class_docstr: Ruleset estimator based on Boolean Rules with Column Generation.
  
  Operates according to an scikit-learn interface with a few additional methods.
class_docstr_tok: ["Ruleset","estimator","based","on","Boolean","Rules","with","Column","Generation",".","Operates","according","to","an","scikit-learn","interface","with","a","few","additional","methods","."]
docstr: Set estimator parameters
docstr_tok: ["Set","estimator","parameters"]
code: def set_params(self, **params):
      """Set estimator parameters"""
      if not params:
          return self
  
      reinit = False
      for k, v in params.items():
          if k in self.valid_params:
              setattr(self, k, v)
          elif k in self.M.valid_params:
              reinit = True
              self.kwargs[k] = v
      if reinit:
          self.init_estimator_()
  
      return self
  
file_path: dowhy/dowhy/causal_refuters/overrule/ruleset.py
imports: from typing import Callable, Dict, List, Optional
  import numpy
  import pandas
  from .BCS.load_process_data_BCS import FeatureBinarizer
  from .BCS.overlap_boolean_rule import OverlapBooleanRule
  from .utils import rule_str, sample_reference
label_desc: Use image node_id 10 for calling the BCSRulesetEstimator obj's underlying member method code with example usage: obj.set_params() and returns: self, self