/
Similarity.py
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/
Similarity.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 22 10:56:15 2019
@author: polo
"""
import json
import numpy as np
import matplotlib.pyplot as plt
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from gensim.summarization import keywords
from LoadTextData import Load_GalLery_Textual_Data,Load_GoogleVision_Labels
from LDA import Preprocessing,remove_stopwords,sent_to_words,lemmatization,PrepareData,LDA,Topics_Words
from ImgurComments import Countries,galeries
DataSet = '/home/polo/.config/spyder-py3/PhD/PhD October 2019/Tourism48'
def LoadTextData(Country,gallery_id):
S ,Data = Load_GalLery_Textual_Data(Country,gallery_id)
S1 ,Data1 = Load_GoogleVision_Labels(Country,gallery_id)
labels = [Preprocessing(x['label']) for x in S1[0]]
labels.append(Preprocessing(S1[1]))
DocList = S[1]
DocList.append(S[0])
for s in S[2]:
DocList.extend(s)
data_lemmatized = PrepareData(DocList)
lda_model,id2word,corpus = LDA(data_lemmatized,num_topics=20)#len(labels))
Topic_Words = Topics_Words(lda_model,num_words=len(labels))
return Topic_Words,labels
def Algebric_Similarity(Country,gallery_id):
Topics,labels = LoadTextData(Country,gallery_id)
setA = set([x.lower() for x in labels])
i = 0
for Topic in Topics:
print ('____________Topic:{}_____________'.format(i))
setB = set(Topic)
overlap = setA & setB
universe = setA | setB
result1 = float(len(overlap)) / len(setA) * 100
result2 = float(len(overlap)) / len(setB) * 100
result3 = float(len(overlap)) / len(universe) * 100
print ('overlap = ',len(overlap))
print ('universe = ',len(universe))
print ('overlap(setA,setB)/setA = ',round(result1, 2))
print ('overlap(setA,setB)/setB = ',round(result2, 2))
print ('overlap(setA,setB)/universe(setA,setB) = ',round(result3, 2))
i = i+1
def FuzzyWazzy_Similarity(Country,gallery_id):
'https://marcobonzanini.com/2015/02/25/fuzzy-string-matching-in-python/'
Topics,labels = LoadTextData(Country,gallery_id)
setA = set([x.lower() for x in labels])
i = 0
for Topic in Topics:
print ('____________Topic:{}_____________'.format(i))
setB = set(Topic)
overlap = 0
for l in setA:
for w in setB:
if fuzz.ratio(l, w) >= 80:
overlap += 1
universe = setA | setB
result1 = float(overlap) / len(setA) * 100
result2 = float(overlap) / len(setB) * 100
result3 = float(overlap) / len(universe) * 100
print ('overlap = ',overlap)
print ('universe = ',len(universe))
print ('overlap(setA,setB)/setA = ',round(result1, 2))
print ('overlap(setA,setB)/setB = ',round(result2, 2))
print ('overlap(setA,setB)/universe(setA,setB) = ',round(result3, 2))
i = i+1
def FuzzyWazzy_SimilarityOverAll(Country,gallery_id):
Topics,labels = LoadTextData(Country,gallery_id)
#print ('=============OverAll Similarity==============')
setA = list(set([x.lower() for x in labels]))
setB = list(set([w for Topic in Topics for w in Topic]))
overlap = 0
universe = 0
for l in setA:
for w in setB:
if fuzz.ratio(l, w) >= 80:
overlap += 1
else:
universe += 1
labels = round(float(overlap) / len(setA) * 100., 2)
comments = round(float(overlap) / len(setB) * 100., 2)
overall = round(float(overlap) / float(universe) * 100., 2)
#print ('overlap = ',overlap)
#print ('universe = ',universe)
#print ('Labels = ',len(setA))
#print ('Comments = ',len(setB))
#print ('overlap(Labels,Comments)/Labels = ',labels)
#print ('overlap(Labels,Comments)/Comments = ',comments)
print ('overlap(Labels,Comments)/Universe(Labels,Comments) = ',overall)
return labels,comments,overall
def keyWords_Labels_Matching(Country,gallery_id):
DocList ,Data = Load_GalLery_Textual_Data(Country,gallery_id)
S1 ,Data1 = Load_GoogleVision_Labels(Country,gallery_id)
data_lemmatized = [w for doc in PrepareData(DocList) for w in doc]
print (data_lemmatized)
fullStr = ' '.join(data_lemmatized)
#labels = [Preprocessing(x['label']) for x in S1[0]]
#labels.append(Preprocessing(S1[1]))
labels = [w for label in PrepareData(S1) for w in label]
setA = list(set(labels))
setB = keywords(fullStr).split('\n')
setB = [w for docs in PrepareData(setB) for w in docs]
overlap = 0
for l in setA:
for w in setB:
if fuzz.ratio(l, w) >= 75:
overlap += 1
universe = []
uni = list(set(setA) | set(setB))
for i in range(len(uni)):
if uni[i] not in universe:
universe.append(uni[i])
for j in range(i+1,len(uni)):
if fuzz.ratio(uni[i], uni[j]) >= 75 and uni[j] not in universe:
universe.append(uni[j])
universe = len(universe)
labels = round(float(overlap) / len(setA) * 100., 2)
comments = round(float(overlap) / len(setB) * 100., 2)
overall = round(float(overlap) / float(universe) * 100., 2)
#print ('overlap = ',overlap)
#print ('universe = ',universe)
#print ('\nLabels = ',len(setA))
#print ('Comments = ',len(setB))
#print ('overlap(Labels,Comments)/Labels = ',labels)
#print ('overlap(Labels,Comments)/Comments = ',comments)
print ('overlap(Labels,Comments)/Universe(Labels,Comments) = ',overall)
return labels,comments,overall,setA,setB
def OverAll_Text_Similarity_DataSet():
Galeries_Matrix = np.array(galeries).reshape(len(Countries),10)
i = 0
for Country in Countries:
print ('============='+Country+'==============')
Slabels = []
Scomments = []
Soverall = []
Similarities = {}
for j in range (10):
print(Galeries_Matrix[i,j])
#labels,comments,overall = FuzzyWazzy_SimilarityOverAll(Country,Galeries_Matrix[i,j])
labels,comments,overall = keyWords_Labels_Matching(Country,Galeries_Matrix[i,j])
Slabels.append(labels)
Scomments.append(comments)
Soverall.append(overall)
Similarities['labels'] = Slabels
Similarities['comments'] = Scomments
Similarities['overall'] = Soverall
with open('LDA Similarities/'+Country+'.json', 'w') as outfile:
json.dump(Similarities, outfile)
#break
i+=1
def Histogramme(Country):
with open('LDA Similarities/'+Country+'.json') as data_file:
Data = json.load(data_file)
#plt.hist(Data['overall'])
x = np.arange(10)
plt.bar(x, Data['labels'])
plt.xticks(x+.2, x)
#OverAll_Text_Similarity_DataSet()
#Histogramme('Algeria')
#labels,comments,overall,setA,setB = keyWords_Labels_Matching('Algeria','x6TwpSQ')
S ,Data = Load_GalLery_Textual_Data('Algeria','x6TwpSQ')
#S1 ,Data1 = Load_GoogleVision_Labels('Algeria','x6TwpSQ')