def on_message(self, message):
logging.info("Received message: "+str(message))
data = json.loads(message)
teacher = data['teacher']
student = data['student']
result = utils.similarity(teacher, student, **data)
self.send(json.dumps(result, default=lambda x: str(x)))
def main():
if len(sys.argv) < 3:
print "Usage: {0} sentence_1 sentence_2 wsd stemmer sim scoring context".format(sys.argv[0])
return
sentence_1 = sys.argv[1]
sentence_2 = sys.argv[2]
lesk_type = "adapted"
if len(sys.argv) >= 4:
lesk_type = sys.argv[3]
stemmer = "lancaster"
if len(sys.argv) >= 5:
stemmer = sys.argv[4]
sim_option = "path"
if len(sys.argv) >= 6:
sim_option = sys.argv[5]
scoring_option = "min"
if len(sys.argv) >= 7:
scoring_option = sys.argv[6]
context = ""
if len(sys.argv) >= 8:
context = sys.argv[7]
result = similarity(
sentence_1, sentence_2,
wsd=lesk_type, stemmer=stemmer,
similarity=sim_option, scoring=scoring_option, context=context)
print "Score: ", result['score']
print "Reason:"
for candidate in result['candidates']:
print candidate['word1']['token'], \
"("+str(candidate['word1']['definition'])+")",\
candidate['match'], \
candidate['word2']['token'], \
"("+str(candidate['word2']['definition'])+")"
def mark(self, **kwargs):
stemmer = kwargs.get('stemmer', None)
wsd = kwargs.get('wsd', None)
sim = kwargs.get('similarity', None)
scorer = kwargs.get('scorer', None)
tentative_threshold_opt = kwargs.get('tentative_threshold_opt', None)
threshold_opt = kwargs.get('threshold_opt', None)
results = []
marking_answers = self.markinganswer_set.all()
tentative_threshold = 0
if tentative_threshold_opt == 'min':
tentative_threshold = 10000
thresholds = []
for answer in marking_answers:
result = utils.similarity(
answer.text, answer.text,
stemmer=stemmer, wsd=wsd, similarity=sim, scoring=scorer)
if tentative_threshold_opt == 'min':
if result['score'] < tentative_threshold:
tentative_threshold = result['score']
if tentative_threshold_opt == 'mean':
thresholds.append(result['score'])
if tentative_threshold_opt == 'max':
if result['score'] > tentative_threshold:
tentative_threshold = result['score']
if tentative_threshold_opt == 'mean':
try:
tentative_threshold = sum(thresholds) / len(thresholds)
except ZeroDivisionError:
tentative_threshold = 0
threshold = threshold_opt if threshold_opt else tentative_threshold
for answer in self.studentanswer_set.all():
max_result = None
max_score = -1
max_markinganswer = None
for marking_answer in marking_answers:
context = self.text + " " + marking_answer.text
result = utils.similarity(
marking_answer.text, answer.text,
stemmer=stemmer, wsd=wsd, similarity=sim, scoring=scorer, context=context)
if result['score'] > max_score:
max_result = result
max_score = result['score']
max_markinganswer = marking_answer
sent_1_terms = [candidate['word1']['token'] for candidate in max_result['candidates']]
sent_2_terms = [candidate['word2']['token'] for candidate in max_result['candidates']]
results.append(
{'answer': answer,
'marking_answer': max_markinganswer,
'result': max_result,
'sent_1_terms': sent_1_terms,
'sent_2_terms': sent_2_terms})
return results, threshold
