class_docstr: Logistic Rule Regression is a directly interpretable supervised learning
  method that performs logistic regression on rule-based features.
docstr: Predict probabilities of Y=1.
  
  Args:
      X (DataFrame): Binarized features with MultiIndex column labels
      Xstd (DataFrame, optional): Standardized numerical features
  Returns:
      array: p -- Predicted probabilities
docstr_tok: ["Predict","probabilities","of","Y=1",".","Args",":","X","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Xstd","(","DataFrame",",","optional",")",":","Standardized","numerical","features","Returns",":","array",":","p","--","Predicted","probabilities"]
code: def predict_proba(self, X, Xstd=None):
      """Predict probabilities of Y=1.
  
      Args:
          X (DataFrame): Binarized features with MultiIndex column labels
          Xstd (DataFrame, optional): Standardized numerical features
      Returns:
          array: p -- Predicted probabilities
      """
      try:
          if self.lr.coef_.shape[1]:
              # Compute conjunctions of features
              A = self.compute_conjunctions(X)
              if self.useOrd:
                  # Selected ordinal features scaled as in training
                  Astd = 0.4 * Xstd.values[:, self.idxNonzeroOrd]
                  # Predict probabilities
                  B = np.concatenate((Astd, A), axis=1)
                  return self.lr.predict_proba(B)[:, 1]
              else:
                  # Predict probabilities
                  return self.lr.predict_proba(A)[:, 1]
          else:
              return np.full(X.shape[0], self.p)
      except AttributeError:
          return np.full(X.shape[0], self.p)
  
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 4 for calling the LogisticRuleRegression obj's underlying member method code with example usage: obj.predict_proba(X, Xstd) and returns: np, np, self, self