def test_grid_gbm_in_spark_pipeline(self):
prostate_frame = self._spark.read.csv(
"file://" +
unit_test_utils.locate("smalldata/prostate/prostate.csv"),
header=True,
inferSchema=True)
algo = H2OGridSearch(labelCol="AGE",
hyperParameters={"_seed": [1, 2, 3]},
ratio=0.8,
algo=H2OGBM(),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
pipeline = Pipeline(stages=[algo])
pipeline.write().overwrite().save(
"file://" + os.path.abspath("build/grid_gbm_pipeline"))
loaded_pipeline = Pipeline.load(
"file://" + os.path.abspath("build/grid_gbm_pipeline"))
model = loaded_pipeline.fit(prostate_frame)
model.write().overwrite().save(
"file://" + os.path.abspath("build/grid_gbm_pipeline_model"))
loaded_model = PipelineModel.load(
"file://" + os.path.abspath("build/grid_gbm_pipeline_model"))
loaded_model.transform(prostate_frame).count()
def createGridForProblemSpecificTesting(algorithm):
algorithm.setLabelCol("CAPSULE")
algorithm.setSplitRatio(0.8)
hyperParameters = {"seed": [1, 2], "ntrees": [3, 5, 10]}
return H2OGridSearch(hyperParameters=hyperParameters,
seed=42,
algo=algorithm)
def testGridSearchGetAlgoIsAbleToReturnAlgorithmOfVariousTypes():
grid = H2OGridSearch(algo=H2ODRFClassifier())
assert grid.getAlgo().__class__.__name__ == "H2ODRFClassifier"
grid.setAlgo(H2OKMeans())
assert grid.getAlgo().__class__.__name__ == "H2OKMeans"
grid.setAlgo(H2OGBMRegressor())
assert grid.getAlgo().__class__.__name__ == "H2OGBMRegressor"
def testGetAlgoViaSetter():
# SW-2276, 3rd call of getAlgo failed
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.setAlgo(H2OGBM().setNtrees(100).setLabelCol("AGE").setSplitRatio(0.8))
grid.getAlgo()
grid.getAlgo()
assert grid.getAlgo().getNtrees() == 100
def testGetGridModels(prostateDataset):
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
algo=H2OGBM(splitRatio=0.8, labelCol="AGE"),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.fit(prostateDataset)
models = grid.getGridModels()
assert len(models) == 3
def testGetGridModelsNoParams(prostateDataset):
grid = H2OGridSearch(algo=H2OGBM(labelCol="AGE", splitRatio=0.8),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.fit(prostateDataset)
params = grid.getGridModelsParams()
assert params.count() == 1
assert params.columns == ['MOJO Model ID']
params.collect() # try materializing
def testGetGridModelsParams(prostateDataset):
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
algo=H2OGBM(splitRatio=0.8, labelCol="AGE"),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.fit(prostateDataset)
params = grid.getGridModelsParams()
assert params.count() == 3
assert params.columns == ['MOJO Model ID', 'seed']
params.collect() # try materializing
def testGetAlgoViaConstructor():
# SW-2276, 3rd call of getAlgo failed
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
algo=H2OGBM(labelCol="AGE",
ntrees=100,
splitRatio=0.8),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.getAlgo()
grid.getAlgo()
assert grid.getAlgo().getNtrees() == 100
def testGetGridModelsMetrics(prostateDataset):
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
algo=H2OGBM(labelCol="AGE", splitRatio=0.8),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
grid.fit(prostateDataset)
metrics = grid.getGridModelsMetrics()
assert metrics.count() == 3
assert metrics.columns == [
'MOJO Model ID', 'MSE', 'MeanResidualDeviance', 'R2', 'RMSE'
]
metrics.collect() # try materializing
def gridSearchTester(algo, prostateDataset):
grid = H2OGridSearch(hyperParameters={"seed": [1, 2, 3]},
algo=algo.setSplitRatio(0.8),
strategy="RandomDiscrete",
maxModels=3,
maxRuntimeSecs=60,
selectBestModelBy="RMSE")
pipeline = Pipeline(stages=[grid])
pipeline.write().overwrite().save("file://" +
os.path.abspath("build/grid_pipeline"))
loadedPipeline = Pipeline.load("file://" +
os.path.abspath("build/grid_pipeline"))
model = loadedPipeline.fit(prostateDataset)
model.write().overwrite().save(
"file://" + os.path.abspath("build/grid_pipeline_model"))
loadedModel = PipelineModel.load(
"file://" + os.path.abspath("build/grid_pipeline_model"))
loadedModel.transform(prostateDataset).count()