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

I just want to post my solution for completeness, and maybe it is useful to one or the other:

class ColumnExtractor(object):

    def transform(self, X):
        cols = X[:,2:4] # column 3 and 4 are "extracted"
        return cols

    def fit(self, X, y=None):
        return self

Then, it can be used in the Pipeline like so:

clf = Pipeline(steps=[
    ('scaler', StandardScaler()),
    ('reduce_dim', ColumnExtractor()),           
    ('classification', GaussianNB())   
    ])

EDIT: General solution

And for a more general solution ,if you want to select and stack multiple columns, you can basically use the following Class as follows:

import numpy as np

class ColumnExtractor(object):

    def __init__(self, cols):
        self.cols = cols

    def transform(self, X):
        col_list = []
        for c in self.cols:
            col_list.append(X[:, c:c+1])
        return np.concatenate(col_list, axis=1)

    def fit(self, X, y=None):
        return self

    clf = Pipeline(steps=[
    ('scaler', StandardScaler()),
    ('dim_red', ColumnExtractor(cols=(1,3))),   # selects the second and 4th column      
    ('classification', GaussianNB())   
    ])