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HashingTF — PySpark 4.0.0 documentation

class pyspark.mllib.feature.HashingTF(numFeatures=1048576)[source]#

Maps a sequence of terms to their term frequencies using the hashing trick.

New in version 1.2.0.

Parameters

numFeaturesint, optional

number of features (default: 2^20)

Notes

The terms must be hashable (can not be dict/set/list…).

Examples

>>> htf = HashingTF(100)
>>> doc = "a a b b c d".split(" ")
>>> htf.transform(doc)
SparseVector(100, {...})

Methods

indexOf(term)

Returns the index of the input term.

setBinary(value)

If True, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: False)

transform(document)

Transforms the input document (list of terms) to term frequency vectors, or transform the RDD of document to RDD of term frequency vectors.

Methods Documentation

indexOf(term)[source]#

Returns the index of the input term.

New in version 1.2.0.

setBinary(value)[source]#

If True, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: False)

New in version 2.0.0.

transform(document)[source]#

Transforms the input document (list of terms) to term frequency vectors, or transform the RDD of document to RDD of term frequency vectors.

New in version 1.2.0.

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