text: from sklearn.utils import shuffle
  
  from sklearn.linear_model import LogisticRegression, SGDClassifier, RidgeClassifier
  from sklearn import neighbors, tree, svm
  from sklearn.base import BaseEstimator
  from sklearn.naive_bayes import GaussianNB
  from sklearn.ensemble import RandomForestClassifier
  
  import numpy as np
  import math
  import logging
  
  
  class CobraClassifier:
      def __init__(self, seed=42, epsilon=0.001, threshold=0.5, machines=None):
          self.seed = seed
          self.epsilon = epsilon
          self.threshold = threshold
          self.machines = machines
  
  
      def fit(self, X, y, X_k=None, y_k=None, X_l=None, y_l=None, flag=False, sample_weight=None):
          self.sample_weight = sample_weight
  
          # if flag == True => X_k values are defined
          self.X, self.y = X, y
          self.X_k, self.y_k = X_k, y_k
          self.X_l, self.y_l = X_l, y_l
          
          self.machine_estimators = {}
  
          if flag == False:
              self.generate_training_data()
  
          # Train machine estimators on the training data (D_k)
          self.setup_machines()
          self.execute_machines()
  
          return self
  
  
      def predict_helper(self, X, alpha):
          res = {}
          for m in self.machines:
              predicted_label = self.machine_estimators[m].predict(X)
              res[m] = {}
  
              for idx in range(len(self.X_l)):
                  if math.fabs(self.machine_predictions[m][idx] - predicted_label) <= self.epsilon:
                      res[m][idx] = 1
                  else:
                      res[m][idx] = 0
          
          filtered_points = []
          for idx in range(0, len(self.X_l)):
              sum = 0
              for m in res:
                  if res[m][idx] == 1:
                      sum += 1
                  if sum >= alpha:
                      filtered_points.append(idx)
                      break
  
          if len(filtered_points) == 0:
              return 0
  
          score = 0
          for idx in filtered_points:
              score += self.y_l[idx]
          score = score / len(filtered_points)
          
          final_label = 1 if score >= self.threshold else 0
          return final_label
  
  
      def predict(self, X, alpha=None):
          n = len(X)
          predicted_labels = np.zeros(n)
          
          if alpha is None:
              alpha = len(self.machines)
  
          for i in range(n):
              predicted_labels[i] = self.predict_helper(X[i].reshape(1, -1), alpha)
          
          return predicted_labels
  
  
      def set_epsilon(self):
          pass
  
  
      def setup_machines(self):
          for m in self.machines:
              if m == 'knn':
                  self.machine_estimators[m] = neighbors.KNeighborsClassifier().fit(self.X_k, self.y_k)
              elif m == 'random_forest':
                  self.machine_estimators[m] = RandomForestClassifier(random_state=self.seed).fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'logistic_regression':
                  self.machine_estimators[m] = LogisticRegression(random_state=self.seed).fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'svm':
                  self.machine_estimators[m] = svm.SVC().fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'decision_trees':
                  self.machine_estimators[m] = tree.DecisionTreeClassifier().fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'naive_bayes':
                  self.machine_estimators[m] = GaussianNB().fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'stochastic_gradient_decision':
                  self.machine_estimators[m] = SGDClassifier().fit(self.X_k, self.y_k, self.sample_weight)
              elif m == 'ridge':
                  self.machine_estimators[m] = RidgeClassifier().fit(self.X_k, self.y_k, self.sample_weight)
  
          return self
  
  
      def execute_machines(self):
          self.machine_predictions = {}
    
          for m in self.machines:
              self.machine_predictions[m] = self.machine_estimators[m].predict(self.X_l)
  
          return self
      
  
      def generate_training_data(self, k=None, l=None):
          """ 
          Splits the data into training (D_k) and testing part (D_l) for execution of models as specified in the COBRA paper
          """
          
          # shuffle the data
          indexes = range(len(self.X))
          self.X, self.y, indexes = shuffle(self.X, self.y, indexes, random_state = self.seed)
  
          if k is None or l is None:
              n = len(self.X)
              k = int(n/2)
              l = int(n/2)
  
          self.X_k, self.y_k = self.X[ : k], self.y[ : k]
          self.X_l, self.y_l = self.X[k : ], self.y[k : ]
  
          if self.sample_weight is not None:
              self.sample_weight = self.sample_weight[ : k]
  
          return self
  
  
  def execute_cobra(X_train, y_train, X_test):
    print("[Executing]: Running Cobra Model ...\n")
  
    # Model Fitting
    model = CobraClassifier(machines = ['knn', 'logistic_regression', 'svm', 'naive_bayes', 'ridge', 'random_forest', 'decision_trees']) 
    model.fit(X_train, y_train)
  
    # Predictions on test dataset
    y_pred = model.predict(X_test)
  
    return y_pred