MinMaxScaler#

class pyspark.ml.feature.MinMaxScaler(*, min=0.0, max=1.0, inputCol=None, outputCol=None)[source]#

Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling. The rescaled value for feature E is calculated as,

Rescaled(e_i) = (e_i - E_min) / (E_max - E_min) * (max - min) + min

For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min)

New in version 1.6.0.

Notes

Since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
>>> mmScaler = MinMaxScaler(outputCol="scaled")
>>> mmScaler.setInputCol("a")
MinMaxScaler...
>>> model = mmScaler.fit(df)
>>> model.setOutputCol("scaledOutput")
MinMaxScalerModel...
>>> model.originalMin
DenseVector([0.0])
>>> model.originalMax
DenseVector([2.0])
>>> model.transform(df).show()
+-----+------------+
|    a|scaledOutput|
+-----+------------+
|[0.0]|       [0.0]|
|[2.0]|       [1.0]|
+-----+------------+
...
>>> minMaxScalerPath = temp_path + "/min-max-scaler"
>>> mmScaler.save(minMaxScalerPath)
>>> loadedMMScaler = MinMaxScaler.load(minMaxScalerPath)
>>> loadedMMScaler.getMin() == mmScaler.getMin()
True
>>> loadedMMScaler.getMax() == mmScaler.getMax()
True
>>> modelPath = temp_path + "/min-max-scaler-model"
>>> model.save(modelPath)
>>> loadedModel = MinMaxScalerModel.load(modelPath)
>>> loadedModel.originalMin == model.originalMin
True
>>> loadedModel.originalMax == model.originalMax
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.

getMax()

Gets the value of max or its default value.

getMin()

Gets the value of min 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.

setMax(value)

Sets the value of max.

setMin(value)

Sets the value of min.

setOutputCol(value)

Sets the value of outputCol.

setParams(self, \*[, min, max, inputCol, ...])

Sets params for this MinMaxScaler.

write()

Returns an MLWriter instance for this ML instance.

Attributes

inputCol

max

min

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.

getMax()#

Gets the value of max or its default value.

New in version 1.6.0.

getMin()#

Gets the value of min or its default value.

New in version 1.6.0.

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.

setMax(value)[source]#

Sets the value of max.

New in version 1.6.0.

setMin(value)[source]#

Sets the value of min.

New in version 1.6.0.

setOutputCol(value)[source]#

Sets the value of outputCol.

setParams(self, \*, min=0.0, max=1.0, inputCol=None, outputCol=None)[source]#

Sets params for this MinMaxScaler.

New in version 1.6.0.

write()#

Returns an MLWriter instance for this ML instance.

Attributes Documentation

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')#
max = Param(parent='undefined', name='max', doc='Upper bound of the output feature range')#
min = Param(parent='undefined', name='min', doc='Lower bound of the output feature range')#
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.