import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
from tests import pyunit_utils

def pubdev_5179():
    h2o.init()

    mnist_original = h2o.import_file(pyunit_utils.locate("smalldata/flow_examples/mnist/test.csv.gz"));
    mnist_original['C785'] = mnist_original['C785'].asfactor()
    predictors = mnist_original.columns[0:-1]
    target = 'C785'
    train, valid, new_data = mnist_original.split_frame(ratios=[.5, .2], seed=1234)
    drf = H2ORandomForestEstimator(model_id='drf', ntrees=3, seed=1234)
    drf.train(x=predictors, y=target, training_frame=train, validation_frame=valid)
    drf_checkpoint = H2ORandomForestEstimator(model_id='drf_checkpoint',checkpoint=drf, ntrees=10, seed=1234)
    drf_checkpoint.train(x=predictors, y=target, training_frame=new_data, validation_frame=valid)


if __name__ == "__main__":
    pyunit_utils.standalone_test(pubdev_5179)
else:
    pubdev_5179()

@@ -118,29 +118,14 @@
 ev_5
-179():%0A    h2o.init()
+352():
 %0A%0A  
@@ -694,10 +694,9 @@
 ees=
-10
+4
 , se
@@ -872,19 +872,19 @@
 pubdev_5
-179
+352
 )%0Aelse:%0A
@@ -899,10 +899,10 @@
 ev_5
-179
+352
 ()%0A

Change test name to pyunit_pubdev_5352