def testTargetEncoderModelProduceSameResultsRegardlessSpecificationOfOutputCols(
trainingDataset, testingDataset):
def trainAndReturnTranformedTestingDataset(targetEncoder):
targetEncoderModel = targetEncoder.fit(trainingDataset)
return targetEncoderModel.transformTrainingDataset(testingDataset)
targetEncoderDefaultOutputCols = H2OTargetEncoder() \
.setInputCols(["RACE", "DPROS", "DCAPS"]) \
.setLabelCol("CAPSULE") \
.setHoldoutStrategy("None") \
.setNoise(0.0)
dataFrameDefaultOutputCols = trainAndReturnTranformedTestingDataset(targetEncoderDefaultOutputCols) \
.withColumnRenamed("RACE_te", "RACE_out") \
.withColumnRenamed("DPROS_te", "DPROS_out") \
.withColumnRenamed("DCAPS_te", "DCAPS_out")
targetEncoderCustomOutputCols = H2OTargetEncoder() \
.setInputCols(["RACE", "DPROS", "DCAPS"]) \
.setOutputCols(["RACE_out", "DPROS_out", "DCAPS_out"]) \
.setLabelCol("CAPSULE") \
.setHoldoutStrategy("None") \
.setNoise(0.0)
dataFrameCustomOutputCols = trainAndReturnTranformedTestingDataset(
targetEncoderCustomOutputCols)
unit_test_utils.assert_data_frames_are_identical(
dataFrameDefaultOutputCols, dataFrameCustomOutputCols)
def testPipelineWithTargetEncoderIsSerializable():
targetEncoder = H2OTargetEncoder(
foldCol="ID",
labelCol="CAPSULE",
inputCols=["RACE", "DPROS", "DCAPS"],
outputCols=["RACE_out", "DPROS_out", "DCAPS_out"],
holdoutStrategy="KFold",
blendedAvgEnabled=True,
blendedAvgInflectionPoint=15.0,
blendedAvgSmoothing=25.0,
noise=0.05,
noiseSeed=123)
gbm = H2OGBM() \
.setLabelCol("CAPSULE") \
.setFeaturesCols(targetEncoder.getOutputCols())
pipeline = Pipeline(stages=[targetEncoder, gbm])
path = "file://" + os.path.abspath(
"build/testPipelineWithTargetEncoderIsSerializable")
pipeline.write().overwrite().save(path)
loadedPipeline = Pipeline.load(path)
[loadedTargetEncoder, loadedGbm] = loadedPipeline.getStages()
assertTargetEncoderAndMOJOModelParamsAreEqual(targetEncoder,
loadedTargetEncoder)
assert gbm.getLabelCol() == loadedGbm.getLabelCol()
assert gbm.getFeaturesCols() == loadedGbm.getFeaturesCols()
def createTargetEncoder():
return H2OTargetEncoder() \
.setInputCols(["RACE", "DPROS", "DCAPS"]) \
.setLabelCol("CAPSULE") \
.setHoldoutStrategy("None") \
.setBlendedAvgEnabled(False) \
.setNoise(5.0) \
.setNoiseSeed(42)
def testPipelineWithTargetEncoderTransformsTrainingAndTestingDatasetWithoutException(trainingDataset, testingDataset):
targetEncoder = H2OTargetEncoder(labelCol="CAPSULE", inputCols=["RACE", "DPROS", "DCAPS"])
gbm = H2OGBM(labelCol="CAPSULE")
pipeline = Pipeline(stages=[targetEncoder, gbm])
model = pipeline.fit(trainingDataset)
model.transform(testingDataset).collect()
def testTargetEncoderConstructorParametersGetPropagatedToLoadedMOJOModel(self):
targetEncoder = H2OTargetEncoder(foldCol="ID", labelCol="CAPSULE", inputCols=["RACE", "DPROS", "DCAPS"], holdoutStrategy = "KFold",
blendedAvgEnabled=True, blendedAvgInflectionPoint=15.0, blendedAvgSmoothing=25.0, noise=0.05, noiseSeed=123)
pipeline = Pipeline(stages=[targetEncoder])
model = pipeline.fit(self._trainingDataset)
path = "file://" + os.path.abspath("build/testTargetEncoderConstructorParametersGetPropagatedToLoadedMOJOModel")
model.write().overwrite().save(path)
loadedModel = PipelineModel.load(path)
mojoModel = loadedModel.stages[0]
self.assertTargetEncoderAndMOJOModelParamsAreEqual(targetEncoder, mojoModel)
def testTargetEncoderMOJOModelCouldBeSavedAndLoaded(trainingDataset, testingDataset):
targetEncoder = H2OTargetEncoder(foldCol="ID", labelCol="CAPSULE", inputCols=["RACE", "DPROS", "DCAPS"],
outputCols=["RACE_out", "DPROS_out", "DCAPS_out"])
model = targetEncoder.fit(trainingDataset)
path = "file://" + os.path.abspath("build/testTargetEncoderMOJOModelCouldBeSavedAndLoaded")
model.write().overwrite().save(path)
loadedModel = H2OTargetEncoderMOJOModel.load(path)
expected = model.transform(testingDataset)
result = loadedModel.transform(testingDataset)
unit_test_utils.assert_data_frames_are_identical(expected, result)
def testTargetEncoderModelWithDisabledNoiseAndTargetEncoderMOJOModelTransformTheTrainingDatasetSameWay(trainingDataset):
targetEncoder = H2OTargetEncoder() \
.setInputCols([["RACE"], ["DPROS", "DCAPS"]]) \
.setLabelCol("CAPSULE") \
.setHoldoutStrategy("None") \
.setNoise(0.0)
targetEncoderModel = targetEncoder.fit(trainingDataset)
transformedByModel = targetEncoderModel.transformTrainingDataset(trainingDataset)
transformedByMOJOModel = targetEncoderModel.transform(trainingDataset)
unit_test_utils.assert_data_frames_are_identical(transformedByModel, transformedByMOJOModel)
def testProducedMOJOModelAndLoadedMOJOModelReturnsSameResult(trainingDataset, testingDataset):
targetEncoder = H2OTargetEncoder(labelCol="CAPSULE", inputCols=[["RACE"], ["DPROS", "DCAPS"]])
pipeline = Pipeline(stages=[targetEncoder])
producedModel = pipeline.fit(trainingDataset)
path = "file://" + os.path.abspath("build/testProducedMOJOModelAndLoadedMOJOModelReturnsSameResult")
producedModel.write().overwrite().save(path)
loadedModel = PipelineModel.load(path)
transformedByProducedModel = producedModel.transform(testingDataset)
transformedByLoadedModel = loadedModel.transform(testingDataset)
unit_test_utils.assert_data_frames_are_identical(transformedByProducedModel, transformedByLoadedModel)
def testTargetEncoderSetterParametersGetPropagatedToLoadedMOJOModel(self):
targetEncoder = H2OTargetEncoder()\
.setFoldCol("ID")\
.setLabelCol("CAPSULE")\
.setInputCols(["RACE", "DPROS", "DCAPS"])\
.setHoldoutStrategy("KFold")\
.setBlendedAvgEnabled(True)\
.setBlendedAvgInflectionPoint(15.0)\
.setBlendedAvgSmoothing(25.0)\
.setNoise(0.05)\
.setNoiseSeed(123)
pipeline = Pipeline(stages=[targetEncoder])
model = pipeline.fit(self._trainingDataset)
path = "file://" + os.path.abspath("build/testTargetEncoderSetterParametersGetPropagatedToLoadedMOJOModel")
model.write().overwrite().save(path)
loadedModel = PipelineModel.load(path)
mojoModel = loadedModel.stages[0]
self.assertTargetEncoderAndMOJOModelParamsAreEqual(targetEncoder, mojoModel)