parameters: (self, input_features=None)
return_statement: return np.array(feature_names, dtype=object)
docstring: Return feature names for output features.
  
          Parameters
          ----------
          input_features : list of str of shape (n_features,)
              String names for input features if available. By default,
              "x0", "x1", ... "xn_features" is used.
  
          Returns
          -------
          output_feature_names : ndarray of shape (n_output_features,)
              Array of feature names.
docstring_summary: Return feature names for output features.
docstring_tokens: Return feature names for output features .
function: def get_feature_names(self, input_features=None):
          """
          Return feature names for output features.
  
          Parameters
          ----------
          input_features : list of str of shape (n_features,)
              String names for input features if available. By default,
              "x0", "x1", ... "xn_features" is used.
  
          Returns
          -------
          output_feature_names : ndarray of shape (n_output_features,)
              Array of feature names.
          """
          check_is_fitted(self)
          cats = self.categories_
          if input_features is None:
              input_features = ['x%d' % i for i in range(len(cats))]
          elif len(input_features) != len(self.categories_):
              raise ValueError(
                  "input_features should have length equal to number of "
                  "features ({}), got {}".format(len(self.categories_),
                                                 len(input_features)))
  
          feature_names = []
          for i in range(len(cats)):
              names = [
                  input_features[i] + '_' + str(t) for t in cats[i]]
              if self.drop is not None:
                  names.pop(self.drop_idx_[i])
              feature_names.extend(names)
  
          return np.array(feature_names, dtype=object)
function_tokens: def get_feature_names ( self , input_features = None ) : check_is_fitted ( self ) cats = self . categories_ if input_features is None : input_features = [ 'x%d' % i for i in range ( len ( cats ) ) ] elif len ( input_features ) != len ( self . categories_ ) : raise ValueError ( "input_features should have length equal to number of " "features ({}), got {}" . format ( len ( self . categories_ ) , len ( input_features ) ) ) feature_names = [ ] for i in range ( len ( cats ) ) : names = [ input_features [ i ] + '_' + str ( t ) for t in cats [ i ] ] if self . drop is not None : names . pop ( self . drop_idx_ [ i ] ) feature_names . extend ( names ) return np . array ( feature_names , dtype = object )