code: def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
      
      try:import pods
      except ImportError:raise ImportWarning('Need pods for example datasets. See https://github.com/sods/ods, or pip install pods.')
      data = pods.datasets.oil()
      X = data['X']
      Xtest = data['Xtest']
      Y = data['Y'][:, 0:1]
      Ytest = data['Ytest'][:, 0:1]
      Y[Y.flatten()==-1] = 0
      Ytest[Ytest.flatten()==-1] = 0
  
      # Create GP model
      m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, num_inducing=num_inducing)
      m.Ytest = Ytest
  
      # Contrain all parameters to be positive
      #m.tie_params('.*len')
      m['.*len'] = 10.
  
      # Optimize
      if optimize:
          m.optimize(messages=1)
      print(m)
  
      #Test
      probs = m.predict(Xtest)[0]
      GPy.util.classification.conf_matrix(probs, Ytest)
      return m
docs: Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.