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465 lines
18 KiB
465 lines
18 KiB
using System;
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using System.Collections.Generic;
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using System.Diagnostics;
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using System.IO;
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using System.Linq;
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using ZeroLevel.HNSW;
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namespace HNSWDemo
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{
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class Program
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{
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public class VectorsDirectCompare
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{
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private readonly IList<float[]> _vectors;
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private readonly Func<float[], float[], float> _distance;
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public VectorsDirectCompare(List<float[]> vectors, Func<float[], float[], float> distance)
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{
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_vectors = vectors;
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_distance = distance;
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}
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public IEnumerable<(int, float)> KNearest(float[] v, int k)
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{
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var weights = new Dictionary<int, float>();
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for (int i = 0; i < _vectors.Count; i++)
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{
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var d = _distance(v, _vectors[i]);
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weights[i] = d;
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}
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return weights.OrderBy(p => p.Value).Take(k).Select(p => (p.Key, p.Value));
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}
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}
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public enum Gender
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{
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Unknown, Male, Feemale
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}
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public class Person
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{
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public Gender Gender { get; set; }
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public int Age { get; set; }
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public long Number { get; set; }
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private static (float[], Person) Generate(int vector_size)
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{
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var rnd = new Random((int)Environment.TickCount);
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var vector = new float[vector_size];
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DefaultRandomGenerator.Instance.NextFloats(vector);
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VectorUtils.NormalizeSIMD(vector);
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var p = new Person();
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p.Age = rnd.Next(15, 80);
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var gr = rnd.Next(0, 3);
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p.Gender = (gr == 0) ? Gender.Male : (gr == 1) ? Gender.Feemale : Gender.Unknown;
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p.Number = CreateNumber(rnd);
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return (vector, p);
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}
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public static List<(float[], Person)> GenerateRandom(int vectorSize, int vectorsCount)
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{
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var vectors = new List<(float[], Person)>();
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for (int i = 0; i < vectorsCount; i++)
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{
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vectors.Add(Generate(vectorSize));
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}
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return vectors;
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}
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static HashSet<long> _exists = new HashSet<long>();
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private static long CreateNumber(Random rnd)
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{
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long start_number;
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do
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{
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start_number = 79600000000L;
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start_number = start_number + rnd.Next(4, 8) * 10000000;
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start_number += rnd.Next(0, 1000000);
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}
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while (_exists.Add(start_number) == false);
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return start_number;
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}
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}
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private static List<float[]> RandomVectors(int vectorSize, int vectorsCount)
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{
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var vectors = new List<float[]>();
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for (int i = 0; i < vectorsCount; i++)
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{
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var vector = new float[vectorSize];
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DefaultRandomGenerator.Instance.NextFloats(vector);
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VectorUtils.NormalizeSIMD(vector);
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vectors.Add(vector);
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}
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return vectors;
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}
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private static Dictionary<int, Person> _database = new Dictionary<int, Person>();
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static void Main(string[] args)
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{
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FilterTest();
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Console.ReadKey();
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}
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static void TransformToCompactWorldTest()
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{
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var count = 10000;
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var dimensionality = 128;
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var samples = RandomVectors(dimensionality, count);
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var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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var ids = world.AddItems(samples.ToArray());
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Console.WriteLine("Start test");
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byte[] dump;
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using (var ms = new MemoryStream())
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{
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world.Serialize(ms);
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dump = ms.ToArray();
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}
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Console.WriteLine($"Full dump size: {dump.Length} bytes");
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ReadOnlySmallWorld<float[]> compactWorld;
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using (var ms = new MemoryStream(dump))
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{
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compactWorld = SmallWorld.CreateReadOnlyWorldFrom<float[]>(NSWReadOnlyOption<float[]>.Create(200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple), ms);
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}
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// Compare worlds outputs
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int K = 200;
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var hits = 0;
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var miss = 0;
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var testCount = 1000;
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var sw = new Stopwatch();
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var timewatchesHNSW = new List<float>();
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var timewatchesHNSWCompact = new List<float>();
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var test_vectors = RandomVectors(dimensionality, testCount);
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foreach (var v in test_vectors)
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{
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sw.Restart();
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var gt = world.Search(v, K).Select(e => e.Item1).ToHashSet();
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sw.Stop();
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timewatchesHNSW.Add(sw.ElapsedMilliseconds);
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sw.Restart();
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var result = compactWorld.Search(v, K).Select(e => e.Item1).ToHashSet();
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sw.Stop();
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timewatchesHNSWCompact.Add(sw.ElapsedMilliseconds);
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foreach (var r in result)
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{
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if (gt.Contains(r))
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{
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hits++;
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}
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else
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{
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miss++;
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}
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}
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}
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byte[] smallWorldDump;
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using (var ms = new MemoryStream())
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{
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compactWorld.Serialize(ms);
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smallWorldDump = ms.ToArray();
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}
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var p = smallWorldDump.Length * 100.0f / dump.Length;
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Console.WriteLine($"Compact dump size: {smallWorldDump.Length} bytes. Decrease: {100 - p}%");
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Console.WriteLine($"HITS: {hits}");
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Console.WriteLine($"MISSES: {miss}");
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Console.WriteLine($"MIN HNSW TIME: {timewatchesHNSW.Min()} ms");
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Console.WriteLine($"AVG HNSW TIME: {timewatchesHNSW.Average()} ms");
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Console.WriteLine($"MAX HNSW TIME: {timewatchesHNSW.Max()} ms");
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Console.WriteLine($"MIN HNSWCompact TIME: {timewatchesHNSWCompact.Min()} ms");
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Console.WriteLine($"AVG HNSWCompact TIME: {timewatchesHNSWCompact.Average()} ms");
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Console.WriteLine($"MAX HNSWCompact TIME: {timewatchesHNSWCompact.Max()} ms");
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}
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static void TransformToCompactWorldTestWithAccuracity()
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{
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var count = 10000;
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var dimensionality = 128;
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var samples = RandomVectors(dimensionality, count);
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var test = new VectorsDirectCompare(samples, CosineDistance.ForUnits);
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var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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var ids = world.AddItems(samples.ToArray());
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Console.WriteLine("Start test");
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byte[] dump;
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using (var ms = new MemoryStream())
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{
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world.Serialize(ms);
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dump = ms.ToArray();
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}
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ReadOnlySmallWorld<float[]> compactWorld;
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using (var ms = new MemoryStream(dump))
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{
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compactWorld = SmallWorld.CreateReadOnlyWorldFrom<float[]>(NSWReadOnlyOption<float[]>.Create(200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple), ms);
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}
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// Compare worlds outputs
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int K = 200;
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var hits = 0;
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var miss = 0;
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var testCount = 2000;
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var sw = new Stopwatch();
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var timewatchesNP = new List<float>();
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var timewatchesHNSW = new List<float>();
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var timewatchesHNSWCompact = new List<float>();
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var test_vectors = RandomVectors(dimensionality, testCount);
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var totalHitsHNSW = new List<int>();
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var totalHitsHNSWCompact = new List<int>();
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foreach (var v in test_vectors)
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{
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var npHitsHNSW = 0;
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var npHitsHNSWCompact = 0;
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sw.Restart();
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var gtNP = test.KNearest(v, K).Select(p => p.Item1).ToHashSet();
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sw.Stop();
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timewatchesNP.Add(sw.ElapsedMilliseconds);
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sw.Restart();
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var gt = world.Search(v, K).Select(e => e.Item1).ToHashSet();
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sw.Stop();
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timewatchesHNSW.Add(sw.ElapsedMilliseconds);
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sw.Restart();
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var result = compactWorld.Search(v, K).Select(e => e.Item1).ToHashSet();
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sw.Stop();
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timewatchesHNSWCompact.Add(sw.ElapsedMilliseconds);
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foreach (var r in result)
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{
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if (gt.Contains(r))
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{
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hits++;
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}
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else
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{
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miss++;
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}
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if (gtNP.Contains(r))
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{
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npHitsHNSWCompact++;
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}
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}
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foreach (var r in gt)
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{
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if (gtNP.Contains(r))
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{
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npHitsHNSW++;
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}
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}
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totalHitsHNSW.Add(npHitsHNSW);
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totalHitsHNSWCompact.Add(npHitsHNSWCompact);
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}
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byte[] smallWorldDump;
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using (var ms = new MemoryStream())
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{
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compactWorld.Serialize(ms);
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smallWorldDump = ms.ToArray();
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}
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var p = smallWorldDump.Length * 100.0f / dump.Length;
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Console.WriteLine($"Full dump size: {dump.Length} bytes");
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Console.WriteLine($"Compact dump size: {smallWorldDump.Length} bytes. Decrease: {100 - p}%");
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Console.WriteLine($"HITS: {hits}");
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Console.WriteLine($"MISSES: {miss}");
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Console.WriteLine($"MIN NP TIME: {timewatchesNP.Min()} ms");
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Console.WriteLine($"AVG NP TIME: {timewatchesNP.Average()} ms");
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Console.WriteLine($"MAX NP TIME: {timewatchesNP.Max()} ms");
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Console.WriteLine($"MIN HNSW TIME: {timewatchesHNSW.Min()} ms");
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Console.WriteLine($"AVG HNSW TIME: {timewatchesHNSW.Average()} ms");
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Console.WriteLine($"MAX HNSW TIME: {timewatchesHNSW.Max()} ms");
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Console.WriteLine($"MIN HNSWCompact TIME: {timewatchesHNSWCompact.Min()} ms");
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Console.WriteLine($"AVG HNSWCompact TIME: {timewatchesHNSWCompact.Average()} ms");
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Console.WriteLine($"MAX HNSWCompact TIME: {timewatchesHNSWCompact.Max()} ms");
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Console.WriteLine($"MIN HNSW Accuracity: {totalHitsHNSW.Min() * 100 / K}%");
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Console.WriteLine($"AVG HNSW Accuracity: {totalHitsHNSW.Average() * 100 / K}%");
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Console.WriteLine($"MAX HNSW Accuracity: {totalHitsHNSW.Max() * 100 / K}%");
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Console.WriteLine($"MIN HNSWCompact Accuracity: {totalHitsHNSWCompact.Min() * 100 / K}%");
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Console.WriteLine($"AVG HNSWCompact Accuracity: {totalHitsHNSWCompact.Average() * 100 / K}%");
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Console.WriteLine($"MAX HNSWCompact Accuracity: {totalHitsHNSWCompact.Max() * 100 / K}%");
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}
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static void SaveRestoreTest()
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{
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var count = 1000;
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var dimensionality = 128;
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var samples = RandomVectors(dimensionality, count);
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var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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var sw = new Stopwatch();
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sw.Start();
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var ids = world.AddItems(samples.ToArray());
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sw.Stop();
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Console.WriteLine($"Insert {ids.Length} items on {sw.ElapsedMilliseconds} ms");
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Console.WriteLine("Start test");
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byte[] dump;
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using (var ms = new MemoryStream())
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{
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world.Serialize(ms);
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dump = ms.ToArray();
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}
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Console.WriteLine($"Full dump size: {dump.Length} bytes");
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byte[] testDump;
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var restoredWorld = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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using (var ms = new MemoryStream(dump))
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{
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restoredWorld.Deserialize(ms);
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}
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using (var ms = new MemoryStream())
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{
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restoredWorld.Serialize(ms);
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testDump = ms.ToArray();
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}
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if (testDump.Length != dump.Length)
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{
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Console.WriteLine($"Incorrect restored size. Got {testDump.Length}. Expected: {dump.Length}");
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return;
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}
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ReadOnlySmallWorld<float[]> compactWorld;
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using (var ms = new MemoryStream(dump))
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{
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compactWorld = SmallWorld.CreateReadOnlyWorldFrom<float[]>(NSWReadOnlyOption<float[]>.Create(200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple), ms);
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}
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byte[] smallWorldDump;
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using (var ms = new MemoryStream())
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{
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compactWorld.Serialize(ms);
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smallWorldDump = ms.ToArray();
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}
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var p = smallWorldDump.Length * 100.0f / dump.Length;
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Console.WriteLine($"Compact dump size: {smallWorldDump.Length} bytes. Decrease: {100 - p}%");
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}
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static void FilterTest()
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{
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var count = 1000;
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var testCount = 100;
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var dimensionality = 128;
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var samples = Person.GenerateRandom(dimensionality, count);
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var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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var ids = world.AddItems(samples.Select(i => i.Item1).ToArray());
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for (int bi = 0; bi < samples.Count; bi++)
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{
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_database.Add(ids[bi], samples[bi].Item2);
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}
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Console.WriteLine("Start test");
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int K = 200;
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var vectors = RandomVectors(dimensionality, testCount);
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var context = new SearchContext().SetActiveNodes(_database.Where(pair => pair.Value.Age > 20 && pair.Value.Age < 50 && pair.Value.Gender == Gender.Feemale).Select(pair => pair.Key));
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var hits = 0;
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var miss = 0;
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foreach (var v in vectors)
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{
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var result = world.Search(v, K, context);
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foreach (var r in result)
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{
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var record = _database[r.Item1];
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if (record.Gender == Gender.Feemale && record.Age > 20 && record.Age < 50)
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{
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hits++;
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}
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else
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{
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miss++;
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}
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}
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}
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Console.WriteLine($"SUCCESS: {hits}");
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Console.WriteLine($"ERROR: {miss}");
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}
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static void AccuracityTest()
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{
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int K = 200;
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var count = 5000;
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var testCount = 1000;
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var dimensionality = 128;
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var totalHits = new List<int>();
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var timewatchesNP = new List<float>();
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var timewatchesHNSW = new List<float>();
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var samples = RandomVectors(dimensionality, count);
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var sw = new Stopwatch();
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var test = new VectorsDirectCompare(samples, CosineDistance.ForUnits);
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var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
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sw.Start();
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var ids = world.AddItems(samples.ToArray());
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sw.Stop();
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Console.WriteLine($"Insert {ids.Length} items on {sw.ElapsedMilliseconds} ms");
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Console.WriteLine("Start test");
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var test_vectors = RandomVectors(dimensionality, testCount);
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foreach (var v in test_vectors)
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{
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sw.Restart();
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var gt = test.KNearest(v, K).ToDictionary(p => p.Item1, p => p.Item2);
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sw.Stop();
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timewatchesNP.Add(sw.ElapsedMilliseconds);
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sw.Restart();
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var result = world.Search(v, K);
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sw.Stop();
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timewatchesHNSW.Add(sw.ElapsedMilliseconds);
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var hits = 0;
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foreach (var r in result)
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{
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if (gt.ContainsKey(r.Item1))
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{
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hits++;
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}
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}
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totalHits.Add(hits);
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}
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Console.WriteLine($"MIN Accuracity: {totalHits.Min() * 100 / K}%");
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Console.WriteLine($"AVG Accuracity: {totalHits.Average() * 100 / K}%");
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Console.WriteLine($"MAX Accuracity: {totalHits.Max() * 100 / K}%");
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Console.WriteLine($"MIN HNSW TIME: {timewatchesHNSW.Min()} ms");
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Console.WriteLine($"AVG HNSW TIME: {timewatchesHNSW.Average()} ms");
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Console.WriteLine($"MAX HNSW TIME: {timewatchesHNSW.Max()} ms");
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Console.WriteLine($"MIN NP TIME: {timewatchesNP.Min()} ms");
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Console.WriteLine($"AVG NP TIME: {timewatchesNP.Average()} ms");
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Console.WriteLine($"MAX NP TIME: {timewatchesNP.Max()} ms");
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}
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}
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}
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