当数据中没有正数 class 时，AUC 未定义 [生病]
AUC is not definied[sic] when there is no positive class in the data
我在尝试训练模型时遇到以下问题：
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 1 NaN 00:00:00.3095729 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary NaN NaN NaN NaN 0.3 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 2 NaN 00:00:00.5833636 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary NaN NaN NaN NaN 0.6 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=- . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 3 NaN 00:00:00.0955045 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary NaN NaN NaN NaN 0.1 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
Exception occured while exploring pipelines:
Training failed with the exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
System.InvalidOperationException: Training failed with the exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
at Microsoft.ML.CLI.CodeGenerator.CodeGenerationHelper.GenerateCode()
at Microsoft.ML.CLI.Program.<>c__DisplayClass1_0.<Main>b__0(NewCommandSettings options)
Please see the log file for more info.
Exiting ...
这是我尝试训练的数据样本的一小部分。即使我只使用这个小子集，训练也会因上述错误而失败。注意："Res" 列是我要求 ML 系统预测的内容。
V1,V2,V3,V4,V5,V6,Res
1.04,0,0,93,0.93,30,1
1.33,3,0.6,81,0.81,37,1
1.2,3,0.6,90,0.9,30,1
1.13,0,0,74,0.74,19,1
1.06,0,0,78,0.78,18,1
1.25,3,0.6,86,0.86,21,1
1.25,4,0.8,89,0.89,18,0
1.25,5,1,96,0.96,23,0
可以看出"Res"这一列确实有正负标签。此外，我尝试将此列中的值更改为 "True/False"、"Yes/No" 和“1/0”，如此处所示。谁能建议解决这个问题？
你是如何加载数据的，这看起来你的标签列是空的，所以可能数据加载不正确。如果使用内置 mlContext.Data.LoadFromTextFile 请记住它默认为 TSV，因此您必须在其配置中自己指定分隔符，如下所示：
mlContext.Data.LoadFromTextFile<YourClass>(@"c:\path\to.file", separatorChar:',',hasHeader:true)
这个问题的解决方案原来是我没有加载足够的数据。验证和测试拆分是自动生成的，但是有一些拆分没有正确的示例 class。
我不知道这是否仍然是最新版本的问题，因为这已经有一段时间了。
加载大量数据！
确保你的测试和训练数据必须有真假两种数据，一旦我在测试和训练中包含真假两种数据，我的问题就解决了file/dataset。
我在尝试训练模型时遇到以下问题：
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 1 NaN 00:00:00.3095729 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary NaN NaN NaN NaN 0.3 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 2 NaN 00:00:00.5833636 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary NaN NaN NaN NaN 0.6 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=- . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
[Source=AutoML, Kind=Trace] 3 NaN 00:00:00.0955045 xf=ColumnConcatenating{ col=Features:V1,V2,V3,V4,V5,V6} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary NaN NaN NaN NaN 0.1 0 |
System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
Exception occured while exploring pipelines:
Training failed with the exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
System.InvalidOperationException: Training failed with the exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
Parameter name: PosSample
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Evaluate(IDataView data, String label, String score, String predictedLabel)
at Microsoft.ML.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent`1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger)
at Microsoft.ML.CLI.CodeGenerator.CodeGenerationHelper.GenerateCode()
at Microsoft.ML.CLI.Program.<>c__DisplayClass1_0.<Main>b__0(NewCommandSettings options)
Please see the log file for more info.
Exiting ...
这是我尝试训练的数据样本的一小部分。即使我只使用这个小子集，训练也会因上述错误而失败。注意："Res" 列是我要求 ML 系统预测的内容。
V1,V2,V3,V4,V5,V6,Res
1.04,0,0,93,0.93,30,1
1.33,3,0.6,81,0.81,37,1
1.2,3,0.6,90,0.9,30,1
1.13,0,0,74,0.74,19,1
1.06,0,0,78,0.78,18,1
1.25,3,0.6,86,0.86,21,1
1.25,4,0.8,89,0.89,18,0
1.25,5,1,96,0.96,23,0
可以看出"Res"这一列确实有正负标签。此外，我尝试将此列中的值更改为 "True/False"、"Yes/No" 和“1/0”，如此处所示。谁能建议解决这个问题？
你是如何加载数据的，这看起来你的标签列是空的，所以可能数据加载不正确。如果使用内置 mlContext.Data.LoadFromTextFile 请记住它默认为 TSV，因此您必须在其配置中自己指定分隔符，如下所示：
mlContext.Data.LoadFromTextFile<YourClass>(@"c:\path\to.file", separatorChar:',',hasHeader:true)
这个问题的解决方案原来是我没有加载足够的数据。验证和测试拆分是自动生成的，但是有一些拆分没有正确的示例 class。
我不知道这是否仍然是最新版本的问题，因为这已经有一段时间了。
加载大量数据！
确保你的测试和训练数据必须有真假两种数据，一旦我在测试和训练中包含真假两种数据，我的问题就解决了file/dataset。