def busquedaPorAsuntoCuerpo(aBuscar):
ix = open_dir("index")
qp = MultifieldParser(["asunto","cuerpo"], schema=ix.schema)
q = qp.parse(unicode(str(aBuscar)))
s=ix.searcher()
results = s.search(q)
return results
def search(self, query):
qp = MultifieldParser(['title', 'content'], schema=self.idx.schema)
q = qp.parse(query)
query_result = self.trainModel.predict(query)
query_label = query_result['outputLabel']
query_output_list = query_result['outputMulti']
query_score = float()
for output in query_output_list:
if output['label'] == query_label:
query_score = float(output['score'])
#print("Query result : " + query_label + "\nQuery score : " + str(query_score))
with self.idx.searcher() as searcher:
search_results = searcher.search(q, limit=None)
article_ids = self.find_matching_political_bias\
(search_results, query_label, query_score)
for article_id in article_ids:
#print(article_id)
print(self.get_article_url(article_id))
print(self.get_article_title(article_id))
print(self.get_article_snippet(article_id))
print(self.get_article_date(article_id))
def search(self, str):
ix = open_dir(indexdir)
with ix.searcher() as searcher:
parser = MultifieldParser(['keywords', 'content', 'title'],
ix.schema)
query = parser.parse(str)
results = searcher.search(query)
return [list(result.values()) for result in results]
def apartado_a(palabras):
ix = open_dir("Index")
queryContent = []
for palabra in palabras:
queryContent.append(Term(""))
query = palabras
with ix.searcher() as searcher:
parser = MultifieldParser(["titulo", "sinopsis"],
ix.schema,
group=qparser.OrGroup)
query = parser.parse(query)
print(query)
results = searcher.search(query)
for r in results:
print(r)
def search():
start_time = time.time()
form = request.form
qstr = request.args.get('q')
page = int(request.args.get('p', "1"))
parser = MultifieldParser(['title', 'content'],
schema=ix.schema,
group=OrGroup)
#termclass=MyFuzzyTerm)
query = parser.parse(qstr)
notes = []
with ix.searcher() as searcher:
corrected = searcher.correct_query(query, qstr)
results = searcher.search(query)
rel = results.estimated_length()
if corrected.string.lower() != qstr.lower():
crs = searcher.search(corrected.query)
if crs.estimated_length() > rel:
notes.append("Did you mean: " + corrected.string)
results = searcher.search_page(query, page, terms=True)
my_cf = ContextFragmenter(maxchars=20, surround=30, charlimit=256)
results.order = SCORE
results.fragmenter = my_cf
# results.formatter = HtmlFormatter()
rsp = [{
"url": item["url"],
"content": item.highlights("content"),
"title": item["title"]
} for item in results]
# return json.dumps(rsp)
# print(json.dumps(rsp))
if rel == 0:
notes.append("Sorry, no result for your query")
else:
elapsed_time = time.time() - start_time
notes.append("%d results found in %.2f seconds" % (rel, elapsed_time))
return render_template("result.html",
result=rsp,
query=qstr,
notes=notes,
nextpage=page + 1,
urlquery=urllib.quote_plus(qstr))
def run_query(query, index):
"""
Queries the index for data with the given text query
@param query The text query to perform on the indexed data
@return A list of HTMl string snippets to return
"""
# Create a searcher object for this index
searcher = index.searcher()
# Create a query parser that will parse multiple fields of the documents
field_boosts = {
'content': 1.0,
'title': 3.0
}
query_parser = MultifieldParser(['content', 'title'], schema=index_schema, fieldboosts=field_boosts, group=OrGroup)
# Build a query object from the query string
query_object = query_parser.parse(query)
# Build a spell checker in this index and add the "content" field to the spell checker
spell_checker = SpellChecker(index.storage)
spell_checker.add_field(index, 'content')
spell_checker.add_field(index, 'title')
# Extract the 'terms' that were found in the query string. This data can be used for highlighting the results
search_terms = [text for fieldname, text in query_object.all_terms()]
# Remove terms that are too short
for search_term in search_terms:
if len(search_term) <= 3:
search_terms.remove(search_term)
# Perform the query itself
search_results = searcher.search(query_object)
# Get an analyzer for analyzing the content of each page for highlighting
analyzer = index_schema['content'].format.analyzer
# Build the fragmenter object, which will automatically split up excerpts. This fragmenter will split up excerpts
# by 'context' in the content
fragmenter = ContextFragmenter(frozenset(search_terms))
# Build the formatter, which will dictate how to highlight the excerpts. In this case, we want to use HTML to
# highlight the results
formatter = HtmlFormatter()
# Iterate through the search results, highlighting and counting the results
result_count = 0
results = []
for search_result in search_results:
# Collect this search result
results.append({
'content': highlight(search_result['content'], search_terms, analyzer, fragmenter, formatter),
'url': search_result['url'],
'title': search_result['title']
})
result_count += 1
# Build a list of 'suggest' words using the spell checker
suggestions = []
for term in search_terms:
suggestions.append(spell_checker.suggest(term))
# Return the list of web pages along with the terms used in the search
return results, search_terms, suggestions, result_count
def run_query(query, index):
"""
Queries the index for data with the given text query
@param query The text query to perform on the indexed data
@return A list of HTMl string snippets to return
"""
# Create a searcher object for this index
searcher = index.searcher()
# Create a query parser that will parse multiple fields of the documents
field_boosts = {'content': 1.0, 'title': 3.0}
query_parser = MultifieldParser(['content', 'title'],
schema=index_schema,
fieldboosts=field_boosts,
group=OrGroup)
# Build a query object from the query string
query_object = query_parser.parse(query)
# Build a spell checker in this index and add the "content" field to the spell checker
spell_checker = SpellChecker(index.storage)
spell_checker.add_field(index, 'content')
spell_checker.add_field(index, 'title')
# Extract the 'terms' that were found in the query string. This data can be used for highlighting the results
search_terms = [text for fieldname, text in query_object.all_terms()]
# Remove terms that are too short
for search_term in search_terms:
if len(search_term) <= 3:
search_terms.remove(search_term)
# Perform the query itself
search_results = searcher.search(query_object)
# Get an analyzer for analyzing the content of each page for highlighting
analyzer = index_schema['content'].format.analyzer
# Build the fragmenter object, which will automatically split up excerpts. This fragmenter will split up excerpts
# by 'context' in the content
fragmenter = ContextFragmenter(frozenset(search_terms))
# Build the formatter, which will dictate how to highlight the excerpts. In this case, we want to use HTML to
# highlight the results
formatter = HtmlFormatter()
# Iterate through the search results, highlighting and counting the results
result_count = 0
results = []
for search_result in search_results:
# Collect this search result
results.append({
'content':
highlight(search_result['content'], search_terms, analyzer,
fragmenter, formatter),
'url':
search_result['url'],
'title':
search_result['title']
})
result_count += 1
# Build a list of 'suggest' words using the spell checker
suggestions = []
for term in search_terms:
suggestions.append(spell_checker.suggest(term))
# Return the list of web pages along with the terms used in the search
return results, search_terms, suggestions, result_count
def page_detail(request, pk):
page = Page.objects.get(pk=pk)
menu = page.menu
comments = Comment.objects.filter(page=page).filter(
visible=True).select_related('user')
if menu.get_children():
menu = menu.get_children()[0]
menus = {'level2': [], 'level3': []}
if menu.level == 2:
for m in menu.get_siblings(include_self=True):
if m == menu:
m.active = True
menus['level2'].append(m)
menus['level3'] = menu.get_children()
elif menu.level == 3:
for m in menu.parent.get_siblings(include_self=True):
if m == menu.parent:
m.active = True
menus['level2'].append(m)
for m in menu.get_siblings(include_self=True):
if m == menu:
m.active = True
menus['level3'].append(m)
if page.keywords:
# Open index dir
ix = whoosh_index.open_dir(settings.PAGE_SEARCH_INDEX)
# Make parser
parser = MultifieldParser(['content', 'name'], schema=ix.schema)
# Configure filter
filter = Term('region', page.region.id)
# Make query string
qstr = page.keywords.replace('+', ' AND ').replace(' -', ' NOT ').replace(' | ', ' OR ')
# Parse query string
query = parser.parse(qstr)
# And... search in index!
with ix.searcher() as searcher:
hits = ix.searcher().search(query, filter=filter, limit=None)
similar_pages = Page.objects.filter(
pk__in=[h.get('id') for h in hits if h.get('id') != page.id]
).order_by('?')[:5]
context = {
'page': page,
'comments': comments,
'menus': menus,
'menu': menu,
'similar_pages': locals().get('similar_pages')
}
# Comments
if request.method == 'POST' and request.POST.get('submit_comment'):
comment_form = CommentForm(request.POST)
if comment_form.is_valid() and request.user.is_authenticated():
comment_form.add_comment()
context['comment_success'] = True
# Clear form
initial = {'user': request.user.id, 'page': page.id}
comment_form = CommentForm(initial=initial)
else:
initial = {'user': request.user.id, 'page': page.id}
comment_form = CommentForm(initial=initial)
try:
context['star_rating'] = StarRating.objects.get(page=page)
except StarRating.DoesNotExist:
context['star_rating'] = StarRating.objects.create(page=page)
context['comment_form'] = comment_form
return render(request, 'page_detail.html', context)
def search_whoosh_files(filename_in):
ngt1 = RegexTokenizer() | NgramFilter(4)
l_aux_i = 0
filename_aux = "dataset_match_" + filename_in
ix1 = open_dir("index_" + filename_in)
#aux max val for progress bar
if filename_in == "jrc_person":
max_val = 3000000
else:
max_val = 3000000
widgets = [
'Progress Searching ' + filename_in + ': ',
Percentage(), ' ',
Bar(marker='0', left='[', right=']'), ' ',
ETA(), ' '
]
pbar = ProgressBar(widgets=widgets, maxval=max_val) #454000
pbar.start()
with ix1.searcher() as searcher:
parser = MultifieldParser(['title'], ix1.schema)
parser.remove_plugin_class(qparser.WildcardPlugin)
parser.remove_plugin_class(qparser.PlusMinusPlugin)
with open("dataset_non_match_" + filename_in + ".csv_tmp",
'w',
encoding="utf-8") as inW2:
with open("dataset_match_" + filename_in + ".csv",
encoding="utf8") as csvfile:
for row in csvfile:
l_aux_i = l_aux_i + 1
if l_aux_i % 20000 == 0:
print("Index search" + str(l_aux_i))
pbar.update(l_aux_i)
l_row_idx = row.split('|')[0]
l_row_aux = row.split('|')[1]
search_list = [token.text for token in ngt1(l_row_aux)]
if len(search_list) > 0:
l_row_str = random.sample(search_list, 1)
query = parser.parse(l_row_str[0])
results = searcher.search(query)
results_aux = []
for result in results:
if result['id'] != l_row_idx:
results_aux.append(
[result['id'], result['title']])
if len(results_aux) > 0:
shuffle(results_aux)
line_new = l_row_idx + "|" + l_row_aux + "|" + results_aux[
0][0] + "|" + results_aux[0][1]
inW2.write(line_new.strip() + '\n')
if len(results_aux) > 1:
if results_aux[1][0] != results_aux[0][0]:
line_new = l_row_idx + "|" + l_row_aux + "|" + results_aux[
1][0] + "|" + results_aux[1][1]
inW2.write(line_new.strip() + '\n')
if len(results_aux) > 2:
if results_aux[2][0] != results_aux[1][0]:
line_new = l_row_idx + "|" + l_row_aux + "|" + results_aux[
2][0] + "|" + results_aux[2][1]
inW2.write(line_new.strip() + '\n')
pbar.finish()
