VarianceThresholdSelector¶

class pyspark.ml.feature.VarianceThresholdSelector(*, featuresCol='features', outputCol=None, varianceThreshold=0.0)[source]¶

Feature selector that removes all low-variance features. Features with a variance not greater than the threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.

New in version 3.1.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame(
...    [(Vectors.dense([6.0, 7.0, 0.0, 7.0, 6.0, 0.0]),),
...     (Vectors.dense([0.0, 9.0, 6.0, 0.0, 5.0, 9.0]),),
...     (Vectors.dense([0.0, 9.0, 3.0, 0.0, 5.0, 5.0]),),
...     (Vectors.dense([0.0, 9.0, 8.0, 5.0, 6.0, 4.0]),),
...     (Vectors.dense([8.0, 9.0, 6.0, 5.0, 4.0, 4.0]),),
...     (Vectors.dense([8.0, 9.0, 6.0, 0.0, 0.0, 0.0]),)],
...    ["features"])
>>> selector = VarianceThresholdSelector(varianceThreshold=8.2, outputCol="selectedFeatures")
>>> model = selector.fit(df)
>>> model.getFeaturesCol()
'features'
>>> model.setFeaturesCol("features")
VarianceThresholdSelectorModel...
>>> model.transform(df).head().selectedFeatures
DenseVector([6.0, 7.0, 0.0])
>>> model.selectedFeatures
[0, 3, 5]
>>> varianceThresholdSelectorPath = temp_path + "/variance-threshold-selector"
>>> selector.save(varianceThresholdSelectorPath)
>>> loadedSelector = VarianceThresholdSelector.load(varianceThresholdSelectorPath)
>>> loadedSelector.getVarianceThreshold() == selector.getVarianceThreshold()
True
>>> modelPath = temp_path + "/variance-threshold-selector-model"
>>> model.save(modelPath)
>>> loadedModel = VarianceThresholdSelectorModel.load(modelPath)
>>> loadedModel.selectedFeatures == model.selectedFeatures
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.
`getFeaturesCol`()	Gets the value of featuresCol 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.
`getVarianceThreshold`()	Gets the value of varianceThreshold 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.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, featuresCol, …])	Sets params for this VarianceThresholdSelector.
`setVarianceThreshold`(value)	Sets the value of `varianceThreshold`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`featuresCol`
`outputCol`
`params`	Returns all params ordered by name.
`varianceThreshold`

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.

getFeaturesCol()¶: Gets the value of featuresCol 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.

getVarianceThreshold()¶: Gets the value of varianceThreshold or its default value.

New in version 3.1.0.

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.

setFeaturesCol(value)[source]¶: Sets the value of featuresCol.

New in version 3.1.0.

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

New in version 3.1.0.

setParams(self, \*, featuresCol="features", outputCol=None, varianceThreshold=0.0)[source]¶: Sets params for this VarianceThresholdSelector.

New in version 3.1.0.

setVarianceThreshold(value)[source]¶: Sets the value of varianceThreshold.

New in version 3.1.0.

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

Attributes Documentation

featuresCol = Param(parent='undefined', name='featuresCol', doc='features 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.

varianceThreshold = Param(parent='undefined', name='varianceThreshold', doc='Param for variance threshold. Features with a variance not greater than this threshold will be removed. The default value is 0.0.')¶

UnivariateFeatureSelectorModel VarianceThresholdSelectorModel