ChiSqSelector¶

class pyspark.ml.feature.ChiSqSelector(*, numTopFeatures: int = 50, featuresCol: str = 'features', outputCol: Optional[str] = None, labelCol: str = 'label', selectorType: str = 'numTopFeatures', percentile: float = 0.1, fpr: float = 0.05, fdr: float = 0.05, fwe: float = 0.05)¶

Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label. The selector supports different selection methods: numTopFeatures, percentile, fpr, fdr, fwe.

numTopFeatures chooses a fixed number of top features according to a chi-squared test.

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 method is numTopFeatures, with the default number of top features set to 50.

Use UnivariateFeatureSelector

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame(
...    [(Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0),
...     (Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0),
...     (Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0)],
...    ["features", "label"])
>>> selector = ChiSqSelector(numTopFeatures=1, outputCol="selectedFeatures")
>>> model = selector.fit(df)
>>> model.getFeaturesCol()
'features'
>>> model.setFeaturesCol("features")
ChiSqSelectorModel...
>>> model.transform(df).head().selectedFeatures
DenseVector([18.0])
>>> model.selectedFeatures
[2]
>>> chiSqSelectorPath = temp_path + "/chi-sq-selector"
>>> selector.save(chiSqSelectorPath)
>>> loadedSelector = ChiSqSelector.load(chiSqSelectorPath)
>>> loadedSelector.getNumTopFeatures() == selector.getNumTopFeatures()
True
>>> modelPath = temp_path + "/chi-sq-selector-model"
>>> model.save(modelPath)
>>> loadedModel = ChiSqSelectorModel.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.
`getFdr`()	Gets the value of fdr or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getFpr`()	Gets the value of fpr or its default value.
`getFwe`()	Gets the value of fwe or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getNumTopFeatures`()	Gets the value of numTopFeatures 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.
`getPercentile`()	Gets the value of percentile or its default value.
`getSelectorType`()	Gets the value of selectorType 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.
`setFdr`(value)	Sets the value of `fdr`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setFpr`(value)	Sets the value of `fpr`.
`setFwe`(value)	Sets the value of `fwe`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setNumTopFeatures`(value)	Sets the value of `numTopFeatures`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, numTopFeatures, …])	Sets params for this ChiSqSelector.
`setPercentile`(value)	Sets the value of `percentile`.
`setSelectorType`(value)	Sets the value of `selectorType`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`fdr`
`featuresCol`
`fpr`
`fwe`
`labelCol`
`numTopFeatures`
`outputCol`
`params`	Returns all params ordered by name.
`percentile`
`selectorType`

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.

getFdr() → float¶: Gets the value of fdr or its default value.

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

getFpr() → float¶: Gets the value of fpr or its default value.

getFwe() → float¶: Gets the value of fwe or its default value.

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

getNumTopFeatures() → int¶: Gets the value of numTopFeatures 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.

getPercentile() → float¶: Gets the value of percentile or its default value.

getSelectorType() → str¶: Gets the value of selectorType 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.

setFdr(value: float) → P¶: Sets the value of fdr. Only applicable when selectorType = “fdr”.

setFeaturesCol(value: str) → P¶: Sets the value of featuresCol.

setFpr(value: float) → P¶: Sets the value of fpr. Only applicable when selectorType = “fpr”.

setFwe(value: float) → P¶: Sets the value of fwe. Only applicable when selectorType = “fwe”.

setLabelCol(value: str) → P¶: Sets the value of labelCol.

setNumTopFeatures(value: int) → P¶: Sets the value of numTopFeatures. Only applicable when selectorType = “numTopFeatures”.

setOutputCol(value: str) → P¶: Sets the value of outputCol.

setParams(self, \*, numTopFeatures=50, featuresCol="features", outputCol=None, labelCol="label", selectorType="numTopFeatures", percentile=0.1, fpr=0.05, fdr=0.05, fwe=0.05)¶: Sets params for this ChiSqSelector.

setPercentile(value: float) → P¶: Sets the value of percentile. Only applicable when selectorType = “percentile”.

setSelectorType(value: str) → P¶: Sets the value of selectorType.

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

fdr = Param(parent='undefined', name='fdr', doc='The upper bound of the expected false discovery rate.')¶

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

fpr = Param(parent='undefined', name='fpr', doc='The highest p-value for features to be kept.')¶

fwe = Param(parent='undefined', name='fwe', doc='The upper bound of the expected family-wise error rate.')¶

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

numTopFeatures = Param(parent='undefined', name='numTopFeatures', doc='Number of features that selector will select, ordered by ascending p-value. If the number of features is < numTopFeatures, then this will select all features.')¶

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.

percentile = Param(parent='undefined', name='percentile', doc='Percentile of features that selector will select, ordered by ascending p-value.')¶

selectorType = Param(parent='undefined', name='selectorType', doc='The selector type. Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.')¶

Bucketizer

ChiSqSelectorModel