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searcher.py
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searcher.py
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from parser_module import Parse
from ranker import Ranker
import utils
from GloVeMethod import GloVeMethod
import linecache
from stemmer import Stemmer
from Word2Vec import Word2VecScrapper
import os
import traceback
class Searcher:
def __init__(self, parser, indexer, model=None):
"""
:param inverted_index: dictionary of inverted index
"""
self._parser = parser
self._indexer = indexer
self._ranker = Ranker()
self._model = model
def relevant_docs_from_posting(self, query): # list of word after parsing
"""
This function loads the posting list and count the amount of relevant documents per term.
:param query: query
:return: dictionary of relevant documents.
"""
# glove = GloVeMethod('../../../../glove.twitter.27B.25d.txt')
# similar_words = glove.most_similar(query)
# for word in similar_words:
# query.append(word[0])
term_in_query = {}
for term in query:
if term not in term_in_query.keys():
term_in_query[term] = 1
else:
term_in_query[term] += 1
try:
if term in self.inverted_index:
self.get_file_from_pointer(self.inverted_index[term])
elif self.stemmer.stem_term(term) in self.inverted_index:
self.get_file_from_pointer(self.inverted_index[self.stemmer.stem_term(term)])
# if term not in term_in_query.keys():
# term_in_query[term] = 1
# else:
# term_in_query[term] += 1
except:
print(traceback.print_exc())
print('term {} not found in posting'.format(term))
#print(self.relevant_docs)
#print(term_in_query)
return self.relevant_docs, term_in_query
def relevant_docs_from_posting_with_word2Vec(self, query): # list of word after parsing
"""
This function loads the posting list and count the amount of relevant documents per term.
:param query: query
:return: dictionary of relevant documents.
"""
w2v = Word2VecScrapper()
word_dict = w2v.get_top_n_dictionary(query)
similar_words = []
#print(word_dict)
if word_dict is not None:
for word in word_dict.keys():
if word_dict[word] is not None:
similar_words.extend(word_dict[word])
query.extend(similar_words)
term_in_query = {}
for term in query:
try:
if term in self.inverted_index:
self.get_file_from_pointer(self.inverted_index[term])
elif self.stemmer.stem_term(term) in self.inverted_index:
self.get_file_from_pointer(self.inverted_index[self.stemmer.stem_term(term)])
if term not in term_in_query.keys():
term_in_query[term] = 1
else:
term_in_query[term] += 1
except:
print('term {} not found in posting'.format(term))
#print(self.relevant_docs)
#print(term_in_query)
return self.relevant_docs, term_in_query
def get_file_from_pointer(self, list_inverted_index):
if len(list_inverted_index) == 3:
pointer = list_inverted_index[2]
self.get_pointer_from_string(pointer)
elif len(list_inverted_index) == 4:
pointer = list_inverted_index[2]
pointer1 = list_inverted_index[3]
self.get_pointer_from_string(pointer, pointer1)
elif len(list_inverted_index) == 5:
pointer = list_inverted_index[2]
pointer1 = list_inverted_index[3]
pointer2 = list_inverted_index[4]
self.get_pointer_from_string(pointer, pointer1, pointer2)
def get_pointer_from_string(self, *filename):
for file in filename:
path, line = file.split(' ')
posting = linecache.getline(os.path.join(self.output_path, path), int(line)+2)
term , tweet_id = posting.split(':')
result = list(eval(tweet_id[1:-2]))
if term not in self.relevant_docs:
self.relevant_docs[term] = result
else:
self.relevant_docs[term].extend(result)
#print(self.relevant_docs)
def add_hashtag(self, query):
result = ["#{}".format(term) for term in query]
return result
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def search(self, query, k=None):
"""
Executes a query over an existing index and returns the number of
relevant docs and an ordered list of search results (tweet ids).
Input:
query - string.
k - number of top results to return, default to everything.
Output:
A tuple containing the number of relevant search results, and
a list of tweet_ids where the first element is the most relavant
and the last is the least relevant result.
"""
relevant_docs, posting_dic = self._relevant_docs_from_posting(query)
n_relevant = len(relevant_docs)
document_score = self._ranker.BM25(relevant_docs, query, posting_dic, self._indexer.tweet_index)
#print(document_score)
ranked_doc_ids = Ranker.rank_relevant_docs(document_score, k)
return n_relevant, ranked_doc_ids
# feel free to change the signature and/or implementation of this function
# or drop altogether.
def _relevant_docs_from_posting(self, query_as_list):
"""
This function loads the posting list and count the amount of relevant documents per term.
:param query_as_list: parsed query tokens
:return: dictionary of relevant documents mapping doc_id to document frequency.
"""
relevant_docs = {}
posting_dic = {}
for term in query_as_list:
posting_list = self._indexer.get_term_posting_list(term)
if posting_list is not None:
doc_id = {}
for posting_term in posting_list:
df = relevant_docs.get(posting_term[0], 0)
relevant_docs[posting_term[0]] = df + 1
doc_id[posting_term[0]] = posting_term[1]
posting_dic[term] = doc_id
else:
posting_dic[term] = {}
return relevant_docs, posting_dic
if __name__ == '__main__':
inverted_index = utils.load_obj('inverted_index')
#print(inverted_index)
s =Searcher('inverted_index')
#print(s.add_hashtag(["SAmuel"]))
#going
s.relevant_docs_from_posting(['fucking', '#coronavirus' , '@JoeBiden'])
#s.get_file_from_pointer([29, 29, 'postingMerge1.txt 1520', 'postingMerge2.txt 1504', 'postingMerge3.txt 1494'])