sklearn passthrough feature selector - python-3.x

Question

I am working with a sklearn pipeline and would like to have a feature selection step that could possibly be set to no feature selection. Is there a sklearn.feature_selection.SelectorMixin object that does nothing?
EDIT: or is there at least a template to develop one, like there can be an estimator?

Answer 1

End of the day I went with something like this which seems to fit my current purpose. Not sure if it would be validated as a proper sklearn.feature_selection selector though:
import numpy as np
import pandas as pd
from sklearn.utils.validation import check_is_fitted
class PassThroughSelector():
"""
Simply selects all columns of the dataframe, allowing
to have the equivalent of no selector without changing
too much of the structure.
Args:
"""
def __init__(self):
pass
def fit(self, x, y=None): # pylint:disable=unused-argument, arguments-differ
"""
Stores a list of selected columns.
Args:
x: training data
y: training y (no effect)
Returns:
self
"""
self.check_x(x)
mask = np.where(np.ones(len(x.columns)), True, False)
self.support_ = mask
self.selected_features_ = x.columns[self.support_].tolist()
return self
def get_support(self, indices = False) -> np.ndarray:
"""Provides a boolean mask of the selected features."""
check_is_fitted(self)
if indices == True:
return np.array([i for i in range(len(self.support_)) if self.support_[i]==True])
else:
return self.support_
def transform(self, x: pd.DataFrame):
"""Selects the features selected in `fit` from a provided dataframe."""
check_is_fitted(self)
self.check_x(x)
return x.loc[:, self.support_]
def get_params(self, deep = True):
return {}