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.')