How are number of iterations and number of partitions releated in Apache spark Word2Vec? -


according mllib.feature.word2vec - spark 1.3.1 documentation [1]:

def setnumiterations(numiterations: int): word2vec.this.type

sets number of iterations (default: 1), should smaller or equal number of partitions.

def setnumpartitions(numpartitions: int): word2vec.this.type

sets number of partitions (default: 1). use small number accuracy.

but in pull request [2]:

to make our implementation more scalable, train each partition separately , merge model of each partition after each iteration. make model more accurate, multiple iterations may needed.

questions:

  • how parameters numiterations & numpartitions effect internal working of algorithm?

  • is there trade-off between setting number of partitions , number of iterations considering following rules ?

    • more accuracy -> more iteration a/c [2]

    • more iteration -> more partition a/c [1]

    • more partition -> less accuracy


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