UnivariateFeatureSelector¶

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

Feature selector based on univariate statistical tests against labels. Currently, Spark supports three Univariate Feature Selectors: chi-squared, ANOVA F-test and F-value. User can choose Univariate Feature Selector by setting 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 ANOVA F-test, 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.

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: 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.

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

Fits a model to the input dataset for each param map in paramMaps.

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

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

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

getLabelType() → str¶: Gets the value of labelType 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.

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

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

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.

setFeatureType(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of featureType.

setFeaturesCol(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of featuresCol.

setLabelCol(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of labelCol.

setLabelType(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of labelType.

setOutputCol(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of outputCol.

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

setSelectionMode(value: str) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of selectionMode.

setSelectionThreshold(value: float) → pyspark.ml.feature.UnivariateFeatureSelector ¶: Sets the value of selectionThreshold.

write() → pyspark.ml.util.JavaMLWriter¶: 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