class_docstr: Select a subset of features.
  
  The ``Subset`` class acts as a transformer that reduces the feature set
  to a subset specified by the user.
  
  Parameters
  ----------
  subset : list
      list of columns indexes to select subset with. Indexes can
      either be of type ``str`` if data accepts slicing on a list of
      strings, otherwise the list should be of type ``int``.
class_docstr_tok: ["Select","a","subset","of","features",".","The","``","Subset","``","class","acts","as","a","transformer","that","reduces","the","feature","set","to","a","subset","specified","by","the","user",".","Parameters","--","--","--","--","--","subset",":","list","list","of","columns","indexes","to","select","subset","with",".","Indexes","can","either","be","of","type","``","str","``","if","data","accepts","slicing","on","a","list","of","strings",",","otherwise","the","list","should","be","of","type","``","int","``","."]
docstr: Learn what format the data is stored in.
  
  Parameters
  ----------
  X : array-like of shape = [n_samples, n_features]
      The whose type will be inferred.
  
  y : array-like of shape = [n_samples, n_features]
      pass-through for Scikit-learn pipeline compatibility.
docstr_tok: ["Learn","what","format","the","data","is","stored","in",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","=","[","n_samples",",","n_features","]","The","whose","type","will","be","inferred",".","y",":","array-like","of","shape","=","[","n_samples",",","n_features","]","pass-through","for","Scikit-learn","pipeline","compatibility","."]
code: def fit(self, X, y=None):
      """Learn what format the data is stored in.
  
      Parameters
      ----------
      X : array-like of shape = [n_samples, n_features]
          The whose type will be inferred.
  
      y : array-like of shape = [n_samples, n_features]
          pass-through for Scikit-learn pipeline compatibility.
      """
      self.is_df_ = X.__class__.__name__ in ["DataFrame", "Series"]
  
      if self.subset is not None:
          self.use_loc_ = any([isinstance(x, str) for x in self.subset])
  
      return self
  
file_path: mlens/mlens/preprocessing/preprocess.py
imports: from __future__ import division, print_function
  from ..externals.sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 2 for calling the Subset obj's underlying member method code with example usage: obj.fit(X, y) and returns: self