LightGmb exception with an error code -1 and an error message of 'bad allocation'. #3615

 public static IDataView GetDataViewAsVector(MLContext mlContext, FileInfo trainingFile, long? maxRows = null)
        {
            var loader = mlContext.Data.CreateTextLoader(options: new TextLoader.Options()
            {
                Columns = new[] {
                    new TextLoader.Column(name:"Label", dataKind: DataKind.String, index: 0),
                    new TextLoader.Column(name:"Features",dataKind:DataKind.Single,minIndex:1,maxIndex:40731)
                },
                HasHeader = false,
                Separators = new[] { '|' },
                UseThreads = true,
                MaxRows=maxRows
            });
            var dv = loader.Load(trainingFile.FullName);
            return dv;
        }

var dataView = DataViewUtils.GetDataViewAsVector(mlContext, trainingFile, null);
dataset = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.1);

var start = DateTime.Now;
(long? training, long? validating) = ((dataset.TrainSet.GetColumn<string>(Label).LongCount(), dataset.TestSet.GetColumn<string>(Label).LongCount()));

this.OnUpdate?.Invoke($"Start training LightGBM on {training.Value:N0} rows of data");
this.OnUpdate?.Invoke($"Testing LightGBM wil be on {validating.Value:N0} rows of data");        

var options =new LightGbmMulticlassTrainer.Options {
                    LabelColumnName   = KeyColumn,
                    FeatureColumnName = Features,
                    Silent= false,
                    Verbose=true,
                    NumberOfThreads=8
                };


var pipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: KeyColumn, inputColumnName: Label)
                // .Append(mlContext.Transforms.Concatenate(outputColumnName: Features, inputColumnNames: Mapper.GetFieldNames()))
                .Append(mlContext.MulticlassClassification.Trainers.LightGbm(options))                          
                .Append(mlContext.Transforms.CopyColumns(inputColumnName: KeyColumn, outputColumnName: nameof(PredictedResult.PredictedLabelIndex)))                         
                ;
           
// Train the model.
var model = pipeline.Fit(dataset.TrainSet);