def test_string_loaded():
DATA_STRS = ["xyz", "beagcfa", "cea", "cb",
"d", "c", "bdaf", "ddcd",
"egbfa", "a", "fba", "bcccfe",
"ab", "bfgbfdc", "bcbbgf", "bfbb"
]
QUERY_STRS = ["abc", "def", "ghik"]
space_type = 'leven'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib.init(
space_type,
space_param,
method_name,
nmslib.DataType.OBJECT_AS_STRING,
nmslib.DistType.INT)
for id, data in enumerate(DATA_STRS):
nmslib.addDataPoint(index, id, data)
print 'Let\'s print a few data entries'
print 'We have added %d data points' % nmslib.getDataPointQty(index)
for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
print nmslib.getDataPoint(index,i)
print 'Let\'s invoke the index-build process'
index_param = ['NN=17', 'initIndexAttempts=3', 'indexThreadQty=4']
query_time_param = ['initSearchAttempts=3']
nmslib.loadIndex(index, index_name)
print "The index %s is loaded" % index_name
nmslib.setQueryTimeParams(index, query_time_param)
print 'Query time parameters are set'
print "Results for the loaded index:"
k = 2
for idx, data in enumerate(QUERY_STRS):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def test_vector_loaded():
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib.init(
space_type,
space_param,
method_name,
nmslib.DataType.DENSE_VECTOR,
nmslib.DistType.FLOAT)
for id, data in enumerate(read_data('sample_dataset.txt')):
pos = nmslib.addDataPoint(index, id, data)
if id != pos:
print 'id %s != pos %s' % (id, pos)
sys.exit(1)
print 'Let\'s print a few data entries'
print 'We have added %d data points' % nmslib.getDataPointQty(index)
for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
print nmslib.getDataPoint(index,i)
print 'Let\'s invoke the index-build process'
query_time_param = ['initSearchAttempts=3']
nmslib.loadIndex(index, index_name)
print "The index %s is loaded" % index_name
nmslib.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the loaded index"
k = 2
for idx, data in enumerate(read_data('sample_queryset.txt')):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def fit(self, X):
if self._method_name == 'vptree':
# To avoid this issue:
# terminate called after throwing an instance of 'std::runtime_error'
# what(): The data size is too small or the bucket size is too big. Select the parameters so that <total # of records> is NOT less than <bucket size> * 1000
# Aborted (core dumped)
self._index_param.append('bucketSize=%d' % min(int(X.shape[0] * 0.0005), 1000))
self._index = nmslib.init(self._nmslib_metric, [], self._method_name, nmslib.DataType.DENSE_VECTOR, nmslib.DistType.FLOAT)
for i, x in enumerate(X):
nmslib.addDataPoint(self._index, i, x.tolist())
if os.path.exists(self._index_name):
print "Loading index from file"
nmslib.loadIndex(self._index, self._index_name)
else:
nmslib.createIndex(self._index, self._index_param)
if self._save_index:
nmslib.saveIndex(self._index, self._index_name)
nmslib.setQueryTimeParams(self._index, self._query_param)
def fit(self, X):
if self._method_name == 'vptree':
# To avoid this issue:
# terminate called after throwing an instance of 'std::runtime_error'
# what(): The data_old size is too small or the bucket size is too big. Select the parameters so that <total # of records> is NOT less than <bucket size> * 1000
# Aborted (core dumped)
self._index_param.append('bucketSize=%d' %
min(int(X.shape[0] * 0.0005), 1000))
self._index = nmslib.init('l2', [], self._method_name,
nmslib.DataType.DENSE_VECTOR,
nmslib.DistType.FLOAT)
for i, x in enumerate(X):
nmslib.addDataPoint(self._index, i, x.tolist())
if os.path.exists(self._index_name):
print('Loading index from file')
nmslib.loadIndex(self._index, self._index_name)
else:
nmslib.createIndex(self._index, self._index_param)
nmslib.saveIndex(self._index, self._index_name)
nmslib.setQueryTimeParams(self._index, self._query_param)
def load(self, fname):
nmslib.loadIndex(self.index, fname)
num_lbls = int(sys.argv[4])
efS = int(sys.argv[5])
num_nbrs = int(sys.argv[6])
write_dist = int(sys.argv[7])
out_dir = sys.argv[8]
num_thread = int(sys.argv[9])
num_out_threads = int(sys.argv[10])
metric_space = sys.argv[11]
lbl_ft_file = sys.argv[12]
index = nmslib.init(method='hnsw',
space='cosinesimil_sparse',
data_type=nmslib.DataType.SPARSE_VECTOR)
data = data_utils.read_sparse_file(lbl_ft_file)
index.addDataPointBatch(data)
nmslib.loadIndex(index, model_file)
index.setQueryTimeParams({'efSearch': efS, 'algoType': 'old'})
start = time.time()
query = data_utils.read_sparse_file(tst_ft_file)
end = time.time()
start = time.time()
nbrs = index.knnQueryBatch(query, k=num_nbrs, num_threads=num_thread)
end = time.time()
print('Time taken to find approx nearest neighbors = %f' % (end - start))
batch_size = int(math.ceil(float(len(nbrs)) / float(num_out_threads)))
for i in range(num_out_threads):
Process(target=write_knn_out,
args=(out_dir, write_dist, num_lbls,