DEEP-13, DEEP-39 AI Model completely reimplemented. Dashboard UI implemented. Namespace style changed

DeepTrace/Controllers/DownloadController.cs

@@ -2,8 +2,8 @@
 using Microsoft.AspNetCore.Mvc;
 using System.Text;
 
-namespace DeepTrace.Controllers
+namespace DeepTrace.Controllers;
-{
+
 [ApiController]
 [Route("api/[controller]")]
 public class DownloadController : Controller
@@ -28,4 +28,3 @@ namespace DeepTrace.Controllers
         };
     }
 }
-}

DeepTrace/Controls/ModelCard.razor

@@ -7,10 +7,16 @@
 @inject IDialogService DialogService
 @inject IModelStorageService ModelService
 @inject ITrainedModelStorageService TrainedModelService
-@inject ILogger<MLProcessor> MLProcessorLogger
 @inject ILogger<ModelCard> Logger
+@inject IMLProcessorFactory MlProcessorFactory
 
-<MudCard Class="mb-3">
+<style>
+    .card {
+        max-width: 250pt;
+    }
+</style>
+
+<MudCard Class="card mb-3">
     <MudCardHeader>
         <CardHeaderContent>
             <MudText Typo="Typo.h6">@Model?.Name</MudText>
@@ -21,6 +27,7 @@
     </MudCardHeader>
     <MudCardContent>    
         <MudText>Current state: @_prediction.PredictedLabel</MudText>
+        <MudText>@_updated.ToString("HH:mm:ss")</MudText>
     </MudCardContent>
 </MudCard>
 
@@ -30,6 +37,8 @@
 
     private ModelDefinition _modelDefinition = new();
     private Prediction _prediction           = new();
+    private IMLProcessor? _mlProcessor;
+    private DateTime _updated                = DateTime.MinValue;
 
     protected override async Task OnAfterRenderAsync(bool firstRender)
     {
@@ -38,6 +47,7 @@
             return;
         }
         _modelDefinition = (await ModelService.Load(Model.Id)) ?? _modelDefinition;
+        _mlProcessor = MlProcessorFactory.Create();
 
         #pragma warning disable CS4014
         Task.Run(PredictionLoop);
@@ -87,15 +97,20 @@
                 await PredictAnomaly(startDate, endDate);
                 startDate = endDate;
             }
-            catch(Exception)
+            catch(Exception e)
             {
-                //ignore
+                Logger.LogError(e, e.Message);
             }
         }
     }
 
     private async Task PredictAnomaly(DateTime startDate, DateTime endDate)
     {
+        if (Model == null || !Model.IsEnabled)
+        {
+            _prediction = new Prediction { PredictedLabel = "Idle" };
+            return;
+        }
 
         // use automatic step value to always request 500 elements
         var seconds = (endDate - startDate).TotalSeconds / 500.0;
@@ -150,8 +165,9 @@
             );
         }
 
-        var mlProcessor = new MLProcessor(MLProcessorLogger);
+        _prediction = await _mlProcessor!.Predict(Model, _modelDefinition, data);
-        _prediction = await mlProcessor.Predict(Model, _modelDefinition, data);
+        _updated = DateTime.Now;
+        await InvokeAsync(StateHasChanged);
     }
 
     private async Task ShowError(string text)

DeepTrace/Controls/TrainingDialog.razor

@@ -14,12 +14,12 @@
         <h4>@Text</h4>
         
         <MudTextField T="string" ReadOnly="true" Text="@_progressText"></MudTextField>
-        @if (_isTraining == false)
+        @if (_isTraining == false && _evaluationMetrics != null)
     {
-            <MudText>MicroAccuracy: @_evaluationMetrics!.MicroAccuracy.ToString("N2")</MudText>
+        <MudText>MicroAccuracy:    @_evaluationMetrics.MicroAccuracy.ToString("N6")</MudText>
-            <MudText>MacroAccuracy: @_evaluationMetrics!.MacroAccuracy.ToString("N2")</MudText>
+        <MudText>MacroAccuracy:    @_evaluationMetrics.MacroAccuracy.ToString("N6")</MudText>
-            <MudText>LogLoss: @_evaluationMetrics!.LogLoss.ToString("N2")</MudText>
+        <MudText>LogLoss:          @_evaluationMetrics.LogLoss.ToString("N6")</MudText>
-            <MudText>LogLossReduction: @_evaluationMetrics!.LogLossReduction.ToString("N2")</MudText>
+        <MudText>LogLossReduction: @_evaluationMetrics.LogLossReduction.ToString("N6")</MudText>
     }
 
         
@@ -47,10 +47,21 @@
         return;
     }
 
+    try
+    {
+
         _evaluationMetrics = await Processor.Train(Model, UpdateProgress);
+    }
+    catch (Exception e)
+    {
+        _progressText = "ERROR: " + e.Message;
+    }
+    finally
+    {
         _isTraining = false;
         await InvokeAsync(StateHasChanged);
     }
+}
 
 private async void UpdateProgress(string message)
 {

DeepTrace/Data/DataSourceDefinition.cs

@@ -29,4 +29,55 @@ public class DataSourceDefinition
     public string Description { get; set; } = string.Empty;
 
     public override string ToString() => Name;
+
+    public List<string> GetColumnNames()
+    {
+        var measureNames = new[] { "min", "max", "avg", "mean" };
+        var columnNames = new List<string>();
+        foreach (var item in Queries)
+        {
+            columnNames.AddRange(measureNames.Select(x => $"{item.Query}_{x}"));
+        }
+        return columnNames;
+    }
+
+    public static string ConvertToCsv(List<TimeSeriesDataSet> source)
+    {
+        var data = "";
+        for (var i = 0; i < source.Count; i++)
+        {
+
+            var queryData = source[i];
+            var min  = queryData.Data.Min(x => x.Value);
+            var max  = queryData.Data.Max(x => x.Value);
+            var avg  = queryData.Data.Average(x => x.Value);
+            var mean = queryData.Data.Sum(x => x.Value) / queryData.Data.Count;
+
+            data += min + "," + max + "," + avg + "," + mean + ",";
+
+        }
+
+        return data.TrimEnd(',');
+    }
+
+    public static float[] ToFeatures(List<TimeSeriesDataSet> source)
+    {
+        var data = new float[source.Count * 4];
+        for (var i = 0; i < source.Count; i++)
+        {
+
+            var queryData = source[i];
+            var min  = queryData.Data.Min(x => x.Value);
+            var max  = queryData.Data.Max(x => x.Value);
+            var avg  = queryData.Data.Average(x => x.Value);
+            var mean = queryData.Data.Sum(x => x.Value) / queryData.Data.Count;
+
+            data[i*4 + 0] = min;
+            data[i*4 + 1] = max;
+            data[i*4 + 2] = avg;
+            data[i*4 + 3] = mean;
+        }
+
+        return data;
+    }
 }

DeepTrace/Data/IntervalDefinition.cs

@@ -1,5 +1,5 @@
-namespace DeepTrace.Data
+namespace DeepTrace.Data;
-{
+
 public class IntervalDefinition
 {
     public IntervalDefinition() { }
@@ -19,4 +19,3 @@
     public List<TimeSeriesDataSet> Data { get; set; } = new();
 
 }
-}

DeepTrace/Data/MLModel1.consumption.cs

@@ -5,8 +5,10 @@ using System;
 using System.Linq;
 using System.IO;
 using System.Collections.Generic;
-namespace DeepTrace
+namespace DeepTrace;
-{
+
+#pragma warning disable CS8618 // Non-nullable field must contain a non-null value when exiting constructor. Consider declaring as nullable.
+
 public partial class MLModel1
 {
     /// <summary>
@@ -164,6 +166,8 @@ namespace DeepTrace
 
     #endregion
 
+#pragma warning restore CS8618 // Non-nullable field must contain a non-null value when exiting constructor. Consider declaring as nullable.
+
     private static string MLNetModelPath = Path.GetFullPath("MLModel1.zip");
 
     public static readonly Lazy<PredictionEngine<ModelInput, ModelOutput>> PredictEngine = new Lazy<PredictionEngine<ModelInput, ModelOutput>>(() => CreatePredictEngine(), true);
@@ -186,4 +190,3 @@ namespace DeepTrace
         return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
     }
 }
-}

DeepTrace/Data/MLModel1.training.cs

@@ -9,8 +9,8 @@ using Microsoft.ML.Trainers.FastTree;
 using Microsoft.ML.Trainers;
 using Microsoft.ML;
 
-namespace DeepTrace
+namespace DeepTrace;
-{
+
 public partial class MLModel1
 {
     /// <summary>
@@ -61,4 +61,3 @@ namespace DeepTrace
         return pipeline;
     }
 }
-}

DeepTrace/Data/ModelDefinition.cs

@@ -2,6 +2,7 @@
 using MongoDB.Bson.Serialization.Attributes;
 using MongoDB.Bson;
 using System.Text;
+using DeepTrace.ML;
 
 namespace DeepTrace.Data;
 
@@ -21,17 +22,10 @@ public class ModelDefinition
     public string AIparameters                             { get; set; } = string.Empty;
     public List<IntervalDefinition> IntervalDefinitionList { get; set; } = new();
 
-    public List<string> GetColumnNames()
+    public List<string> GetColumnNames() => DataSource.GetColumnNames()
-    {
+        .Concat(new[] { "Name" })
-        var measureNames = new[] { "min", "max", "avg", "mean" };
+        .ToList()
-        var columnNames = new List<string>();
+        ;
-        foreach (var item in DataSource.Queries)
-        {
-            columnNames.AddRange(measureNames.Select(x => $"{item.Query}_{x}"));
-        }
-        columnNames.Add("Name");
-        return columnNames;
-    }
 
     public string ToCsv()
     {
@@ -45,30 +39,24 @@ public class ModelDefinition
         foreach (var currentInterval in IntervalDefinitionList)
         {
             var source = currentInterval.Data;
-            string data = ConvertToCsv(source);
+            string data = DataSourceDefinition.ConvertToCsv(source);
-            data += "," + currentInterval.Name;
+            data += $",\"{currentInterval.Name}\"";
             writer.AppendLine(data);
         }
 
         return writer.ToString();
     }
 
-    public static string ConvertToCsv(List<TimeSeriesDataSet> source)
+    public IEnumerable<MLInputData> ToInput()
     {
-        var data = "";
+        foreach (var currentInterval in IntervalDefinitionList)
-        for (var i = 0; i < source.Count; i++)
         {
-
+            var source = currentInterval.Data;
-            var queryData = source[i];
+            yield return new MLInputData
-            var min = queryData.Data.Min(x => x.Value);
+            {
-            var max = queryData.Data.Max(x => x.Value);
+                Features       = DataSourceDefinition.ToFeatures(source),
-            var avg = queryData.Data.Average(x => x.Value);
+                Label = currentInterval.Name
-            var mean = queryData.Data.Sum(x => x.Value) / queryData.Data.Count;
+            };
-
+        }
-            data += min + "," + max + "," + avg + "," + mean + ",";
-
-        }
-
-        return data+"\"ignoreMe\"";
     }
 }

DeepTrace/Data/Prediction.cs

@@ -4,9 +4,9 @@ namespace DeepTrace.Data;
 
 public class Prediction
 {
-    [ColumnName(@"PredictedLabel")]
+    [ColumnName("PredictedLabel")]
-    public string PredictedLabel { get; set; }
+    public string PredictedLabel { get; set; } = string.Empty;
 
-    [ColumnName(@"Score")]
+    [ColumnName("Score")]
-    public float[] Score { get; set; }
+    public float[] Score { get; set; } = Array.Empty<float>();
 }

DeepTrace/Data/TrainedModelDefinition.cs

@@ -1,14 +1,14 @@
 using MongoDB.Bson.Serialization.Attributes;
 using MongoDB.Bson;
 
-namespace DeepTrace.Data
+namespace DeepTrace.Data;
-{
+
 public class TrainedModelDefinition
 {
     [BsonId]
     public ObjectId? Id { get; set; }
     public bool IsEnabled { get; set; } = false;
     public string Name { get; set; } = string.Empty;
+    public DataSourceDefinition? DataSource{ get; set;}
     public byte[] Value { get; set; } = Array.Empty<byte>(); //base64
 }
-}

DeepTrace/ML/EstimatorBuilder.cs

@@ -1,47 +1,31 @@
 using DeepTrace.Data;
 using Microsoft.ML;
+using Microsoft.ML.Data;
 using Microsoft.ML.Trainers;
 
 
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
-{
+
 public class EstimatorBuilder : IEstimatorBuilder
 {
     public IEstimator<ITransformer> BuildPipeline(MLContext mlContext, ModelDefinition model)
     {
-            IEstimator<ITransformer>? pipeline = null;
+        return
-            var ds = model.DataSource;
+            mlContext.Transforms.NormalizeMinMax(inputColumnName: nameof(MLInputData.Features),outputColumnName: "Features")
-
+            .Append(mlContext.Transforms.Conversion.MapValueToKey(inputColumnName: nameof(MLInputData.Label), outputColumnName: "Label"))
-            var measureNames = new[] { "min", "max", "avg", "mean" };
+//            .AppendCacheCheckpoint(mlContext)
-            var columnNames = new List<string>();
+            .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(
-            foreach (var item in ds.Queries)
+                binaryEstimator: mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
+                    new LbfgsLogisticRegressionBinaryTrainer.Options
                     {
-                var estimators = measureNames.Select(x => mlContext.Transforms.Text.FeaturizeText(inputColumnName: $"{item.Query}_{x}", outputColumnName: $"{item.Query}_{x}"));
+                        L1Regularization  = 1F,
-                columnNames.AddRange(measureNames.Select(x => $"{item.Query}_{x}"));
+                        L2Regularization  = 1F,
-
+                        LabelColumnName   = "Label",
-                foreach (var e in estimators)
+                        FeatureColumnName = "Features"
-                {
-                    if (pipeline == null)
-                    {
-                        pipeline = e;
                     }
-                    else
+                    ))
-                    {
+            )
-                        pipeline = pipeline.Append(e);
+            .Append(mlContext.Transforms.Conversion.MapKeyToValue(nameof(MLOutputData.PredictedLabel), inputColumnName: "PredictedLabel"));
-                    }
-                }
-
-            }
-
-            pipeline = pipeline!
-                .Append(mlContext.Transforms.Concatenate(@"Features", columnNames.ToArray()))
-                .Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: @"Name", inputColumnName: @"Name"))
-                .Append(mlContext.Transforms.NormalizeMinMax(@"Features", @"Features"))
-                .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator: mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(new LbfgsLogisticRegressionBinaryTrainer.Options() { L1Regularization = 1F, L2Regularization = 1F, LabelColumnName = @"Name", FeatureColumnName = @"Features" }), labelColumnName: @"Name"))
-                .Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: @"PredictedLabel", inputColumnName: @"PredictedLabel"));
-
-            return pipeline;
 
     }
 }
-}

DeepTrace/ML/IEstimatorBuilder.cs

@@ -1,10 +1,9 @@
 using DeepTrace.Data;
 using Microsoft.ML;
 
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
-{
+
 public interface IEstimatorBuilder
 {
     IEstimator<ITransformer> BuildPipeline(MLContext mlContext, ModelDefinition model);
 }
-}

DeepTrace/ML/IMLProcessor.cs

@@ -10,3 +10,8 @@ public interface IMLProcessor
     void Import(byte[] data);
     Task<Prediction> Predict(TrainedModelDefinition trainedModel, ModelDefinition model, List<TimeSeriesDataSet> data);
 }
+
+public interface IMLProcessorFactory
+{
+    IMLProcessor Create();
+}

DeepTrace/ML/IMeasure.cs

@@ -1,11 +1,10 @@
 using PrometheusAPI;
 
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
-{
+
 public interface IMeasure
 {
     public string Name { get; }
     void Reset();
     float Calculate(IEnumerable<TimeSeries> data);
 }
-}

DeepTrace/ML/MLEvaluationMetrics.cs

@@ -1,5 +1,5 @@
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
-{
+
 public class MLEvaluationMetrics
 {
     public MLEvaluationMetrics() 
@@ -13,4 +13,3 @@
     public double LogLossReduction { get; set; }
 
 }
-}

DeepTrace/ML/MLHelpers.cs

@@ -7,6 +7,21 @@ namespace DeepTrace.ML;
 
 public record ModelRecord(MLContext Context, DataViewSchema Schema, ITransformer Transformer);
 
+public class MLInputData
+{
+    public string Label { get; set; } = "Normal operation";
+    public float[] Features { get; set; } = Array.Empty<float>();
+
+}
+
+public class MLOutputData
+{
+    public string PredictedLabel { get; set; } = string.Empty;
+
+    public float[] Score { get; set; } = Array.Empty<float>();
+}
+
+
 public static class MLHelpers
 {
     public static byte[] ExportSingleModel( ModelRecord model)
@@ -32,10 +47,22 @@ public static class MLHelpers
 
         await File.WriteAllTextAsync(fileName, csv);
 
-        return LoadFromCsv(mlContext, model, fileName);
+        return (LoadFromCsv(mlContext, model, fileName), fileName);
     }
 
-    public static (IDataView View, string FileName) LoadFromCsv(MLContext mlContext, ModelDefinition model, string fileName)
+    public static Task<IDataView> ToInput(MLContext mlContext, ModelDefinition model)
+    {
+        var input = model.ToInput().ToList();
+
+        // VectorType attribute with dynamic dimension
+        // https://github.com/dotnet/machinelearning/issues/164
+        var schemaDef = SchemaDefinition.Create(typeof(MLInputData));
+        schemaDef["Features"].ColumnType = new VectorDataViewType(NumberDataViewType.Single, input.First().Features.Length );
+
+        return Task.FromResult(mlContext.Data.LoadFromEnumerable(input, schemaDef));
+    }
+
+    public static IDataView LoadFromCsv(MLContext mlContext, ModelDefinition model, string fileName)
     {
         var columnNames = model.GetColumnNames();
         var columns = columnNames
@@ -43,8 +70,14 @@ public static class MLHelpers
             .ToArray()
             ;
 
-        var view = mlContext.Data.LoadFromTextFile(fileName, columns, separatorChar: ',', hasHeader: true, allowQuoting: true, trimWhitespace: true);
+        var view = mlContext.Data.LoadFromTextFile(
+            fileName,
+            columns,
+            separatorChar: ',',
+            hasHeader: true,
+            allowQuoting: true,
+            trimWhitespace: true);
 
-        return (view, fileName);
+        return view;
     }
 }

DeepTrace/ML/MLProcessor.cs

@@ -1,32 +1,51 @@
 using DeepTrace.Data;
 using Microsoft.ML;
-using Microsoft.ML.Data;
-using PrometheusAPI;
 using System.Data;
-using static DeepTrace.MLModel1;
 
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
+
+internal class MLProcessorFactory : IMLProcessorFactory
 {
+    private readonly ILogger<MLProcessor> _logger;
+    private IEstimatorBuilder _estimatorBuilder;
+
+    public MLProcessorFactory(ILogger<MLProcessor> logger, IEstimatorBuilder estimatorBuilder)
+    {
+        _logger = logger;
+        _estimatorBuilder = estimatorBuilder;
+    }
+
+    public IMLProcessor Create() => new MLProcessor(_logger, _estimatorBuilder);
+}
+
+/// <summary>
+/// Wrapper for ML.NET operations.
+/// </summary>
 public class MLProcessor : IMLProcessor
 {
+    private readonly ILogger<MLProcessor> _logger;
     private MLContext                     _mlContext        = new MLContext();
-        private EstimatorBuilder _estimatorBuilder = new EstimatorBuilder();
+    private IEstimatorBuilder             _estimatorBuilder;
     private DataViewSchema?               _schema;
     private ITransformer?                 _transformer;
     private static string                 _signature        = "DeepTrace-Model-v1-" + typeof(MLProcessor).Name;
-        private readonly ILogger<MLProcessor> _logger;
+    private PredictionEngine<MLInputData, MLOutputData>? _predictionEngine;
 
-        public MLProcessor(ILogger<MLProcessor> logger)
+    public MLProcessor(ILogger<MLProcessor> logger, IEstimatorBuilder estimatorBuilder)
     {
         _logger           = logger;
+        _estimatorBuilder = estimatorBuilder;
     }
 
     private string Name { get; set; } = "TestModel";
 
     public async Task<MLEvaluationMetrics> Train(ModelDefinition modelDef, Action<string> log)
     {
+        _logger.LogInformation("Training started");
+
+        Name = modelDef.Name;
         var pipeline = _estimatorBuilder.BuildPipeline(_mlContext, modelDef);
-            var (data, filename) = await MLHelpers.Convert(_mlContext, modelDef);
+        var data = await MLHelpers.ToInput(_mlContext, modelDef);
 
         DataOperationsCatalog.TrainTestData dataSplit = _mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
 
@@ -35,13 +54,13 @@ namespace DeepTrace.ML
         {
             _schema      = data.Schema;
             _transformer = pipeline.Fit(dataSplit.TrainSet);
+
             return Evaluate(dataSplit.TestSet);
         } 
         finally
         {
-                File.Delete(filename);
+            _logger.LogInformation("Training finished");
         }
-           
     }
 
     private void LogEvents(Action<string> log, LoggingEventArgs e)
@@ -56,8 +75,10 @@ namespace DeepTrace.ML
 
     private MLEvaluationMetrics Evaluate(IDataView testData)
     {
+        // https://learn.microsoft.com/en-us/dotnet/api/microsoft.ml.standardtrainerscatalog.lbfgslogisticregression?view=ml-dotnet
+
         var predictions       = _transformer!.Transform(testData);
-            var metrics = _mlContext.MulticlassClassification.Evaluate(predictions, "Name");
+        var metrics           = _mlContext.MulticlassClassification.Evaluate(predictions, nameof(MLInputData.Label));
         var evaluationMetrics = new MLEvaluationMetrics()
         {
             MicroAccuracy    = metrics.MicroAccuracy,
@@ -110,28 +131,25 @@ namespace DeepTrace.ML
         (_mlContext, _schema, _transformer) = MLHelpers.ImportSingleModel(bytes);
     }
 
-        public async Task<Prediction> Predict(TrainedModelDefinition trainedModel, ModelDefinition model, List<TimeSeriesDataSet> data)
+    public Task<Prediction> Predict(TrainedModelDefinition trainedModel, ModelDefinition model, List<TimeSeriesDataSet> data)
     {
+        Name = trainedModel.Name;
+
+        if (_transformer == null )
             Import(trainedModel.Value);
-            var headers = string.Join(",", model.GetColumnNames().Select(x => $"\"{x}\""));
-            var row = ModelDefinition.ConvertToCsv(data);
 
-            var csv = headers+"\n"+row;
+        if (_predictionEngine == null)
-            var fileName = Path.GetTempFileName();
-            try
         {
-                await File.WriteAllTextAsync(fileName, csv);
+            _predictionEngine = _mlContext.Model.CreatePredictionEngine<MLInputData, MLOutputData>(_transformer, _schema);
-
-                var (dataView, _) = MLHelpers.LoadFromCsv(_mlContext, model, fileName);
-
-                var predictionEngine = _mlContext.Model.CreatePredictionEngine<IDataView, Prediction>(_transformer);
-                var prediction = predictionEngine.Predict(dataView);
-                return prediction;
         }
-            finally
+
+        var input = new MLInputData 
         {
-                File.Delete(fileName);
+            Features = DataSourceDefinition.ToFeatures(data)
-            }
+        };
-        }
+        
+        var prediction = _predictionEngine.Predict( input );
+        
+        return Task.FromResult( new Prediction { PredictedLabel = prediction.PredictedLabel, Score = prediction.Score } );
     }
 }

DeepTrace/ML/Measures.cs

@@ -1,7 +1,7 @@
 using PrometheusAPI;
 
-namespace DeepTrace.ML
+namespace DeepTrace.ML;
-{
+
 public class MeasureMin : IMeasure
 {
     public string Name => "Min";
@@ -85,5 +85,3 @@ namespace DeepTrace.ML
 /// </summary>
 public class MeasureDiffSum : MeasureDiff<MeasureSum> { }
 public class MeasureDiffMedian : MeasureDiff<MeasureMedian> { }
-
-}

DeepTrace/Pages/DataSources.razor

@@ -58,8 +58,8 @@
                             int pos = i;
 
                             <MudItem xs="10">
-                                @*<MudTextField Label="Query" @bind-Value="_queryForm.Source.Queries[pos].Query" Variant="Variant.Text" InputType="InputType.Search" Lines="2" />*@
+                                <MudTextField Label="Query" @bind-Value="_queryForm.Source.Queries[pos].Query" Variant="Variant.Text" InputType="InputType.Search" Lines="2" />
-                                <MudAutocomplete Label="Query" @bind-Value="_queryForm.Source.Queries[pos].Query" Lines="1" Variant="Variant.Text" SearchFunc="@SearchForQuery"></MudAutocomplete>
+                                @*<MudAutocomplete Label="Query" @bind-Value="_queryForm.Source.Queries[pos].Query" Lines="1" Variant="Variant.Text" SearchFunc="@SearchForQuery"></MudAutocomplete>*@
                             </MudItem>
                             <MudItem xs="1">
                                 <MudIconButton Icon="@Icons.Material.Outlined.Add" Variant="Variant.Outlined" aria-label="add" OnClick="@(() => AddQuery(pos))" />

DeepTrace/Pages/Error.cshtml.cs

@@ -2,8 +2,8 @@
 using Microsoft.AspNetCore.Mvc.RazorPages;
 using System.Diagnostics;
 
-namespace DeepTrace.Pages
+namespace DeepTrace.Pages;
-{
+
 [ResponseCache(Duration = 0, Location = ResponseCacheLocation.None, NoStore = true)]
 [IgnoreAntiforgeryToken]
 public class ErrorModel : PageModel
@@ -24,4 +24,3 @@ namespace DeepTrace.Pages
         RequestId = Activity.Current?.Id ?? HttpContext.TraceIdentifier;
     }
 }
-}

DeepTrace/Pages/Training.razor

@@ -18,7 +18,7 @@
 @inject IEstimatorBuilder EstimatorBuilder
 @inject NavigationManager NavManager
 @inject IJSRuntime Js
-@inject ILogger<MLProcessor> MLProcessorLogger
+@inject IMLProcessorFactory MlProcessorFactory
 
 
 <PageTitle>Training</PageTitle>
@@ -531,14 +531,14 @@
 
     private async Task HandleTrain()
     {
-        var mlProcessor = new MLProcessor(MLProcessorLogger);
-        MLProcessorLogger.LogInformation("Training started");
-        
         var options = new DialogOptions
             {
                 CloseOnEscapeKey = true
             };
         var parameters = new DialogParameters();
+        
+        var mlProcessor = MlProcessorFactory.Create();
+
         parameters.Add(nameof(Controls.TrainingDialog.Text), _modelForm!.CurrentModel.Name);
         parameters.Add(nameof(Controls.TrainingDialog.Processor), mlProcessor);
         parameters.Add(nameof(Controls.TrainingDialog.Model), _modelForm.CurrentModel);
@@ -546,7 +546,6 @@
         var d = DialogService.Show<Controls.TrainingDialog>("Training", parameters, options);
         var res = await d.Result;
 
-        MLProcessorLogger.LogInformation("Training finished");
         var bytes = mlProcessor.Export();
         
         //save to Mongo

DeepTrace/Program.cs

@@ -29,6 +29,7 @@ builder.Services
     .AddSingleton<IModelStorageService, ModelStorageService>()
     .AddSingleton<ITrainedModelStorageService, TrainedModelStorageService>()
     .AddSingleton<IEstimatorBuilder, EstimatorBuilder>()
+    .AddSingleton<IMLProcessorFactory, MLProcessorFactory>()
     ;
 
 var app = builder.Build();

DeepTrace/Services/DataSourceStorageService.cs

@@ -1,8 +1,8 @@
 using MongoDB.Bson;
 using MongoDB.Driver;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 public class DataSourceStorageService : IDataSourceStorageService
 {
 
@@ -55,4 +55,3 @@ namespace DeepTrace.Services
         await collection.DeleteOneAsync($"_id = {source.Id}");
     }
 }
-}

DeepTrace/Services/IDataSourceStorageService.cs

@@ -2,8 +2,8 @@
 using MongoDB.Bson.Serialization.Attributes;
 using MongoDB.Bson;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 public class DataSourceStorage : DataSourceDefinition, IEquatable<DataSourceStorage>
 {
     [BsonId]
@@ -35,4 +35,3 @@ namespace DeepTrace.Services
     Task<List<DataSourceStorage>> Load();
     Task Store(DataSourceStorage source);
 }
-}

DeepTrace/Services/IModelStorageService.cs

@@ -3,8 +3,8 @@ using MongoDB.Bson;
 using DeepTrace.Data;
 using System.Text;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 
 public interface IModelStorageService
 {
@@ -13,4 +13,3 @@ namespace DeepTrace.Services
     Task<ModelDefinition?> Load(BsonObjectId id);
     Task Store(ModelDefinition source);
 }
-}

DeepTrace/Services/ITrainedModelStorageService.cs

@@ -1,11 +1,10 @@
 using DeepTrace.Data;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 public interface ITrainedModelStorageService
 {
     Task Delete(TrainedModelDefinition source, bool ignoreNotStored = false);
     Task<List<TrainedModelDefinition>> Load();
     Task Store(TrainedModelDefinition source);
 }
-}

DeepTrace/Services/ModelStorageService.cs

@@ -2,8 +2,8 @@
 using MongoDB.Bson;
 using MongoDB.Driver;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 public class ModelStorageService : IModelStorageService
 {
 
@@ -30,8 +30,9 @@ namespace DeepTrace.Services
     {
         var db = _client.GetDatabase(MongoDBDatabaseName);
         var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
-            var res = (await (await collection.FindAsync($"{{_id:ObjectId(\"{id}\")}}")).ToListAsync()).FirstOrDefault();
+
-            return res;
+        var res = await (await collection.FindAsync($"{{ _id : ObjectId(\"{id}\") }}")).ToListAsync();
+        return res.FirstOrDefault();
     }
 
     public async Task Store(ModelDefinition source)
@@ -65,4 +66,3 @@ namespace DeepTrace.Services
         await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
     }
 }
-}

DeepTrace/Services/TrainedModelStorageService.cs

@@ -2,8 +2,8 @@
 using MongoDB.Bson;
 using MongoDB.Driver;
 
-namespace DeepTrace.Services
+namespace DeepTrace.Services;
-{
+
 public class TrainedModelStorageService: ITrainedModelStorageService
 {
     private const string MongoDBDatabaseName = "DeepTrace";
@@ -55,4 +55,3 @@ namespace DeepTrace.Services
         await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
     }
 }
-}

PrometheusAPI/JsonSetializerSetup.cs

@@ -6,8 +6,8 @@ using System.Text.Json.Serialization;
 using System.Text.Json;
 using System.Threading.Tasks;
 
-namespace PrometheusAPI
+namespace PrometheusAPI;
-{
+
 public static class JsonSetializerSetup
 {
     private static JsonSerializerOptions _options = new JsonSerializerOptions
@@ -22,4 +22,3 @@ namespace PrometheusAPI
 
     public static JsonSerializerOptions Options => _options;
 }
-}

PrometheusAPI/PrometheusClient.cs

@@ -1,7 +1,7 @@
 using System.Text.Json;
 
-namespace PrometheusAPI
+namespace PrometheusAPI;
-{
+
 public class PrometheusClient
 {
     private readonly HttpClient _client;
@@ -116,4 +116,3 @@ namespace PrometheusAPI
     }
 
 }
-}

PrometheusAPI/TimeSeriesCoverter.cs

@@ -1,8 +1,8 @@
 using System.Text.Json;
 using System.Text.Json.Serialization;
 
-namespace PrometheusAPI
+namespace PrometheusAPI;
-{
+
 internal class TimeSeriesCoverter : JsonConverter<TimeSeries?>
 {
     public override TimeSeries? Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
@@ -49,4 +49,3 @@ namespace PrometheusAPI
 
     }
 }
-}