我在 ML.NET 中对分类数据进行聚类时遇到了困难。

使用 var predictor = mlContext.Model.CreatePredictionEngine(model) 这一行代码时，出现了异常 “System.InvalidOperationException: ‘Incompatible features column type: ‘Vector’ vs ‘Vector””

我对机器学习还比较新手，能有人帮助我吗？

谢谢！

class Program{    static void Main(string[] args)    {        var mlContext = new MLContext();        var samples = new[]        {            new DataPoint {Education = "0-5yrs", ZipCode = "98005"},            new DataPoint {Education = "0-5yrs", ZipCode = "98052"},            new DataPoint {Education = "6-11yrs", ZipCode = "98005"},            new DataPoint {Education = "6-11yrs", ZipCode = "98052"},            new DataPoint {Education = "11-15yrs", ZipCode = "98005"}        };        IDataView data = mlContext.Data.LoadFromEnumerable(samples);        var multiColumnKeyPipeline =            mlContext.Transforms.Categorical.OneHotEncoding(                new[]                {                    new InputOutputColumnPair("Education"),                    new InputOutputColumnPair("ZipCode")                });        IDataView transformedData =            multiColumnKeyPipeline.Fit(data).Transform(data);        string featuresColumnName = "Features";        var pipeline = mlContext.Transforms            .Concatenate(featuresColumnName, "Education", "ZipCode")            .Append(mlContext.Clustering.Trainers.KMeans(featuresColumnName, numberOfClusters: 2));        var model = pipeline.Fit(transformedData);        var predictor = mlContext.Model.CreatePredictionEngine<TransformedData, ClusterPredictionItem>(model);    }    private class DataPoint    {        public string Education { get; set; }        public string ZipCode { get; set; }    }    private class TransformedData    {        public float Education { get; set; }        public float ZipCode { get; set; }    }    internal class ClusterPredictionItem    {    }}

回答：

我怀疑你遇到问题的原因是你将管道分成了几部分，并且你的实际训练基于转换后的 IDataView，而没有将其作为管道的一部分。如果你将 onehotencoding 和训练器合并到一个管道中，你可以简化你的代码：

 IDataView data = mlContext.Data.LoadFromEnumerable(samples);        string featuresColumnName = "Features";        var pipeline = mlContext.Transforms.Categorical.OneHotEncoding(            new[]            {                new InputOutputColumnPair("Education"),                new InputOutputColumnPair("ZipCode")            }).Append(mlContext.Transforms.Concatenate("Features", "Education", "ZipCode"))            .Append(mlContext.Clustering.Trainers.KMeans(featuresColumnName, numberOfClusters: 2));        var model = pipeline.Fit(data);        var predictor = mlContext.Model.CreatePredictionEngine<DataPoint, ClusterPredictionItem>(model);

这样应该可以避免异常的发生。