How can I use a custom feature selection function in scikit-learn's `pipeline`

Question

Let\'s say that I want to compare different dimensionality reduction approaches for a particular (supervised) dataset that consists of n>2 features via cross-validation and by u

Answer 1

You can use the following custom transformer to select the columns specified:

#Custom Transformer that extracts columns passed as an argument to its constructor

class FeatureSelector( BaseEstimator, TransformerMixin ):

    #Class Constructor 
    def __init__( self, feature_names ):
        self._feature_names = feature_names 

    #Return self nothing else to do here    
    def fit( self, X, y = None ):
        return self 

    #Method that describes what we need this transformer to do
    def transform( self, X, y = None ):
        return X[ self._feature_names ]`

Here feature_names is the list of features which you want to select For more details, you can refer to this link [1]: https://towardsdatascience.com/custom-transformers-and-ml-data-pipelines-with-python-20ea2a7adb65