class_docstr: RIPPER (Repeated Incremental Pruning to Produce Error Reduction) is a heuristic rule induction algorithm
  based on separate-and-conquer. The explainer outputs a rule set in Disjunctive Normal Form (DNF) for a single
  target concept.
  
  References:
      .. [#ML95] `William W Cohen, "Fast Effective Rule Induction"
          Machine Learning: Proceedings of the Twelfth International Conference, 1995.
          <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.2612&rep=rep1&type=pdf>
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docstr: Export rule set to technical interchange format trxf from internal representation
  for the positive value (i.e. label value) it has been fitted for.
  
  When the internal rule set is empty an empty dnf rule set with the internal pos value
  is returned.
  
  Returns:
      trxf.DnfRuleSet
docstr_tok: ["Export","rule","set","to","technical","interchange","format","trxf","from","internal","representation","for","the","positive","value","(","i.e",".","label","value",")","it","has","been","fitted","for",".","When","the","internal","rule","set","is","empty","an","empty","dnf","rule","set","with","the","internal","pos","value","is","returned",".","Returns",":","trxf.DnfRuleSet"]
code: def explain(self):
      """
      Export rule set to technical interchange format trxf from internal representation
      for the positive value (i.e. label value) it has been fitted for.
  
      When the internal rule set is empty an empty dnf rule set with the internal pos value
      is returned.
  
      Returns:
          trxf.DnfRuleSet
      """
      assert self.target_label is not None, (
          "Not fitted or not fitted for a specific pos value. Use export_rules "
          "in the latter case. "
      )
  
      if len(self._rule_map.items()) == 0:
          return DnfRuleSet([], self.target_label)
      for label, rules in self._rule_map.items():
          if label == self.target_label:
              return self._rules_to_trxf_dnf_ruleset(rules, label)
      raise Exception(
          "No rules found for label: " + str(self.target_label)
      )
  
imports: import logging
  import logging.config
  from collections import OrderedDict
  import numpy
  from aix360.algorithms.dise import DISExplainer
  from aix360.algorithms.rule_induction.ripper.base import _encoding_for_parallel, init_encoder, encode_nominal, _split_instances
  from aix360.algorithms.rule_induction.ripper.binding import append_literal, _bind_literal, _bind_rule, unbound_rule_list_index, _unbound, _unbound_rule_list, _rule_list_predict, LE, EQ, _find_best_literal, Literal, _filter_contradicted_instances
  from aix360.algorithms.rule_induction.ripper.mdl import _mdl
  from aix360.algorithms.rule_induction.ripper.pruning import _pruning_irep, _minimize_rule_list, _pruning_optimization
  from aix360.algorithms.rule_induction.trxf.core.conjunction import Conjunction
  from aix360.algorithms.rule_induction.trxf.core.dnf_ruleset import DnfRuleSet
  from aix360.algorithms.rule_induction.trxf.core.feature import Feature
  from aix360.algorithms.rule_induction.trxf.core.predicate import Predicate, Relation
  from pandas import DataFrame, Series
label_desc: Use image node_id 6 for calling the RipperExplainer obj's underlying member method code with example usage: obj.explain() and returns: DnfRuleSet, self