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_string():
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'
method_param = [
'NN=17', 'initIndexAttempts=3', 'initSearchAttempts=1',
'indexThreadQty=4'
]
index = nmslib.initIndex(len(DATA_STRS), space_type, space_param,
method_name, method_param, nmslib.DataType.STRING,
nmslib.DistType.INT)
for pos, data in enumerate(DATA_STRS):
#print pos, data
nmslib.setData(index, pos, data)
nmslib.buildIndex(index)
k = 2
for idx, data in enumerate(QUERY_STRS):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def test_vector():
n = 4500
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
method_param = [
'NN=17', 'initIndexAttempts=3', 'initSearchAttempts=1',
'indexThreadQty=4'
]
index = nmslib.initIndex(n, space_type, space_param, method_name,
method_param, nmslib.DataType.VECTOR,
nmslib.DistType.FLOAT)
for pos, data in enumerate(read_data('sample_dataset.txt')):
if pos >= n:
break
#print pos, data
nmslib.setData(index, pos, data)
print 'here'
nmslib.buildIndex(index)
k = 2
for idx, data in enumerate(read_data('sample_queryset.txt')):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def test_string_fresh(batch=True):
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)
if batch:
print 'DATA_STRS', DATA_STRS
positions = nmslib.addDataPointBatch(index, np.arange(len(DATA_STRS), dtype=np.int32), DATA_STRS)
else:
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.createIndex(index, index_param)
nmslib.setQueryTimeParams(index, query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 2
if batch:
num_threads = 10
res = nmslib.knnQueryBatch(index, num_threads, k, QUERY_STRS)
for idx, data in enumerate(QUERY_STRS):
res = nmslib.knnQuery(index, k, data)
print idx, data, res, [DATA_STRS[i] for i in res]
nmslib.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib.freeIndex(index)
def test_object_as_string_fresh(batch=True):
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
if os.path.isfile(index_name):
os.remove(index_name)
index = nmslib.init(
space_type,
space_param,
method_name,
nmslib.DataType.OBJECT_AS_STRING,
nmslib.DistType.FLOAT)
if batch:
data = [s for s in read_data_as_string('sample_dataset.txt')]
positions = nmslib.addDataPointBatch(index, np.arange(len(data), dtype=np.int32), data)
else:
for id, data in enumerate(read_data_as_string('sample_dataset.txt')):
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.createIndex(index, index_param)
print 'The index is created'
nmslib.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 3
for idx, data in enumerate(read_data_as_string('sample_queryset.txt')):
print idx, nmslib.knnQuery(index, k, data)
nmslib.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib.freeIndex(index)
def test_sparse_vector_fresh():
space_type = 'cosinesimil_sparse'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '_sparse.index'
if os.path.isfile(index_name):
os.remove(index_name)
index = nmslib.init(
space_type,
space_param,
method_name,
nmslib.DataType.SPARSE_VECTOR,
nmslib.DistType.FLOAT)
for id, data in enumerate(read_sparse_data('sample_sparse_dataset.txt')):
nmslib.addDataPoint(index, id, data)
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.createIndex(index, index_param)
print 'The index is created'
nmslib.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 3
for idx, data in enumerate(read_sparse_data('sample_sparse_queryset.txt')):
print idx, nmslib.knnQuery(index, k, data)
nmslib.saveIndex(index, index_name)
print "The index %s is saved" % index_name
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 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 = ['efSearch=50']
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 test_vector():
n = 4500
space_type = "cosinesimil"
space_param = []
method_name = "small_world_rand"
method_param = ["NN=17", "initIndexAttempts=3", "initSearchAttempts=1", "indexThreadQty=4"]
index = nmslib.initIndex(
n, space_type, space_param, method_name, method_param, nmslib.DataType.VECTOR, nmslib.DistType.FLOAT
)
for pos, data in enumerate(read_data("sample_dataset.txt")):
if pos >= n:
break
# print pos, data
nmslib.setData(index, pos, data)
print "here"
nmslib.buildIndex(index)
k = 2
for idx, data in enumerate(read_data("sample_queryset.txt")):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def test_string():
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"
method_param = ["NN=17", "initIndexAttempts=3", "initSearchAttempts=1", "indexThreadQty=4"]
index = nmslib.initIndex(
len(DATA_STRS), space_type, space_param, method_name, method_param, nmslib.DataType.STRING, nmslib.DistType.INT
)
for pos, data in enumerate(DATA_STRS):
# print pos, data
nmslib.setData(index, pos, data)
nmslib.buildIndex(index)
k = 2
for idx, data in enumerate(QUERY_STRS):
print idx, nmslib.knnQuery(index, k, data)
nmslib.freeIndex(index)
def bench_sparse_vector(batch=True):
dim = 20000
dataset = np.random.binomial(1, 0.01, size=(40000, dim))
queryset = np.random.binomial(1, 0.009, size=(1000, dim))
print 'dataset[0]:', [[i, v] for i, v in enumerate(dataset[0]) if v > 0]
k = 3
q0 = queryset[0]
res = []
for i in range(dataset.shape[0]):
res.append([i, distance.cosine(q0, dataset[i,:])])
res.sort(key=lambda x: x[1])
print 'q0 res', res[:k]
data_matrix = csr_matrix(dataset, dtype=np.float32)
query_matrix = csr_matrix(queryset, dtype=np.float32)
data_to_return = range(dataset.shape[0])
with TimeIt('building MultiClusterIndex'):
cp = snn.MultiClusterIndex(data_matrix, data_to_return)
with TimeIt('knn search'):
res = cp.search(query_matrix, k=k, return_distance=False)
print res[:5]
for i in res[0]:
print int(i), distance.cosine(q0, dataset[int(i),:])
#space_type = 'cosinesimil_sparse'
space_type = 'cosinesimil_sparse_fast'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '_sparse.index'
if os.path.isfile(index_name):
os.remove(index_name)
index = nmslib.init(space_type,
space_param,
method_name,
nmslib.DataType.SPARSE_VECTOR,
nmslib.DistType.FLOAT)
if batch:
with TimeIt('batch add'):
positions = nmslib.addDataPointBatch(index, np.arange(len(dataset), dtype=np.int32), data_matrix)
print 'positions', positions
else:
d = []
q = []
with TimeIt('preparing'):
for data in dataset:
d.append([[i, v] for i, v in enumerate(data) if v > 0])
for data in queryset:
q.append([[i, v] for i, v in enumerate(data) if v > 0])
with TimeIt('adding points'):
for id, data in enumerate(d):
nmslib.addDataPoint(index, id, data)
print 'Let\'s invoke the index-build process'
index_param = ['NN=17', 'initIndexAttempts=3', 'indexThreadQty=4']
query_time_param = ['initSearchAttempts=3']
with TimeIt('building index'):
nmslib.createIndex(index, index_param)
print 'The index is created'
nmslib.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
with TimeIt('knn query'):
if batch:
num_threads = 10
res = nmslib.knnQueryBatch(index, num_threads, k, query_matrix)
for idx, v in enumerate(res):
if idx < 5:
print idx, v
if idx == 0:
for i in v:
print 'q0', i, distance.cosine(q0, dataset[i,:])
else:
for idx, data in enumerate(q):
res = nmslib.knnQuery(index, k, data)
if idx < 5:
print idx, res
nmslib.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib.freeIndex(index)
def test_vector_fresh(fast=True):
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
if os.path.isfile(index_name):
os.remove(index_name)
index = nmslib.init(
space_type,
space_param,
method_name,
nmslib.DataType.DENSE_VECTOR,
nmslib.DistType.FLOAT)
start = time.time()
if fast:
data = read_data_fast('sample_dataset.txt')
print 'data.shape', data.shape
positions = nmslib.addDataPointBatch(index, np.arange(len(data), dtype=np.int32), data)
else:
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)
end = time.time()
print 'added data in %s secs' % (end - start)
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.createIndex(index, index_param)
print 'The index is created'
nmslib.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 3
start = time.time()
if fast:
num_threads = 10
query = read_data_fast('sample_queryset.txt')
res = nmslib.knnQueryBatch(index, num_threads, k, query)
for idx, v in enumerate(res):
print idx, v
else:
for idx, data in enumerate(read_data('sample_queryset.txt')):
print idx, nmslib.knnQuery(index, k, data)
end = time.time()
print 'querying done in %s secs' % (end - start)
nmslib.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib.freeIndex(index)
def query2(q, k=10, m=3):
return nmslib.knnQuery(index2, k, q.tolist())
def query(self, v, n):
import nmslib
return nmslib.knnQuery(self._index, n, v.tolist())