def show_pk_article(request, pk, keyword):
a = Article.objects.get(pk=pk)
tokens = string_to_tokens(a.abstract)
keyword_stem = string_to_tokens(keyword)
pos = []
for w in tokens:
if keyword_stem[0][0] == w[0]:
pos.append(w[1])
_abstract = ""
# a bug happened because \n
for j in a.abstract:
if (j != '\n'):
_abstract += j
else:
_abstract += ' '
render_dict = {
'title': a.title,
'abstract': _abstract.split(' '),
'keyword': keyword,
'pos': pos
}
return render(request, 'search_engine/show_an_article.html', render_dict)
def create_revindex(request):
a = Article.objects.all()
word_freq = {}
for i in a:
for j in string_to_tokens(i.abstract):
if j[0] != 'nan':
w = Word(context = j[0], pos_in_a_article = j[1])
w.save()
w.position.add(i)
return HttpResponse('Create Reverse index success')
def create_stem_freq(request):
a = Article.objects.all()
word_freq = {}
for i in a:
for j in string_to_tokens(i.abstract):
if j[0] != 'nan':
if j[0] in word_freq.keys():
word_freq[j[0]] += 1
else:
word_freq[j[0]] = 0
# sort the dict by value
word_freq = {k : v for k, v in sorted(word_freq.items(), key=lambda item: item[1], reverse=True)}
#save to database
for j in word_freq.keys():
sf = StemFreq(word = j, frequency = word_freq[j])
sf.save()
return HttpResponse('StemFreq created')
def get_subset(keyword):
from search_engine.parsing_utils import string_to_tokens
corrected_keyword = keyword
# query for the data subset
keywords_cleaned = string_to_tokens(corrected_keyword)
articles_pk = [] # get the target articles' pk
for i in keywords_cleaned:
w = Word.objects.filter(context=i[0])
for j in w:
mode = j.position.get()
# append article pk
if mode.pk not in articles_pk:
articles_pk.append(mode.pk)
words_freq = {}
words = []
# get the target articles' words
for i in articles_pk:
w = Word.objects.filter(position__id=i)
for j in w:
if j.context not in words_freq.keys():
words_freq[j.context] = 1
else:
words_freq[j.context] += 1
words.append(j.context)
# sort the dict by value
words_freq = {
k: v
for k, v in sorted(
words_freq.items(), key=lambda item: item[1], reverse=True)
}
return words_freq, words
def stemmed(keyword):
from search_engine.parsing_utils import string_to_tokens
keywords_cleaned = string_to_tokens(keyword)
for i in keywords_cleaned:
return i[0]
def show_articles(request, first=False):
# POST => process the form data from user
# if request.method == 'POST':
if not first and request.method == 'POST':
form = WordForm(request.POST)
if form.is_valid():
# parse and clean (stemming..etc) the keywords
keywords_cleaned = string_to_tokens(form.cleaned_data['keywords'])
# search keywords in db (Model => Word)
all_words = []
for i in keywords_cleaned:
# retrive all word
q_set = Word.objects.filter(context=i[0])
for j in q_set:
temp = {}
temp['word'] = j.context # keyword name
temp['pos'] = j.pos_in_a_article # position in the doc
# look up in which docs
# get many to many table
q_art = j.position.get()
# when calculating the which doc, we do ( pk - current_firstpk )
q3 = Article.objects.filter()[0]
article_firstpk = q3.pk
temp['docs'] = q_art.pk - article_firstpk
all_words.append(temp)
# now we render the show page
all_articles = Article.objects.all()
len_article = len(all_articles) #count the num of articles
arts = []
_str = ""
# a bug happened because \n
for i in all_articles:
for j in i.abstract:
if (j != '\n'):
_str += j
else:
_str += ' '
arts.append(_str)
_str = ""
arts = [i.split(' ') for i in arts] # articles break into words
# count sentence num
tot_sc = 0
sep_sc = []
for sc in all_articles:
tot_sc += count_sent(sc.abstract)
sep_sc.append(count_sent(sc.abstract))
# count the num of words
tot_words = 0
len_sep_words = []
for wo in arts:
tot_words += len(wo)
len_sep_words.append(len(wo))
# make sure which docs has keywords
# format : [doc1, doc2, doc3] (type int)
key_docs = []
for i in all_words:
if i['docs'] not in key_docs:
key_docs.append(i['docs'])
# make sure where the keywords are in the coresponding doc
# format : {doc1 : [pos1, pos2], doc2 : [pos3, pos4]} (type int)
key_pos = {}
for i in all_words:
if i['docs'] not in key_pos.keys():
key_pos[i['docs']] = [
i['pos'],
]
else:
key_pos[i['docs']].append(i['pos'])
form = WordForm()
#count
words = {
'form': form,
'len_article': len_article,
'tot_sc': tot_sc,
'sep_sc': sep_sc,
'len_words': tot_words,
'sep_words': len_sep_words,
'articles': arts,
'num_result': len(all_words),
'keylines': key_docs,
'keywords': key_pos
}
# structure of words
# {
# 'articles' : a list of articles to show on template,
# 'keylines' : a list of which line has words to highlight,
# 'keywords' : a dict of which word to highlight in the corresponding line,
# key is int , value is list
# }
# debug
# return JsonResponse({'keylines' : key_docs , 'keywords' : key_pos})
return render(request, 'search_engine/show_articles.html', words)
else:
form = WordForm()
all_articles = Article.objects.all()
len_article = len(all_articles) #count the num of articles
arts = [i.abstract.split(' ')
for i in all_articles] # articles break into words
# count the num of words
tot_words = 0
len_sep_words = []
# count sent num
tot_sc = 0
sep_sc = []
for sc in all_articles:
tot_sc += count_sent(sc.abstract)
sep_sc.append(count_sent(sc.abstract))
for wo in arts:
tot_words += len(wo)
len_sep_words.append(len(wo))
words = {
'form': form,
'articles': [i.abstract.split(' ') for i in all_articles],
'tot_sc': tot_sc,
'sep_sc': sep_sc,
'len_article': len_article,
'len_words': tot_words,
'sep_words': len_sep_words,
}
# return HttpResponse(loader.get_template('search_engine/show_articles.html').render(words , request))
return render(request, 'search_engine/show_articles.html', words)
def get_keywords(request):
# POST => process the form data from user
if request.method == 'POST':
form = WordForm(request.POST)
if form.is_valid():
# parse and clean (stemming..etc) the keywords
keywords_cleaned = string_to_tokens(form.cleaned_data['keywords'])
# search keywords in db (Model => Word)
all_words = []
for i in keywords_cleaned:
# retrive all word
q_set = Word.objects.filter(context=i[0])
for j in q_set:
temp = {}
temp['word'] = j.context # keyword name
temp['pos'] = j.pos_in_a_article # position in the doc
# look up in which docs
# get many to many table
q_art = j.position.get()
# when calculating the which doc, we do ( pk - current_firstpk )
q3 = Article.objects.filter()[0]
article_firstpk = q3.pk
temp['docs'] = q_art.pk - article_firstpk
all_words.append(temp)
# now we render the show page
all_articles = Article.objects.all()
# make sure which docs has keywords
# format : [doc1, doc2, doc3] (type int)
key_docs = []
for i in all_words:
if i['docs'] not in key_docs:
key_docs.append(i['docs'])
# make sure where the keywords are in the coresponding doc
# format : {doc1 : [pos1, pos2], doc2 : [pos3, pos4]} (type int)
key_pos = {}
for i in all_words:
if i['docs'] not in key_pos.keys():
key_pos[i['docs']] = [
i['pos'],
]
else:
key_pos[i['docs']].append(i['pos'])
words = {
'articles': [i.abstract.split(' ') for i in all_articles],
'num_result': len(all_words),
'keylines': key_docs,
'keywords': key_pos
}
return HttpResponse(
loader.get_template('search_engine/show_articles.html').render(
words, request))
# return HttpResponse(all_words)
# GET => create blank form
else:
form = WordForm()
return render(request, 'search_engine/search_page.html', {'form': form})
def zipf_ct(request):
# form = WordForm()
keyword = 'ct'
corrected_keywords = [
'mask', 'children', '2003', '2019', 'pneumothorax', 'pneumonia',
'image', 'adult', 'bat', 'immune', 'wuhan', 'vaccin'
]
return_dict = {}
# query for the data subset
for ck in corrected_keywords:
keywords_cleaned = string_to_tokens(ck)
articles_pk = [] # get the target articles' pk
w = Word.objects.filter(context=keywords_cleaned[0][0])
for j in w:
mode = j.position.get()
# append article pk
if mode.pk not in articles_pk:
articles_pk.append(mode.pk)
words = {}
# get the target articles' words
for i in articles_pk:
w = Word.objects.filter(position__id=i)
for j in w:
if j.context not in words.keys():
words[j.context] = 1
else:
words[j.context] += 1
# sort the dict by value
words = {
k: v
for k, v in sorted(
words.items(), key=lambda item: item[1], reverse=True)
}
# stemming words
_words = list(words.keys())
_freq = list(words.values())
keyword_rank = 0
# calculate keyword ranking in each subset
for i in range(len(_words)):
if _words[i] == keyword:
keyword_rank = i
return_dict['{}_words'.format(ck)] = _words
return_dict['{}_freq'.format(ck)] = _freq
return_dict['{}_subset_article_num'.format(ck)] = len(_words)
return_dict['{}_rank'.format(ck)] = keyword_rank
# return JsonResponse(return_dict)
return render(request, 'search_engine/chart_ct.html', return_dict)
def zipf_search(request, subset=0):
# for spelling correction
from spellchecker import SpellChecker
spell = SpellChecker()
form = WordForm()
corrected_keyword = ''
search_title = ''
# user search
if request.method == 'POST':
form = WordForm(request.POST)
# get search word
if form.is_valid():
# parse and clean (stemming..etc) the keywords
origin_keyword = form.cleaned_data['keywords']
misspelled = spell.unknown([origin_keyword])
corrected_keyword = ''
search_title = ''
if len(misspelled) != 0:
for w in misspelled:
corrected_keyword = spell.correction(w)
search_title = 'Do you mean \"{}\". Showing search results for {}'.format(
corrected_keyword, corrected_keyword)
else:
corrected_keyword = origin_keyword
search_title = 'search results for {}'.format(origin_keyword)
# predefine subset
elif subset == 0:
corrected_keyword = 'mask'
search_title = 'search results for mask'
elif subset == 1:
corrected_keyword = '2005'
search_title = 'search results for 2005'
elif subset == 2:
corrected_keyword = 'children'
search_title = 'search results for children'
# query for the data subset
keywords_cleaned = string_to_tokens(corrected_keyword)
titles = {} # get the target articles names
articles_pk = [] # get the target articles' pk
articles_raw_words = {} # get the target articles' abstract raw words
for i in keywords_cleaned:
w = Word.objects.filter(context=i[0])
for j in w:
mode = j.position.get()
title = mode.title
# append article pk
if mode.pk not in articles_pk:
articles_pk.append(mode.pk)
# append raw abstract words
for k in mode.abstract.split(' '):
if k not in articles_raw_words.keys():
articles_raw_words[k] = 1
else:
articles_raw_words[k] += 1
# append article title
if title not in titles.keys():
titles[title] = 1
else:
titles[title] += 1
# sort the dict by value
titles = {
k: v
for k, v in sorted(
titles.items(), key=lambda item: item[1], reverse=True)
}
articles_raw_words = {
k: v
for k, v in sorted(
articles_raw_words.items(), key=lambda item: item[1], reverse=True)
}
titles_name = list(titles.keys())
titles_freq = list(titles.values())
# get sort article pk
articles_pk = []
for i in titles_name:
a = Article.objects.filter(title=i)[0]
articles_pk.append(a.pk)
words = {}
# get the target articles' words
for i in articles_pk:
w = Word.objects.filter(position__id=i)
for j in w:
if j.context not in words.keys():
words[j.context] = 1
else:
words[j.context] += 1
# sort the dict by value
words = {
k: v
for k, v in sorted(
words.items(), key=lambda item: item[1], reverse=True)
}
# calculate zipf
# of = OriginFreq.objects.filter(word == )
# sf = StemFreq.objects.all()
title = str(corrected_keyword)
# stemming words
top100_words = list(words.keys())[:100]
freq = list(words.values())
# calculate stemmed covid19 set word rank
stem_covid_set_freq = []
stem_covid_set_rank = []
stem_covid_set_first_pk = StemFreq.objects.filter()[0].pk
for i in top100_words:
a = StemFreq.objects.get(word=i)
stem_covid_set_freq.append(a.frequency)
stem_covid_set_rank.append(a.pk - stem_covid_set_first_pk)
# origin words
top100_words_raw = list(articles_raw_words.keys())[:100]
freq_raw = list(articles_raw_words.values())
# calculate original covid19 set word rank
origin_covid_set_freq = []
origin_covid_set_rank = []
origin_covid_set_first_pk = OriginFreq.objects.filter()[0].pk
for i in top100_words_raw:
a = OriginFreq.objects.get(word=i)
origin_covid_set_freq.append(a.frequency)
origin_covid_set_rank.append(a.pk - origin_covid_set_first_pk)
return_dict = {
'search_title': search_title,
'titles_name': titles_name,
'titles_freq': titles_freq,
'chart_title': title,
'form': form,
'top_words': top100_words,
'freq': freq,
'top_words_raw': top100_words_raw,
'freq_raw': freq_raw,
'stem_covid_set_freq': stem_covid_set_freq,
'stem_covid_set_rank': stem_covid_set_rank,
'origin_covid_set_freq': origin_covid_set_freq,
'origin_covid_set_rank': origin_covid_set_rank,
'article_pk': articles_pk
}
return render(request, 'search_engine/chart_search.html', return_dict)