def top_tfidf_words(queryset_person, document_collection):
stopwords = functions.get_stopwords()
# fast way to join lists: http://stackoverflow.com/questions/716477/join-list-of-lists-in-python
person_words = question_queryset_to_unicode_text(queryset_person) #list(itertools.chain.from_iterable([tokenizer.tokenize(question.tittel) for question in queryset_person])) # wordpunct_tokenize ser ut til å fungere bedre enn mange andre. men en regexp er bedre?
#list(itertools.chain.from_iterable([tokenizer.tokenize(question.tittel) for question in queryset_everybody]))
evaluated_words = {} # dict to hold words
#print "evaluerer ord"
if len(person_words) > 30:
#print len(set(person_words))
for word in set(person_words): # just once for each word
#print "ord: %s og value %s" % (word, dc.tf_idf(word, person_words))
if word.lower() not in stopwords: # får ord som har, få, som top.. prøver med stoppord
evaluated_words[word] = document_collection.tf_idf(word, person_words)
#print evaluated_words
sorted_words = sorted(evaluated_words.items(), key=lambda x: x[1], reverse=True)[:29]
max_tfidf = sorted_words[0][1]
result = [{'tag': x, 'tfidf': y, 'size': functions.font_size_from_percent(round(float(y)/max_tfidf*100,2)) } for x, y in sorted_words]
else:
result = [{'tag': 'For lite data til å lage ordsky.', 'tfidf': 0, 'size': 7}]
return result
def create_weigted_list(queryset):
''' create a weigthed list of top word from questions from a particular user '''
# first get a list of stop words
stopwords = functions.get_stopwords()
pure_words = question_queryset_to_unicode_text(queryset)
counts = defaultdict(int)
for word in pure_words:
if word.lower() not in stopwords:
counts[word] += 1
pairs = [(x,y) for x,y in counts.items() if y >= 3] # remove shorter than 3 accounts
if len(pairs) > 1:
# if we have data to work with:
sorted_stems = sorted(pairs, key = lambda x: x[1], reverse=True)[:30] # Sort (stem,count) pairs based on count, pick top n words
max_value = sorted_stems[0][1] # max_value, min_value = sorted_stems[0][1], sorted_stems[-1][1]
#print sorted_stems, max_value
result = [{'tag': x, 'freq': y, 'size': functions.font_size_from_percent(round(float(y)/max_value*100,2)) } for x, y in sorted_stems] # round(n,2) <- rounds off float.
else:
# too little data to do this
result = [{'tag': 'For lite data til å lage ordsky, ingen enkeltord er brukt mer enn 3 ganger.', 'freq': 0, 'size': 7}]
return result