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.

>>> 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
>>> hashingTF.indexOf("b")
5

New in version 1.3.0.

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

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.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

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.