title: ValueError: Stop argument for islice() must be None or an integer: 0 <= x <= sys.maxsize on topic coherence
body: <p>im following this tutorials <a href="https://towardsdatascience.com/evaluate-topic-model-in-python-latent-dirichlet-allocation-lda-7d57484bb5d0" rel="noreferrer">https://towardsdatascience.com/evaluate-topic-model-in-python-latent-dirichlet-allocation-lda-7d57484bb5d0</a> and find problem. so my purpose on this code to make iterate it over the range of topics, alpha, and beta parameter values. so I can determine the optimal number of topics from the coherence score generated by alpha and beta</p>
  
  <pre><code>def compute_coherence_values(corpus, dictionary, k, a, b):
  
  lda_model = gensim.models.LdaMulticore(corpus=corpus,
                                         id2word=id2word,
                                         num_topics=10, 
                                         random_state=100,
                                         chunksize=100,
                                         passes=10,
                                         alpha=a,
                                         eta=b,
                                         per_word_topics=True)
  
  coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')
  
  return coherence_model_lda.get_coherence()
  </code></pre>
  
  <p>and then</p>
  
  <pre><code>import numpy as np
  import tqdm
  grid = {}
  grid['Validation_Set'] = {}
  # Topics range
  min_topics = 2
  max_topics = 11
  step_size = 1
  topics_range = range(min_topics, max_topics, step_size)
  # Alpha parameter
  alpha = list(np.arange(0.01, 1, 0.3))
  alpha.append('symmetric')
  alpha.append('asymmetric')
  # Beta parameter
  beta = list(np.arange(0.01, 1, 0.3))
  beta.append('symmetric')
  # Validation sets
  num_of_docs = len(corpus)
  corpus_sets = [# gensim.utils.ClippedCorpus(corpus, num_of_docs*0.25), 
                 # gensim.utils.ClippedCorpus(corpus, num_of_docs*0.5), 
                 gensim.utils.ClippedCorpus(corpus, num_of_docs*0.75), 
                 corpus]
  corpus_title = ['75% Corpus', '100% Corpus']
  model_results = {'Validation_Set': [],
                   'Topics': [],
                   'Alpha': [],
                   'Beta': [],
                   'Coherence': []
                  }
  # Can take a long time to run
  if 1 == 1:
      pbar = tqdm.tqdm(total=540)
  
      # iterate through validation corpuses
      for i in range(len(corpus_sets)):
          # iterate through number of topics
          for k in topics_range:
              # iterate through alpha values
              for a in alpha:
                  # iterare through beta values
                  for b in beta:
                      # get the coherence score for the given parameters
                      cv = compute_coherence_values(corpus=corpus_sets[i], dictionary=id2word, 
                                                    k=k, a=a, b=b)
                      # Save the model results
                      model_results['Validation_Set'].append(corpus_title[i])
                      model_results['Topics'].append(k)
                      model_results['Alpha'].append(a)
                      model_results['Beta'].append(b)
                      model_results['Coherence'].append(cv)
  
                      pbar.update(1)
      pd.DataFrame(model_results).to_csv('lda_tuning_results.csv', index=False)
      pbar.close()
  </code></pre>
  
  <p>come out this error <strong>ValueError: Stop argument for islice() must be None or an integer: 0 &lt;= x &lt;= sys.maxsize.</strong></p>