def _compute_onetomany_relations(tokens):
relations = defaultdict(list)
for token in tokens:
for other in tokens:
if other == token:
continue
if (token in relations[other]
or DB.sismember(pair_key(token), other)):
relations[token].append(other)
return relations
def try_fuzzy(helper, tokens, include_common=True):
if not helper.bucket_dry or not tokens:
return
helper.debug('Fuzzy on. Trying with %s.', tokens)
tokens.sort(key=lambda t: len(t), reverse=True)
allkeys = helper.keys[:]
if include_common:
# As we are in fuzzy, try to narrow as much as possible by adding
# unused common tokens.
allkeys.extend(
[t.db_key for t in helper.common if t.db_key not in helper.keys])
for try_one in tokens:
if helper.bucket_full:
break
keys = allkeys[:]
if try_one.db_key in keys:
keys.remove(try_one.db_key)
if try_one.isdigit():
continue
helper.debug('Going fuzzy with %s and %s', try_one, keys)
neighbors = make_fuzzy(try_one, max=helper.fuzzy)
if len(keys):
# Only retain tokens that have been seen in the index at least
# once with the other tokens.
DB.sadd(helper.pid, *neighbors)
interkeys = [pair_key(k[2:]) for k in keys]
interkeys.append(helper.pid)
fuzzy_words = DB.sinter(interkeys)
DB.delete(helper.pid)
# Keep the priority we gave in building fuzzy terms (inversion
# first, then substitution, etc.).
fuzzy_words = [w.decode() for w in fuzzy_words]
fuzzy_words.sort(key=lambda x: neighbors.index(x))
else:
# The token we are considering is alone.
fuzzy_words = []
for neighbor in neighbors:
key = dbkeys.token_key(neighbor)
count = DB.zcard(key)
if count:
fuzzy_words.append(neighbor)
if fuzzy_words:
helper.debug('Found fuzzy candidates %s', fuzzy_words)
fuzzy_keys = [dbkeys.token_key(w) for w in fuzzy_words]
for key in fuzzy_keys:
if helper.bucket_dry:
helper.add_to_bucket(keys + [key])
def try_fuzzy(helper, tokens, include_common=True):
if not helper.bucket_dry or not tokens:
return
helper.debug('Fuzzy on. Trying with %s.', tokens)
tokens.sort(key=lambda t: len(t), reverse=True)
allkeys = helper.keys[:]
if include_common:
# As we are in fuzzy, try to narrow as much as possible by adding
# unused commons tokens.
common = [t for t in helper.common if t.db_key not in helper.keys]
allkeys.extend([t.db_key for t in common])
for try_one in tokens:
if helper.bucket_full:
break
keys = allkeys[:]
if try_one.db_key in keys:
keys.remove(try_one.db_key)
if try_one.isdigit():
continue
helper.debug('Going fuzzy with %s', try_one)
neighbors = make_fuzzy(try_one, max=helper.fuzzy)
if len(keys):
# Only retains tokens that have been seen in the index at least
# once with the other tokens.
DB.sadd(helper.query, *neighbors)
interkeys = [pair_key(k[2:]) for k in keys]
interkeys.append(helper.query)
fuzzy_words = DB.sinter(interkeys)
DB.delete(helper.query)
# Keep the priority we gave in building fuzzy terms (inversion
# first, then substitution, etc.).
fuzzy_words = [w.decode() for w in fuzzy_words]
fuzzy_words.sort(key=lambda x: neighbors.index(x))
else:
# The token we are considering is alone.
fuzzy_words = []
for neighbor in neighbors:
key = dbkeys.token_key(neighbor)
count = DB.zcard(key)
if count:
fuzzy_words.append(neighbor)
helper.debug('Found fuzzy candidates %s', fuzzy_words)
fuzzy_keys = [dbkeys.token_key(w) for w in fuzzy_words]
for key in fuzzy_keys:
if helper.bucket_dry:
helper.add_to_bucket(keys + [key])
def autocomplete(helper, tokens, skip_commons=False, use_geohash=False):
helper.debug('Autocompleting %s', helper.last_token)
keys = [t.db_key for t in tokens if not t.is_last]
pair_keys = [pair_key(t) for t in tokens if not t.is_last]
key = edge_ngram_key(helper.last_token)
autocomplete_tokens = DB.sinter(pair_keys + [key])
helper.debug('Found tokens to autocomplete %s', autocomplete_tokens)
for token in autocomplete_tokens:
key = dbkeys.token_key(token.decode())
if skip_commons\
and token_key_frequency(key) > config.COMMON_THRESHOLD:
helper.debug('Skip common token to autocomplete %s', key)
continue
if not helper.bucket_overflow or helper.last_token in helper.not_found:
helper.debug('Trying to extend bucket. Autocomplete %s', key)
extra_keys = [key]
if use_geohash and helper.geohash_key:
extra_keys.append(helper.geohash_key)
helper.add_to_bucket(keys + extra_keys)
def autocomplete(helper, tokens, skip_commons=False, use_geohash=False):
helper.debug('Autocompleting %s', helper.last_token)
# helper.last_token.autocomplete()
keys = [t.db_key for t in tokens if not t.is_last]
pair_keys = [pair_key(t) for t in tokens if not t.is_last]
key = edge_ngram_key(helper.last_token)
autocomplete_tokens = DB.sinter(pair_keys + [key])
helper.debug('Found tokens to autocomplete %s', autocomplete_tokens)
for token in autocomplete_tokens:
key = dbkeys.token_key(token.decode())
if skip_commons\
and token_key_frequency(key) > config.COMMON_THRESHOLD:
helper.debug('Skip common token to autocomplete %s', key)
continue
if not helper.bucket_overflow or helper.last_token in helper.not_found:
helper.debug('Trying to extend bucket. Autocomplete %s', key)
extra_keys = [key]
if use_geohash and helper.geohash_key:
extra_keys.append(helper.geohash_key)
helper.add_to_bucket(keys + extra_keys)
