class_docstr: Base class for missing values in supervised learning.
class_docstr_tok: ["Base","class","for","missing","values","in","supervised","learning","."]
docstr: Set up (Run only once)
docstr_tok: ["Set","up","(","Run","only","once",")"]
code: def setUpClass(self):
      """
      Set up (Run only once)
      """
      self.target = "y"
      self.sf = tc.SFrame()
      self.sf["y"] = tc.SArray([1, 2, 1], int)
      self.sf["int"] = tc.SArray([1, 2, 3], int)
      self.sf["float"] = tc.SArray([1, 2, 3], float)
      self.sf["dict"] = tc.SArray(
          [{"1": 3, "2": 2}, {"2": 1}, {}], dict
      )
      self.sf["array"] = tc.SArray(
          [[1, 2], [3, 4], [5, 6]], array.array
      )
      self.sf["str"] = tc.SArray(["1", "2", "3"], str)
  
      test_sf = tc.SFrame()
      test_sf["y"] = tc.SArray([2], int)
      test_sf["int"] = tc.SArray([2], int)
      test_sf["float"] = tc.SArray([2], float)
      test_sf["dict"] = tc.SArray([{"1": 1, "2": 1}], dict)
      test_sf["array"] = tc.SArray([[3, 4]], array.array)
      test_sf["str"] = tc.SArray(["2"], str)
      self.test_sf = test_sf
  
      self.model = None
      self.support_missing_value = False
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import turicreate
  import array
  from turicreate.toolkits._main import ToolkitError
label_desc: Use image node_id 1 for calling the SupervisedLearningMissingValueTest obj's underlying member method code with example usage: obj.setUpClass() without return types