pyspark.mllib.feature.
IDF
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.
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)
fit
Computes the inverse document frequency.
Methods Documentation
pyspark.RDD
an RDD of term frequency vectors