KMeans cluster analysis is non-deterministic when using KMeansYinyang initialization, even with fixed MLContext seed #6375

using Microsoft.ML;
using Microsoft.ML.Data;

public class ModelData
{
    public float Value1 { get; set; }
    public float Value2 { get; set; }
}

public class ClusterPrediction
{
    [ColumnName("PredictedLabel")]
    public uint PredictedClusterId;

    [ColumnName("Score")]
    public float[] Distances = null!;

    [ColumnName("Features")]
    public float[] Features = null!;
}

var data = Enumerable.Range(0, 60).Select(x => new ModelData { Value1 = Random.Shared.Next(0, 2000), Value2 = Random.Shared.Next(0, 7) }).ToList();

const string FeaturesColumnName = "Features";
const int ClusterCount = 4;

public PredictionEngine<ModelData, ClusterPrediction> CreateModel(IEnumerable<ModelData> data)
{
    var mlContext = new MLContext(seed: 0);

    var dataView = mlContext.Data.LoadFromEnumerable(data);

    IEstimator<ITransformer> pipeline = mlContext.Transforms
        .Concatenate(FeaturesColumnName, new[] { nameof(ModelData.Value1), nameof(ModelData.Value2) })
        .Append(mlContext.Clustering.Trainers.KMeans(FeaturesColumnName, numberOfClusters: ClusterCount));

    var model = pipeline.Fit(dataView);

    return mlContext.Model.CreatePredictionEngine<ModelData, ClusterPrediction>(model);
}

using System.Linq;

for (var i = 0; i < 10; i++)
{
    var engine = CreateModel(data);

    var clusterCounts = data.Select(d => engine.Predict(d).PredictedClusterId).ToLookup(x => (int)x);

    Console.WriteLine(string.Join(" ", Enumerable.Range(1, ClusterCount).Select(x => $"Cluster {x}: {clusterCounts[x].Count()} items")));
}