def __generate_conreq_rank(self, result, clean_query):
"""Determines string similarity and combined with a weight of the original rank"""
try:
clean_title = clean_string(result["title"])
# Multiplier if whole substring was found within the search result
if clean_title.find(clean_query) != -1:
query_substring_multiplier = 0.1
else:
query_substring_multiplier = 1
# Generate similarity rank
result["conreqSimilarityRank"] = (
# Round the values to look pretty
self.__round(
# String similarity between the query and result
(
self.__damerau.distance(clean_query, clean_title)
# Use sonarr/radarr's original rank as a weight/bias
* (result["arrOriginalRank"] / 10))
# Bias towards full substring matches
* query_substring_multiplier) + 1)
except:
log.handler("Failed to generate conreq rank!", log.ERROR, _logger)
try:
result["conreqSimilarityRank"] = result["arrOriginalRank"]
except:
result["conreqSimilarityRank"] = 1
def generate_cache_key(cache_name, cache_args, cache_kwargs, key):
"""Generates a key to be used with django caching"""
return clean_string(
cache_name
+ "_args"
+ str(cache_args)
+ "_kwargs"
+ str(cache_kwargs)
+ "_key"
+ str(key)
)
def __set_conreq_rank(self, query, results):
# Determine string similarity and combined with a weight of the original rank
clean_query = clean_string(query)
thread_list = []
for result in results:
thread = Thread(target=self.__generate_conreq_rank,
args=[result, clean_query])
thread.start()
thread_list.append(thread)
# Wait for computation to complete
for thread in thread_list:
thread.join()
# Sort them by the new ranking metric
return sorted(results, key=lambda i: i["conreqSimilarityRank"])