找不到列 'PredictedLabel'

Question

我正在使用 ML.Net 并尝试对一些数据进行聚类，但我遇到的异常是在标题中：“找不到列 'PredictedLabel'”。但是我的预测中确实有这个专栏 class!

    public class ClusterPrediction
    {
        public uint Label { get; set; }
        // Predicted label from the trainer.
        //[ColumnName("PredictedLabel")] <-- doesn't make a differnce if I have this or not
        public uint PredictedLabel { get; set; }
        public float[] Score { get; set; } = new float[0];
        public float[] Features { get; set; } = new float[0];
    }

以下是我设置管道的方式：

            var clusterSchema = SchemaDefinition.Create(typeof(InputCombinedClusterData));
            for (int i = 0; i < 5; i++)
            {
                var col = clusterSchema[i];
                var itemType = ((VectorDataViewType)col.ColumnType).ItemType;
                col.ColumnType = new VectorDataViewType(itemType, InputCombinedClusterData.LOOKBACK);
            }

            var trainingData = mlContext.Data.LoadFromEnumerable(data.TrainingData);
            var options = new KMeansTrainer.Options
            {
                // will tune this later 
                NumberOfClusters = 15,
                OptimizationTolerance = 1e-9f,
                MaximumNumberOfIterations = 10000,
            };

            var fundamentalCols = new List<string> { tons of columns };

            string featuresColumnName = "Features";
            var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("Sector")
                .Append(mlContext.Transforms.Categorical.OneHotEncoding("Industry"));

            foreach (var col in fundamentalCols)
            {
                pipeline.Append(mlContext.Transforms.NormalizeMinMax(col));
            }
            
            pipeline.Append(mlContext.Transforms.Concatenate(featuresColumnName, "Open", "High", "Low", "Close", "Volume", "Sector", "Industry", {lots more columns}));
            pipeline.Append(mlContext.Clustering.Trainers.KMeans(options));

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // test the model
            var testData = mlContext.Data.LoadFromEnumerable(data.TestingData);

            // Run the model on test data set
            var transformedTestData = model.Transform(testData);

            // Goes BOOM here
            var predictions = mlContext.Data.CreateEnumerable<ClusterPrediction>(
                transformedTestData, reuseRowObject: false).ToList();

我确实有一个替代的聚类训练器，它确实有效，但它的列数要少得多。我没有看到为什么当两个培训师都使用相同的预测 class 时，上面有更多列的那个会因为找不到 PredictedLabel 列而爆炸？这是一个可供参考的作品：

            var clusterSchema = SchemaDefinition.Create(typeof(InputPriceClusterData));
            for (int i = 0; i < 5; i++)
            {
                var col = clusterSchema[i];
                var itemType = ((VectorDataViewType)col.ColumnType).ItemType;
                col.ColumnType = new VectorDataViewType(itemType, InputPriceClusterData.LOOKBACK);
            }

            var trainingData = mlContext.Data.LoadFromEnumerable(data.TrainingData);
            var options = new KMeansTrainer.Options
            {
                NumberOfClusters = 15,
                OptimizationTolerance = 1e-9f,
                MaximumNumberOfIterations = 10000
            };

            // Define the trainer.
            string featuresColumnName = "Features";
            var pipeline = mlContext.Transforms
                .Concatenate(featuresColumnName, "Open", "High", "Low", "Close", "Volume")
                .Append(mlContext.Clustering.Trainers.KMeans(options));

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // test the model
            var testData = mlContext.Data.LoadFromEnumerable(data.TestingData);

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<ClusterPrediction>(
                transformedTestData, reuseRowObject: false).ToList();

对于最初的破教练，我错过了什么？附录的顺序？

Answer 1

可能有点伤感，但管道上的 .Append 方法不会像 someIList.Sort() 那样进行就地追加。我还发现我也可以对所有列对进行批处理以进行标准化。一口气解决了所有问题：

            string featuresColumnName = "Features";
            var colPairs = new List<InputOutputColumnPair>();
            foreach (var col in fundamentalCols)
            {
                colPairs.Add(new InputOutputColumnPair(col, col));
            }

            var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("Sector")
                .Append(mlContext.Transforms.Categorical.OneHotEncoding("Industry"))
                .Append(mlContext.Transforms.NormalizeMinMax(colPairs.ToArray()))
                .Append(mlContext.Transforms.Concatenate(featuresColumnName, "Open", "High", "Low", "Close", "Volume", "Sector", "Industry", {boatload of columns}))
                .Append(mlContext.Clustering.Trainers.KMeans(options));

找不到列 'PredictedLabel'

Could not find column 'PredictedLabel'

c#

ml.net