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VSM.py
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VSM.py
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import os
import nltk
import pathlib
import json
from tkinter import *
from collections import OrderedDict
from nltk.stem import WordNetLemmatizer
from nltk.stem import PorterStemmer
import math
import numpy as np
def pre_processing(path,id): #preprocessing each document and creating indexes of each document
contractions = {
"ain't": "am not / are not",
"aren't": "are not / am not",
"can't": "cannot",
"can't've": "cannot 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 had / he would",
"he'd've": "he would have",
"he'll": "he shall / he will",
"he'll've": "he shall have / he will have",
"he's": "he has / he is",
"how'd": "how did",
"how'd'y": "how do you",
"how'll": "how will",
"how's": "how has / how is",
"i'd": "I had / I would",
"i'd've": "I would have",
"i'll": "I shall / I will",
"i'll've": "I shall have / I will have",
"i'm": "I am",
"i've": "I have",
"isn't": "is not",
"it'd": "it had / it would",
"it'd've": "it would have",
"it'll": "it shall / it will",
"it'll've": "it shall have / it will have",
"it's": "it has / 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 had / she would",
"she'd've": "she would have",
"she'll": "she shall / she will",
"she'll've": "she shall have / she will have",
"she's": "she has / she is",
"should've": "should have",
"shouldn't": "should not",
"shouldn't've": "should not have",
"so've": "so have",
"so's": "so as / so is",
"that'd": "that would / that had",
"that'd've": "that would have",
"that's": "that has / that is",
"there'd": "there had / there would",
"there'd've": "there would have",
"there's": "there has / there is",
"they'd": "they had / they would",
"they'd've": "they would have",
"they'll": "they shall / they will",
"they'll've": "they shall have / they will have",
"they're": "they are",
"they've": "they have",
"to've": "to have",
"wasn't": "was not",
"we'd": "we had / 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 shall / what will",
"what'll've": "what shall have / what will have",
"what're": "what are",
"what's": "what has / what is",
"what've": "what have",
"when's": "when has / when is",
"when've": "when have",
"where'd": "where did",
"where's": "where has / where is",
"where've": "where have",
"who'll": "who shall / who will",
"who'll've": "who shall have / who will have",
"who's": "who has / who is",
"who've": "who have",
"why's": "why has / 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 had / you would",
"you'd've": "you would have",
"you'll": "you shall / you will",
"you'll've": "you shall have / you will have",
"you're": "you are",
"you've": "you have"
}
file_content=""
with open(path, 'r',encoding='utf-8') as f: #reading the documents
file_content=f.read()
f.close()
# print(file_content)
words = []
l=file_content.split()
for word in l: #removing contractions
if word in contractions:
words.append(contractions[word])
else:
words.append(word)
file_content = ""
file_content = ' '.join(words)
new = ""
punctuations = '''!()[]{};:'"\,<‘>./?@#$%^&*_~“”’'''
file_content.replace("\n", " ")
for w in file_content: #removing punctuatations and stemming
if w not in punctuations:
new = new + w
new = new.replace("—", "")
new=new.replace("-","")
new = new.lower()
words_dict = nltk.word_tokenize(new)
ps= PorterStemmer()
wd=[]
for i in words_dict:
i=ps.stem(i)
wd.append(i)
words_dict1 = list(dict.fromkeys(wd))
stopwords = ["am", "is", "the", "of", "all", "and", "to", "can", "be", "as", "once", "for", "at", "a", "are",
"has", "have", "had", "up", "his", "her", "in", "on", "no", "we", "do"]
context = dict()
idf_index=dict()
N=50
for i in wd: #removing stopwords and creating index of the given document
if i not in stopwords:
if i in context:
context[i][id]=context[i][id]+1
else:
context[i]=dict()
context[i][id]=1
return context #returning indexe
def indexing(h1,h2): #merging the indexes of two document
for i in h1:
if i in h2:
h2[i].update(h1[i])
else:
h2[i]=h1[i]
return h2 #returning the combined dictionary
def merge_inverted(dict1,dict2): #merging the indexes of two document
for i in dict1.keys():
if i in dict2:
dict2[i]=dict1[i]+dict2[i]
else:
dict2[i]=dict1[i]
return dict2 #returning the combined index
def create_indexes(): #creating the both complete indexes of all the documents
ps=PorterStemmer()
dataset=os.getcwd()
dataset=dataset+'\ShortStories'
idf_index=dict()
inverted_index=dict()
N=50
for txt_file in os.listdir(dataset): #traversing the given path
doc_id=int(txt_file.split('.')[0])
inverted=pre_processing(os.path.join(dataset,txt_file),str(doc_id))
inverted_index=indexing(inverted_index,inverted)
for word in inverted_index:
# idf_index[word]=round(math.log10(N/len(inverted_index[word].keys())),5)
idf_index[word]=round(math.log10(len(inverted_index[word].keys()))/N,3)
return inverted_index,idf_index #returning both indexes
def cosine_sim(q_vect,doc_vect,alpha):
dot=np.dot(q_vect,doc_vect)
similar=round(dot/(np.linalg.norm(q_vect)*np.linalg.norm(doc_vect)),3)
if similar>=float(alpha):
return similar
else:
return -1
def main1(Query,alpha,root,l1):
file1=os.getcwd()+'\doctf_index.json'
file2=os.getcwd()+'\idf_index.json'
tf=dict()
idf=dict()
if not os.path.exists(file2): #if index are not created it will create and save the model
tf,idf=create_indexes()
json_object_tf = json.dumps(tf)
json_object_idf = json.dumps(idf)
with open("doctf_index.json", "w") as outfile:
outfile.write(json_object_tf)
with open("idf_index.json", "w") as outf:
outf.write(json_object_idf)
else: #or else it will only load the json object if file exists
with open('doctf_index.json', 'r') as openfile:
tf = json.load(openfile)
with open('idf_index.json', 'r') as openfile:
idf = json.load(openfile)
words_len=len(tf)
print(words_len)
vectors=dict()
for i in range(1,51):
vectors[str(i)]= np.zeros(words_len,dtype=float)
count=0
for word in tf:
if str(i) in tf[word]:
vectors[str(i)][count]=float(tf[word][str(i)])*float(idf[word])
count+=1
query=Query
alpha1=0.005
if alpha!="":
alpha1=float(alpha)
q1=nltk.word_tokenize(query)
ps=PorterStemmer()
q_final=dict()
for k in q1:
if k not in q_final:
k=ps.stem(k)
q_final[k]=1
else:
q_final[k]+=1
q_vect=np.zeros(words_len,dtype=float)
counter=0
for w in idf:
if w in q_final:
q_vect[counter]=q_final[w]
counter+=1
result=[]
for docs in vectors:
sim=cosine_sim(q_vect,vectors[docs],alpha1)
if sim!=-1:
result.append((docs,sim))
result.sort(key=lambda x: x[1],reverse=True)
document_count=len(result)
print(result)
a='Result Set of query: '+Query
Label(root,text=a,font=('Helvetica',25,'bold'),fg='blue').pack(pady=5,padx=5)
fi=' '.join(str([x[0] for x in result]))
l1.delete("1.0",END)
l1.insert(END,fi)
l1.insert(END,"\nTotal number of Document retrieved: "+str(document_count))
result=[]