class_docstr: An estimator that supports building rules as described in
  A Simple, Fast, and Effective Rule Learner (1999). Intended to be used
  as part of the SlipperRulesClassifier.
class_docstr_tok: ["An","estimator","that","supports","building","rules","as","described","in","A","Simple",",","Fast",",","and","Effective","Rule","Learner","(","1999",")",".","Intended","to","be","used","as","part","of","the","SlipperRulesClassifier","."]
docstr: objective function for prune rule routine
  eq 7 from Cohen & Singer (1999)
docstr_tok: ["objective","function","for","prune","rule","routine","eq","7","from","Cohen","&","Singer","(","1999",")"]
code: def _prune_rule_obj(self, X, y, rule):
      """
      objective function for prune rule routine
      eq 7 from Cohen & Singer (1999)
      """
  
      V_plus, V_minus = self._get_design_matrices(X, y, rule)
      C_R = self._sample_weight(V_plus, V_minus)
      return (
          (1 - V_plus - V_minus)
          + V_plus * np.exp(-C_R)
          + V_minus * np.exp(C_R)
      )
  
imports: import random
  import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.model_selection import train_test_split
  from imodels.util.arguments import check_fit_arguments
label_desc: Use image node_id 12 for calling the SlipperBaseEstimator obj's underlying member method code with example usage: obj._prune_rule_obj(X, y, rule) and returns: unknown