class_docstr: BooleanRuleCG is a directly interpretable supervised learning method
  for binary classification that learns a Boolean rule in disjunctive
  normal form (DNF) or conjunctive normal form (CNF) using column generation (CG).
  AIX360 implements a heuristic beam search version of BRCG that is less 
  computationally intensive than the published integer programming version [#NeurIPS2018]_.
  
  References:
      .. [#NeurIPS2018] `S. Dash, O. Günlük, D. Wei, "Boolean decision rules via
         column generation." Neural Information Processing Systems (NeurIPS), 2018.
         <https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation.pdf>`_
docstr: Predict class labels.
  
  Args:
      X (DataFrame): Binarized features with MultiIndex column labels
  Returns:
      array: y -- Predicted labels
docstr_tok: ["Predict","class","labels",".","Args",":","X","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Returns",":","array",":","y","--","Predicted","labels"]
code: def predict(self, X):
      """Predict class labels.
  
      Args:
          X (DataFrame): Binarized features with MultiIndex column labels
      Returns:
          array: y -- Predicted labels
      """
      # Compute conjunctions of features
      A = self.compute_conjunctions(X)
      # Predict labels
      if self.CNF:
          # Flip labels since model is actually a DNF for Y=0
          return 1 - (np.dot(A, self.w) > 0)
      else:
          return (np.dot(A, self.w) > 0).astype(int)
  
file_path: AIX360/aix360/algorithms/rbm/boolean_rule_cg.py
imports: import os
  import numpy
  import pandas
  import cvxpy
  from sklearn.metrics import confusion_matrix, accuracy_score
  from sklearn.base import BaseEstimator, ClassifierMixin
  import time
  from .beam_search import beam_search, beam_search_K1
label_desc: Use image node_id 4 for calling the BooleanRuleCG obj's underlying member method code with example usage: obj.predict(X) and returns: unknown, unknown