-
Notifications
You must be signed in to change notification settings - Fork 0
/
ngrams_derive.py
161 lines (131 loc) · 5.68 KB
/
ngrams_derive.py
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
from nltk.collocations import BigramCollocationFinder, TrigramCollocationFinder
from nltk.metrics import BigramAssocMeasures, TrigramAssocMeasures
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import nltk
import csv
import re
with open('data/travel/quora.txt') as f:
words_quora = [word for line in f for word in line.split()]
with open('data/travel/wikihow.txt') as f:
words_wiki = [re.sub(r'[^\w\s]', '', word) for line in f for word in line.split()]
with open('data/travel/stackexchange.txt') as f:
words_stackexchange = [re.sub(r'[^\w\s]', '', word) for line in f for word in line.split()]
with open('data/travel/reddit.txt') as f:
words_reddit = [re.sub(r'[^\w\s]', '', word) for line in f for word in line.split()]
wiki_sentence = []
for w in words_wiki:
if w.lower() not in stop_words:
wiki_sentence.append(w.lower())
quora_sentence = []
for w in words_quora:
if w.lower() not in stop_words:
quora_sentence.append(w.lower())
stackexchange_sentence = []
for w in words_stackexchange:
if w.lower() not in stop_words:
stackexchange_sentence.append(w.lower())
reddit_sentence = []
for w in words_reddit:
if w.lower() not in stop_words:
reddit_sentence.append(w.lower())
finder = TrigramCollocationFinder.from_words(wiki_sentence)
trigram_wiki = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)
finder = TrigramCollocationFinder.from_words(quora_sentence)
trigram_quora = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)
finder = BigramCollocationFinder.from_words(wiki_sentence)
bigram_wiki = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)
finder = BigramCollocationFinder.from_words(quora_sentence)
bigram_quora = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)
finder = BigramCollocationFinder.from_words(reddit_sentence)
bigram_reddit = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)
finder = BigramCollocationFinder.from_words(stackexchange_sentence)
bigram_stackexchange = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)
finder = TrigramCollocationFinder.from_words(reddit_sentence)
trigram_reddit = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)
finder = TrigramCollocationFinder.from_words(stackexchange_sentence)
trigram_stackexchange = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)
universal = trigram_wiki + bigram_wiki + trigram_quora + bigram_quora + trigram_stackexchange + bigram_stackexchange + trigram_reddit + bigram_reddit
# with open("Trigram_Quora.csv", "w") as f:
# writer = csv.writer(f)
# writer.writerows(trigram_quora)
# with open("Trigram_Wiki.csv", "w") as f:
# writer = csv.writer(f)
# writer.writerows(trigram_wiki)
# with open("Bigram_Quora.csv", "w") as f:
# writer = csv.writer(f)
# writer.writerows(bigram_quora)
# with open("Bigram_Wiki.csv", "w") as f:
# writer = csv.writer(f)
# writer.writerows(bigram_wiki)
import spacy
nlp = spacy.load('en_core_web_lg')
import math
from subprocess import Popen, PIPE
def caterr(nue):
try:
return int(math.ceil(((1/nue)-1)))
except ZeroDivisionError:
return 10
print("calculating Similarity matrix")
similarity_mat = [[nlp(' '.join(k)).similarity(nlp(' '.join(i)))
for k in universal] for i in universal]
distance_mat = [[caterr(k) for k in i] for i in similarity_mat]
with open("data/ddcrp/Universal_Pool.csv", "w") as f:
writer = csv.writer(f)
writer.writerows(universal)
with open('data/ddcrp/Distance_Matrix.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(distance_mat)
print("DDCRP starting")
process = Popen(['Rscript', 'ddcrp/ddcrp.R'], stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
print(stderr)
print("Cluster starting")
try:
with open("cluster.txt", "r") as f:
ddcrp_clust = f.readlines()
ddcrp_clust = ddcrp_clust[1:]
op = {}
for i in ddcrp_clust:
line = i.split(' ')
if line[1] not in op:
op[line[1]] = []
op[line[1]].append(universal[int(line[2])-1])
for key, value in op.items():
val = list(set(value))
op[key] = val
print("Writing output")
opfile = open("output.txt", "w")
for key, value in op.items():
quora_score = 0
wiki_score = 0
reddit_score = 0
stackexchange_score = 0
val_list = []
for i in value:
val_list.append(i)
if i in bigram_quora or i in trigram_quora:
quora_score += 1
if i in bigram_wiki or i in trigram_wiki:
wiki_score += 1
if i in bigram_reddit or i in trigram_reddit:
reddit_score += 1
if i in bigram_stackexchange or i in trigram_stackexchange:
stackexchange_score += 1
total_score = quora_score + wiki_score + reddit_score + stackexchange_score
# quora_prob = quora_score/(quora_score + wiki_score)
# wiki_prob = wiki_score/(quora_score + wiki_score)
# ent_quora = quora_prob*(math.log(quora_prob, 2)) if quora_prob != 0 else 0
# ent_wiki = wiki_prob*(math.log(wiki_prob, 2)) if wiki_prob != 0 else 0
# score = ent_quora + ent_wiki
# if quora_prob == wiki_prob:
# lead = "Wiki + Quora"
# elif quora_prob > wiki_prob:
# lead = "Quora"
# elif quora_prob < wiki_prob:
# lead = "Wiki"
score_list = {"Q": str(round(quora_score/total_score, 2)), "W": str(round(wiki_score/total_score, 2)), "R": str(round(reddit_score/total_score, 2)), "S": str(round(stackexchange_score/total_score, 2))}
opfile.write("Score = " + str(score_list) + " --- Cluster " + str(val_list) + "\n")
except Exception as e:
print(e)