What hashing function does Spark use for HashingTF

Question

Spark MLLIb has a HashingTF() function that computes document term frequencies based on a hashed value of each of the terms.

1) what function does it use to do the hashing?

2) How can I achieve the same hashed value from Python?

3) If I want to compute the hashed output for a given single input, without computing the term frequency, how can I do this?

Answer 1

If you're in doubt is it usually good to check the source. The bucket for a given term is determined as follows:

def indexOf(self, term):
    """ Returns the index of the input term. """
    return hash(term) % self.numFeatures

As you can see it is just a plain old hash module number of buckets.

Final hash is just a vector of counts per bucket (I've omitted docstring and RDD case for brevity):

def transform(self, document):
    freq = {}
    for term in document:
        i = self.indexOf(term)
        freq[i] = freq.get(i, 0) + 1.0
    return Vectors.sparse(self.numFeatures, freq.items())

If you want to ignore frequencies then you can use set(document) as an input, but I doubt there is much to gain here. To create set you'll have to compute hash for each element anyway.

Answer 2

It seems to me that there is something else going on under the hood other than what the source that zero323 linked. I found that hashing and then doing the modulus as the source code did wouldn't give me the same indices as hashingTF generates. At least for single characters, what I had to do was convert the char to the ascii code, like so: (Python 2.7)

index = ord('a') # 97

Which corresponds to what hashingtf outputs for the index. If I did the same thing as hashingtf appears to do, which is:

index = hash('a') % 1<<20 # 897504

I would get very clearly the wrong index.