class HasUserY(Params):
userY = Param(Params._dummy(), "userY", "User-specified initial matrix Y.",
H2OTypeConverters.toNullableDataFrame())
def getUserY(self):
return self.getOrDefault(self.userY)
def setUserY(self, value):
return self._set(userY=value)
class HasBetaConstraints(Params):
betaConstraints = Param(
Params._dummy(), "betaConstraints",
"Data frame of beta constraints enabling to set special conditions over the model coefficients.",
H2OTypeConverters.toNullableDataFrame())
def getBetaConstraints(self):
return self.getOrDefault(self.betaConstraints)
def setBetaConstraints(self, value):
return self._set(betaConstraints=value)
class HasCalibrationDataFrame(Params):
calibrationDataFrame = Param(
Params._dummy(), "calibrationDataFrame",
"Calibration data frame for Platt Scaling. "
"To enable usage of the data frame, set the parameter calibrateModel to True.",
H2OTypeConverters.toNullableDataFrame())
def getCalibrationDataFrame(self):
return self.getOrDefault(self.calibrationDataFrame)
def setCalibrationDataFrame(self, value):
return self._set(calibrationDataFrame=value)
class H2OCommonParams(H2OBaseMOJOParams):
##
# Param definitions
##
validationDataFrame = Param(
Params._dummy(), "validationDataFrame",
"A data frame dedicated for a validation of the trained model. If the parameters is not set,"
+ "a validation frame created via the 'splitRatio' parameter.",
H2OTypeConverters.toNullableDataFrame())
splitRatio = Param(
Params._dummy(), "splitRatio",
"Accepts values in range [0, 1.0] which determine how large part of dataset is used for training"
" and for validation. For example, 0.8 -> 80% training 20% validation.",
H2OTypeConverters.toFloat())
columnsToCategorical = Param(
Params._dummy(), "columnsToCategorical",
"List of columns to convert to categorical before modelling",
H2OTypeConverters.toListString())
##
# Getters
##
def getValidationDataFrame(self):
return self.getOrDefault(self.validationDataFrame)
def getSplitRatio(self):
return self.getOrDefault(self.splitRatio)
def getColumnsToCategorical(self):
return self.getOrDefault(self.columnsToCategorical)
##
# Setters
##
def setValidationDataFrame(self, value):
return self._set(validationDataFrame=value)
def setSplitRatio(self, value):
return self._set(splitRatio=value)
def setColumnsToCategorical(self, value, *args):
assert_is_type(value, [str], str)
if isinstance(value, str):
prepared_array = [value]
else:
prepared_array = value
for arg in args:
prepared_array.append(arg)
return self._set(columnsToCategorical=value)
# Setters for parameters which are defined on MOJO as well
def setPredictionCol(self, value):
return self._set(predictionCol=value)
def setDetailedPredictionCol(self, value):
return self._set(detailedPredictionCol=value)
def setWithDetailedPredictionCol(self, value):
warnings.warn(
"The method will be removed without a replacement in the version 3.34."
"Detailed prediction columns is enabled by default.",
DeprecationWarning)
return self
def setFeaturesCols(self, value):
return self._set(featuresCols=value)
def setConvertUnknownCategoricalLevelsToNa(self, value):
return self._set(convertUnknownCategoricalLevelsToNa=value)
def setConvertInvalidNumbersToNa(self, value):
return self._set(convertInvalidNumbersToNa=value)
def setNamedMojoOutputColumns(self, value):
return self._set(namedMojoOutputColumns=value)
def setWithContributions(self, value):
return self._set(withContributions=value)
def setWithLeafNodeAssignments(self, value):
return self._set(withLeafNodeAssignments=value)
def setWithStageResults(self, value):
return self._set(withStageResults=value)