text: Title: Gensim sort_by_descending_frequency changes most_similar results
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  #### Problem description
  
  It seems that when retrieving the most similar word vectors, sorting by word frequency will change the results in `Gensim`. 
  
  #### Steps/code/corpus to reproduce
  
  
  Before sorting: 
  
      from gensim.models import FastText
      from gensim.test.utils import common_texts  # some example sentences
      print(len(common_texts))
      model = FastText(vector_size=4, window=3, min_count=1)  # instantiate
      model.build_vocab(corpus_iterable=common_texts)
      model.train(corpus_iterable=common_texts, total_examples=len(common_texts), epochs=1)  
      
      model.wv.most_similar(positive=["human"])
  
  >     [('interface', 0.7432922720909119),
  >      ('minors', 0.6719315052032471),
  >      ('time', 0.3513716757297516),
  >      ('computer', 0.05815044790506363),
  >      ('response', -0.11714297533035278),
  >      ('graph', -0.15643596649169922),
  >      ('eps', -0.2679084539413452),
  >      ('survey', -0.34035828709602356),
  >      ('trees', -0.63677978515625),
  >      ('user', -0.6500451564788818)]
  
  However, if I sort the vectors by descending frequency: 
  
      model.wv.sort_by_descending_frequency()
      
      model.wv.most_similar(positive=["human"])
  
  >     [('minors', 0.9638221263885498),
  >      ('time', 0.6335864067077637),
  >      ('interface', 0.40014874935150146),
  >      ('computer', 0.03224882856011391),
  >      ('response', -0.14850640296936035),
  >      ('graph', -0.2249641716480255),
  >      ('survey', -0.26847705245018005),
  >      ('user', -0.45202943682670593),
  >      ('eps', -0.497650682926178),
  >      ('trees', -0.6367797255516052)]
  
  The most similar word ranking as well as the word similarities change. Any idea why? 
  
  Include full tracebacks, logs and datasets if necessary. Please keep the examples minimal ("minimal reproducible example").
  
  If your problem is with a specific Gensim model (word2vec, lsimodel, doc2vec, fasttext, ldamodel etc), include the following:
  
  ```python
  print(my_model.lifecycle_events)
  [{'params': 'FastText(vocab=0, vector_size=4, alpha=0.025)', 'datetime': '2021-07-20T09:46:56.158863', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'created'}, {'msg': 'effective_min_count=1 retains 12 unique words (100.0%% of original 12, drops 0)', 'datetime': '2021-07-20T09:46:56.159995', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'prepare_vocab'}, {'msg': 'effective_min_count=1 leaves 29 word corpus (100.0%% of original 29, drops 0)', 'datetime': '2021-07-20T09:46:56.160040', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'prepare_vocab'}, {'msg': 'downsampling leaves estimated 3.5001157321504532 word corpus (12.1%% of prior 29)', 'datetime': '2021-07-20T09:46:56.160376', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'prepare_vocab'}, {'update': False, 'trim_rule': 'None', 'datetime': '2021-07-20T09:46:56.233809', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'build_vocab'}, {'msg': 'training model with 3 workers on 12 vocabulary and 4 features, using sg=0 hs=0 sample=0.001 negative=5 window=3', 'datetime': '2021-07-20T09:46:56.234068', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'train'}, {'msg': 'training on 29 raw words (3 effective words) took 0.0s, 1377 effective words/s', 'datetime': '2021-07-20T09:46:56.236277', 'gensim': '4.0.1', 'python': '3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n[GCC 7.3.0]', 'platform': 'Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen', 'event': 'train'}]
  ```
  
  #### Versions
  
  Linux-3.10.0-1160.31.1.el7.csd3.x86_64-x86_64-with-redhat-7.9-Nitrogen
  Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) 
  [GCC 7.3.0]
  Bits 64
  NumPy 1.18.1
  SciPy 1.4.1
  gensim 4.0.1
  FAST_VERSION 0