在没有范围技巧的情况下将额外的参数传递给GenericUnivariateSelect [英] passing an extra argument to GenericUnivariateSelect without scope tricks

问题描述

如果我应用答案中建议的make_scorer解决方法，这是完整的追溯...

here is the complete traceback if I apply the make_scorer workaround suggested in the answers...

`File "________python/anaconda-2.7.11-64/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 880, in runfile
    execfile(filename, namespace)

  File ""________python/anaconda-2.7.11-64/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
    builtins.execfile(filename, *where)

  File ""________/main_"________.py", line 43, in <module>
    "_________index.fit(X,Y ,g=g,L=L)

  File ""________/Core.py", line 95, in fit
    X_preprocessed=self.preprocessing.fit_transform(X,y)

  File ""________python/anaconda-2.7.11-64/lib/python2.7/site-packages/sklearn/pipeline.py", line 303, in fit_transform
    return last_step.fit_transform(Xt, y, **fit_params)

  File ""________/python/anaconda-2.7.11-64/lib/python2.7/site-packages/sklearn/base.py", line 497, in fit_transform
    return self.fit(X, y, **fit_params).transform(X)

  File "Base/Base.py", "________
    score_func_ret = self.score_func(X, y)

TypeError: __call__() takes at least 4 arguments (3 given)`

---

我正在研究sklearn管道.

---

I am working on a sklearn pipeline.

custom_filter=GenericUnivariateSelect(Custom_Score,mode='MinScore',param=0.9)   
custom_filter._selection_modes.update({'MinScore': SelectMinScore})
MyProcessingPipeline=Pipeline(steps=[...
                           ('filter_step', None),
                           ....])
ProcessingParams.update({'filter_step':custom_filter})
MyProcessingPipeline.set_params(**ProcessingParams)

其中SelectMinScore是自定义BaseFilter.

我需要基于Custom_Score执行单变量特征选择，必须必须接收一个额外的参数，在这里称为XX

I need to perform univariate feature selection based on a Custom_Score, which must receive an extra argument, called XX in here

def Custom_Score(X,Y,XX=_XX ):
      # do stuff
      return my_score

不幸的是，Akleik sklearn API不允许将额外的参数传递给管道步骤的参数.

Unfortunately, AFAIK the sklearn API does not allow extra arguments to be passed a parameter of a parameter of a step of a pipeline.

我尝试过

MyProcessingPipeline({'filter_step':custom_filter(XX=_XX)})

但这打破了传递级联的论点(我相信).

but that breaks argument passing cascade (I believe).

到目前为止，我已经通过编写包装器解决了这个问题，其中_XX是我需要的数据，不幸的是，在定义时，该数据必须在函数的范围内. 因此，我最终在main函数中定义了该函数，以便_XX存在并且可以传递它.

So far, I have solved this by writing a wrapper, where _XX is the data I need which unfortunately need to be in the scope of the function at definition time. So I have ended up defining the function within my main function so that _XX exists and it can be passed.

def Custom_Score_Wrapped(X,Y):
            return Custom_Score(X,Y,XX=_XX )

我认为这是一个非常肮脏的解决方法.

I think this is a really dirty workaround.

正确的方法是什么?

推荐答案

您可以在调用make_scorer()函数时简单地传递额外的争论. 例如，您选中此选项链接.在示例中，它使用了fbeta_score. 现在，fbeta需要一个附加参数beta，该参数是在像这样调用make_scorer()函数时设置的:

You can simply pass the extra arguement while calling the make_scorer() function. For example, you check this link. In the example it makes use of fbeta_score. Now fbeta requires an additional parameter, beta which is set while calling the make_scorer() function like this :

ftwo_scorer = make_scorer(fbeta_score, beta=2)

因此，在您的情况下，这应该可行:

So in your case, this should work:

def Custom_Score(X,Y,XX):
  # do stuff
  return my_score

my_scorer = make_scorer(Custom_Score,XX=_XX)

这篇关于在没有范围技巧的情况下将额外的参数传递给GenericUnivariateSelect的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！