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: Make candidate rules for grow routine to compare scores
docstr_tok: ["Make","candidate","rules","for","grow","routine","to","compare","scores"]
code: def _make_candidate(self, X, y, curr_rule, feat, A_c):
      """Make candidate rules for grow routine to compare scores"""
  
      # make candidate rules
      candidates = [curr_rule.copy() for _ in range(3)]
      candidates = [
          x
          + [
              {
                  "feature": int(feat),
                  "operator": operator,
                  "pivot": float(A_c),
              }
          ]
          for x, operator in zip(candidates, [">", "<", "=="])
      ]
  
      # pick best condition
      Zs = [self._grow_rule_obj(X, y, r) for r in candidates]
      return candidates[Zs.index(max(Zs))]
  
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 2 for calling the SlipperBaseEstimator obj's underlying member method code with example usage: obj._make_candidate(X, y, curr_rule, feat, A_c) and returns: candidates