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features.py
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features.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Feb 15 22:20:38 2019
@author: acer
"""
import pandas as pd
from nltk.corpus import wordnet
from itertools import product
import numpy
from nltk import bigrams
from nltk.corpus import wordnet_ic
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
#from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics.pairwise import manhattan_distances
from sklearn.metrics.pairwise import euclidean_distances
brown_ic = wordnet_ic.ic('ic-brown.dat')
def tf_idf(sen1,sen2):
documents = [sen1, sen2]
vectorizer = TfidfVectorizer(use_idf=True)
sp_m = vectorizer.fit_transform(documents)
#print(sp_m)
doc_term_matrix = sp_m.todense()
#print("dense matrix",doc_term_matrix)
vectorframe = pd.DataFrame(doc_term_matrix,
columns=vectorizer.get_feature_names(),
index=['ans1', 'ans2'])
#print(vectorframe)
return vectorframe
def count_vector(sen1,sen2):
documents = [sen1, sen2]
count_vectorizer = CountVectorizer()
sparse_matrix = count_vectorizer.fit_transform(documents)
#print("sparse matrix",sparse_matrix)
# OPTIONAL: Convert Sparse Matrix to Pandas Dataframe if you want to see the word frequencies.
doc_term_matrix = sparse_matrix.todense()
#print("dense matrix",doc_term_matrix)
vectorframe = pd.DataFrame(doc_term_matrix,
columns=count_vectorizer.get_feature_names(),
index=['ans1', 'ans2'])
#print(vectorframe)
return vectorframe
def cosine_sim(vector1,vector2):
arr=cosine_similarity(vector1, vector2)
val=arr[0][1]
return val
def jaccard_sim(str1, str2):
a = set(str1.split())
b = set(str2.split())
c = a.intersection(b)
return float(len(c)) / (len(a) + len(b) - len(c))
def manhattan_dis(v1):
arr = manhattan_distances(v1, sum_over_features=True)
#print(arr)
r1=arr[0][1]
return r1
def euclidean_dis(v1):
res=euclidean_distances(v1,squared=True)
#print(res)
r1=res[0][1]
return r1
def bigram(sen1,sen2):
string_bigrams1 =list( bigrams(sen1.split()))
string_bigrams2 = list(bigrams(sen2.split()))
#print(*map(' '.join, string_bigrams1), sep=', ')
#print("one",string_bigrams1)
#print("two",string_bigrams2)
count=0
c=0
for i in string_bigrams1:
c=c+1
for j in string_bigrams2:
if(i==j):
count=count+1
d=count/c
return d
def wordnet_wup_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
d = wordnet.wup_similarity(sense1, sense2)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index
def wordnet_path_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
d = wordnet.path_similarity(sense1, sense2)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index
def wordnet_lch_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
d=None
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
pos1=sense1.pos()
pos2=sense2.pos()
if(pos1=='s' or pos2=='s'):
continue
if(pos1 == pos2):
d = wordnet.lch_similarity(sense1, sense2,brown_ic)
#d = wordnet.lch_similarity(sense1, sense2)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index
def wordnet_res_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
d=None
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
pos1=sense1.pos()
pos2=sense2.pos()
if(pos1=='s' or pos2=='s' or pos1=='r' or pos2=='r' or pos1=='a' or pos2=='a'):
continue
if(pos1 == pos2):
d = wordnet.res_similarity(sense1, sense2,brown_ic)
#d = wordnet.res_similarity(sense1, sense2,brown_ic)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index
def wordnet_jcn_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
d=None
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
pos1=sense1.pos()
pos2=sense2.pos()
if(pos1=='s' or pos2=='s' or pos1=='r' or pos2=='r' or pos1=='a' or pos2=='a'):
continue
if(pos1 == pos2):
d = wordnet.jcn_similarity(sense1, sense2,brown_ic)
#d = wordnet.res_similarity(sense1, sense2,brown_ic)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index
def wordnet_lin_sim(list1,list2):
sims = []
final = []
#print(list1)
#print(list2)
for word1 in list1:
simi =[]
for word2 in list2:
sims = []
#print(word1)
#print(word2)
syns1 = wordnet.synsets(word1)
#print("synset1",syns1)
#print(list1[0])
syns2 = wordnet.synsets(word2)
#print(wordFromList2[0])
#print("synset2",syns2)
d=None
for sense1, sense2 in product(syns1, syns2):
#print("sense1",sense1)
#print("sense2",sense2)
pos1=sense1.pos()
pos2=sense2.pos()
if(pos1=='s' or pos2=='s' or pos1=='r' or pos2=='r' or pos1=='a' or pos2=='a'):
continue
if(pos1 == pos2):
d = wordnet.lin_similarity(sense1, sense2,brown_ic)
#d = wordnet.lin_similarity(sense1, sense2,brown_ic)
if d != None:
sims.append(d)
#print(sims)
#print(max(sims))
if sims != []:
max_sim = max(sims)
#print(max_sim)
simi.append(max_sim)
if simi != []:
max_final = max(simi)
final.append(max_final)
##---------------Final Output---------------##
similarity_index = numpy.mean(final)
similarity_index = round(similarity_index , 2)
#print("Sentence 1: ",str1)
#print("Sentence 2: ",str2)
#print("Similarity index value : ", similarity_index)
return similarity_index