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: return all indices for which the passed rule holds on X 
docstr_tok: ["return","all","indices","for","which","the","passed","rule","holds","on","X"]
code: def _rule_predict(self, X, rule):
      """return all indices for which the passed rule holds on X"""
  
      preds = np.zeros(X.shape[0])
      positive_cases = set(range(X.shape[0]))
  
      for condition in rule:
          outputs = set(list(self._condition_classify(X, condition)))
          positive_cases = positive_cases.intersection(outputs)
  
      preds[list(positive_cases)] = 1
  
      return preds
  
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 4 for calling the SlipperBaseEstimator obj's underlying member method code with example usage: obj._rule_predict(X, rule) and returns: preds