class_docstr: Base class for missing values in supervised learning.
class_docstr_tok: ["Base","class","for","missing","values","in","supervised","learning","."]
docstr: Test predict topk with missing value
docstr_tok: ["Test","predict","topk","with","missing","value"]
code: def test_predict_topk(self):
      """
      Test predict topk with missing value
      """
      if self.model is None or not hasattr(self.model, "predict_topk"):
          return
  
      model = self.model
      test_sf = self.test_sf.copy()
  
      # Should pass
      model.predict_topk(test_sf, k=1, missing_value_action="auto")
      model.predict_topk(test_sf, k=1, missing_value_action="impute")
      model.predict_topk(test_sf, k=1, missing_value_action="error")
  
      # Missing value in each col type
      # -------------------------------
      for col in ["int", "float", "dict", "array", "str"]:
          test_sf_with_na = self.fill_with_na(test_sf, col)
          model.predict_topk(test_sf_with_na, k=1)
  
      for col in ["int", "float", "array"]:
          test_sf_with_na = self.fill_with_na(test_sf, col)
          self.assertRaises(
              ToolkitError,
              lambda: model.predict_topk(
                  test_sf_with_na, k=1, missing_value_action="error"
              ),
          )
  
      # Missing columns
      # ----------------------------------
      for col in ["int", "float", "dict", "array", "str"]:
          del test_sf[col]
          model.predict_topk(test_sf, k=1)
  
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 11 for calling the SupervisedLearningMissingValueTest obj's underlying member method code with example usage: obj.test_predict_topk() without return types