docstr: Train a rule induction algorithm and generate a trxf DnfRuleSet
  
  @param X: pandas dataframe representing features
  @param y: pandas series representing labels
  @param to_learn: one of the unique values of y we want to learn, representing the positive class
  @param kwargs: algorithm-specific parameters
  @return: a trxf DnfRuleSet
docstr_tok: ["Train","a","rule","induction","algorithm","and","generate","a","trxf","DnfRuleSet","@","param","X",":","pandas","dataframe","representing","features","@","param","y",":","pandas","series","representing","labels","@","param","to_learn",":","one","of","the","unique","values","of","y","we","want","to","learn",",","representing","the","positive","class","@","param","kwargs",":","algorithm-specific","parameters","@","return",":","a","trxf","DnfRuleSet"]
code: def generate(
      self, X: pd.DataFrame, y: pd.Series, to_learn: Any, **kwargs
  ) -> DnfRuleSet:
      """
      Train a rule induction algorithm and generate a trxf DnfRuleSet
  
      @param X: pandas dataframe representing features
      @param y: pandas series representing labels
      @param to_learn: one of the unique values of y we want to learn, representing the positive class
      @param kwargs: algorithm-specific parameters
      @return: a trxf DnfRuleSet
      """
      raise NotImplementedError
  
imports: import abc
  from typing import Any
  import pandas
  from aix360.algorithms.rule_induction.trxf.core.dnf_ruleset import DnfRuleSet
label_desc: Use image node_id 2 for calling the RuleSetGenerator obj's underlying member method code with example usage: obj.generate(X, y, to_learn) without return types