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 (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.
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 featuresCol 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 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
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.
- 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.
- 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 typeParam
.
- 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.')#
- uid#
A unique id for the object.