pyspark.ml.feature.
RobustScalerModel
Model fitted by RobustScaler.
RobustScaler
New in version 3.0.0.
Methods
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
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()
getInputCol
Gets the value of inputCol or its default value.
getLower()
getLower
Gets the value of lower or its default value.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()
getOutputCol
Gets the value of outputCol or its default value.
getParam(paramName)
getParam
Gets a param by its name.
getRelativeError()
getRelativeError
Gets the value of relativeError or its default value.
getUpper()
getUpper
Gets the value of upper or its default value.
getWithCentering()
getWithCentering
Gets the value of withCentering or its default value.
getWithScaling()
getWithScaling
Gets the value of withScaling or its default value.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setInputCol(value)
setInputCol
Sets the value of inputCol.
inputCol
setOutputCol(value)
setOutputCol
Sets the value of outputCol.
outputCol
transform(dataset[, params])
transform
Transforms the input dataset with optional parameters.
write()
write
Returns an MLWriter instance for this ML instance.
Attributes
lower
median
Median of the RobustScalerModel.
params
Returns all params ordered by name.
range
Quantile range of the RobustScalerModel.
relativeError
upper
withCentering
withScaling
Methods Documentation
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.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
extra param values
merged param map
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
transformed dataset
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param