docstr: Restrict the features to those in support using feature selection.
  
  This function modifies the estimator in-place.
docstr_tok: ["Restrict","the","features","to","those","in","support","using","feature","selection",".","This","function","modifies","the","estimator","in-place","."]
code: def restrict(self, support):
      """Restrict the features to those in support using feature selection.
  
      This function modifies the estimator in-place.
  
      """
      if self.has_been_restricted == True:
          return self
  
      new_numerical_cols = []
      new_categorical_cols = []
      new_additional_numerical_cols = []
      new_feature_names = []
      new_vocab = {}
  
      for idx, val in enumerate(support):
          if val == True:
              feature_name = self.feature_names_[idx]
              if self.separator in feature_name:
                  base_feature_name = feature_name[
                      : feature_name.rfind(self.separator)
                  ]
              else:
                  base_feature_name = feature_name
              new_feature_names.append(feature_name)
              new_vocab[feature_name] = len(new_vocab)
              if feature_name in self.numerical_columns:
                  new_numerical_cols.append(feature_name)
              elif (
                  base_feature_name in self.categorical_columns
                  and base_feature_name not in new_categorical_cols
              ):
                  new_categorical_cols.append(base_feature_name)
              elif feature_name in self.additional_numerical_cols:
                  new_additional_numerical_cols.append(feature_name)
  
      self.feature_names_ = new_feature_names
      self.vocabulary_ = new_vocab
      self.numerical_columns = new_numerical_cols
      self.categorical_columns = new_categorical_cols
      self.additional_numerical_cols = new_additional_numerical_cols
  
      self.has_been_restricted = True
      return self
  
imports: from array import array
  import numbers
  from operator import itemgetter
  import numpy
  import pandas
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.externals import six
  from auto_ml.utils import CustomLabelEncoder
label_desc: Use image node_id 9 for calling the DataFrameVectorizer obj's underlying member method code with example usage: obj.restrict(support) and returns: self, self