old_contents: import sys
  sys.path.insert(1, "../../")
  import h2o,tests
  
  def hexdev_394(ip,port):
    path = "smalldata/covtype/covtype.20k.data"
    trainraw = h2o.lazy_import(path)
    tsetup = h2o.parse_setup(trainraw)
    tsetup["column_types"][10] = "ENUM"
    tsetup["column_types"][11] = "ENUM"
    tsetup["column_types"][12] = "ENUM"
    train = h2o.parse_raw(tsetup)
    
    cols = train.col_names  # This returned space for first column name
    x_cols = [colname for colname in cols if colname != "C55"]
    x_cols
    
    
    splits = train.split_frame()
    newtrain = splits[0]
    newvalid = splits[1]
    newtrain_x = newtrain[x_cols]
    newtrain_y = newtrain[54].asfactor()
    newvalid_x = newvalid[x_cols]
    newvalid_y = newvalid[54].asfactor()
    
    
    my_gbm = h2o.gbm(y=newtrain_y,
                     validation_y=newvalid_y,
                     x=newtrain_x,
                     validation_x=newvalid_x,
                     distribution =  "multinomial",
                     ntrees=100,
                     learn_rate=0.1,
                     max_depth=6)
    
    split1, split2 = train.split_frame()
    
    newtrain_x = split1[x_cols]
    newtrain_y = split1[54].asfactor()
    newvalid_x = split2[x_cols]
    newvalid_y = split2[54].asfactor()
    
    my_gbm = h2o.gbm(y=newtrain_y,
                     validation_y=newvalid_y,
                     x=newtrain_x,
                     validation_x=newvalid_x,
                     distribution = "multinomial",
                     ntrees=100,
                     learn_rate=0.1,
                     max_depth=6) 
  
    print "KEEPING FRAME???"
    print train._keep
  
  if __name__ == "__main__":
      tests.run_test(sys.argv, hexdev_394)
  
new_contents: import sys
  sys.path.insert(1, "../../")
  import h2o,tests
  
  def hexdev_394(ip,port):
    path = h2o.locate("smalldata/covtype/covtype.20k.data")
    trainraw = h2o.lazy_import(path)
    tsetup = h2o.parse_setup(trainraw)
    tsetup["column_types"][10] = "ENUM"
    tsetup["column_types"][11] = "ENUM"
    tsetup["column_types"][12] = "ENUM"
    train = h2o.parse_raw(tsetup)
    
    cols = train.col_names  # This returned space for first column name
    x_cols = [colname for colname in cols if colname != "C55"]
    x_cols
    
    
    splits = train.split_frame()
    newtrain = splits[0]
    newvalid = splits[1]
    newtrain_x = newtrain[x_cols]
    newtrain_y = newtrain[54].asfactor()
    newvalid_x = newvalid[x_cols]
    newvalid_y = newvalid[54].asfactor()
    
    
    my_gbm = h2o.gbm(y=newtrain_y,
                     validation_y=newvalid_y,
                     x=newtrain_x,
                     validation_x=newvalid_x,
                     distribution =  "multinomial",
                     ntrees=100,
                     learn_rate=0.1,
                     max_depth=6)
    
    split1, split2 = train.split_frame()
    
    newtrain_x = split1[x_cols]
    newtrain_y = split1[54].asfactor()
    newvalid_x = split2[x_cols]
    newvalid_y = split2[54].asfactor()
    
    my_gbm = h2o.gbm(y=newtrain_y,
                     validation_y=newvalid_y,
                     x=newtrain_x,
                     validation_x=newvalid_x,
                     distribution = "multinomial",
                     ntrees=100,
                     learn_rate=0.1,
                     max_depth=6) 
  
    print "KEEPING FRAME???"
    print train._keep
  
  if __name__ == "__main__":
      tests.run_test(sys.argv, hexdev_394)
  
subject: use locate on the path