class_docstr: PUBDEV-1843: Grid testing.  Subtask 5.
  
  This class is created to test the gridsearch for kmeans algo over differ validation datasets.  Only one test
  is performed here.
  
  Test Descriptions:
      a. choose a fixed set of parameters to use in the hyper-parameters for gridsearch.  However, must grid over
         the validation_frame parameter.
      b. Next, build H2O kmeans models using grid search and check out the performance metrics with the different
         validation datasets.
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docstr: This function performs all initializations necessary:
  load the training/validation data sets and set the training set indices
docstr_tok: ["This","function","performs","all","initializations","necessary",":","load","the","training\/validation","data","sets","and","set","the","training","set","indices"]
code: def setup_data(self):
      """
      This function performs all initializations necessary:
      load the training/validation data sets and set the training set indices
      """
      self.training1_data = h2o.import_file(
          path=pyunit_utils.locate(self.training1_filenames)
      )
      self.x_indices = list(range(self.training1_data.ncol))
  
      self.hyper_params["validation_frame"] = []
  
      for fname in self.validation_filenames:
          temp = h2o.import_file(path=pyunit_utils.locate(fname))
          self.hyper_params["validation_frame"].append(temp.frame_id)
  
file_path: h2o-3/h2o-py/dynamic_tests/testdir_algos/kmeans/pyunit_NOFEATURE_kmeans_gridsearch_over_validation_frame.py
imports: import sys
  import os
  from builtins import range
  import time
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.kmeans import H2OKMeansEstimator
  from h2o.grid.grid_search import H2OGridSearch
label_desc: Use image node_id 2 for calling the Test_kmeans_grid_search obj's underlying member method code with example usage: obj.setup_data() without return types