ChiSqSelector¶
-
class
pyspark.ml.feature.
ChiSqSelector
(numTopFeatures=50, featuresCol='features', outputCol=None, labelCol='label', selectorType='numTopFeatures', percentile=0.1, fpr=0.05, fdr=0.05, fwe=0.05)[source]¶ 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.
>>> 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
New in version 2.0.0.
Methods
Attributes
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
extra – Extra parameters to copy to the new instance
- Returns
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
extra – extra param values
- Returns
merged param map
-
fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – 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
fitted model(s)
New in version 1.3.0.
-
fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
-
getFdr
()¶ Gets the value of fdr or its default value.
New in version 2.2.0.
-
getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
-
getFpr
()¶ Gets the value of fpr or its default value.
New in version 2.1.0.
-
getFwe
()¶ Gets the value of fwe or its default value.
New in version 2.2.0.
-
getLabelCol
()¶ Gets the value of labelCol or its default value.
-
getNumTopFeatures
()¶ Gets the value of numTopFeatures or its default value.
New in version 2.0.0.
-
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.
-
getPercentile
()¶ Gets the value of percentile or its default value.
New in version 2.1.0.
-
getSelectorType
()¶ Gets the value of selectorType or its default value.
New in version 2.1.0.
-
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.
-
setFdr
(value)[source]¶ Sets the value of
fdr
. Only applicable when selectorType = “fdr”.New in version 2.2.0.
-
setFeaturesCol
(value)[source]¶ Sets the value of
featuresCol
.
-
setFpr
(value)[source]¶ Sets the value of
fpr
. Only applicable when selectorType = “fpr”.New in version 2.1.0.
-
setFwe
(value)[source]¶ Sets the value of
fwe
. Only applicable when selectorType = “fwe”.New in version 2.2.0.
-
setNumTopFeatures
(value)[source]¶ Sets the value of
numTopFeatures
. Only applicable when selectorType = “numTopFeatures”.New in version 2.0.0.
-
setParams
(self, numTopFeatures=50, featuresCol='features', outputCol=None, labelCol='labels', selectorType='numTopFeatures', percentile=0.1, fpr=0.05, fdr=0.05, fwe=0.05)[source]¶ Sets params for this ChiSqSelector.
New in version 2.0.0.
-
setPercentile
(value)[source]¶ Sets the value of
percentile
. Only applicable when selectorType = “percentile”.New in version 2.1.0.
-
setSelectorType
(value)[source]¶ Sets the value of
selectorType
.New in version 2.1.0.
-
write
()¶ 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 typeParam
.
-
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 of the ChisqSelector. Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.')¶