old_contents: import sys
  import os
  sys.path.insert(1, os.path.join('..','..','h2o-py'))
  #sys.path.insert(0, os.path.join('..','..','h2o-py'))
  import h2o
  
  ######################################################
  # Parse command-line args.
  #
  # usage:  python test_name.py --usecloud ipaddr:port
  #
  
  ip_port = sys.argv[2].split(":")
  print ip_port
  ip = ip_port[0]
  port = int(ip_port[1])
  
  ######################################################
  #
  # Sample Running GBM on prostate.csv
  
  # Connect to a pre-existing cluster
  h2o.init(ip=ip, port=port)
  
  df = h2o.import_frame(path="../../../smalldata/logreg/prostate.csv")
  df.describe()
  
  # Remove ID from training frame
  del df['ID']
  
  # For VOL & GLEASON, a zero really means "missing"
  vol = df['VOL']
  vol[vol == 0] = None
  gle = df['GLEASON']
  gle[gle == 0] = None
  
  # Convert CAPSULE to a logical factor
  df['CAPSULE'] = df['CAPSULE'].asfactor()
  
  # Test/train split
  r = vol.runif()
  train = df[r < 0.8]
  test  = df[r >= 0.8]
  
  # See that the data is ready
  train.describe()
  test.describe()
  
  
  # Run GBM
  gbm = h2o.gbm(           y=train["CAPSULE"],
                validation_y=test ["CAPSULE"],
                           x=train[1:],
                validation_x=test [1:],
                ntrees=50,
                max_depth=5,
                learn_rate=0.1,
  			  distribution="bernoulli")
  mm = gbm.model_performance(test)
  mm.show()
  
new_contents: import sys
  import os
  sys.path.insert(1, os.path.join('..','..','h2o-py'))
  #sys.path.insert(0, os.path.join('..','..','h2o-py'))
  import h2o
  
  ######################################################
  # Parse command-line args.
  #
  # usage:  python test_name.py --usecloud ipaddr:port
  #
  
  ip_port = sys.argv[2].split(":")
  print ip_port
  ip = ip_port[0]
  port = int(ip_port[1])
  
  ######################################################
  #
  # Sample Running GBM on prostate.csv
  
  # Connect to a pre-existing cluster
  h2o.init(ip=ip, port=port)
  
  df = h2o.import_frame(path="../../../smalldata/logreg/prostate.csv")
  df.describe()
  
  # Remove ID from training frame
  df = df.drop('ID')
  
  # For VOL & GLEASON, a zero really means "missing"
  vol = df['VOL']
  vol[vol == 0] = float("nan")
  gle = df['GLEASON']
  gle[gle == 0] = float("nan")
  
  # Convert CAPSULE to a logical factor
  df['CAPSULE'] = df['CAPSULE'].asfactor()
  
  # Test/train split
  r = vol.runif()
  train = df[r < 0.8]
  test  = df[r >= 0.8]
  
  # See that the data is ready
  train.describe()
  test.describe()
  
  
  # Run GBM
  gbm = h2o.gbm(           y=train["CAPSULE"],
                validation_y=test ["CAPSULE"],
                           x=train[1:],
                validation_x=test [1:],
                ntrees=50,
                max_depth=5,
                learn_rate=0.1,
  			  distribution="bernoulli")
  mm = gbm.model_performance(test)
  mm.show()
  
subject: update gradle build py test