MaxAbsScaler#

class pyspark.ml.feature.MaxAbsScaler(*, inputCol=None, outputCol=None)[source]#

Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.

New in version 2.0.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(Vectors.dense([1.0]),), (Vectors.dense([2.0]),)], ["a"])
>>> maScaler = MaxAbsScaler(outputCol="scaled")
>>> maScaler.setInputCol("a")
MaxAbsScaler...
>>> model = maScaler.fit(df)
>>> model.setOutputCol("scaledOutput")
MaxAbsScalerModel...
>>> model.transform(df).show()
+-----+------------+
|    a|scaledOutput|
+-----+------------+
|[1.0]|       [0.5]|
|[2.0]|       [1.0]|
+-----+------------+
...
>>> scalerPath = temp_path + "/max-abs-scaler"
>>> maScaler.save(scalerPath)
>>> loadedMAScaler = MaxAbsScaler.load(scalerPath)
>>> loadedMAScaler.getInputCol() == maScaler.getInputCol()
True
>>> loadedMAScaler.getOutputCol() == maScaler.getOutputCol()
True
>>> modelPath = temp_path + "/max-abs-scaler-model"
>>> model.save(modelPath)
>>> loadedModel = MaxAbsScalerModel.load(modelPath)
>>> loadedModel.maxAbs == model.maxAbs
True
>>> loadedModel.transform(df).take(1) == model.transform(df).take(1)
True

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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getInputCol`()	Gets the value of inputCol 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.
`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`.
`setParams`(self, \*[, inputCol, outputCol])	Sets params for this MaxAbsScaler.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`inputCol`
`outputCol`
`params`	Returns all params ordered by name.

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

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)#: 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=None)#

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

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

getInputCol()#: Gets the value of inputCol or its default value.

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

getOutputCol()#: Gets the value of outputCol or its default value.

getParam(paramName)#: Gets a param by its name.

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.

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

classmethod 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)[source]#: Sets the value of inputCol.

setOutputCol(value)[source]#: Sets the value of outputCol.

setParams(self, \*, inputCol=None, outputCol=None)[source]#: Sets params for this MaxAbsScaler.

New in version 2.0.0.

write()#: Returns an MLWriter instance for this ML instance.

Attributes Documentation

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

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.

uid#: A unique id for the object.