MaxAbsScaler

class pyspark.ml.connect.feature.MaxAbsScaler(*, inputCol=None, outputCol=None)

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 3.5.0.

Examples

>>> from pyspark.ml.connect.feature import MaxAbsScaler
>>> scaler = MaxAbsScaler(inputCol='features', outputCol='scaled_features')
>>> dataset = spark.createDataFrame([
...     ([1.0, 2.0],),
...     ([2.0, -1.0],),
...     ([-3.0, -2.0],),
... ], schema=['features'])
>>> scaler_model = scaler.fit(dataset)
>>> transformed_dataset = scaler_model.transform(dataset)
>>> transformed_dataset.show(truncate=False)
+------------+--------------------------+
|features    |scaled_features           |
+------------+--------------------------+
|[1.0, 2.0]  |[0.3333333333333333, 1.0] |
|[2.0, -1.0] |[0.6666666666666666, -0.5]|
|[-3.0, -2.0]|[-1.0, -1.0]              |
+------------+--------------------------+

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.
fit(dataset[, params])	Fits a model to the input dataset with optional parameters.
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)	Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.
loadFromLocal(path)	Load Estimator / Transformer / Model / Evaluator from provided local path.
save(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.
saveToLocal(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided local path.
set(param, value)	Sets a parameter in the embedded param map.

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. The default implementation creates a shallow copy using copy.copy(), and then copies the embedded and extra parameters over and returns the copy. Subclasses should override this method if the default approach is not sufficient.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

Params

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 3.5.0.

Parameters

datasetpyspark.sql.DataFrame or py:class:pandas.DataFrame

input dataset, it can be either pandas dataframe or spark dataframe.

paramsa dict of param values, optional

an optional param map that overrides embedded params.

Returns

Transformer

fitted model

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)

Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.

New in version 3.5.0.

classmethod loadFromLocal(path)

Load Estimator / Transformer / Model / Evaluator from provided local path.

New in version 3.5.0.

save(path, *, overwrite=False)

Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.

New in version 3.5.0.

saveToLocal(path, *, overwrite=False)

Save Estimator / Transformer / Model / Evaluator to provided local path.

New in version 3.5.0.

set(param, value)

Sets a parameter in the embedded param map.

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.