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 fit(self, X):
import nmslib
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._method_param.append('bucketSize=%d' % min(int(X.shape[0] * 0.0005), 1000))
self._index = nmslib.initIndex(X.shape[0], self._nmslib_metric, [], self._method_name, self._method_param, nmslib.DataType.VECTOR, nmslib.DistType.FLOAT)
for i, x in enumerate(X):
nmslib.setData(self._index, i, x.tolist())
nmslib.buildIndex(self._index)
def fit(self, X):
import nmslib
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._method_param.append('bucketSize=%d' %
min(int(X.shape[0] * 0.0005), 1000))
self._index = nmslib.initIndex(X.shape[0], self._nmslib_metric, [],
self._method_name, self._method_param,
nmslib.DataType.VECTOR,
nmslib.DistType.FLOAT)
for i, x in enumerate(X):
nmslib.setData(self._index, i, x.tolist())
nmslib.buildIndex(self._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 test_save_and_load(data, init_nn=3, init_index=3, init_search=3):
import nmslib
reload(nmslib)
n = data.shape[0]
space_type = 'l2'
space_param = []
method_name = 'small_world_rand'
method_param = ['NN=%d'%init_nn,
'initIndexAttempts=%d'%init_index,
'initSearchAttempts=%d'%init_search,
'indexThreadQty=4',
'graphFileName=savedGraph.txt',
'saveGraphFile=1',
'loadGraphFile=0']
index = nmslib.initIndex(n,
space_type,
space_param,
method_name,
method_param,
nmslib.DataType.VECTOR,
nmslib.DistType.FLOAT)
t0 = time.time()
for pos, d in enumerate(data):
nmslib.setData(index, pos, d.tolist())
nmslib.buildIndex(index)
print 'Building %i dataset took %1.4f' % (data.shape[0], time.time()-t0)
def query(q, k=10, m=3):
return nmslib.knnQuery(index, k, q.tolist())
print 'building score: '
print test_method(query)
nmslib.freeIndex(index)
method_param = ['NN=%d'%init_nn,
'initIndexAttempts=%d'%init_index,
'initSearchAttempts=%d'%init_search,
'indexThreadQty=1',
'graphFileName=savedGraph.txt',
'saveGraphFile=0',
'loadGraphFile=1']
index2 = nmslib.initIndex(n,
space_type,
space_param,
method_name,
method_param,
nmslib.DataType.VECTOR,
nmslib.DistType.FLOAT)
t0 = time.time()
for pos, d in enumerate(data):
nmslib.setData(index2, pos, d.tolist())
nmslib.buildIndex(index2)
print 'Building %i dataset took %1.4f' % (data.shape[0], time.time()-t0)
def query2(q, k=10, m=3):
return nmslib.knnQuery(index2, k, q.tolist())
print 'loading score: '
print test_method(query2)
nmslib.freeIndex(index2)
