一个带有额外参数的管道，用于指定 PCA 步骤的切换，以便我可以通过 GridSearch 等对其进行优化.我不在 sklearn 源代码中看到这样的实现，但有什么解决方法吗?

I wonder if we can set up an "optional" step in sklearn.pipeline. For example, for a classification problem, I may want to try an ExtraTreesClassifier with AND without a PCA transformation ahead of it. In practice, it might be a pipeline with an extra parameter specifying the toggle of the PCA step, so that I can optimize on it via GridSearch and etc. I don't see such an implementation in sklearn source, but is there any work-around?

此外，由于管道中后续步骤的可能参数值可能取决于前一步中的参数(例如，ExtraTreesClassifier.max_features 的有效值取决于 PCA.n_components)，是否可以在 sklearn.pipeline 和 sklearn.grid_search 中指定这样的条件依赖?

Furthermore, since the possible parameter values of a following step in pipeline might depend on the parameters in a previous step (e.g., valid values of ExtraTreesClassifier.max_features depend on PCA.n_components), is it possible to specify such a conditional dependency in sklearn.pipeline and sklearn.grid_search?

谢谢！

推荐答案

• Pipeline 步骤目前无法在网格搜索中成为可选步骤，但您可以将 PCA 类包装到您自己的 OptionalPCA 组件中使用布尔参数在请求时关闭 PCA 作为快速解决方法.您可能想查看 hyperopt 以设置更复杂的搜索空间.我认为默认情况下它具有很好的 sklearn 集成来支持这种模式，但我再也找不到文档了.也许看看这个演讲.

  • Pipeline steps cannot currently be made optional in a grid search but you could wrap the PCA class into your own OptionalPCA component with a boolean parameter to turn off PCA when requested as a quick workaround. You might want to have a look at hyperopt to setup more complex search spaces. I think it has good sklearn integration to support this kind of patterns by default but I cannot find the doc anymore. Maybe have a look at this talk.

    对于依赖参数问题，GridSearchCV 支持参数树来处理这种情况如文档中所示.

    For the dependent parameters problem, GridSearchCV supports trees of parameters to handle this case as demonstrated in the documentation.

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