Ndtyjky

I am performing topic modelling and using functions to get the top keywords in the topic models as shown below.

def getTopKWords(self, K):



    results  = 

    """

    returns top K discriminative words for topic t

    ie words v for which p(v|t) is maximum

    """

    index = 

    key_terms = 







    pseudocounts = np.copy(self.n_vt)

    normalizer = np.sum(pseudocounts, (0))

    pseudocounts /= normalizer[np.newaxis, :]

    for t in range(self.numTopics):

        topWordIndices = pseudocounts[:, t].argsort()[-1:-(K+1):-1]

        vocab = self.vectorizer.get_feature_names()

        print (t, [vocab[i] for i in topWordIndices])

   ## Code for storing the values in a single list

   return results

The above functions gives me the code as shown in the fig

0 ['computer', 'laptop', 'mac', 'use', 'bought', 'like', 'warranty', 'screen', 'way', 'just']

1 ['laptop', 'computer', 'use', 'just', 'like', 'time', 'great', 'windows', 'macbook', 'months']

2 ['computer', 'great', 'laptop', 'mac', 'buy', 'just', 'macbook', 'use', 'pro', 'windows']

3 ['laptop', 'computer', 'great', 'time', 'battery', 'use', 'apple', 'love', 'just', 'work']

It results from the 4 time the loop runs and print index and all keywords in each vocab.

Now, I want to return a single list from the function which returns me the following output.

return   [keyword1, keyword2, keyword3, keyword4]

where, keyword1/2/3/4 are the words which were occuring the most in vocab lists with index 0, 1,2,3 in output.

You can use collection.Counter:

from collections import Counter



a = ['computer', 'laptop', 'mac', 'use', 'bought', 'like', 

     'warranty', 'screen', 'way', 'just']

b = ['laptop', 'computer', 'use', 'just', 'like', 'time', 

     'great', 'windows', 'macbook', 'months']

c = ['computer', 'great', 'laptop', 'mac', 'buy', 'just', 

     'macbook', 'use', 'pro', 'windows']

d = ['laptop', 'computer', 'great', 'time', 'battery', 'use', 

     'apple', 'love', 'just', 'work']



def get_most_common(*kwargs):

    """Accepts iterables, feeds all into Counter and returns the Counter instance"""

    c = Counter()

    for k in kwargs:

        c.update(k)

    return c



# get the most common ones 

mc = get_most_common(a,b,c,d).most_common()



# print top 4 keys

top4 = [k for k,v in mc[0:4]]

print (top4)

Output:

['computer', 'laptop', 'use', 'just']

 some_results =  # store stuff

for t in range(self.numTopics):

    topWordIndices = pseudocounts[:, t].argsort()[-1:-(K+1):-1]

    vocab = self.vectorizer.get_feature_names()

    print (t, [vocab[i] for i in topWordIndices])

      some_results.append( [vocab[i] for i in topWordIndices] )



  mc = get_most_common(*some_results).most_common()

  return [k for k,v in mc[0:4]]