According to sklearn.pipeline.Pipeline documentation, the class whose instance is a pipeline element should implement fit() and transform(). I managed to create a custom class that has these methods and works fine with a single pipeline.

Now I want to use that Pipeline object as the estimator argument for GridSearchCV. The latter requires the custom class to have set_params() method, since I want to search over the range of custom instance parameters, as opposed to using a single instance of my custom class.

After I added set_params, I got an error message "set_params() takes 0 positional arguments but 1 was given". If anyone has done it, please post a successful example when a custom class works with both Pipeline and GridSearchCV. All I can find online are examples of classes that are a part of sklearn.

  • 1
    $\begingroup$ Generally you should just inherit from BaseEstimator to get set_params and get_params for free; the only caveat is that then your __init__ method's signature should contain precisely the attributes that get set in that method. Try stackoverflow.com/search?q=%5Bscikit-learn%5D+custom+class for some examples? $\endgroup$
    – Ben Reiniger
    May 16, 2022 at 14:10

1 Answer 1


Originally, I wanted to create a (SelectFromModel, LogisticRegression) pipeline. SelectFromModel object is constructed based on a pre-selected RandomForestClassifier. The problem is that GridSearchCV always calls fit() on each element in the pipeline. SFM object is already fitted and it's expensive to call fit() again. Besides, if SFM is constructed with prefit=True, calling fit() on it generates an error because the client can only call transform() in that case.

The solution (thanks to Ben) is as follows:

from sklearn.base import BaseEstimator
from sklearn.feature_selection import SelectFromModel

class WrapperSFM_22(BaseEstimator):    
    def __init__(self, max_features = None):
         # Note that the estimator arg refers to a global object.       
         self.sfm_object = SelectFromModel(estimator = best_rf_2.best_estimator_, max_features = max_features, 
                                           threshold = -np.inf, prefit = True)  
         # I was forced to add this by an error message.
         # Looks like one has to create an attribute for each such parameter.
         self.max_features = max_features
    def fit(self, X, y=None, **fit_params):
        # Do nothing
        return self
    def transform(self, X):      
        return self.sfm_object.transform(X)

    def set_params(self, **params):

    def get_params(self, deep=True):
        param_dict = self.sfm_object.get_params()
        result = {"max_features" : param_dict["max_features"]}
        return result

After that, one can use an instance of WrapperSFM_22 with GridSearchCV to search over max_features.

  • $\begingroup$ Although the solution I posted technically works with GridSearchCV, it doesn't look like GridSearchCV passes the max_features parameter properly. $\endgroup$
    – James
    May 16, 2022 at 21:54
  • $\begingroup$ I still use the new class, but for GridSearchCV I create a list of its instances, each element with a different max_features, and then include that list in the grid via "passthrough" trick. $\endgroup$
    – James
    May 17, 2022 at 17:47

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