File size: 10,318 Bytes
e84a10b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f38db99
e84a10b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
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)