def subtest(self, d, metric, topk, search_L, threshold):
metric_names = {faiss.METRIC_L1: 'L1',
faiss.METRIC_L2: 'L2',
faiss.METRIC_INNER_PRODUCT: 'IP'}
topk = 10
nt, nb, nq = 2000, 1000, 200
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
gt_index = faiss.IndexFlat(d, metric)
gt_index.add(xb)
gt_D, gt_I = gt_index.search(xq, topk)
K = 16
index = faiss.IndexNNDescentFlat(d, K, metric)
index.nndescent.S = 10
index.nndescent.R = 32
index.nndescent.L = K + 20
index.nndescent.iter = 5
index.verbose = False
index.nndescent.search_L = search_L;
index.add(xb)
D, I = index.search(xq, topk)
recalls = 0
for i in range(nq):
for j in range(topk):
for k in range(topk):
if I[i, j] == gt_I[i, k]:
recalls += 1
break
recall = 1.0 * recalls / (nq * topk)
print('Metric: {}, L: {}, Recall@{}: {}'.format(
metric_names[metric], search_L, topk, recall))
assert recall > threshold, '{} <= {}'.format(recall, threshold)
def subtest(self, d, K, metric):
metric_names = {faiss.METRIC_L1: 'L1',
faiss.METRIC_L2: 'L2',
faiss.METRIC_INNER_PRODUCT: 'IP'}
nb = 1000
_, xb, _ = get_dataset_2(d, 0, nb, 0)
_, knn = faiss.knn(xb, xb, K + 1, metric)
knn = knn[:, 1:]
index = faiss.IndexNNDescentFlat(d, K, metric)
index.nndescent.S = 10
index.nndescent.R = 32
index.nndescent.L = K + 20
index.nndescent.iter = 5
index.verbose = True
index.add(xb)
graph = index.nndescent.final_graph
graph = faiss.vector_to_array(graph)
graph = graph.reshape(nb, K)
recalls = 0
for i in range(nb):
for j in range(K):
for k in range(K):
if graph[i, j] == knn[i, k]:
recalls += 1
break
recall = 1.0 * recalls / (nb * K)
print('Metric: {}, knng accuracy: {}'.format(metric_names[metric], recall))
assert recall > 0.99
