def testing(request, type_id):
type = QuestionType.objects.get(pk=type_id)
import codecs
#
module_dir = os.path.dirname(__file__) # get current directory
file_path = os.path.join(module_dir, 'results/results_type_'+type_id+'.txt')
output_file = codecs.open(file_path, 'a', encoding="utf-8")
type_str = "\n \n" + type.QuestionType + "\n \n"
output_file.writelines(type_str)
questions_ids = [q.id for q in Question.objects.filter(QuestionTypeID=type_id)]
# print questions
for question_id in questions_ids:
question = Question.objects.get(pk=question_id)
comprehension = Comprehension.objects.get(pk=question.Comprehension_id)
# print comprehension
# Split Question tags string into individual tags
tag_parts = question.QuestionTagsOnly.split('\\')
# Fetch compound words from the dictionary and count their matches against question words
compound_words = Dictionary.objects.filter(CompoundWord=True)
question_words = question.QuestionText.split(' ')
question_compound_words_count = len([match for match in question_words if match in compound_words])
question_word_tags = [{'word': w, 'tag': t} for w, t in zip(question_words, tag_parts) if t != "RD_PUNC"]
all_named_entities = [dict.Word for dict in Dictionary.objects.filter(NamedEntity=True)]
question_named_entities = [m for m in question_words if m in all_named_entities]
question_named_entity_types = [ne.name for ne in NamedEntityType.objects.filter(
dictionary__Word__in=question_named_entities).distinct()]
question_bag = {'question_word_tags': question_word_tags, 'punctuation_index': 1,
'question_compound_words_count': question_compound_words_count,
'question_named_entity_types': question_named_entity_types,
'question_named_entities': question_named_entities}
cog_bag = []
import sentence_formulation as sf
sentences = sf.formulate(question.QuestionText, comprehension.ComprehensionsText)
probable_answers = []
answers_cog_list = []
for sentence_index, sentence in enumerate(sentences['sentences']):
words = sentence.split()
sentence_words = sentence.split()
# print sentence_words
sentence_compound_words_count = len([match for match in sentence_words if match in compound_words])
sentence_named_entities = [m for m in sentence_words if m in all_named_entities]
sentence_named_entity_types = [ne.name for ne in NamedEntityType.objects.filter(
dictionary__Word__in=sentence_named_entities).distinct()]
del sentence_words[-1]
# punctuations = [iter + 1 for iter, punc in enumerate(sentence_words) if punc == u'|']
# print punctuations
# punctuation_index = punctuations[-1]
sentence_word_tags = []
for word in sentence_words:
obj = Dictionary.objects.filter(Word=word).first()
if obj != None:
sentence_word_tags.append({'word': obj.Word, 'tag': obj.WordType})
sentence_bag = {'sentence_word_tags': sentence_word_tags, 'punctuation_index': 1,
'sentence_compound_words_count': sentence_compound_words_count,
'sentence_named_entity_types': sentence_named_entity_types,
'sentence_named_entities': sentence_named_entities}
cog = cogm.calculate_cogm(question_bag, sentence_bag)
distance_similarity = cogm.euclidean_distance_similarity(cog['sentence_cog']['cogX'], cog['sentence_cog']['cogY'],
cog['question_cog']['cogX'], cog['question_cog']['cogY'])
cog['euclidean_distance'] = distance_similarity['euclidean_distance']
cog['similarity'] = distance_similarity['similarity']
cog['bonus_value'] = cog['sentence_cog']['bonus_value']
cog['question_cog']['question'] = question.QuestionText
cog['sentence_cog']['sentence'] = sentence
cog['matched_bigrams'] = cog['sentence_cog']['matched_bigrams']
cog_bag.append(cog)
cog_bag = sorted(cog_bag, key=itemgetter('similarity'), reverse=True)
try:
if len(cog_bag) > 0:
output = re.sub(r'([\'\"\,])', '', cog_bag[0]['question_cog']['question']) + ", (" + \
str(cog_bag[0]['question_cog']['X1']) + " " + \
str(cog_bag[0]['question_cog']['Y1']) + ") " + "(" + str(cog_bag[0]['question_cog']['X2']) + " " + \
str(cog_bag[0]['question_cog']['Y2']) + ") " + "(" + str(cog_bag[0]['question_cog']['X3']) + " " + \
str(cog_bag[0]['question_cog']['Y3']) + "), " + str(cog_bag[0]['question_cog']['lexical_density']) + \
", " + str(cog_bag[0]['question_cog']['readability_index']) + \
str(cog_bag[0]['question_cog']['q_lex']) + ", (" + str(cog_bag[0]['sentence_cog']['cogX']) + \
" " + str(cog_bag[0]['question_cog']['cogY']) + "), " + str(cog_bag[0]['euclidean_distance']) + ", " + \
str(cog_bag[0]['similarity']), "\n"
output += re.sub(r'([\'\"\,])', '', cog_bag[0]['sentence_cog']['sentence']) + ", (" + \
str(cog_bag[0]['sentence_cog']['X1']) + " " + \
str(cog_bag[0]['sentence_cog']['Y1']) + ") " + "(" + str(cog_bag[0]['sentence_cog']['X2']) + " " + \
str(cog_bag[0]['sentence_cog']['Y2']) + ") " + "(" + str(cog_bag[0]['sentence_cog']['X3']) + " " + \
str(cog_bag[0]['sentence_cog']['Y3']) + "), " + str(cog_bag[0]['sentence_cog']['lexical_density']) + \
", " + str(cog_bag[0]['sentence_cog']['readability_index']) + \
str(cog_bag[0]['sentence_cog']['s_lex']) + ", (" + str(cog_bag[0]['sentence_cog']['cogX']) + \
" " + str(cog_bag[0]['sentence_cog']['cogY']) + "), " + str(cog_bag[0]['euclidean_distance']) + ", " + \
str(cog_bag[0]['similarity']), "\n"
output_file.writelines(output)
except (IndexError, ValueError):
pass
# print output_list
output_file.close()
# types = QuestionType.objects.annotate(count=Count('questiontypes')).order_by('-count').values('id', 'count')
return render(request, 'qa/testing.html', {})
def questiondetail(request, question_id):
try:
from nltk.corpus import stopwords
question = Question.objects.get(pk=question_id)
question_types = question.QuestionTypeID.all()
comprehension = Comprehension.objects.get(pk=question.Comprehension_id)
# Split Question tags string into individual tags
tag_parts = question.QuestionTagsOnly.split('\\')
# Find Punctuation Index (W3)
# punctuations = [i+1 for i, x in enumerate(tag_parts) if x == 'RD_PUNC']
# punctuation_index = punctuations[-1]
#Fetch compound words from the dictionary and count their matches against question words
compound_words = Dictionary.objects.filter(CompoundWord=True)
question_words = question.QuestionText.split(' ')
question_compound_words_count = len([match for match in question_words if match in compound_words])
question_word_tags = [{'word':w, 'tag':t} for w,t in zip(question_words, tag_parts) if t != "RD_PUNC"]
all_named_entities = [dict.Word for dict in Dictionary.objects.filter(NamedEntity=True)]
question_named_entities = [m for m in question_words if m in all_named_entities]
question_named_entity_types = [ne.name for ne in NamedEntityType.objects.filter(dictionary__Word__in=question_named_entities).distinct()]
question_bag = {'question_word_tags': question_word_tags, 'punctuation_index': 1,
'question_compound_words_count': question_compound_words_count,
'question_named_entity_types': question_named_entity_types,
'question_named_entities': question_named_entities }
# Break paragraph into sentences
# sentences = [s.strip() + " |" for s in comprehension.ComprehensionsText.split('|')]
# del sentences[-1]
cog_bag = []
import sentence_formulation as sf
# sentences = st.who_logic(question.QuestionText, comprehension.ComprehensionsText)
sentences = sf.formulate(question.QuestionText, comprehension.ComprehensionsText)
# print sentences
probable_answers = []
answers_cog_list = []
for sentence_index, sentence in enumerate(sentences['sentences']):
words = sentence.split()
# sentence_words = [word for word in words if word not in stopwords.words('punjabi')]
sentence_words = sentence.split()
# print ",".join(sentence_words)
sentence_compound_words_count = len([match for match in sentence_words if match in compound_words])
sentence_named_entities = [m for m in sentence_words if m in all_named_entities]
sentence_named_entity_types = [ne.name for ne in NamedEntityType.objects.filter(dictionary__Word__in=sentence_named_entities).distinct()]
# # Find Punctuation Index (W3)
# punctuations = [iter + 1 for iter, punc in enumerate(sentence_words) if punc == '|']
# punctuation_index = punctuations[-1]
del sentence_words[-1]
# Split Question tags string into individual tags
sentence_word_tags = []
for word in sentence_words:
obj = Dictionary.objects.filter(Word=word).first()
if obj != None:
sentence_word_tags.append({'word': obj.Word, 'tag': obj.WordType})
sentence_bag = {'sentence_word_tags': sentence_word_tags, 'punctuation_index': 1,
'sentence_compound_words_count': sentence_compound_words_count,
'sentence_named_entity_types': sentence_named_entity_types,
'sentence_named_entities': sentence_named_entities}
# sentence_tags = []
# for part in tag_parts:
# for p in part:
# sentence_tags.append(p)
#
# sentence_word_tags = [{'word': w, 'tag': t} for w, t in zip(sentence_words, sentence_tags)]
# for t in sentence_word_tags:
# print t['word'], t['tag']
#sentence_tags.append('RD_PUNC')
#print sentence_compound_words_count
# bonus_value = bonus.get_bonus(question_words, question_named_entity_types, sentence_words,sentence_named_entity_types, sentence_tags)
# sentence_cog = cogm.calculate_cogm(sentence_words, bonus_value, sentence_compound_words_count, sentence_named_entity_types, sentence_tags, sentence_named_entities)
cog = cogm.calculate_cogm(question_bag, sentence_bag)
distance_similarity = cogm.euclidean_distance_similarity(cog['sentence_cog']['cogX'], cog['sentence_cog']['cogY'],
cog['question_cog']['cogX'], cog['question_cog']['cogY'])
cog['euclidean_distance'] = distance_similarity['euclidean_distance']
cog['similarity'] = distance_similarity['similarity']
cog['bonus_value'] = cog['sentence_cog']['bonus_value']
cog['question_cog']['question'] = question.QuestionText
cog['sentence_cog']['sentence'] = sentence
cog['matched_bigrams'] = cog['sentence_cog']['matched_bigrams']
# cog['question_bonus_distance'] = cogm.euclidean_distance(cog['question_cog']['bonus_value']['bonus_x'], cog['question_cog']['bonus_value']['bonus_y'],
# cog['question_cog']['cogX'], cog['question_cog']['cogY'])
# cog['sentence_bonus_distance'] = cogm.euclidean_distance(cog['sentence_cog']['bonus_value']['bonus_x'],
# cog['sentence_cog']['bonus_value']['bonus_y'],
# cog['sentence_cog']['cogX'],
# cog['sentence_cog']['cogY'])
# import math
# cog['inter_bonus_distance'] = math.sqrt(math.pow(cog['question_bonus_distance'] - cog['sentence_bonus_distance'], 2))
# probable_answer = {'sentence': sentence, 'sentence_tags': sentence_tags,'cogX': sentence_cog['cogX'], 'cogY': sentence_cog['cogY'], 'cog': [sentence_cog['cogX'], sentence_cog['cogY']],
# 'sentence_index': sentence_index + 1, 'lexical_density': sentence_cog['lexical_density'],
# 'readability_index': sentence_cog['readability_index'], 'punctuation_index': punctuation_index, 'euclidean_distance': euclidean_distance, 'bonus': bonus_value, 'sentence_cog': sentence_cog }
# #print probable_answer
# answers_cog_list.append([sentence_cog['cogX'],sentence_cog['cogY']])
#print sentence
cog_bag.append(cog)
# print cog
cog_bag = sorted(cog_bag, key=itemgetter('similarity'), reverse=True)
# import codecs
#
# module_dir = os.path.dirname(__file__) # get current directory
# file_path = os.path.join(module_dir, 'results.txt')
# output_file = codecs.open(file_path, 'a', encoding="utf-8")
# for c in cog_bag:
# output = "Question" + "\n" + \
# "X1= " + str(c['question_cog']['X1']) + "\n" + \
# "Y1= " + str(c['question_cog']['Y1']) + "\n" + \
# "X2= " + str(c['question_cog']['X2']) + "\n" + \
# "Y2= " + str(c['question_cog']['Y2']) + "\n" + \
# "X3= " + str(c['question_cog']['X3']) + "\n" + \
# "Y3= " + str(c['question_cog']['Y3']) + "\n" + \
# "W1= " + str(c['question_cog']['lexical_density']) + "\n" + \
# "W2= " + str(c['question_cog']['readability_index']) + "\n" + \
# "W3= " + str(c['question_cog']['q_lex']) + "\n\n" + \
# "Sentence" + "\n" + \
# "X1= " + str(c['sentence_cog']['X1']) + "\n" + \
# "Y1= " + str(c['sentence_cog']['Y1']) + "\n" + \
# "X2= " + str(c['sentence_cog']['X2']) + "\n" + \
# "Y2= " + str(c['sentence_cog']['Y2']) + "\n" + \
# "X3= " + str(c['sentence_cog']['X3']) + "\n" + \
# "Y3= " + str(c['sentence_cog']['Y3']) + "\n" + \
# "W1= " + str(c['sentence_cog']['lexical_density']) + "\n" + \
# "W2= " + str(c['sentence_cog']['readability_index']) + "\n" + \
# "W3= " + str(c['sentence_cog']['s_lex']) + "\n\n\n\n"
# output_file.writelines(str(output))
# output_file.close()
# import codecs
#
# module_dir = os.path.dirname(__file__) # get current directory
# file_path = os.path.join(module_dir, 'out4.txt')
# output_file = codecs.open(file_path, 'a', encoding="utf-8")
# output = re.sub(r'([\'\"\,])', '', cog_bag[0]['question_cog']['question']) + ", (" + \
# str(cog_bag[0]['question_cog']['X1']) + " " + \
# str(cog_bag[0]['question_cog']['Y1']) + ") " + "(" + str(cog_bag[0]['question_cog']['X2']) + " " + \
# str(cog_bag[0]['question_cog']['Y2']) + ") " + "(" + str(cog_bag[0]['question_cog']['X3']) + " " + \
# str(cog_bag[0]['question_cog']['Y3']) + "), " + str(cog_bag[0]['question_cog']['lexical_density']) + \
# ", " + str(cog_bag[0]['question_cog']['readability_index']) + \
# str(cog_bag[0]['question_cog']['q_lex']) + ", (" + str(cog_bag[0]['sentence_cog']['cogX']) + \
# " " + str(cog_bag[0]['question_cog']['cogY']) + "), " + str(cog_bag[0]['euclidean_distance']), "\n"
# output += re.sub(r'([\'\"\,])', '', cog_bag[0]['sentence_cog']['sentence']) + ", (" + \
# str(cog_bag[0]['sentence_cog']['X1']) + " " + \
# str(cog_bag[0]['sentence_cog']['Y1']) + ") " + "(" + str(cog_bag[0]['sentence_cog']['X2']) + " " + \
# str(cog_bag[0]['sentence_cog']['Y2']) + ") " + "(" + str(cog_bag[0]['sentence_cog']['X3']) + " " + \
# str(cog_bag[0]['sentence_cog']['Y3']) + "), " + str(cog_bag[0]['sentence_cog']['lexical_density']) + \
# ", " + str(cog_bag[0]['sentence_cog']['readability_index']) + \
# str(cog_bag[0]['sentence_cog']['s_lex']) + ", (" + str(cog_bag[0]['sentence_cog']['cogX']) + \
# " " + str(cog_bag[0]['sentence_cog']['cogY']) + "), " + str(cog_bag[0]['euclidean_distance']), "\n"
# output_file.writelines(output)
# output_file.close()
# cog_bag = sorted(cog_bag, key=itemgetter('euclidean_distance'))
# cog_bag = sorted(cog_bag, key=itemgetter('bonus_value', 'euclidean_distance'), reverse=True)
# cog_bag = sorted(cog_bag, key=lambda element: (element['euclidean_distance'], -element['bonus_value']))
#print answers_cog
#random.shuffle(answers)
# nearest_answer = cogm.find_nearest_vector(answers_cog, [question_cog['cogX'], question_cog['cogY']])
#print nearest_answer
except Question.DoesNotExist:
raise Http404('Question does not exist')
#return render(request, 'qa/questiondetail.html', {'comprehension': comprehension, 'question': question, 'X':[X1,X2,X3], 'Y':[Y1,Y2,Y3], 'graph': graph, 'question_types': question_types})
# return render(request, 'qa/questiondetail.html', {'comprehension': comprehension, 'question': question, 'q_cog_dump': ujson.dumps(question_cog), 'question_types': question_types, 'answers': answers, 'answers_dump': ujson.dumps(answers)})
return render(request, 'qa/questiondetail.html',
{'comprehension': comprehension, 'question': question, 'cog_bag': cog_bag,
'question_types': question_types, \
# 'answers': answers, 'answers_dump': ujson.dumps(answers)
})
