Removing unnecessary test

python_bindings/integration_tests/test_nmslib.py

@@ -227,7 +227,7 @@ def gen_sparse_data():
     np.savetxt('sample_sparse_queryset.txt', query, delimiter='\t')
 
 def test_sparse_vector_fresh():
-    space_type = 'cosinesimil_sparse'
+    space_type = 'cosinesimil_sparse_fast'
     space_param = []
     method_name = 'small_world_rand'
     index_name  = method_name + '_sparse.index'
@@ -443,99 +443,6 @@ def test_object_as_string_fresh(batch=True):
 
     nmslib.freeIndex(index)
 
-
-def bench_sparse_vector(batch=True):
-    # delay importing these so CI can import module
-    from scipy.sparse import csr_matrix
-    from scipy.spatial import distance
-
-    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])
-
-    #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', 'efConstruction=50', 'indexThreadQty=4']
-    query_time_param = ['efSearch=50']
-
-    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)
-
 if __name__ == '__main__':
 
     print('DENSE_VECTOR', nmslib.DataType.DENSE_VECTOR)
@@ -562,5 +469,4 @@ def bench_sparse_vector(batch=True):
     test_object_as_string_fresh()
     test_object_as_string_fresh(False)
 
-    bench_sparse_vector()