def run(self):
"""Entry point for the SimilarityScorer.
Returns:
A dict with metadata about the processed data set or None if no
data_types has been configured.
"""
# Exit early if there is no data_type to process.
if not self._config:
return
# Event generator for streaming results.
events = self.event_stream(query_string=self._config.query,
return_fields=[self._config.field])
lsh, minhashes = similarity.new_lsh_index(
events,
field=self._config.field,
delimiters=self._config.delimiters,
num_perm=self._config.num_perm,
threshold=self._config.threshold)
total_num_events = len(minhashes)
for key, minhash in minhashes.items():
event_id, event_type, index_name = key
event_dict = dict(_id=event_id,
_type=event_type,
_index=index_name)
event = interface.Event(event_dict, self.datastore)
score = similarity.calculate_score(lsh, minhash, total_num_events)
attributes_to_add = {'similarity_score': score}
event.add_attributes(attributes_to_add)
return dict(index=self._config.index_name,
data_type=self._config.data_type,
num_events_processed=total_num_events)
def run(self):
"""Entry point for the SimilarityScorer.
Returns:
A dict with metadata about the processed data set or None if no
data_types has been configured.
"""
if not self._config:
return 'No data_type specified.'
# Event generator for streaming results.
events = self.event_stream(query_string=self._config.query,
return_fields=[self._config.field])
lsh, minhashes = similarity.new_lsh_index(
events,
field=self._config.field,
delimiters=self._config.delimiters,
num_perm=self._config.num_perm,
threshold=self._config.threshold)
total_num_events = len(minhashes)
for key, minhash in minhashes.items():
event_id, event_type, index_name = key
event_dict = dict(_id=event_id,
_type=event_type,
_index=index_name)
event = interface.Event(event_dict, self.datastore)
score = similarity.calculate_score(lsh, minhash, total_num_events)
attributes_to_add = {'similarity_score': score}
event.add_attributes(attributes_to_add)
# Commit the event to the datastore.
event.commit()
msg = 'Similarity scorer processed {0:d} events for data_type {1:s}'
return msg.format(total_num_events, self._config.data_type)
def run(self):
"""Entry point for the SimilarityScorer.
Returns:
A dict with metadata about the processed data set or None if no
data_types has been configured.
"""
# Exit early if there is no data_type to process.
if not self._config:
return None
# Event generator for streaming results.
events = self.event_stream(
query_string=self._config.query,
return_fields=[self._config.field]
)
lsh, minhashes = similarity.new_lsh_index(
events, field=self._config.field,
delimiters=self._config.delimiters, num_perm=self._config.num_perm,
threshold=self._config.threshold)
total_num_events = len(minhashes)
for key, minhash in minhashes.items():
event_id, event_type, index_name = key
event_dict = dict(_id=event_id, _type=event_type, _index=index_name)
event = interface.Event(event_dict, self.datastore)
score = similarity.calculate_score(lsh, minhash, total_num_events)
attributes_to_add = {'similarity_score': score}
event.add_attributes(attributes_to_add)
# Commit the event to the datastore.
event.commit()
msg = 'Similarity scorer processed {0:d} events for data_type {1:s}'
return msg.format(total_num_events, self._config.data_type)
