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: Predict whether or not X lies in the overlap region (1 = True).
  
  :param x: Samples of covariates
  :type x: Pandas DataFrame or Numpy Array, shape (n, d)
docstr_tok: ["Predict","whether","or","not","X","lies","in","the","overlap","region","(","1","=","True",")",".",":","param","x",":","Samples","of","covariates",":","type","x",":","Pandas","DataFrame","or","Numpy","Array",",","shape","(","n",",","d",")"]
code: def predict(self, x):
      """
      Predict whether or not X lies in the overlap region (1 = True).
  
      :param x: Samples of covariates
      :type x: Pandas DataFrame or Numpy Array, shape (n, d)
      """
      # Construct features dataframe
      data = (
          x
          if isinstance(x, pd.DataFrame)
          else pd.DataFrame(
              dict([("x%d" % i, x[:, i]) for i in range(x.shape[1])])
          )
      )
  
      X = self.FeatureBinarizer.transform(data).fillna(0)
  
      preds = self.M.predict(X)
  
      return preds
  
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 6 for calling the BCSRulesetEstimator obj's underlying member method code with example usage: obj.predict(x) and returns: preds