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packages/leann-backend-hnsw/third_party/faiss/tests/test_ivfpq_indexing.cpp

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+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <cstdio>
+#include <cstdlib>
+#include <random>
+
+#include <gtest/gtest.h>
+
+#include <faiss/IndexFlat.h>
+#include <faiss/IndexIVFPQ.h>
+
+TEST(IVFPQ, accuracy) {
+    // dimension of the vectors to index
+    int d = 64;
+
+    // size of the database we plan to index
+    size_t nb = 1000;
+
+    // make a set of nt training vectors in the unit cube
+    // (could be the database)
+    size_t nt = 1500;
+
+    // make the index object and train it
+    faiss::IndexFlatL2 coarse_quantizer(d);
+
+    // a reasonable number of cetroids to index nb vectors
+    int ncentroids = 25;
+
+    faiss::IndexIVFPQ index(&coarse_quantizer, d, ncentroids, 16, 8);
+
+    // index that gives the ground-truth
+    faiss::IndexFlatL2 index_gt(d);
+
+    std::mt19937 rng;
+    std::uniform_real_distribution<> distrib;
+
+    { // training
+
+        std::vector<float> trainvecs(nt * d);
+        for (size_t i = 0; i < nt * d; i++) {
+            trainvecs[i] = distrib(rng);
+        }
+        index.verbose = true;
+        index.train(nt, trainvecs.data());
+    }
+
+    { // populating the database
+
+        std::vector<float> database(nb * d);
+        for (size_t i = 0; i < nb * d; i++) {
+            database[i] = distrib(rng);
+        }
+
+        index.add(nb, database.data());
+        index_gt.add(nb, database.data());
+    }
+
+    int nq = 200;
+    int n_ok;
+
+    { // searching the database
+
+        std::vector<float> queries(nq * d);
+        for (size_t i = 0; i < nq * d; i++) {
+            queries[i] = distrib(rng);
+        }
+
+        std::vector<faiss::idx_t> gt_nns(nq);
+        std::vector<float> gt_dis(nq);
+
+        index_gt.search(nq, queries.data(), 1, gt_dis.data(), gt_nns.data());
+
+        index.nprobe = 5;
+        int k = 5;
+        std::vector<faiss::idx_t> nns(k * nq);
+        std::vector<float> dis(k * nq);
+
+        index.search(nq, queries.data(), k, dis.data(), nns.data());
+
+        n_ok = 0;
+        for (int q = 0; q < nq; q++) {
+            for (int i = 0; i < k; i++)
+                if (nns[q * k + i] == gt_nns[q])
+                    n_ok++;
+        }
+        EXPECT_GT(n_ok, nq * 0.4);
+    }
+}