c# - ml.net 中分类数据的聚类

Question

我正在努力处理 ML.NET 中的分类数据集群。

var Predictor = mlContext.Model.CreatePredictionEngine(model) 行失败，并出现异常“System.InvalidOperationException: '不兼容的特征列类型:'Vector' 与 'Vector''”

我对机器学习很陌生，有人可以帮忙吗？

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
    {
    }
}

Answer 1

我怀疑您会看到一些问题，因为如果您将 onehotencoding 和 trainer 合并到一个中，您会看到一些问题，因为您划分管道的方式并将实际训练基于从转换中产生的 IDataView，而不是管道的一部分您可以通过管道简化代码:

 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);

它应该可以正常工作。