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make_index_and_queries.py
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make_index_and_queries.py
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from __future__ import unicode_literals
import json
import parsivar
import xlrd
import xlsxwriter
from hazm import *
import hazm
data_set = 'D:\ترم7\بازیابی\Project-1st Phase\IR-1st-Phase\IR1_7k_news.xlsx'
data_reader = xlrd.open_workbook(data_set)
content = data_reader.sheet_by_index(0)
data_writer = xlsxwriter.Workbook(data_set)
worksheet = data_writer.add_worksheet()
number_of_rows = content.nrows
normalizer = parsivar.Normalizer(statistical_space_correction=True, date_normalizing_needed=True,
pinglish_conversion_needed=True)
tokenizer = parsivar.Tokenizer()
stemmer = parsivar.FindStems()
stemmer_hazm = Stemmer()
hazm_normalaizer = hazm.Normalizer()
lemmatizer = hazm.Lemmatizer()
positional_index = dict()
positional_index_with_stops = dict()
garbage = ['۱', '۲', '۳', '۴', '۵', '۶', '۷', '۸', '۹', '۰', 'a', 'b', 'c', 'd', 'e', 't', 'o', 'p', 'x', 'y', 'z',
'https', '،', '.', ':', '**', '-', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '?', '**', '[', ']',
'(', ')', '://', '/?', '=', '&', '/', '؛', '&', '/', '.', '_', '،', '?**', ":", "%", ">>", "<<", "#", "!",
"*", "«", "»"]
def add_to_index(token, position, docID):
# print(positional_index)
if token not in positional_index:
positions = {}
positions[str(docID)] = []
positions[str(docID)].append(1)
positions[str(docID)].append([position])
print(token + ">>>>>")
print(positions)
repeat_num = 1
dict_value = [positions, repeat_num]
positional_index[token] = dict_value
else:
positions = positional_index[token][0]
if (str(docID)) in positions:
positions[str(docID)][1].append(position)
positions[str(docID)][0] += 1
else:
positions[str(docID)] = []
positions[str(docID)].append(1)
positions[str(docID)].append([position])
print(token + ">>>>>")
print(positions)
positional_index[token][0] = positions
positional_index[token][1] += 1
def add_to_index_with_stop(token, position, docID):
if token not in positional_index_with_stops:
positions = {}
positions[str(docID)] = []
positions[str(docID)].append(1)
positions[str(docID)].append([position])
print(token + ">>>>>")
print(positions)
repeat_num = 1
dict_value = [positions, repeat_num]
positional_index_with_stops[token] = dict_value
else:
positions = positional_index_with_stops[token][0]
if (str(docID)) in positions:
if (position not in positions[str(docID)][1]):
positions[str(docID)][1].append(position)
positions[str(docID)][0] += 1
else:
positions[str(docID)] = []
positions[str(docID)].append(1)
positions[str(docID)].append([position])
print(token + ">>>>>")
print(positions)
positional_index_with_stops[token][0] = positions
positional_index_with_stops[token][1] += 1
def make_index():
term_doc = []
for i in range(1, number_of_rows):
temp = data_reader.sheet_by_index(0).cell(i, 0).value
temp = normalizer.normalize(temp)
temp_tokens = tokenizer.tokenize_words(temp)
print(str(i) + " : " + str(len(temp_tokens)))
print(temp_tokens)
for j in range(len(temp_tokens)):
term_doc.append((temp_tokens[j], i, j + 1))
for (token, docID, position) in term_doc:
file = open("stop_words.txt", encoding="utf-8")
if ((token in file.read())):
if ((str(token) not in garbage)):
print("stop :" + token)
stem_token = stemmer.convert_to_stem(token)
if "&" in stem_token:
mazi, mozare = stem_token.split("&")
add_to_index_with_stop(mazi, position, docID)
add_to_index_with_stop(mozare, position, docID)
else:
add_to_index_with_stop(stem_token, position, docID)
else:
stem_token = stemmer.convert_to_stem(token)
if "&" in stem_token:
mazi, mozare = stem_token.split("&")
add_to_index(mazi, position, docID)
add_to_index(mozare, position, docID)
add_to_index_with_stop(mazi, position, docID)
add_to_index_with_stop(mozare, position, docID)
else:
stem_token = stemmer_hazm.stem(stem_token)
add_to_index(stem_token, position, docID)
add_to_index_with_stop(stem_token, position, docID)
# writing dictionary to file
f = open("index_with_stops.json", "w", encoding="utf-8")
json.dump(positional_index_with_stops, f)
f.close()
def answer_multi_word_query(query, positional_index):
docs = dict()
normal_query = normalizer.normalize(query)
tokens = tokenizer.tokenize_words(normal_query)
answers = dict()
for t in tokens:
stem_token = stemmer_hazm.stem(t)
if stem_token in positional_index:
docs_and_positions = positional_index[stem_token][0]
print(docs_and_positions)
for doc in docs_and_positions:
positions = docs_and_positions[doc][1]
i = tokens.index(t) + 1
count = 1
if (i == len(tokens)):
if doc not in answers:
answers[doc] = count
flag = True
test = False
if i < len(tokens):
if doc in positional_index[stemmer_hazm.stem(tokens[i])][0]:
while flag:
find = False
if doc in positional_index[stemmer_hazm.stem(tokens[i])][0]:
print(tokens[i])
print(t)
for p1 in positions:
for p2 in positional_index[stemmer_hazm.stem(tokens[i])][0][doc][1]:
distance = abs(p1 - p2)
if distance == i - tokens.index(t):
if test:
if p1 == pos1:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
else:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
if find == True:
print(doc)
print("here1")
print("=============================")
count += 1
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
# print(answers[doc])
i += 1
if i == len(tokens):
flag = False
else:
test = True
continue
else:
print("here2")
print("=============================")
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
flag = False
else:
flag = False
else:
if doc not in answers:
answers[doc] = count
if doc in answers and answers[doc] < count:
answers[doc] = count
answers = dict(sorted(answers.items(), key=lambda item: item[1], reverse=True))
print(answers)
file_name = str(query) + ".txt"
f = open(file_name, "a", encoding="utf-8")
for d in answers:
print(d)
relative_sentences = []
title = data_reader.sheet_by_index(0).cell(int(d), 2).value
news_content = hazm_normalaizer.normalize(data_reader.sheet_by_index(0).cell(int(d), 0).value)
sentences = sent_tokenize(str(news_content))
# f.write("Title of the news is : {}\n".format(title))
for t in tokens:
for s in sentences:
if stemmer_hazm.stem(t) in s:
if s not in relative_sentences:
relative_sentences.append(s)
# relative_sentences += [s for s in sentences if t in s]
print("Title of the news is : {}".format(title))
f.write("Title of the news is : {}\n".format(title))
print("senteces which are relative :")
for s in relative_sentences:
print(s)
f.write("{} \n".format(s))
f.write("-----------------------------------------------------------------------------------------\n")
print("---------------------------------------")
def answer_multi_word_query2(query, positional_index):
docs = dict()
normal_query = normalizer.normalize(query)
tokens = tokenizer.tokenize_words(normal_query)
answers = dict()
for t in tokens:
stem_token = stemmer.convert_to_stem(t)
if "&" not in stem_token:
if stem_token in positional_index:
docs_and_positions = positional_index[stem_token][0]
print(docs_and_positions)
for doc in docs_and_positions:
positions = docs_and_positions[doc][1]
i = tokens.index(t) + 1
count = 1
if (i == len(tokens)):
if doc not in answers:
answers[doc] = count
flag = True
test = False
if i < len(tokens):
if doc in positional_index[stemmer.convert_to_stem(tokens[i])][0]:
while flag:
find = False
if doc in positional_index[stemmer.convert_to_stem(tokens[i])][0]:
print(tokens[i])
print(t)
for p1 in positions:
for p2 in positional_index[stemmer.convert_to_stem(tokens[i])][0][doc][1]:
distance = abs(p1 - p2)
if distance == i - tokens.index(t):
if test:
if p1 == pos1:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
else:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
if find == True:
print(doc)
print("here1")
print("=============================")
count += 1
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
# print(answers[doc])
i += 1
if i == len(tokens):
flag = False
else:
test = True
continue
else:
print("here2")
print("=============================")
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
flag = False
else:
flag = False
else:
if doc not in answers:
answers[doc] = count
if doc in answers and answers[doc] < count:
answers[doc] = count
else:
verb_stems = stem_token.split("&")
print(verb_stems)
for v in verb_stems:
if v in positional_index:
docs_and_positions = positional_index[v][0]
print(docs_and_positions)
for doc in docs_and_positions:
positions = docs_and_positions[doc][1]
i = tokens.index(t) + 1
count = 1
if (i == len(tokens)):
if doc not in answers:
answers[doc] = count
flag = True
test = False
if i < len(tokens):
if doc in positional_index[stemmer.convert_to_stem(tokens[i])][0]:
while flag:
find = False
if doc in positional_index[stemmer.convert_to_stem(tokens[i])][0]:
print(tokens[i])
print(t)
for p1 in positions:
for p2 in positional_index[stemmer.convert_to_stem(tokens[i])][0][doc][1]:
distance = abs(p1 - p2)
if distance == i - tokens.index(t):
if test:
if p1 == pos1:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
else:
print(p1)
print(p2)
pos1 = p1
pos2 = p2
find = True
if find == True:
print(doc)
print("here1")
print("=============================")
count += 1
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
# print(answers[doc])
i += 1
if i == len(tokens):
flag = False
else:
test = True
continue
else:
print("here2")
print("=============================")
if (doc in answers and answers[doc] < count):
answers[doc] = count
elif doc not in answers:
answers[doc] = count
flag = False
else:
flag = False
else:
if doc not in answers:
answers[doc] = count
if doc in answers and answers[doc] < count:
answers[doc] = count
answers = dict(sorted(answers.items(), key=lambda item: item[1], reverse=True))
print(answers)
file_name = str(query) + ".txt"
f = open(file_name, "a", encoding="utf-8")
for d in answers:
print(d)
relative_sentences = []
title = data_reader.sheet_by_index(0).cell(int(d), 2).value
news_content = hazm_normalaizer.normalize(data_reader.sheet_by_index(0).cell(int(d), 0).value)
sentences = sent_tokenize(str(news_content))
for t in tokens:
for s in sentences:
if stemmer_hazm.stem(t) in s:
if s not in relative_sentences:
relative_sentences.append(s)
print("Title of the news is : {}".format(title))
f.write("Title of the news is : {}\n".format(title))
print("senteces which are relative :")
for s in relative_sentences:
print(s)
f.write("{} \n".format(s))
f.write("-----------------------------------------------------------------------------------------\n")
print("---------------------------------------")
def main():
# if (os.stat("index.json").st_size == 0):
# make_index()
# else:
# reading dictionary from file
f = open('index.json', encoding='utf-8')
data = json.load(f)
positional_index = data
f.close()
print(positional_index["رفت"])
query = input("Enter your query")
answer_multi_word_query2(query, positional_index)
if __name__ == "__main__":
main()