class SearchEngine:
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation, but you must have a parser and an indexer.
def __init__(self, config=None):
self._config = config
self._parser = Parse()
self._indexer = Indexer(config)
self._model = None
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def build_index_from_parquet(self, fn):
"""
Reads parquet file and passes it to the parser, then indexer.
Input:
fn - path to parquet file
Output:
No output, just modifies the internal _indexer object.
"""
df = pd.read_parquet(fn, engine="pyarrow")
documents_list = df.values.tolist()
# Iterate over every document in the file
number_of_documents = 0
for idx, document in enumerate(documents_list):
# parse the document
parsed_document = self._parser.parse_doc(document)
number_of_documents += 1
# index the document data
self._indexer.add_new_doc(parsed_document)
self._indexer.add_square_Wij()
to_Save=(self._indexer.inverted_idx, self._indexer.postingDict, self._indexer.num_of_docs, self._indexer.avg_Size_doc,self._indexer.doc_info)
utils.save_obj(to_Save, "index_4")
print('Finished parsing and indexing.')
#print(sorted( self._indexer.inverted_idx,key=lambda x: self._indexer.inverted_idx[x]))
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def load_index(self, fn):
"""
Loads a pre-computed index (or indices) so we can answer queries.
Input:
fn - file name of pickled index.
"""
obj = utils.load_obj(fn)
self._indexer.inverted_idx = obj[0]
self._indexer.postingDict = obj[1]
self._indexer.num_of_docs = obj[2]
self._indexer.avg_Size_doc = obj[3]
self._indexer.doc_info = obj[4]
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def load_precomputed_model(self,model_dir=None):
"""
Loads a pre-computed model (or models) so we can answer queries.
This is where you would load models like word2vec, LSI, LDA, etc. and
assign to self._model, which is passed on to the searcher at query time.
"""
pass
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def search(self, query,k=2000):
"""
Executes a query over an existing index and returns the number of
relevant docs and an ordered list of search results.
Input:
query - string.
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.
"""
searcher = Searcher(self._parser, self._indexer)
return searcher.search(query,k)
def main(self,output_path,stemming,query_to_check,num_docs_to_retrieve):
self.build_index_from_parquet("data/benchmark_data_train.snappy.parquet")
if isinstance(query_to_check, list):
queries = query_to_check
elif isinstance(query_to_check, str):
if query_to_check.endswith(".txt"):
try:
with open(query_to_check, "r",encoding="utf-8") as queries:
queries = queries.readlines()
query2 = []
for q in queries:
if (q != "\n"):
query2.append(q)
queries=query2
except FileNotFoundError as e:
print(e)
else:
queries = [query_to_check]
else:
return
if (stemming):
output_path = output_path + "/WithStem"
else:
output_path = output_path + "/WithoutStem"
query_num = 1
queries = pd.read_csv(os.path.join('data', 'queries_train.tsv'), sep='\t')
for i, row in queries.iterrows():
q_id = row['query_id']
q_keywords = row['keywords']
start = time.time()
mylist = self.search(q_keywords, num_docs_to_retrieve)
answer_to_run = mylist[1]
for doc_tuple in answer_to_run:
print('tweet id: {}'.format(doc_tuple))
query_num += 1
print("time that toke to retrieve :" + str(time.time() - start))
