IDF¶

class pyspark.mllib.feature.IDF(minDocFreq: int = 0)[source]¶

Inverse document frequency (IDF).

The standard formulation is used: idf = log((m + 1) / (d(t) + 1)), where m is the total number of documents and d(t) is the number of documents that contain term t.

This implementation supports filtering out terms which do not appear in a minimum number of documents (controlled by the variable minDocFreq). For terms that are not in at least minDocFreq documents, the IDF is found as 0, resulting in TF-IDFs of 0.

New in version 1.2.0.

Parameters

minDocFreqint: minimum of documents in which a term should appear for filtering

Examples

>>> n = 4
>>> freqs = [Vectors.sparse(n, (1, 3), (1.0, 2.0)),
...          Vectors.dense([0.0, 1.0, 2.0, 3.0]),
...          Vectors.sparse(n, [1], [1.0])]
>>> data = sc.parallelize(freqs)
>>> idf = IDF()
>>> model = idf.fit(data)
>>> tfidf = model.transform(data)
>>> for r in tfidf.collect(): r
SparseVector(4, {1: 0.0, 3: 0.5754})
DenseVector([0.0, 0.0, 1.3863, 0.863])
SparseVector(4, {1: 0.0})
>>> model.transform(Vectors.dense([0.0, 1.0, 2.0, 3.0]))
DenseVector([0.0, 0.0, 1.3863, 0.863])
>>> model.transform([0.0, 1.0, 2.0, 3.0])
DenseVector([0.0, 0.0, 1.3863, 0.863])
>>> model.transform(Vectors.sparse(n, (1, 3), (1.0, 2.0)))
SparseVector(4, {1: 0.0, 3: 0.5754})

Methods

fit(dataset)

Computes the inverse document frequency.

Methods Documentation

fit(dataset: pyspark.rdd.RDD[VectorLike]) → pyspark.mllib.feature.IDFModel [source]¶

Computes the inverse document frequency.

New in version 1.2.0.

Parameters

datasetpyspark.RDD: an RDD of term frequency vectors

IDFModel

Word2Vec