class SearchEngine:
GLOVE_PATH_SERVER = '../../../../glove.twitter.27B.25d.txt'
GLOVE_PATH_LOCAL = '.\model/model.txt'
def __init__(self, config=None):
self._config = config
self._parser = Parse(False)
self.reader = ReadFile(corpus_path=config.get__corpusPath())
self._indexer = Indexer(config)
self.model = self.initialize_glove_dict()
self._indexer.set_glove_dict(self.model)
def initialize_glove_dict(self):
glove_dict = {}
with open(self.GLOVE_PATH_LOCAL, 'r', encoding='utf-8') as f:
for line in tqdm(f):
values = line.split()
word = values[0]
vector = np.asarray(values[1:], "float32")
glove_dict[word] = vector
return glove_dict
# 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 tqdm(enumerate(documents_list)):
# parse the document
parsed_document = self._parser.parse_doc(document)
if parsed_document is None:
continue
number_of_documents += 1
# index the document data
self._indexer.add_new_doc(parsed_document)
tuple_to_save = self._indexer.fix_inverted_index()
utils.save_pickle_tuple(tuple_to_save, 'idx_engine1',
self._config.get_out_path())
print('Finished parsing and indexing.')
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def load_precomputed_model(self, model_path):
"""
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
def load_index(self, fn):
return self._indexer.load_index(fn)
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def search(self, query):
"""
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.
"""
self._indexer.inverted_idx, self._indexer.document_dict = self.load_index(
'idx_engine1.pkl')
searcher = Searcher(self._parser, self._indexer, model=self.model)
# TODO check about K
query_as_list = self._parser.parse_sentence(query)
l_res = searcher.search(query_as_list[0])
t_ids = [tup[1] for tup in l_res]
return len(l_res), t_ids
class SearchEngine:
def __init__(self, config=None):
self._config = config
self._parser = Parse(False)
self.reader = ReadFile(corpus_path=config.get__corpusPath())
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 tqdm(enumerate(documents_list)):
# parse the document
parsed_document = self._parser.parse_doc(document)
if parsed_document is None:
continue
number_of_documents += 1
# index the document data
self._indexer.add_new_doc(parsed_document)
tuple_to_save = self._indexer.fix_inverted_index()
utils.save_pickle_tuple(tuple_to_save, 'idx_engine2',
self._config.get_out_path())
print('Finished parsing and indexing.')
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation as you see fit.
def load_precomputed_model(self, model_path):
"""
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
def load_index(self, fn):
return self._indexer.load_index(fn)
def search(self, query):
"""
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.
"""
self._indexer.inverted_idx, self._indexer.document_dict = self.load_index(
'idx_engine2.pkl')
searcher = Searcher(self._parser, self._indexer, model=self.model)
# TODO check about K
query_as_list = self._parser.parse_sentence(query)
list_copy = list(query_as_list[0])
tagged_words = pos_tag(list_copy)
for word in tagged_words:
wn_tag = Wordnet.get_wordnet_pos(word[1])
synonym = Wordnet.get_closest_term(word[0], wn_tag)
if synonym is not None:
list_copy.append(synonym)
l_res = searcher.search(list_copy)
t_ids = [tup[1] for tup in l_res]
return len(l_res), t_ids
