Python Gensim how to make affinity with WMD work faster with multiprocessing

Question

I am trying to run gensim affinity with WMD faster. Typically, this is what is in the docs: Corpus example:

    my_corpus = ["Human machine interface for lab abc computer applications",
>>>              "A survey of user opinion of computer system response time",
>>>              "The EPS user interface management system",
>>>              "System and human system engineering testing of EPS",
>>>              "Relation of user perceived response time to error measurement",
>>>              "The generation of random binary unordered trees",
>>>              "The intersection graph of paths in trees",
>>>              "Graph minors IV Widths of trees and well quasi ordering",
>>>              "Graph minors A survey"]

my_query = 'Human and artificial intelligence software programs'
my_tokenized_query =['human','artificial','intelligence','software','programs']

model = a trained word2Vec model on about 100,000 documents similar to my_corpus.
model = Word2Vec.load(word2vec_model)

---

from gensim import Word2Vec
from gensim.similarities import WmdSimilarity

def init_instance(my_corpus,model,num_best):
    instance = WmdSimilarity(my_corpus, model,num_best = 1)
    return instance
instance[my_tokenized_query]

the best consistent document "Human machine interface for lab abc computer applications"that is great.

However, the function instanceabove takes a very long time. Therefore, I thought about how to break the case into parts N, and then make it WMDon each s num_best = 1, and then at the end its part with the maximum score will be the most similar.

    from multiprocessing import Process, Queue ,Manager

    def main( my_query,global_jobs,process_tmp):
        process_query = gensim.utils.simple_preprocess(my_query)

        def worker(num,process_query,return_dict):  
            instance=init_instance\
(my_corpus[num*chunk+1:num*chunk+chunk], model,1)
            x = instance[process_query][0][0]
            y = instance[process_query][0][1]
            return_dict[x] = y
        manager = Manager()
        return_dict = manager.dict()

        for num in range(num_workers):
            process_tmp = Process(target=worker, args=(num,process_query,return_dict))
            global_jobs.append(process_tmp)
            process_tmp.start()
        for proc in global_jobs:
            proc.join()

        return_dict = dict(return_dict)
        ind = max(return_dict.iteritems(), key=operator.itemgetter(1))[0]
        print corpus[ind]
        >>> "Graph minors A survey"

The problem with this is that, despite the fact that it outputs something, it does not give me a good similar request from my corps, even if it receives the maximum similarity of all parts.

Am I doing something wrong?

Answer 1

: chunk - : . chunk = 600...

chunk static, num_workers.

10001 / 600 = 16,6683333333 = 17 num_workers

process cores .
17 cores, .

cores , :

num_workers = os.cpu_count()
chunk = chunksize(my_corpus, num_workers)

---

• , :

  #process_query = gensim.utils.simple_preprocess(my_query)
  process_query = my_tokenized_query
  

• worker Index 0..n.
  return_dict[x] , . return_dict my_corpus. :

  #return_dict[x] = y
  return_dict[ (num * chunk)+x ] = y
  

• +1 , .
  , chunk, :

  def chunksize(iterable, num_workers):
      c_size, extra = divmod(len(iterable), num_workers)
      if extra:
          c_size += 1
      if len(iterable) == 0:
          c_size = 0
      return c_size
  
  #Usage
  chunk = chunksize(my_corpus, num_workers)
  ...
  #my_corpus_chunk = my_corpus[num*chunk+1:num*chunk+chunk]
  my_corpus_chunk = my_corpus[num * chunk:(num+1) * chunk]
  

: 10 , Tuple = (Index worker num = 0, Index worker num = 1)

multiprocessing, chunk=5:
  02,09: (3, 8), 01,03: (3, 5):
   EPS
  04,06,07: (0, 8), 05,08: (0, 5), 10: (0, 7):
   abc

     

multiprocessing, chunk=5:
  01: (3, 6), 02: (3, 5), 05,08,10: (3, 7), 07,09: (3, 8):
   EPS
  03,04,06: (0, 5):
   abc

     

multiprocessing, chunking:
  01,02,03,04,06,07,08: (3, -1):
   EPS
  05,09,10: (0, -1):
   abc

Python: 3.4.2

Answer 2

Python 2.7: . WMD - :

    wmd_instances = []
    if wmd_instance_count > len(wmd_corpus):
        wmd_instance_count = len(wmd_corpus)
    chunk_size = int(len(wmd_corpus) / wmd_instance_count)
    for i in range(0, wmd_instance_count):
        if i == wmd_instance_count -1:
            wmd_instance = WmdSimilarity(wmd_corpus[i*chunk_size:], wmd_model, num_results)
        else:
            wmd_instance = WmdSimilarity(wmd_corpus[i*chunk_size:chunk_size], wmd_model, num_results)
        wmd_instances.append(wmd_instance)
    wmd_logic.setWMDInstances(wmd_instances, chunk_size)

'wmd_instance_count' - , . . , - , "wmd_instance_count" -threads , sims:

def perform_query_for_job_on_instance(wmd_logic, wmd_instances, query, jobID, instance):
    wmd_instance = wmd_instances[instance]
    sims = wmd_instance[query]
    wmd_logic.set_mt_thread_result(jobID, instance, sims)

'wmd_logic' - , :

def set_mt_thread_result(self, jobID, instance, sims):
    res = []
    #
    # We need to scale the found ids back to our complete corpus size...
    #
    for sim in sims:
        aSim = (int(sim[0] + (instance * self.chunk_size)), sim[1])
        res.append(aSim)

, , . "wmd_instance_count" , , -10 - .

, .