VarianceThresholdSelector

class pyspark.ml.feature.VarianceThresholdSelector(*, featuresCol: str = 'features', outputCol: Optional[str] = None, varianceThreshold: float = 0.0)

Feature selector that removes all low-variance features. Features with a (sample) 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: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

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: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

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: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

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

:py:class:`Transformer` or a list ofpy:class:Transformer

fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

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() → str

Gets the value of featuresCol or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

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

getOutputCol() → str

Gets the value of outputCol or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getVarianceThreshold() → float

Gets the value of varianceThreshold or its default value.

New in version 3.1.0.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

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

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → pyspark.ml.feature.VarianceThresholdSelector

Sets the value of featuresCol.

New in version 3.1.0.

setOutputCol(value: str) → pyspark.ml.feature.VarianceThresholdSelector

Sets the value of outputCol.

New in version 3.1.0.

setParams(self, \*, featuresCol="features", outputCol=None, varianceThreshold=0.0)

Sets params for this VarianceThresholdSelector.

New in version 3.1.0.

setVarianceThreshold(value: float) → pyspark.ml.feature.VarianceThresholdSelector

Sets the value of varianceThreshold.

New in version 3.1.0.

write() → pyspark.ml.util.JavaMLWriter

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.')

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