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dotnet / machinelearning-samples

Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
https://dot.net/ml
MIT License
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Error training faild #500

Closed vardeg2017 closed 5 years ago

vardeg2017 commented 5 years ago

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 ...`

vardeg2017 commented 5 years ago

invalid branch I'm new to github