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.EvaluatorBase1.AucAggregatorBase1.ComputeWeightedAuc(Double& unweighted) at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase1.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, IMetricsAgent1 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.EvaluatorBase1.AucAggregatorBase1.ComputeWeightedAuc(Double& unweighted)
at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish()
at Microsoft.ML.Data.BinaryClassifierEvaluator.<>cDisplayClass32_0.b0(UInt32 stratColKey, ReadOnlyMemory1 stratColVal, Aggregator agg) at Microsoft.ML.Data.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries) at Microsoft.ML.Data.EvaluatorBase1.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.<>cDisplayClass32_0.b0(UInt32 stratColKey, ReadOnlyMemory1 stratColVal, Aggregator agg) at Microsoft.ML.Data.EvaluatorBase1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data)
at Microsoft.ML.Data.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func2 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, IMetricsAgent1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger) at Microsoft.ML.Data.EvaluatorBase1.AucAggregatorBase1.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.EvaluatorBase1.AucAggregatorBase1.ComputeWeightedAuc(Double& unweighted) at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish() at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory1 stratColVal, Aggregator agg)
at Microsoft.ML.Data.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries)
at Microsoft.ML.Data.EvaluatorBase1.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, IMetricsAgent1 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.b5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable1 trees, IEnumerable1 treeWeights, Double bias) at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure() at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor() at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor) at Microsoft.ML.Data.EstimatorChain1.Fit(IDataView input)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent1 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.<CreateTreeEnsembleFromInternalDataStructure>b__5_0(InternalRegressionTree tree) at System.Linq.Enumerable.SelectListIterator2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable1 trees, IEnumerable1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase4.TrainModelCore(TrainContext context) at Microsoft.ML.Trainers.TrainerEstimatorBase2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain1.Fit(IDataView input) at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent1 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.b5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIterator2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source)
at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable1 trees, IEnumerable1 treeWeights, Double bias) at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure() at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor() at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase4.TrainModelCore(TrainContext context)
at Microsoft.ML.Trainers.TrainerEstimatorBase2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor) at Microsoft.ML.Data.EstimatorChain1.Fit(IDataView input)
at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent1 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.<CreateTreeEnsembleFromInternalDataStructure>b__5_0(InternalRegressionTree tree) at System.Linq.Enumerable.SelectListIterator2.ToList()
at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable1 trees, IEnumerable1 treeWeights, Double bias)
at Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure()
at Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.CreatePredictor()
at Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase4.TrainModelCore(TrainContext context) at Microsoft.ML.Trainers.TrainerEstimatorBase2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor)
at Microsoft.ML.Data.EstimatorChain1.Fit(IDataView input) at Microsoft.ML.AutoML.RunnerUtil.TrainAndScorePipeline[TMetrics](MLContext context, SuggestedPipeline pipeline, IDataView trainData, IDataView validData, String labelColumn, IMetricsAgent1 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 ...`
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:b0(UInt32 stratColKey, ReadOnlyMemoryb 0(UInt32 stratColKey, ReadOnlyMemoryb5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIteratorb 5_0(InternalRegressionTree tree)
at System.Linq.Enumerable.SelectListIteratorb__0(NewCommandSettings options)
Please see the log file for more info.
Exiting ...`
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.AucAggregatorBase1.ComputeWeightedAuc(Double& unweighted) at Microsoft.ML.Data.BinaryClassifierEvaluator.<>c__DisplayClass32_0.<GetAggregatorConsolidationFuncs>b__0(UInt32 stratColKey, ReadOnlyMemory
1 stratColVal, Aggregator agg) at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish() at Microsoft.ML.Data.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func
2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries) at Microsoft.ML.Data.EvaluatorBase1.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.EvaluatorBase1.AucAggregatorBase
1.ComputeWeightedAuc(Double& unweighted) at Microsoft.ML.Data.BinaryClassifierEvaluator.Aggregator.Finish() at Microsoft.ML.Data.BinaryClassifierEvaluator.<>cDisplayClass32_0.1 stratColVal, Aggregator agg) at Microsoft.ML.Data.EvaluatorBase
1.ProcessData(IDataView data, RoleMappedSchema schema, Func2 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.<>cDisplayClass32_0.1 stratColVal, Aggregator agg) at Microsoft.ML.Data.EvaluatorBase
1.Microsoft.ML.Data.IEvaluator.Evaluate(RoleMappedData data) at Microsoft.ML.Data.EvaluatorBase1.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, IMetricsAgent1 metricsAgent, ITransformer preprocessorTransform, FileInfo modelFileInfo, DataViewSchema modelInputSchema, AutoMLLogger logger) at Microsoft.ML.Data.EvaluatorBase
1.AucAggregatorBase1.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.AucAggregatorBase1.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.EvaluatorBase1.ProcessData(IDataView data, RoleMappedSchema schema, Func
2 activeColsIndices, TAgg aggregator, AggregatorDictionaryBase[] dictionaries) at Microsoft.ML.Data.EvaluatorBase1.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.2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable
1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable
1 trees, IEnumerable1 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.TrainerEstimatorBase2.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, IMetricsAgent1 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.<CreateTreeEnsembleFromInternalDataStructure>b__5_0(InternalRegressionTree tree) at System.Linq.Enumerable.SelectListIterator
2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble
1..ctor(IEnumerable1 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.LightGbmTrainerBase4.TrainModelCore(TrainContext context) at Microsoft.ML.Trainers.TrainerEstimatorBase
2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor) at Microsoft.ML.Data.EstimatorChain1.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.2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable
1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble1..ctor(IEnumerable
1 trees, IEnumerable1 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.TrainerEstimatorBase2.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, IMetricsAgent1 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.<CreateTreeEnsembleFromInternalDataStructure>b__5_0(InternalRegressionTree tree) at System.Linq.Enumerable.SelectListIterator
2.ToList() at System.Linq.Enumerable.ToList[TSource](IEnumerable1 source) at Microsoft.ML.Trainers.FastTree.TreeEnsemble
1..ctor(IEnumerable1 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.LightGbmTrainerBase4.TrainModelCore(TrainContext context) at Microsoft.ML.Trainers.TrainerEstimatorBase
2.TrainTransformer(IDataView trainSet, IDataView validationSet, IPredictor initPredictor) at Microsoft.ML.Data.EstimatorChain1.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.