old_contents: import sys
  sys.path.insert(1, "../../")
  import h2o, tests
  
  def javapredict_gbm_xlarge():
  
      hdfs_name_node = tests.hadoop_namenode()
      hdfs_file_name = "/datasets/z_repro.csv.gz"
      url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_file_name)
  
      params = {'ntrees':22, 'max_depth':37, 'min_rows':1, 'sample_rate':0.1} # 651MB pojo
      print "Parameter list:"
      for k,v in zip(params.keys(), params.values()): print "{0}, {1}".format(k,v)
  
      train =  h2o.import_file(url)
      test = train[range(0,10),:]
      x = range(1,train.ncol)
      y = 0
  
      tests.javapredict("gbm", "numeric", train, test, x, y, **params)
  
  if __name__ == "__main__":
      tests.run_test(sys.argv, javapredict_gbm_xlarge)
new_contents: import sys
  sys.path.insert(1, "../../")
  import h2o, tests
  
  def javapredict_gbm_xlarge():
  
      hdfs_name_node = tests.hadoop_namenode()
      hdfs_file_name = "/datasets/z_repro.csv"
      url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_file_name)
  
      params = {'ntrees':22, 'max_depth':37, 'min_rows':1, 'sample_rate':0.1} # 651MB pojo
      print "Parameter list:"
      for k,v in zip(params.keys(), params.values()): print "{0}, {1}".format(k,v)
  
      train =  h2o.import_file(url)
      test = train[range(0,10),:]
      x = range(1,train.ncol)
      y = 0
  
      tests.javapredict("gbm", "numeric", train, test, x, y, **params)
  
  if __name__ == "__main__":
      tests.run_test(sys.argv, javapredict_gbm_xlarge)
subject: fix file path in pyunit