CrossValidate引发错误System.ArgumentOutOfRangeException:“score column”score“not found parameter name:name”

我正在跟踪githublabeler引发异常的结果: System.ArgumentOutOfRangeException:未找到“分数”列“分数” 参数名称:name' 调用时出错

mlContext.MulticlassClassification.CrossValidate(...)

我知道它是在要求它,但是githublabeler没有提供它。

我已经修改了从列表中加载的代码,其中t是一个具有几个hundard float值的viewmodel,我的实现如下:

var trainData = mlContext.CreateStreamingDataView(data);
var features = trainData.Schema.GetColumns()
    .Select(tuple => tuple.column.Name)
    .Where(name => name != labelName)
    .ToArray();


var trainingPipeline = mlContext.Transforms.Conversion.MapValueToKey(labelName, "Label")
                        .Append(mlContext.Transforms.Concatenate("Features", features))
                        .AppendCacheCheckpoint(mlContext);

IEstimator<ITransformer> trainer = null;
var selectedStrategy = MachineLearningMethods.OVAAveragedPerceptronTrainer;
switch (selectedStrategy)
{
    case MachineLearningMethods.SdcaMultiClassTrainer:
        trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label,
                                                                                                DefaultColumnNames.Features);
        break;
    case MachineLearningMethods.OVAAveragedPerceptronTrainer:
        {
            // Create a binary classification trainer.
            var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification
                                                    .Trainers.AveragedPerceptron(DefaultColumnNames.Label,
                                                                                    DefaultColumnNames.Features,
                                                                                    numIterations: 10                                                                                            
                                                                                );

            trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(averagedPerceptronBinaryTrainer);

            break;
        }
    default:
        break;
}


trainingPipeline.Append(trainer).Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

Trace.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(data: trainData
                                                                            , estimator: trainingPipeline
                                                                            , numFolds: 6
                                                                            , labelColumn: "Label");
PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValidationResults);

Trace.WriteLine("=============== Training the model ===============");
var model = trainingPipeline.Fit(trainData);