StandardScaler#
- class pyspark.ml.feature.StandardScaler(*, withMean=False, withStd=True, inputCol=None, outputCol=None)[source]#
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
The “unit std” is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
New in version 1.4.0.
Examples
>>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"]) >>> standardScaler = StandardScaler() >>> standardScaler.setInputCol("a") StandardScaler... >>> standardScaler.setOutputCol("scaled") StandardScaler... >>> model = standardScaler.fit(df) >>> model.getInputCol() 'a' >>> model.setOutputCol("output") StandardScalerModel... >>> model.mean DenseVector([1.0]) >>> model.std DenseVector([1.4142]) >>> model.transform(df).collect()[1].output DenseVector([1.4142]) >>> standardScalerPath = temp_path + "/standard-scaler" >>> standardScaler.save(standardScalerPath) >>> loadedStandardScaler = StandardScaler.load(standardScalerPath) >>> loadedStandardScaler.getWithMean() == standardScaler.getWithMean() True >>> loadedStandardScaler.getWithStd() == standardScaler.getWithStd() True >>> modelPath = temp_path + "/standard-scaler-model" >>> model.save(modelPath) >>> loadedModel = StandardScalerModel.load(modelPath) >>> loadedModel.std == model.std True >>> loadedModel.mean == model.mean 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.
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.
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.
Gets the value of outputCol or its default value.
getParam
(paramName)Gets a param by its name.
Gets the value of withMean or its default value.
Gets the value of withStd 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
.setParams
(self, \*[, withMean, withStd, ...])Sets params for this StandardScaler.
setWithMean
(value)Sets the value of
withMean
.setWithStd
(value)Sets the value of
withStd
.write
()Returns an MLWriter instance for this ML instance.
Attributes
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
- dataset
pyspark.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.
- dataset
- Returns
Transformer
or a list ofTransformer
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
- dataset
pyspark.sql.DataFrame
input dataset.
- paramMaps
collections.abc.Sequence
A Sequence of param maps.
- dataset
- 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.
- getWithMean()#
Gets the value of withMean or its default value.
New in version 1.4.0.
- getWithStd()#
Gets the value of withStd or its default value.
New in version 1.4.0.
- 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.
- setParams(self, \*, withMean=False, withStd=True, inputCol=None, outputCol=None)[source]#
Sets params for this StandardScaler.
New in version 1.4.0.
- 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 typeParam
.
- withMean = Param(parent='undefined', name='withMean', doc='Center data with mean')#
- withStd = Param(parent='undefined', name='withStd', doc='Scale to unit standard deviation')#
- uid#
A unique id for the object.