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 rules activated by x
  
  :param x: Samples of covariates
  :type x: Pandas DataFrame or Numpy Array, shape (n, d)
  :return: Matrix with binary values, of shape (n, r), where r is the total number of rules considered by the
      estimator, and where 1 indicates that the sample matches the rule, and 0 indicates otherwise.
  :rtype: Numpy Array, shape (n, r)
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code: def predict_rules(self, x):
      """
      Predict rules activated by x
  
      :param x: Samples of covariates
      :type x: Pandas DataFrame or Numpy Array, shape (n, d)
      :return: Matrix with binary values, of shape (n, r), where r is the total number of rules considered by the
          estimator, and where 1 indicates that the sample matches the rule, and 0 indicates otherwise.
      :rtype: Numpy Array, shape (n, r)
      """
      # 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)
  
      return self.M.predict_rules(X)
  
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 7 for calling the BCSRulesetEstimator obj's underlying member method code with example usage: obj.predict_rules(x) and returns: self