HashingTF#

class pyspark.ml.feature.HashingTF(*, numFeatures=262144, binary=False, inputCol=None, outputCol=None)[source]#

Maps a sequence of terms to their term frequencies using the hashing trick. Currently we use Austin Appleby’s MurmurHash 3 algorithm (MurmurHash3_x86_32) to calculate the hash code value for the term object. Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the numFeatures parameter; otherwise the features will not be mapped evenly to the columns.

New in version 1.3.0.

Examples

>>> df = spark.createDataFrame([(["a", "b", "c"],)], ["words"])
>>> hashingTF = HashingTF(inputCol="words", outputCol="features")
>>> hashingTF.setNumFeatures(10)
HashingTF...
>>> hashingTF.transform(df).head().features
SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0})
>>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs
SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0})
>>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
>>> hashingTF.transform(df, params).head().vector
SparseVector(5, {0: 1.0, 2: 1.0, 3: 1.0})
>>> hashingTFPath = temp_path + "/hashing-tf"
>>> hashingTF.save(hashingTFPath)
>>> loadedHashingTF = HashingTF.load(hashingTFPath)
>>> loadedHashingTF.getNumFeatures() == hashingTF.getNumFeatures()
True
>>> loadedHashingTF.transform(df).take(1) == hashingTF.transform(df).take(1)
True
>>> hashingTF.indexOf("b")
5

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.
`getBinary`()	Gets the value of binary or its default value.
`getInputCol`()	Gets the value of inputCol 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.
`indexOf`(term)	Returns the index of the input term.
`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.
`setBinary`(value)	Sets the value of `binary`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setNumFeatures`(value)	Sets the value of `numFeatures`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, numFeatures, binary, ...])	Sets params for this HashingTF.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`binary`
`inputCol`
`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 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)#: 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

extradict, optional: extra param values

Returns

dict: merged param map

getBinary()[source]#: Gets the value of binary or its default value.

New in version 2.0.0.

getInputCol()#: Gets the value of inputCol 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. 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.

indexOf(term)[source]#: Returns the index of the input term.

New in version 3.0.0.

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.

setBinary(value)[source]#: Sets the value of binary.

New in version 2.0.0.

setInputCol(value)[source]#: Sets the value of inputCol.

setNumFeatures(value)[source]#: Sets the value of numFeatures.

setOutputCol(value)[source]#: Sets the value of outputCol.

setParams(self, \*, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None)[source]#: Sets params for this HashingTF.

New in version 1.3.0.

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

binary = Param(parent='undefined', name='binary', doc='If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default False.')#

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')#

numFeatures = Param(parent='undefined', name='numFeatures', doc='Number of features. Should be greater than 0.')#

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.

uid#: A unique id for the object.