class_docstr: This class implements a simple classifier constructed from a list of DNF rule sets.
  Based on the specified RuleSelectionMethod, this classifier predicts the label for a given input.
class_docstr_tok: ["This","class","implements","a","simple","classifier","constructed","from","a","list","of","DNF","rule","sets",".","Based","on","the","specified","RuleSelectionMethod",",","this","classifier","predicts","the","label","for","a","given","input","."]
docstr: Compute rule level metrics for the provided input, and update the Rules with them
  @param X: pandas data frame to compute required confidence score for PMML exported rules
  @param y: pandas series to hold labels to compute required confidence score for PMML exported rules
docstr_tok: ["Compute","rule","level","metrics","for","the","provided","input",",","and","update","the","Rules","with","them","@","param","X",":","pandas","data","frame","to","compute","required","confidence","score","for","PMML","exported","rules","@","param","y",":","pandas","series","to","hold","labels","to","compute","required","confidence","score","for","PMML","exported","rules"]
code: def update_rules_with_metrics(
      self, X: pd.DataFrame, y: pd.Series
  ) -> None:
      """
      Compute rule level metrics for the provided input, and update the Rules with them
      @param X: pandas data frame to compute required confidence score for PMML exported rules
      @param y: pandas series to hold labels to compute required confidence score for PMML exported rules
      """
      new_rules = []
      for rule_set in self._rule_sets:
          metric_list = compute_rule_metrics(rule_set, X, y)
          for rule in metric_list:
              confidence = self._compute_confidence(rule)
              weight = self._compute_weight(rule)
              new_rules.append(
                  Rule(
                      conjunction=rule.conjunction,
                      label=rule_set.then_part,
                      record_count=rule.n_records,
                      nb_correct=rule.n_correct,
                      confidence=confidence,
                      weight=weight,
                  )
              )
      self._rules = new_rules
  
imports: import enum
  from collections import namedtuple
  from typing import Dict, List, Any
  import pandas
  from aix360.algorithms.rule_induction.trxf.core.dnf_ruleset import DnfRuleSet
  from aix360.algorithms.rule_induction.trxf.metrics import compute_rule_metrics
label_desc: Use image node_id 5 for calling the RuleSetClassifier obj's underlying member method code with example usage: obj.update_rules_with_metrics(X, y) without return types