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| import re | |
| from bs4 import BeautifulSoup | |
| import distance | |
| from fuzzywuzzy import fuzz | |
| import pickle | |
| import numpy as np | |
| cv = pickle.load(open('cv.pkl','rb')) | |
| def test_common_words(q1,q2): | |
| w1 = set(map(lambda word: word.lower().strip(), q1.split(" "))) | |
| w2 = set(map(lambda word: word.lower().strip(), q2.split(" "))) | |
| return len(w1 & w2) | |
| def test_total_words(q1,q2): | |
| w1 = set(map(lambda word: word.lower().strip(), q1.split(" "))) | |
| w2 = set(map(lambda word: word.lower().strip(), q2.split(" "))) | |
| return (len(w1) + len(w2)) | |
| def test_fetch_token_features(q1, q2): | |
| SAFE_DIV = 0.0001 | |
| STOP_WORDS = pickle.load(open('stopwords.pkl','rb')) | |
| token_features = [0.0] * 8 | |
| # Converting the Sentence into Tokens: | |
| q1_tokens = q1.split() | |
| q2_tokens = q2.split() | |
| if len(q1_tokens) == 0 or len(q2_tokens) == 0: | |
| return token_features | |
| # Get the non-stopwords in Questions | |
| q1_words = set([word for word in q1_tokens if word not in STOP_WORDS]) | |
| q2_words = set([word for word in q2_tokens if word not in STOP_WORDS]) | |
| # Get the stopwords in Questions | |
| q1_stops = set([word for word in q1_tokens if word in STOP_WORDS]) | |
| q2_stops = set([word for word in q2_tokens if word in STOP_WORDS]) | |
| # Get the common non-stopwords from Question pair | |
| common_word_count = len(q1_words.intersection(q2_words)) | |
| # Get the common stopwords from Question pair | |
| common_stop_count = len(q1_stops.intersection(q2_stops)) | |
| # Get the common Tokens from Question pair | |
| common_token_count = len(set(q1_tokens).intersection(set(q2_tokens))) | |
| token_features[0] = common_word_count / (min(len(q1_words), len(q2_words)) + SAFE_DIV) | |
| token_features[1] = common_word_count / (max(len(q1_words), len(q2_words)) + SAFE_DIV) | |
| token_features[2] = common_stop_count / (min(len(q1_stops), len(q2_stops)) + SAFE_DIV) | |
| token_features[3] = common_stop_count / (max(len(q1_stops), len(q2_stops)) + SAFE_DIV) | |
| token_features[4] = common_token_count / (min(len(q1_tokens), len(q2_tokens)) + SAFE_DIV) | |
| token_features[5] = common_token_count / (max(len(q1_tokens), len(q2_tokens)) + SAFE_DIV) | |
| # Last word of both question is same or not | |
| token_features[6] = int(q1_tokens[-1] == q2_tokens[-1]) | |
| # First word of both question is same or not | |
| token_features[7] = int(q1_tokens[0] == q2_tokens[0]) | |
| return token_features | |
| def test_fetch_length_features(q1, q2): | |
| length_features = [0.0] * 3 | |
| # Converting the Sentence into Tokens: | |
| q1_tokens = q1.split() | |
| q2_tokens = q2.split() | |
| if len(q1_tokens) == 0 or len(q2_tokens) == 0: | |
| return length_features | |
| # Absolute length features | |
| length_features[0] = abs(len(q1_tokens) - len(q2_tokens)) | |
| # Average Token Length of both Questions | |
| length_features[1] = (len(q1_tokens) + len(q2_tokens)) / 2 | |
| strs = list(distance.lcsubstrings(q1, q2)) | |
| length_features[2] = len(strs[0]) / (min(len(q1), len(q2)) + 1) | |
| return length_features | |
| def test_fetch_fuzzy_features(q1, q2): | |
| fuzzy_features = [0.0] * 4 | |
| # fuzz_ratio | |
| fuzzy_features[0] = fuzz.QRatio(q1, q2) | |
| # fuzz_partial_ratio | |
| fuzzy_features[1] = fuzz.partial_ratio(q1, q2) | |
| # token_sort_ratio | |
| fuzzy_features[2] = fuzz.token_sort_ratio(q1, q2) | |
| # token_set_ratio | |
| fuzzy_features[3] = fuzz.token_set_ratio(q1, q2) | |
| return fuzzy_features | |
| def preprocess(q): | |
| q = str(q).lower().strip() | |
| # Replace certain special characters with their string equivalents | |
| q = q.replace('%', ' percent') | |
| q = q.replace('$', ' dollar ') | |
| q = q.replace('₹', ' rupee ') | |
| q = q.replace('€', ' euro ') | |
| q = q.replace('@', ' at ') | |
| # The pattern '[math]' appears around 900 times in the whole dataset. | |
| q = q.replace('[math]', '') | |
| # Replacing some numbers with string equivalents (not perfect, can be done better to account for more cases) | |
| q = q.replace(',000,000,000 ', 'b ') | |
| q = q.replace(',000,000 ', 'm ') | |
| q = q.replace(',000 ', 'k ') | |
| q = re.sub(r'([0-9]+)000000000', r'\1b', q) | |
| q = re.sub(r'([0-9]+)000000', r'\1m', q) | |
| q = re.sub(r'([0-9]+)000', r'\1k', q) | |
| # Decontracting words | |
| # https://en.wikipedia.org/wiki/Wikipedia%3aList_of_English_contractions | |
| # https://stackoverflow.com/a/19794953 | |
| contractions = { | |
| "ain't": "am not", | |
| "aren't": "are not", | |
| "can't": "can not", | |
| "can't've": "can not have", | |
| "'cause": "because", | |
| "could've": "could have", | |
| "couldn't": "could not", | |
| "couldn't've": "could not have", | |
| "didn't": "did not", | |
| "doesn't": "does not", | |
| "don't": "do not", | |
| "hadn't": "had not", | |
| "hadn't've": "had not have", | |
| "hasn't": "has not", | |
| "haven't": "have not", | |
| "he'd": "he would", | |
| "he'd've": "he would have", | |
| "he'll": "he will", | |
| "he'll've": "he will have", | |
| "he's": "he is", | |
| "how'd": "how did", | |
| "how'd'y": "how do you", | |
| "how'll": "how will", | |
| "how's": "how is", | |
| "i'd": "i would", | |
| "i'd've": "i would have", | |
| "i'll": "i will", | |
| "i'll've": "i will have", | |
| "i'm": "i am", | |
| "i've": "i have", | |
| "isn't": "is not", | |
| "it'd": "it would", | |
| "it'd've": "it would have", | |
| "it'll": "it will", | |
| "it'll've": "it will have", | |
| "it's": "it is", | |
| "let's": "let us", | |
| "ma'am": "madam", | |
| "mayn't": "may not", | |
| "might've": "might have", | |
| "mightn't": "might not", | |
| "mightn't've": "might not have", | |
| "must've": "must have", | |
| "mustn't": "must not", | |
| "mustn't've": "must not have", | |
| "needn't": "need not", | |
| "needn't've": "need not have", | |
| "o'clock": "of the clock", | |
| "oughtn't": "ought not", | |
| "oughtn't've": "ought not have", | |
| "shan't": "shall not", | |
| "sha'n't": "shall not", | |
| "shan't've": "shall not have", | |
| "she'd": "she would", | |
| "she'd've": "she would have", | |
| "she'll": "she will", | |
| "she'll've": "she will have", | |
| "she's": "she is", | |
| "should've": "should have", | |
| "shouldn't": "should not", | |
| "shouldn't've": "should not have", | |
| "so've": "so have", | |
| "so's": "so as", | |
| "that'd": "that would", | |
| "that'd've": "that would have", | |
| "that's": "that is", | |
| "there'd": "there would", | |
| "there'd've": "there would have", | |
| "there's": "there is", | |
| "they'd": "they would", | |
| "they'd've": "they would have", | |
| "they'll": "they will", | |
| "they'll've": "they will have", | |
| "they're": "they are", | |
| "they've": "they have", | |
| "to've": "to have", | |
| "wasn't": "was not", | |
| "we'd": "we would", | |
| "we'd've": "we would have", | |
| "we'll": "we will", | |
| "we'll've": "we will have", | |
| "we're": "we are", | |
| "we've": "we have", | |
| "weren't": "were not", | |
| "what'll": "what will", | |
| "what'll've": "what will have", | |
| "what're": "what are", | |
| "what's": "what is", | |
| "what've": "what have", | |
| "when's": "when is", | |
| "when've": "when have", | |
| "where'd": "where did", | |
| "where's": "where is", | |
| "where've": "where have", | |
| "who'll": "who will", | |
| "who'll've": "who will have", | |
| "who's": "who is", | |
| "who've": "who have", | |
| "why's": "why is", | |
| "why've": "why have", | |
| "will've": "will have", | |
| "won't": "will not", | |
| "won't've": "will not have", | |
| "would've": "would have", | |
| "wouldn't": "would not", | |
| "wouldn't've": "would not have", | |
| "y'all": "you all", | |
| "y'all'd": "you all would", | |
| "y'all'd've": "you all would have", | |
| "y'all're": "you all are", | |
| "y'all've": "you all have", | |
| "you'd": "you would", | |
| "you'd've": "you would have", | |
| "you'll": "you will", | |
| "you'll've": "you will have", | |
| "you're": "you are", | |
| "you've": "you have" | |
| } | |
| q_decontracted = [] | |
| for word in q.split(): | |
| if word in contractions: | |
| word = contractions[word] | |
| q_decontracted.append(word) | |
| q = ' '.join(q_decontracted) | |
| q = q.replace("'ve", " have") | |
| q = q.replace("n't", " not") | |
| q = q.replace("'re", " are") | |
| q = q.replace("'ll", " will") | |
| # Removing HTML tags | |
| q = BeautifulSoup(q) | |
| q = q.get_text() | |
| # Remove punctuations | |
| pattern = re.compile('\W') | |
| q = re.sub(pattern, ' ', q).strip() | |
| return q | |
| def query_point_creator(q1, q2): | |
| input_query = [] | |
| # preprocess | |
| q1 = preprocess(q1) | |
| q2 = preprocess(q2) | |
| # fetch basic features | |
| input_query.append(len(q1)) | |
| input_query.append(len(q2)) | |
| input_query.append(len(q1.split(" "))) | |
| input_query.append(len(q2.split(" "))) | |
| input_query.append(test_common_words(q1, q2)) | |
| input_query.append(test_total_words(q1, q2)) | |
| input_query.append(round(test_common_words(q1, q2) / test_total_words(q1, q2), 2)) | |
| # fetch token features | |
| token_features = test_fetch_token_features(q1, q2) | |
| input_query.extend(token_features) | |
| # fetch length based features | |
| length_features = test_fetch_length_features(q1, q2) | |
| input_query.extend(length_features) | |
| # fetch fuzzy features | |
| fuzzy_features = test_fetch_fuzzy_features(q1, q2) | |
| input_query.extend(fuzzy_features) | |
| # bow feature for q1 | |
| q1_bow = cv.transform([q1]).toarray() | |
| # bow feature for q2 | |
| q2_bow = cv.transform([q2]).toarray() | |
| return np.hstack((np.array(input_query).reshape(1, 22), q1_bow, q2_bow)) |