FeatureHasher#

classpyspark.ml.feature.FeatureHasher(*,numFeatures=262144,inputCols=None,outputCol=None,categoricalCols=None)[source]#

Feature hashing projects a set of categorical or numerical features into a feature vector ofspecified dimension (typically substantially smaller than that of the original featurespace). This is done using the hashing trick (https://en.wikipedia.org/wiki/Feature_hashing)to map features to indices in the feature vector.

The FeatureHasher transformer operates on multiple columns. Each column may contain eithernumeric or categorical features. Behavior and handling of column data types is as follows:

• Numeric columns:

  For numeric features, the hash value of the column name is used to map thefeature value to its index in the feature vector. By default, numeric featuresare not treated as categorical (even when they are integers). To treat themas categorical, specify the relevant columns incategoricalCols.

• String columns:

  For categorical features, the hash value of the string “column_name=value”is used to map to the vector index, with an indicator value of1.0.Thus, categorical features are “one-hot” encoded(similarly to usingOneHotEncoder withdropLast=false).

• Boolean columns:

  Boolean values are treated in the same way as string columns. That is,boolean features are represented as “column_name=true” or “column_name=false”,with an indicator value of1.0.

Null (missing) values are ignored (implicitly zero in the resulting feature vector).

Since a simple modulo is used to transform the hash function to a vector index,it is advisable to use a power of two as thenumFeatures parameter;otherwise the features will not be mapped evenly to the vector indices.

New in version 2.3.0.

Examples

>>>data=[(2.0,True,"1","foo"),(3.0,False,"2","bar")]>>>cols=["real","bool","stringNum","string"]>>>df=spark.createDataFrame(data,cols)>>>hasher=FeatureHasher()>>>hasher.setInputCols(cols)FeatureHasher...>>>hasher.setOutputCol("features")FeatureHasher...>>>hasher.transform(df).head().featuresSparseVector(262144, {174475: 2.0, 247670: 1.0, 257907: 1.0, 262126: 1.0})>>>hasher.setCategoricalCols(["real"]).transform(df).head().featuresSparseVector(262144, {171257: 1.0, 247670: 1.0, 257907: 1.0, 262126: 1.0})>>>hasherPath=temp_path+"/hasher">>>hasher.save(hasherPath)>>>loadedHasher=FeatureHasher.load(hasherPath)>>>loadedHasher.getNumFeatures()==hasher.getNumFeatures()True>>>loadedHasher.transform(df).head().features==hasher.transform(df).head().featuresTrue

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.
getCategoricalCols()	Gets the value of binary or its default value.
getInputCols()	Gets the value of inputCols or its default value.
getNumFeatures()	Gets the value of numFeatures 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.
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 ofread().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.
setCategoricalCols(value)	Sets the value ofcategoricalCols.
setInputCols(value)	Sets the value ofinputCols.
setNumFeatures(value)	Sets the value ofnumFeatures.
setOutputCol(value)	Sets the value ofoutputCol.
setParams(self, \*[, numFeatures, ...])	Sets params for this FeatureHasher.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

categoricalCols
inputCols
numFeatures
outputCol
params	Returns all params ordered by name.

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 someextra params. This implementation first calls Params.copy andthen make a copy of the companion Java pipeline component withextra params. So both the Python wrapper and the Java pipelinecomponent 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 optionaldefault value and user-supplied value in a string.

explainParams()#

Returns the documentation of all params with their optionallydefault values and user-supplied values.

extractParamMap(extra=None)#

Extracts the embedded default param values and user-suppliedvalues, and then merges them with extra values from input intoa flat param map, where the latter value is used if there existconflicts, i.e., with ordering: default param values <user-supplied values < extra.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getCategoricalCols()[source]#

Gets the value of binary or its default value.

New in version 2.3.0.

getInputCols()#

Gets the value of inputCols or its default value.

getNumFeatures()#

Gets the value of numFeatures or its default value.

getOrDefault(param)#

Gets the value of a param in the user-supplied param map or itsdefault 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.

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 hasa default value.

isSet(param)#

Checks whether a param is explicitly set by user.

classmethodload(path)#

Reads an ML instance from the input path, a shortcut ofread().load(path).

classmethodread()#

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.

setCategoricalCols(value)[source]#

Sets the value ofcategoricalCols.

New in version 2.3.0.

setInputCols(value)[source]#

Sets the value ofinputCols.

setNumFeatures(value)[source]#

Sets the value ofnumFeatures.

setOutputCol(value)[source]#

Sets the value ofoutputCol.

setParams(self,\*,numFeatures=1<<18,inputCols=None,outputCol=None,categoricalCols=None)[source]#

Sets params for this FeatureHasher.

New in version 2.3.0.

transform(dataset,params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write()#

Returns an MLWriter instance for this ML instance.

Attributes Documentation

categoricalCols=Param(parent='undefined',name='categoricalCols',doc='numericcolumnstotreatascategorical')#

inputCols=Param(parent='undefined',name='inputCols',doc='inputcolumnnames.')#

numFeatures=Param(parent='undefined',name='numFeatures',doc='Numberoffeatures.Shouldbegreaterthan0.')#

outputCol=Param(parent='undefined',name='outputCol',doc='outputcolumnname.')#

params#

Returns all params ordered by name. The default implementationusesdir() to get all attributes of typeParam.

uid#

A unique id for the object.