UnivariateFeatureSelector¶

class pyspark.ml.feature.UnivariateFeatureSelector(*, featuresCol='features', outputCol=None, labelCol='label', selectionMode='numTopFeatures')[source]¶

The user can set featureType and labelType, and Spark will pick the score function based on the specified featureType and labelType.

The following combination of featureType and labelType are supported:

featureType categorical and labelType categorical, Spark uses chi-squared, i.e. chi2 in sklearn.
featureType continuous and labelType categorical, Spark uses ANOVATest, i.e. f_classif in sklearn.
featureType continuous and labelType continuous, Spark uses F-value, i.e. f_regression in sklearn.

The UnivariateFeatureSelector supports different selection modes: numTopFeatures, percentile, fpr, fdr, fwe.

numTopFeatures chooses a fixed number of top features according to a according to a hypothesis.
percentile is similar but chooses a fraction of all features instead of a fixed number.
fpr chooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.
fdr uses the Benjamini-Hochberg procedure to choose all features whose false discovery rate is below a threshold.
fwe chooses all features whose p-values are below a threshold. The threshold is scaled by 1 / numFeatures, thus controlling the family-wise error rate of selection.

By default, the selection mode is numTopFeatures.

New in version 3.1.1.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame(
...    [(Vectors.dense([1.7, 4.4, 7.6, 5.8, 9.6, 2.3]), 3.0),
...     (Vectors.dense([8.8, 7.3, 5.7, 7.3, 2.2, 4.1]), 2.0),
...     (Vectors.dense([1.2, 9.5, 2.5, 3.1, 8.7, 2.5]), 1.0),
...     (Vectors.dense([3.7, 9.2, 6.1, 4.1, 7.5, 3.8]), 2.0),
...     (Vectors.dense([8.9, 5.2, 7.8, 8.3, 5.2, 3.0]), 4.0),
...     (Vectors.dense([7.9, 8.5, 9.2, 4.0, 9.4, 2.1]), 4.0)],
...    ["features", "label"])
>>> selector = UnivariateFeatureSelector(outputCol="selectedFeatures")
>>> selector.setFeatureType("continuous").setLabelType("categorical").setSelectionThreshold(1)
UnivariateFeatureSelector...
>>> model = selector.fit(df)
>>> model.getFeaturesCol()
'features'
>>> model.setFeaturesCol("features")
UnivariateFeatureSelectorModel...
>>> model.transform(df).head().selectedFeatures
DenseVector([7.6])
>>> model.selectedFeatures
[2]
>>> selectorPath = temp_path + "/selector"
>>> selector.save(selectorPath)
>>> loadedSelector = UnivariateFeatureSelector.load(selectorPath)
>>> loadedSelector.getSelectionThreshold() == selector.getSelectionThreshold()
True
>>> modelPath = temp_path + "/selector-model"
>>> model.save(modelPath)
>>> loadedModel = UnivariateFeatureSelectorModel.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.
`getFeatureType`()	Gets the value of featureType or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getLabelType`()	Gets the value of labelType 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.
`getSelectionMode`()	Gets the value of selectionMode or its default value.
`getSelectionThreshold`()	Gets the value of selectionThreshold 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.
`setFeatureType`(value)	Sets the value of `featureType`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setLabelType`(value)	Sets the value of `labelType`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, featuresCol, …])	Sets params for this UnivariateFeatureSelector.
`setSelectionMode`(value)	Sets the value of `selectionMode`.
`setSelectionThreshold`(value)	Sets the value of `selectionThreshold`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`featureType`
`featuresCol`
`labelCol`
`labelType`
`outputCol`
`params`	Returns all params ordered by name.
`selectionMode`
`selectionThreshold`

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.

getFeatureType()¶: Gets the value of featureType or its default value.

New in version 3.1.1.

getFeaturesCol()¶: Gets the value of featuresCol or its default value.

getLabelCol()¶: Gets the value of labelCol or its default value.

getLabelType()¶: Gets the value of labelType or its default value.

New in version 3.1.1.

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.

getSelectionMode()¶: Gets the value of selectionMode or its default value.

New in version 3.1.1.

getSelectionThreshold()¶: Gets the value of selectionThreshold or its default value.

New in version 3.1.1.

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.

setFeatureType(value)[source]¶: Sets the value of featureType.

New in version 3.1.1.

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

setLabelCol(value)[source]¶: Sets the value of labelCol.

setLabelType(value)[source]¶: Sets the value of labelType.

New in version 3.1.1.

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

setParams(self, \*, featuresCol="features", outputCol=None, labelCol="labels", selectionMode="numTopFeatures")[source]¶: Sets params for this UnivariateFeatureSelector.

New in version 3.1.1.

setSelectionMode(value)[source]¶: Sets the value of selectionMode.

New in version 3.1.1.

setSelectionThreshold(value)[source]¶: Sets the value of selectionThreshold.

New in version 3.1.1.

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

Attributes Documentation

featureType = Param(parent='undefined', name='featureType', doc='The feature type. Supported options: categorical, continuous.')¶

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

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')¶

labelType = Param(parent='undefined', name='labelType', doc='The label type. Supported options: categorical, continuous.')¶

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.

selectionMode = Param(parent='undefined', name='selectionMode', doc='The selection mode. Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.')¶

selectionThreshold = Param(parent='undefined', name='selectionThreshold', doc='The upper bound of the features that selector will select.')¶

Tokenizer UnivariateFeatureSelectorModel