text: import numpy as np
  import copy
  from sklearn.svm import SVC
  from sklearn.preprocessing import MultiLabelBinarizer
  
  class CalibratedLabelRanking:
      """Calibratedn Label Ranking
      Reference Paper:
          Min-Ling Zhang and Zhi-Hua Zhou. A Review on Multi-Label Learning Algorithms
          Johannes Fürnkranz. Multilabel classification via calibrated label ranking
      """
      def __init__(self, classifier=SVC(kernel='rbf')):
          self.classifier = classifier
  
      def fit(self, X, y):
          self.virtualLabel = y.shape[1]
          self.label_count = y.shape[1]
          self.m = X.shape[0]
          self.X = X
          self.y = y
          self.realLabelClassifiers = []
          self.virtualLabelClassifiers = []
          self.trainRealLabelClassifiers()
          self.trainVirtualLabelClassifiers()
          return self
  
      def trainRealLabelClassifiers(self):
          for i in range(self.label_count):
              for j in range(i+1, self.label_count):
                  X_subset = []
                  y_subset = []
                  classifier = copy.deepcopy(self.classifier)
                  for k in range(self.m):
                      if self.y[k, i] == self.y[k, j]:
                          continue
                      elif self.y[k, i] == 1:
                          X_subset.append(self.X[k, :])
                          y_subset.append(i)
                      else:
                          X_subset.append(self.X[k, :])
                          y_subset.append(j)
                  try:
                      classifier.fit(np.vstack(X_subset), np.array(y_subset))
                  except ValueError:
                      if y_subset[0] == i:
                          classifier = i
                      else:
                          classifier = j
                  self.realLabelClassifiers.append(classifier)
  
      def trainVirtualLabelClassifiers(self):
          for i in range(self.label_count):
              classifier = copy.deepcopy(self.classifier)
              y_temp= self.y[:, i]
              y_subset = []
              for j in range(self.m):
                  y_subset.append(i if y_temp[j] == 1 else self.virtualLabel)
              classifier.fit(self.X, y_subset)
              self.virtualLabelClassifiers.append(classifier)
  
      def predict(self, X_pre):
          result = np.zeros((X_pre.shape[0], self.label_count))
          threshold = self.voteForVirtualLabel(X_pre)
          votes = self.voteForRealLabel(X_pre)
          for i in range(X_pre.shape[0]):
              result[i, votes[i,:] > threshold[i]] = 1
          return result
  
      def voteForVirtualLabel(self, X_pre):
          votes = np.zeros(X_pre.shape[0])
          for i in range(len(self.virtualLabelClassifiers)):
              predict = self.virtualLabelClassifiers[i].predict(X_pre)
              for j in range(X_pre.shape[0]):
                  if predict[j] == self.virtualLabel:
                      votes[j] += 1
          return votes
  
      def voteForRealLabel(self, X_pre):
          votes = np.zeros((X_pre.shape[0], self.label_count))
          for i in range(len(self.realLabelClassifiers)):
              if isinstance(self.realLabelClassifiers[i], int):
                  predict = np.array([self.realLabelClassifiers[i]] * X_pre.shape[0])
              else:
                  predict = self.realLabelClassifiers[i].predict(X_pre)
              for j in range(X_pre.shape[0]):
                  votes[j, predict[j]] += 1
          for j in range(len(self.virtualLabelClassifiers)):
              predict = self.virtualLabelClassifiers[j].predict(X_pre)
              for j in range(X_pre.shape[0]):
                  if predict[j] < self.virtualLabel:
                      votes[j, predict[j]] += 1
          return votes
              