AIQuizGenerator / mcq.py
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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
import random
import spacy
import zipfile
import os
import json
from sense2vec import Sense2Vec
import requests
from collections import OrderedDict
import string
import pke
import nltk
from nltk import FreqDist
nltk.download('brown')
nltk.download('stopwords')
nltk.download('popular')
from nltk.corpus import stopwords
from nltk.corpus import brown
from similarity.normalized_levenshtein import NormalizedLevenshtein
from nltk.tokenize import sent_tokenize
from flashtext import KeywordProcessor
def MCQs_available(word,s2v):
word = word.replace(" ", "_")
sense = s2v.get_best_sense(word)
if sense is not None:
return True
else:
return False
def edits(word):
"All edits that are one edit away from `word`."
letters = 'abcdefghijklmnopqrstuvwxyz '+string.punctuation
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def sense2vec_get_words(word,s2v):
output = []
word_preprocessed = word.translate(word.maketrans("","", string.punctuation))
word_preprocessed = word_preprocessed.lower()
word_edits = edits(word_preprocessed)
word = word.replace(" ", "_")
sense = s2v.get_best_sense(word)
most_similar = s2v.most_similar(sense, n=15)
compare_list = [word_preprocessed]
for each_word in most_similar:
append_word = each_word[0].split("|")[0].replace("_", " ")
append_word = append_word.strip()
append_word_processed = append_word.lower()
append_word_processed = append_word_processed.translate(append_word_processed.maketrans("","", string.punctuation))
if append_word_processed not in compare_list and word_preprocessed not in append_word_processed and append_word_processed not in word_edits:
output.append(append_word.title())
compare_list.append(append_word_processed)
out = list(OrderedDict.fromkeys(output))
return out
def get_options(answer,s2v):
distractors =[]
try:
distractors = sense2vec_get_words(answer,s2v)
if len(distractors) > 0:
print(" Sense2vec_distractors successful for word : ", answer)
return distractors,"sense2vec"
except:
print (" Sense2vec_distractors failed for word : ",answer)
return distractors,"None"
def tokenize_sentences(text):
sentences = [sent_tokenize(text)]
sentences = [y for x in sentences for y in x]
# Remove any short sentences less than 20 letters.
sentences = [sentence.strip() for sentence in sentences if len(sentence) > 20]
return sentences
def get_sentences_for_keyword(keywords, sentences):
keyword_processor = KeywordProcessor()
keyword_sentences = {}
for word in keywords:
word = word.strip()
keyword_sentences[word] = []
keyword_processor.add_keyword(word)
for sentence in sentences:
keywords_found = keyword_processor.extract_keywords(sentence)
for key in keywords_found:
keyword_sentences[key].append(sentence)
for key in keyword_sentences.keys():
values = keyword_sentences[key]
values = sorted(values, key=len, reverse=True)
keyword_sentences[key] = values
delete_keys = []
for k in keyword_sentences.keys():
if len(keyword_sentences[k]) == 0:
delete_keys.append(k)
for del_key in delete_keys:
del keyword_sentences[del_key]
return keyword_sentences
def is_far(words_list,currentword,thresh,normalized_levenshtein):
threshold = thresh
score_list =[]
for word in words_list:
score_list.append(normalized_levenshtein.distance(word.lower(),currentword.lower()))
if min(score_list)>=threshold:
return True
else:
return False
def filter_phrases(phrase_keys,max,normalized_levenshtein ):
filtered_phrases =[]
if len(phrase_keys)>0:
filtered_phrases.append(phrase_keys[0])
for ph in phrase_keys[1:]:
if is_far(filtered_phrases,ph,0.7,normalized_levenshtein ):
filtered_phrases.append(ph)
if len(filtered_phrases)>=max:
break
return filtered_phrases
def get_nouns_multipartite(text):
out = []
extractor = pke.unsupervised.MultipartiteRank()
extractor.load_document(input=text, language='en')
pos = {'PROPN', 'NOUN'}
stoplist = list(string.punctuation)
stoplist += stopwords.words('english')
extractor.candidate_selection(pos=pos)
# 4. build the Multipartite graph and rank candidates using random walk,
# alpha controls the weight adjustment mechanism, see TopicRank for
# threshold/method parameters.
try:
extractor.candidate_weighting(alpha=1.1,
threshold=0.75,
method='average')
except:
return out
keyphrases = extractor.get_n_best(n=10)
for key in keyphrases:
out.append(key[0])
return out
def get_phrases(doc):
phrases={}
for np in doc.noun_chunks:
phrase =np.text
len_phrase = len(phrase.split())
if len_phrase > 1:
if phrase not in phrases:
phrases[phrase]=1
else:
phrases[phrase]=phrases[phrase]+1
phrase_keys=list(phrases.keys())
phrase_keys = sorted(phrase_keys, key= lambda x: len(x),reverse=True)
phrase_keys=phrase_keys[:50]
return phrase_keys
def get_keywords(nlp,text,max_keywords,s2v,fdist,normalized_levenshtein,no_of_sentences):
doc = nlp(text)
max_keywords = int(max_keywords)
keywords = get_nouns_multipartite(text)
keywords = sorted(keywords, key=lambda x: fdist[x])
keywords = filter_phrases(keywords, max_keywords,normalized_levenshtein )
phrase_keys = get_phrases(doc)
filtered_phrases = filter_phrases(phrase_keys, max_keywords,normalized_levenshtein )
total_phrases = keywords + filtered_phrases
total_phrases_filtered = filter_phrases(total_phrases, min(max_keywords, 2*no_of_sentences),normalized_levenshtein )
answers = []
for answer in total_phrases_filtered:
if answer not in answers and MCQs_available(answer,s2v):
answers.append(answer)
answers = answers[:max_keywords]
return answers
def generate_questions_mcq(keyword_sent_mapping,device,tokenizer,model,sense2vec,normalized_levenshtein):
batch_text = []
answers = keyword_sent_mapping.keys()
for answer in answers:
txt = keyword_sent_mapping[answer]
context = "context: " + txt
text = context + " " + "answer: " + answer + " </s>"
batch_text.append(text)
encoding = tokenizer.batch_encode_plus(batch_text, pad_to_max_length=True, return_tensors="pt")
print ("Running model for generation")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
with torch.no_grad():
outs = model.generate(input_ids=input_ids,
attention_mask=attention_masks,
max_length=150)
output_array ={}
output_array["questions"] =[]
# print(outs)
for index, val in enumerate(answers):
individual_question ={}
out = outs[index, :]
dec = tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
Question = dec.replace("question:", "")
Question = Question.strip()
individual_question["question_statement"] = Question
individual_question["question_type"] = "MCQ"
individual_question["answer"] = val
individual_question["id"] = index+1
individual_question["options"], individual_question["options_algorithm"] = get_options(val, sense2vec)
individual_question["options"] = filter_phrases(individual_question["options"], 10,normalized_levenshtein)
index = 3
individual_question["extra_options"]= individual_question["options"][index:]
individual_question["options"] = individual_question["options"][:index]
individual_question["context"] = keyword_sent_mapping[val]
if len(individual_question["options"])>0:
output_array["questions"].append(individual_question)
return output_array
def generate_normal_questions(keyword_sent_mapping,device,tokenizer,model): #for normal one word questions
batch_text = []
answers = keyword_sent_mapping.keys()
for answer in answers:
txt = keyword_sent_mapping[answer]
context = "context: " + txt
text = context + " " + "answer: " + answer + " </s>"
batch_text.append(text)
encoding = tokenizer.batch_encode_plus(batch_text, pad_to_max_length=True, return_tensors="pt")
print ("Running model for generation")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
with torch.no_grad():
outs = model.generate(input_ids=input_ids,
attention_mask=attention_masks,
max_length=150)
output_array ={}
output_array["questions"] =[]
for index, val in enumerate(answers):
individual_quest= {}
out = outs[index, :]
dec = tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
Question= dec.replace('question:', '')
Question= Question.strip()
individual_quest['Question']= Question
individual_quest['Answer']= val
individual_quest["id"] = index+1
individual_quest["context"] = keyword_sent_mapping[val]
output_array["questions"].append(individual_quest)
return output_array
def random_choice():
a = random.choice([0,1])
return bool(a)