def POST(self):
# accepts the human marked form
# takes the YES/NO signal and if YES sends to Solr
# either way uses the YES/NO to train another epoch of the
# machine learning algorithm. The constants will be stored in
# the database.
web.header('Content-type', 'application/json')
request_input = web.input(answer='',
url='',
title='',
image='',
emotional_score=-1,
quality_score=-1,
body='')
url = request_input.url
title = request_input.title
image = request_input.image
emotional_score = request_input.emotional_score
quality_score = request_input.quality_score
body = request_input.body
answer = request_input.answer
if answer == '' or url == '' or title == '' or image == '' or emotional_score == -1 or quality_score == -1 or body == '':
return web.badrequest()
if "yes" == answer:
Store.Record.create(CosineSimilarity.text_to_vector(body), 1)
Scraper.store_in_solr(url, emotional_score, quality_score,
(title, body, image))
else:
Store.Record.create(CosineSimilarity.text_to_vector(body), 0)
def get_quality_score(self, bodyList):
# we will measure quality by looking at relevancy and the quality of the writing
# relevancy will be judged as a normalized score.
# return reduce(lambda acc, x: acc + CosineSimilarity.similarity(x, GOLD_STD), bodyList, 0)
cosine_sim_value = reduce(lambda acc, x: acc + CosineSimilarity.similarity(x, GOLD_STD), bodyList, 0)
nearest_neighbor_class = (NearestNeighborFilter()).classify(CosineSimilarity.text_to_vector(" ".join(bodyList)))
nearest_neighbor_class = nearest_neighbor_class[0] # get the actual value.
# we are going to return a weighted average of the two filters
print "Combined score: {0}".format(0.58 * cosine_sim_value + 0.42 * nearest_neighbor_class)
return 0.58 * cosine_sim_value + 0.42 * nearest_neighbor_class
def POST(self):
# accepts the human marked form
# takes the YES/NO signal and if YES sends to Solr
# either way uses the YES/NO to train another epoch of the
# machine learning algorithm. The constants will be stored in
# the database.
web.header('Content-type', 'application/json')
request_input = web.input(answer='', url='', title='', image='', emotional_score=-1, quality_score=-1, body='')
url = request_input.url
title = request_input.title
image = request_input.image
emotional_score = request_input.emotional_score
quality_score = request_input.quality_score
body = request_input.body
answer = request_input.answer
if answer == '' or url == '' or title == '' or image == '' or emotional_score == -1 or quality_score == -1 or body == '':
return web.badrequest()
if "yes" == answer:
Store.Record.create(CosineSimilarity.text_to_vector(body), 1)
Scraper.store_in_solr(url, emotional_score, quality_score, (title, body, image))
else:
Store.Record.create(CosineSimilarity.text_to_vector(body), 0)
