forked from danaSror/Search_Engine
/
search_engine.py
83 lines (69 loc) · 2.63 KB
/
search_engine.py
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from reader import ReadFile
from configuration import ConfigClass
from parser_module import Parse
from indexer import Indexer
from searcher import Searcher
import utils
import pandas as pd
from posting_merge import PostingsMerge
from tqdm import tqdm
def run_engine(config):
"""
:param config:
:return:
"""
number_of_documents = 0
r = ReadFile(corpus_path=config.get__corpusPath())
p = Parse(config.toStem)
indexer = Indexer(config)
paruet_list = r.read_all_parquet()
for list in paruet_list:
#for i in tqdm(range(0,len(list))): # for every doc
for i in range(0, len(list)): # for every doc
# parse the document
parsed_document = p.parse_doc(list[i])
if parsed_document is None:
continue
number_of_documents += 1
# index the document data
indexer.add_new_doc(parsed_document)
#print('Finished parsing and indexing. Starting to export files')
indexer.save_postings() # saves the remaining posting file .
PostingsMerge(indexer).chunks_merging()
utils.save_dict_as_pickle(indexer.inverted_idx, "inverted_idx", config.get_out_path())
def load_index(out_path=''):
"""
load inverted index
:param out_path:
:return:
"""
#print('Load inverted index and document dictionary')
inverted_index = utils.load_pickle_as_dict("inverted_idx", out_path)
#print('Done')
return inverted_index
def search_and_rank_query(query, inverted_index, k, config=None):
"""
This function search for relevant docs according to the query and rank them
:param query:
:param inverted_index:
:param k:
:param config:
:return:
"""
p = Parse(config.toStem)
query_as_list = p.parse_sentence(query)
searcher = Searcher(inverted_index, config)
relevant_docs = searcher.relevant_docs_from_posting(query_as_list)
ranked_docs = searcher.ranker.rank_relevant_doc(relevant_docs)
return searcher.ranker.retrieve_top_k(ranked_docs, k)
def main(corpus_path, output_path, stemming, queries, num_docs_to_retrieve):
if queries is not None:
config = ConfigClass(corpus_path, output_path, stemming)
run_engine(config)
query_list = utils.load_queries_list(queries)
inverted_index = load_index(output_path)
for idx in range(1, len(query_list)+1):
print("query {}:".format(idx))
for doc_tuple in search_and_rank_query(query_list[idx-1], inverted_index, k=num_docs_to_retrieve, config=config):
print('\ttweet id: {} | score : {} '
.format(doc_tuple[0] , doc_tuple[1]))