class HammingIndex(object):
def __init__(self,
document,
feature,
version=None,
path='',
db_size_bytes=1000000000,
listen=False,
writeonly=False,
**extra_data):
super(HammingIndex, self).__init__()
self.document = document
self.feature = feature
self.db_size_bytes = db_size_bytes
self.path = path
self.extra_data = extra_data
self.writeonly = writeonly
version = version or self.feature.version
self.hamming_db_path = os.path.join(
self.path, 'index.{self.feature.key}.{version}'.format(**locals()))
try:
self.event_log = document.event_log
except AttributeError:
self.event_log = None
try:
self.hamming_db = HammingDb(self.hamming_db_path,
code_size=None,
writeonly=self.writeonly)
except ValueError:
self.hamming_db = None
self.encoder = TimeSliceEncoder()
self.decoder = TimeSliceDecoder()
self.thread = None
if listen:
self.listen()
def close(self):
try:
self.stop()
except:
pass
try:
self.hamming_db.close()
except:
pass
def __del__(self):
self.close()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def __len__(self):
if self.hamming_db is None:
return 0
return len(self.hamming_db)
def stop(self):
self.event_log.unsubscribe()
def listen(self):
self.thread = threading.Thread(target=self._listen)
self.thread.daemon = True
self.thread.start()
def _init_hamming_db(self, code=None):
if self.hamming_db is not None:
return
code_size = len(code) if code else None
self.hamming_db = HammingDb(self.hamming_db_path,
code_size=code_size,
writeonly=self.writeonly)
def _synchronously_process_events(self):
self._listen(raise_when_empty=True)
def add_all(self):
for doc in self.document:
self.add(doc._id)
def _collect_extra_data(self, _id, ts):
if not self.extra_data:
return None
doc = self.document(_id)
return dict(((key, func(doc, ts))
for key, func in self.extra_data.iteritems()))
def add(self, _id, timestamp=''):
# load the feature from the feature database
feature = self.feature(_id=_id, persistence=self.document)
try:
arr = ConstantRateTimeSeries(feature)
except ValueError:
arr = feature
# extract codes and timeslices from the feature
for ts, data in arr.iter_slices():
code = self.encode_query(data)
encoded_ts = dict(_id=_id, **self.encoder.dict(ts))
extra_data = self._collect_extra_data(_id, ts)
if extra_data:
encoded_ts['extra_data'] = extra_data
self._init_hamming_db(code)
self.hamming_db.append(code, json.dumps(encoded_ts))
self.hamming_db.set_metadata('timestamp', bytes(timestamp))
def _listen(self, raise_when_empty=False):
if self.hamming_db is not None:
last_timestamp = self.hamming_db.get_metadata('timestamp') or ''
else:
last_timestamp = ''
if not self.event_log:
raise ValueError(
'{self.document} must have an event log configured'.format(
**locals()))
subscription = self.event_log.subscribe(
last_id=last_timestamp, raise_when_empty=raise_when_empty)
for timestamp, data in subscription:
# parse the data from the event stream
data = json.loads(data)
_id, name, version = data['_id'], data['name'], data['version']
# ensure that it's about the feature we're subscribed to
if name != self.feature.key or version != self.feature.version:
continue
self.add(_id, timestamp)
def _parse_result(self, result):
d = json.loads(result)
ts = TimeSlice(**self.decoder.kwargs(d))
if not self.extra_data:
return d['_id'], ts
return d['_id'], ts, d['extra_data']
def decode_query(self, binary_query):
packed = np.fromstring(binary_query, dtype=np.uint8)
return np.unpackbits(packed)
def encode_query(self, feature):
if isinstance(feature, str):
return feature
elif feature.dtype == np.uint64:
return feature.tostring()
elif feature.dtype == np.uint8 or feature.dtype == np.bool:
return np.packbits(feature).tostring()
else:
raise ValueError(
'feature must be a raw bit string, an already packed uint64'
'array, or an "unpacked" uint8 or bool array')
def random_search(self, n_results, multithreaded=False, sort=False):
self._init_hamming_db()
code, raw_results = self.hamming_db.random_search(n_results,
multithreaded,
sort=sort)
parsed_results = (self._parse_result(r) for r in raw_results)
return SearchResults(code, parsed_results)
def search(self, feature, n_results, multithreaded=False, sort=False):
self._init_hamming_db()
code = self.encode_query(feature)
raw_results = self.hamming_db.search(code,
n_results,
multithreaded,
sort=sort)
parsed_results = (self._parse_result(r) for r in raw_results)
return SearchResults(code, parsed_results)
def test_can_set_and_get_metadata(self):
db = HammingDb(self._path, code_size=16)
db.set_metadata('cat', 'dog')
self.assertEqual('dog', db.get_metadata('cat'))