class_docstr: Logistic Rule Regression is a directly interpretable supervised learning
  method that performs logistic regression on rule-based features.
docstr: Compute conjunctions of features as specified in self.z.
  
  Args:
      X (DataFrame): Binarized features with MultiIndex column labels
  Returns:
      array: A -- Feature conjunction values, shape (X.shape[0], self.z.shape[1])
docstr_tok: ["Compute","conjunctions","of","features","as","specified","in","self.z",".","Args",":","X","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Returns",":","array",":","A","--","Feature","conjunction","values",",","shape","(","X.shape","[","0","]",",","self.z.shape","[","1","]",")"]
code: def compute_conjunctions(self, X):
      """Compute conjunctions of features as specified in self.z.
  
      Args:
          X (DataFrame): Binarized features with MultiIndex column labels
      Returns:
          array: A -- Feature conjunction values, shape (X.shape[0], self.z.shape[1])
      """
      try:
          A = 1 - (((1 - X) @ self.z) > 0)
      except AttributeError:
          print("Attribute 'z' does not exist, please fit model first.")
      # Scale conjunctions as done in training
      A = (
          A
          * self.lambda0
          / (self.lambda0 + self.lambda1 * self.z.sum().values)
      )
      return A
  
file_path: AIX360/aix360/algorithms/rbm/logistic_regression.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator, ClassifierMixin
  from .beam_search import beam_search_K1
label_desc: Use image node_id 3 for calling the LogisticRuleRegression obj's underlying member method code with example usage: obj.compute_conjunctions(X) and returns: A