RobustScalerModel

class pyspark.ml.feature.RobustScalerModel(java_model: Optional[JavaObject] = None)

Model fitted by RobustScaler.

New in version 3.0.0.

Methods

clear(param)	Clears a param from the param map if it has been explicitly set.
copy([extra])	Creates a copy of this instance with the same uid and some extra params.
explainParam(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()	Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
getInputCol()	Gets the value of inputCol or its default value.
getLower()	Gets the value of lower or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()	Gets the value of outputCol or its default value.
getParam(paramName)	Gets a param by its name.
getRelativeError()	Gets the value of relativeError or its default value.
getUpper()	Gets the value of upper or its default value.
getWithCentering()	Gets the value of withCentering or its default value.
getWithScaling()	Gets the value of withScaling or its default value.
hasDefault(param)	Checks whether a param has a default value.
hasParam(paramName)	Tests whether this instance contains a param with a given (string) name.
isDefined(param)	Checks whether a param is explicitly set by user or has a default value.
isSet(param)	Checks whether a param is explicitly set by user.
load(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)	Sets a parameter in the embedded param map.
setInputCol(value)	Sets the value of inputCol.
setOutputCol(value)	Sets the value of outputCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

inputCol
lower
median	Median of the RobustScalerModel.
outputCol
params	Returns all params ordered by name.
range	Quantile range of the RobustScalerModel.
relativeError
upper
withCentering
withScaling

Methods Documentation

clear(param: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

JavaParams

Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getInputCol() → str

Gets the value of inputCol or its default value.

getLower() → float

Gets the value of lower or its default value.

New in version 3.0.0.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str

Gets the value of outputCol or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getRelativeError() → float

Gets the value of relativeError or its default value.

getUpper() → float

Gets the value of upper or its default value.

New in version 3.0.0.

getWithCentering() → bool

Gets the value of withCentering or its default value.

New in version 3.0.0.

getWithScaling() → bool

Gets the value of withScaling or its default value.

New in version 3.0.0.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setInputCol(value: str) → pyspark.ml.feature.RobustScalerModel

Sets the value of inputCol.

New in version 3.0.0.

setOutputCol(value: str) → pyspark.ml.feature.RobustScalerModel

Sets the value of outputCol.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')

lower: pyspark.ml.param.Param[float] = Param(parent='undefined', name='lower', doc='Lower quantile to calculate quantile range')

median

Median of the RobustScalerModel.

New in version 3.0.0.

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

range

Quantile range of the RobustScalerModel.

New in version 3.0.0.

relativeError = Param(parent='undefined', name='relativeError', doc='the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1]')

upper: pyspark.ml.param.Param[float] = Param(parent='undefined', name='upper', doc='Upper quantile to calculate quantile range')

withCentering: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='withCentering', doc='Whether to center data with median')

withScaling: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='withScaling', doc='Whether to scale the data to quantile range')

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