Training step failed: AutoML should return more meaningful errors which can be understood easily by users

Model Builder 16.0.1905.641
OS Windows 10 Pro 17134.765
VS Studion 2019 16.1.1
I made very simple sample - XOR data set. Trying with csv format with "," seporated and tsv - no matter.
Here is my tsv data set:
**x y z
1 0 1
0 1 1
1 1 0
0 0 0**
When i choose binary-classification on a trin step i got this:
`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
[Source=AutoML, Kind=Trace] Channel started
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
Parameter name: PosSample
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=ColumnConcatenating{ col=Features:x,y} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:x,y} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ . Exception: System.ArgumentOutOfRangeException: AUC is not definied when there is no positive class in the data
at Microsoft.ML.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
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.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.AutoML.BinaryMetricsAgent.EvaluateMetrics(IDataView data, String labelColumn)
[Source=AutoML, Kind=Trace] 1 ­ҐязЁб«® 00:00:00.6932896 xf=ColumnConcatenating{ col=Features:x,y} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® 0,7 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.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:x,y} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:x,y} 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.BinaryClassifierEvaluator.<>c__DisplayClass32_0.b__0(UInt32 stratColKey, ReadOnlyMemory`1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase`1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.EvaluatorBase`1.ProcessData(IDataView data, RoleMappedSchema schema, Func`2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
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.Data.EvaluatorBase`1.AucAggregatorBase`1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
[Source=AutoML, Kind=Trace] 2 ­ҐязЁб«® 00:00:06.9448234 xf=ColumnConcatenating{ col=Features:x,y} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® 7,0 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.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:x,y} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Error] Pipeline crashed: xf=ColumnConcatenating{ col=Features:x,y} tr=LightGbmBinary{} cache=- . Exception: System.ArgumentNullException: Value cannot be null.
Parameter name: items
at System.Collections.Immutable.Requires.FailArgumentNullException(String parameterName)
at System.Collections.Immutable.ImmutableArray.Create[T](T[] items, Int32 start, Int32 length)
at Microsoft.ML.Trainers.FastTree.RegressionTreeBase..ctor(InternalRegressionTree tree)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.<>c.b__5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator`2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable`1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble`1..ctor(IEnumerable`1 trees, IEnumerable`1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase`4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase`2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain`1.Fit(IDataView input)
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 ­ҐязЁб«® 00:00:00.1836263 xf=ColumnConcatenating{ col=Features:x,y} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® ­ҐязЁб«® 0,2 0 |
System.ArgumentNullException: Value cannot be null.
at System.Collections.Immutable.Requires.FailArgumentNullException(String parameterName)
Parameter name: items
at System.Collections.Immutable.ImmutableArray.Create[T](T[] items, Int32 start, Int32 length)
at Microsoft.ML.Trainers.FastTree.RegressionTreeBase..ctor(InternalRegressionTree tree)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.<>c.b__5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator`2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable`1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble`1..ctor(IEnumerable`1 trees, IEnumerable`1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase`4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase`2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain`1.Fit(IDataView input)
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.ArgumentNullException: Value cannot be null.
Parameter name: items
at System.Collections.Immutable.Requires.FailArgumentNullException(String parameterName)
at System.Collections.Immutable.ImmutableArray.Create[T](T[] items, Int32 start, Int32 length)
at Microsoft.ML.Trainers.FastTree.RegressionTreeBase..ctor(InternalRegressionTree tree)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.<>c.b__5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator`2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable`1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble`1..ctor(IEnumerable`1 trees, IEnumerable`1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase`4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase`2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain`1.Fit(IDataView input)
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.ArgumentNullException: Value cannot be null.
Parameter name: items
at System.Collections.Immutable.Requires.FailArgumentNullException(String parameterName)
at System.Collections.Immutable.ImmutableArray.Create[T](T[] items, Int32 start, Int32 length)
at Microsoft.ML.Trainers.FastTree.RegressionTreeBase..ctor(InternalRegressionTree tree)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.<>c.b__5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator`2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable`1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble`1..ctor(IEnumerable`1 trees, IEnumerable`1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase`4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase`2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain`1.Fit(IDataView input)
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.b__0(NewCommandSettings options)
Please see the log file for more info.
Exiting ...`