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

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This commit is contained in:
Andrey Shabarshov 2023-07-30 16:20:41 +01:00
parent af6c75a5ac
commit bfb3de9331
36 changed files with 1180 additions and 1088 deletions
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41
DeepTrace/Controllers/DownloadController.cs
View File

@ -2,30 +2,29 @@
using Microsoft.AspNetCore.Mvc;
using System.Text;
namespace DeepTrace.Controllers
namespace DeepTrace.Controllers;
[ApiController]
[Route("api/[controller]")]
public class DownloadController : Controller
{
[ApiController]
[Route("api/[controller]")]
public class DownloadController : Controller
private readonly IModelStorageService _modelService;
public DownloadController(IModelStorageService modelService)
{
private readonly IModelStorageService _modelService;
_modelService = modelService;
}
public DownloadController(IModelStorageService modelService)
[HttpGet("mldata/{modelName}")]
public async Task<FileContentResult> GetMLDataCsv([FromRoute] string modelName)
{
var ModelDefinition = await _modelService.Load();
var model = ModelDefinition.FirstOrDefault(x=>x.Name==modelName) ?? throw new ApplicationException($"Model {modelName} not found");
var csv = model.ToCsv();
return new(Encoding.UTF8.GetBytes(csv),"text/csv")
{
_modelService = modelService;
}
[HttpGet("mldata/{modelName}")]
public async Task<FileContentResult> GetMLDataCsv([FromRoute] string modelName)
{
var ModelDefinition = await _modelService.Load();
var model = ModelDefinition.FirstOrDefault(x=>x.Name==modelName) ?? throw new ApplicationException($"Model {modelName} not found");
var csv = model.ToCsv();
return new(Encoding.UTF8.GetBytes(csv),"text/csv")
{
FileDownloadName = modelName+".csv"
};
}
FileDownloadName = modelName+".csv"
};
}
}

36
DeepTrace/Controls/Dialog.razor
View File

@ -4,31 +4,31 @@
</DialogContent>
<DialogActions>
@if( IsYesNoCancel )
{
<MudButton Color="MudBlazor.Color.Primary" OnClick="Yes">Yes</MudButton>
<MudButton OnClick="No">No</MudButton>
<MudButton OnClick="Cancel">Cancel</MudButton>
}
else
{
if (AllowCancel)
{
<MudButton Color="MudBlazor.Color.Primary" OnClick="Yes">Yes</MudButton>
<MudButton OnClick="No">No</MudButton>
<MudButton OnClick="Cancel">Cancel</MudButton>
}
else
{
if (AllowCancel)
{
<MudButton OnClick="Cancel">Cancel</MudButton>
}
<MudButton Color="MudBlazor.Color.Primary" OnClick="Submit">Ok</MudButton>
}
<MudButton Color="MudBlazor.Color.Primary" OnClick="Submit">Ok</MudButton>
}
</DialogActions>
</MudDialog>
@code {
[CascadingParameter] MudDialogInstance? MudDialog { get; set; }
[Parameter] public bool AllowCancel { get; set; }
[Parameter] public string Text { get; set; } = "";
[Parameter] public bool IsYesNoCancel { get; set; } = false;
[CascadingParameter] MudDialogInstance? MudDialog { get; set; }
[Parameter] public bool AllowCancel { get; set; }
[Parameter] public string Text { get; set; } = "";
[Parameter] public bool IsYesNoCancel { get; set; } = false;
void Submit() => MudDialog?.Close(DialogResult.Ok(true));
void Submit() => MudDialog?.Close(DialogResult.Ok(true));
void Cancel() => MudDialog?.Cancel();
void Cancel() => MudDialog?.Cancel();
void Yes() => MudDialog?.Close(DialogResult.Ok(true));
void No() => MudDialog?.Close(DialogResult.Ok(false));
void Yes() => MudDialog?.Close(DialogResult.Ok(true));
void No() => MudDialog?.Close(DialogResult.Ok(false));
}

30
DeepTrace/Controls/ModelCard.razor
View File

@ -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>
@ -29,7 +36,9 @@
public TrainedModelDefinition? Model { get; set; }
private ModelDefinition _modelDefinition = new();
private Prediction _prediction = 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)

294
DeepTrace/Controls/TimeSeriesChart.razor
View File

@ -9,8 +9,8 @@
OnZoomed="OnZoomed"
>
@foreach (var ts in _currentData.Series)
{
<ApexPointSeries TItem="TimeSeries"
{
<ApexPointSeries TItem="TimeSeries"
Name="@ts.Name"
Items="@ts.Data"
SeriesType="SeriesType.Line"
@ -18,164 +18,164 @@
YAggregate="@(e => (decimal)e.Sum(e => e.Value))"
ShowDataLabels="false"
/>
}
}
</ApexChart>
@code {
[CascadingParameter]
protected bool IsDarkMode { get; set; }
[CascadingParameter]
protected bool IsDarkMode { get; set; }
[Parameter] public TimeSeriesData? Data { get; set; }
[Parameter] public TimeSeriesData? Data { get; set; }
[Parameter] public DateTime? MinDate { get; set; }
[Parameter] public DateTime? MaxDate { get; set; }
[Parameter] public EventCallback<DateTime?> MinDateChanged { get; set; }
[Parameter] public EventCallback<DateTime?> MaxDateChanged { get; set; }
[Parameter] public DateTime? MinDate { get; set; }
[Parameter] public DateTime? MaxDate { get; set; }
[Parameter] public EventCallback<DateTime?> MinDateChanged { get; set; }
[Parameter] public EventCallback<DateTime?> MaxDateChanged { get; set; }
private ApexChart<TimeSeries>? _chart;
private ApexChartOptions<TimeSeries>? _options;
private TimeSeriesData _currentData = new() { Series = { new () } };
private ApexChart<TimeSeries>? _chart;
private ApexChartOptions<TimeSeries>? _options;
private TimeSeriesData _currentData = new() { Series = { new () } };
protected override void OnInitialized()
{
_options = CreateOptions();
base.OnInitialized();
}
protected override void OnInitialized()
{
_options = CreateOptions();
base.OnInitialized();
}
protected override async Task OnParametersSetAsync()
{
Console.WriteLine("OnParametersSet");
protected override async Task OnParametersSetAsync()
{
Console.WriteLine("OnParametersSet");
await UpdateChart();
await base.OnParametersSetAsync();
}
await UpdateChart();
await base.OnParametersSetAsync();
}
private async Task UpdateChart()
{
if (Data == _currentData)
return;
private async Task UpdateChart()
{
if (Data == _currentData)
return;
_currentData = Data ?? new() { Series = { new() } }; ;
_options = CreateOptions();
_currentData = Data ?? new() { Series = { new() } }; ;
_options = CreateOptions();
if (_chart == null)
return;
if (_chart == null)
return;
//await InvokeAsync(StateHasChanged);
await _chart!.UpdateSeriesAsync();
await _chart!.UpdateOptionsAsync(true, true, true);
await InvokeAsync(StateHasChanged);
}
private ApexChartOptions<TimeSeries> CreateOptions()
{
var backgroundColor = IsDarkMode ? "var(--mud-palette-surface)" : "#f3f3f3";
var gridColor = IsDarkMode ? "var(--mud-palette-drawer-background)" : "#f3f3f3";
var borderColor = IsDarkMode ? "var(--mud-palette-text-primary)" : "#e7e7e7";
var lineColors = _currentData.Series.Select( x => x.Color).ToList();
var mode = IsDarkMode
? Mode.Dark
: Mode.Light
;
var options = new ApexChartOptions<TimeSeries>
{
Chart = new()
{
Background = backgroundColor,
Toolbar = new()
{
Show = true
},
DropShadow = new()
{
Enabled = false,
Color = "",
Top = 18,
Left = 7,
Blur = 10,
Opacity = 0.2d
}
},
DataLabels = new()
{
Enabled = false
},
Tooltip = new ApexCharts.Tooltip
{
Y = new ()
{
Formatter = @"function(value, opts) {
if (value === undefined) {return '';}
return Number(value).toLocaleString();}",
},
X = new ()
{
Formatter = @"function(value, opts) {
if (value === undefined) {return '';}
return (new Date(value)).toISOString();}",
}
},
Xaxis = new()
{
Type = XAxisType.Datetime
},
Grid = new()
{
BorderColor = borderColor,
Row = new()
{
Colors = new List<string> { gridColor, "transparent" },
Opacity = 0.5d
}
},
Colors = lineColors,
//Markers = new() { Shape = ShapeEnum.Circle, Size = 2, FillOpacity = new Opacity(0.8d) },
Stroke = new() { Curve = Curve.Straight, Width = 2 },
Legend = new()
{
Position = LegendPosition.Top,
HorizontalAlign = ApexCharts.Align.Right,
Floating = true,
OffsetX = -5,
OffsetY = -25
},
Theme = new()
{
Mode = mode,
Palette = PaletteType.Palette8,
}
};
return options;
}
private void OnZoomed(ZoomedData<TimeSeries> zoomedData)
{
if (zoomedData.XAxis?.Min == null && zoomedData.XAxis?.Max == null)
return;
DateTimeOffset xMin;
DateTimeOffset xMax;
xMin = zoomedData.XAxis?.Min == null
? _currentData!.Series.First().Data.Min(e => e.TimeStamp.Date)
: DateTimeOffset.FromUnixTimeMilliseconds((long)zoomedData.XAxis.Min)
;
xMax = zoomedData.XAxis?.Max == null
? _currentData!.Series.First().Data.Max(e => e.TimeStamp.Date)
: DateTimeOffset.FromUnixTimeMilliseconds((long)zoomedData.XAxis.Max)
;
MinDate = xMin.UtcDateTime;
MinDateChanged.InvokeAsync(MinDate);
MaxDate = xMax.UtcDateTime;
MaxDateChanged.InvokeAsync(MaxDate);
}
//await InvokeAsync(StateHasChanged);
await _chart!.UpdateSeriesAsync();
await _chart!.UpdateOptionsAsync(true, true, true);
await InvokeAsync(StateHasChanged);
}
private ApexChartOptions<TimeSeries> CreateOptions()
{
var backgroundColor = IsDarkMode ? "var(--mud-palette-surface)" : "#f3f3f3";
var gridColor = IsDarkMode ? "var(--mud-palette-drawer-background)" : "#f3f3f3";
var borderColor = IsDarkMode ? "var(--mud-palette-text-primary)" : "#e7e7e7";
var lineColors = _currentData.Series.Select( x => x.Color).ToList();
var mode = IsDarkMode
? Mode.Dark
: Mode.Light
;
var options = new ApexChartOptions<TimeSeries>
{
Chart = new()
{
Background = backgroundColor,
Toolbar = new()
{
Show = true
},
DropShadow = new()
{
Enabled = false,
Color = "",
Top = 18,
Left = 7,
Blur = 10,
Opacity = 0.2d
}
},
DataLabels = new()
{
Enabled = false
},
Tooltip = new ApexCharts.Tooltip
{
Y = new ()
{
Formatter = @"function(value, opts) {
if (value === undefined) {return '';}
return Number(value).toLocaleString();}",
},
X = new ()
{
Formatter = @"function(value, opts) {
if (value === undefined) {return '';}
return (new Date(value)).toISOString();}",
}
},
Xaxis = new()
{
Type = XAxisType.Datetime
},
Grid = new()
{
BorderColor = borderColor,
Row = new()
{
Colors = new List<string> { gridColor, "transparent" },
Opacity = 0.5d
}
},
Colors = lineColors,
//Markers = new() { Shape = ShapeEnum.Circle, Size = 2, FillOpacity = new Opacity(0.8d) },
Stroke = new() { Curve = Curve.Straight, Width = 2 },
Legend = new()
{
Position = LegendPosition.Top,
HorizontalAlign = ApexCharts.Align.Right,
Floating = true,
OffsetX = -5,
OffsetY = -25
},
Theme = new()
{
Mode = mode,
Palette = PaletteType.Palette8,
}
};
return options;
}
private void OnZoomed(ZoomedData<TimeSeries> zoomedData)
{
if (zoomedData.XAxis?.Min == null && zoomedData.XAxis?.Max == null)
return;
DateTimeOffset xMin;
DateTimeOffset xMax;
xMin = zoomedData.XAxis?.Min == null
? _currentData!.Series.First().Data.Min(e => e.TimeStamp.Date)
: DateTimeOffset.FromUnixTimeMilliseconds((long)zoomedData.XAxis.Min)
;
xMax = zoomedData.XAxis?.Max == null
? _currentData!.Series.First().Data.Max(e => e.TimeStamp.Date)
: DateTimeOffset.FromUnixTimeMilliseconds((long)zoomedData.XAxis.Max)
;
MinDate = xMin.UtcDateTime;
MinDateChanged.InvokeAsync(MinDate);
MaxDate = xMax.UtcDateTime;
MaxDateChanged.InvokeAsync(MaxDate);
}
}

63
DeepTrace/Controls/TrainingDialog.razor
View File

@ -14,13 +14,13 @@
<h4>@Text</h4>
<MudTextField T="string" ReadOnly="true" Text="@_progressText"></MudTextField>
@if (_isTraining == false)
{
<MudText>MicroAccuracy: @_evaluationMetrics!.MicroAccuracy.ToString("N2")</MudText>
<MudText>MacroAccuracy: @_evaluationMetrics!.MacroAccuracy.ToString("N2")</MudText>
<MudText>LogLoss: @_evaluationMetrics!.LogLoss.ToString("N2")</MudText>
<MudText>LogLossReduction: @_evaluationMetrics!.LogLossReduction.ToString("N2")</MudText>
}
@if (_isTraining == false && _evaluationMetrics != null)
{
<MudText>MicroAccuracy: @_evaluationMetrics.MicroAccuracy.ToString("N6")</MudText>
<MudText>MacroAccuracy: @_evaluationMetrics.MacroAccuracy.ToString("N6")</MudText>
<MudText>LogLoss: @_evaluationMetrics.LogLoss.ToString("N6")</MudText>
<MudText>LogLossReduction: @_evaluationMetrics.LogLossReduction.ToString("N6")</MudText>
}
</DialogContent>
@ -29,32 +29,43 @@
</DialogActions>
</MudDialog>
@code {
[CascadingParameter] MudDialogInstance? MudDialog { get; set; }
[Parameter] public MLProcessor? Processor { get; set; }
[Parameter] public ModelDefinition? Model { get; set; }
[Parameter] public string Text { get; set; } = "";
[CascadingParameter] MudDialogInstance? MudDialog { get; set; }
[Parameter] public MLProcessor? Processor { get; set; }
[Parameter] public ModelDefinition? Model { get; set; }
[Parameter] public string Text { get; set; } = "";
private string _progressText = "";
private bool _isTraining = true;
private MLEvaluationMetrics? _evaluationMetrics;
private string _progressText = "";
private bool _isTraining = true;
private MLEvaluationMetrics? _evaluationMetrics;
void Submit() => MudDialog?.Close(DialogResult.Ok(true));
void Submit() => MudDialog?.Close(DialogResult.Ok(true));
protected override async Task OnAfterRenderAsync(bool firstRender)
protected override async Task OnAfterRenderAsync(bool firstRender)
{
if (!firstRender || Processor==null || Model==null)
{
return;
}
try
{
if (!firstRender || Processor==null || Model==null)
{
return;
}
_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)
{
_progressText = message;
await InvokeAsync(StateHasChanged);
}
}
private async void UpdateProgress(string message)
{
_progressText = message;
await InvokeAsync(StateHasChanged);
}
}

51
DeepTrace/Data/DataSourceDefinition.cs
View File

@ -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;
}
}

35
DeepTrace/Data/IntervalDefinition.cs
View File

@ -1,22 +1,21 @@
namespace DeepTrace.Data
namespace DeepTrace.Data;
public class IntervalDefinition
{
public class IntervalDefinition
public IntervalDefinition() { }
public IntervalDefinition(DateTime from, DateTime to, string name)
{
public IntervalDefinition() { }
public IntervalDefinition(DateTime from, DateTime to, string name)
{
From = from;
To = to;
Name = name;
}
public DateTime From { get; set; } = DateTime.MinValue;
public DateTime To { get; set; } = DateTime.MaxValue;
public string Name { get; set; } = string.Empty;
public List<TimeSeriesDataSet> Data { get; set; } = new();
From = from;
To = to;
Name = name;
}
public DateTime From { get; set; } = DateTime.MinValue;
public DateTime To { get; set; } = DateTime.MaxValue;
public string Name { get; set; } = string.Empty;
public List<TimeSeriesDataSet> Data { get; set; } = new();
}

255
DeepTrace/Data/MLModel1.consumption.cs
View File

@ -5,185 +5,188 @@ 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
{
public partial class MLModel1
/// <summary>
/// model input class for MLModel1.
/// </summary>
#region model input class
public class ModelInput
{
/// <summary>
/// model input class for MLModel1.
/// </summary>
#region model input class
public class ModelInput
{
[ColumnName(@"Q1min")]
public string Q1min { get; set; }
[ColumnName(@"Q1min")]
public string Q1min { get; set; }
[ColumnName(@"Q1max")]
public string Q1max { get; set; }
[ColumnName(@"Q1max")]
public string Q1max { get; set; }
[ColumnName(@"Q1avg")]
public string Q1avg { get; set; }
[ColumnName(@"Q1avg")]
public string Q1avg { get; set; }
[ColumnName(@"Q1mean")]
public string Q1mean { get; set; }
[ColumnName(@"Q1mean")]
public string Q1mean { get; set; }
[ColumnName(@"Q2min")]
public string Q2min { get; set; }
[ColumnName(@"Q2min")]
public string Q2min { get; set; }
[ColumnName(@"Q2max")]
public string Q2max { get; set; }
[ColumnName(@"Q2max")]
public string Q2max { get; set; }
[ColumnName(@"Q2avg")]
public string Q2avg { get; set; }
[ColumnName(@"Q2avg")]
public string Q2avg { get; set; }
[ColumnName(@"Q2mean")]
public string Q2mean { get; set; }
[ColumnName(@"Q2mean")]
public string Q2mean { get; set; }
[ColumnName(@"Q3min")]
public string Q3min { get; set; }
[ColumnName(@"Q3min")]
public string Q3min { get; set; }
[ColumnName(@"Q3max")]
public string Q3max { get; set; }
[ColumnName(@"Q3max")]
public string Q3max { get; set; }
[ColumnName(@"Q3avg")]
public string Q3avg { get; set; }
[ColumnName(@"Q3avg")]
public string Q3avg { get; set; }
[ColumnName(@"Q3mean")]
public string Q3mean { get; set; }
[ColumnName(@"Q3mean")]
public string Q3mean { get; set; }
[ColumnName(@"Q4min")]
public string Q4min { get; set; }
[ColumnName(@"Q4min")]
public string Q4min { get; set; }
[ColumnName(@"Q4max")]
public string Q4max { get; set; }
[ColumnName(@"Q4max")]
public string Q4max { get; set; }
[ColumnName(@"Q4avg")]
public string Q4avg { get; set; }
[ColumnName(@"Q4avg")]
public string Q4avg { get; set; }
[ColumnName(@"Q4mean")]
public string Q4mean { get; set; }
[ColumnName(@"Q4mean")]
public string Q4mean { get; set; }
[ColumnName(@"Q5min")]
public string Q5min { get; set; }
[ColumnName(@"Q5min")]
public string Q5min { get; set; }
[ColumnName(@"Q5max")]
public string Q5max { get; set; }
[ColumnName(@"Q5max")]
public string Q5max { get; set; }
[ColumnName(@"Q5avg")]
public string Q5avg { get; set; }
[ColumnName(@"Q5avg")]
public string Q5avg { get; set; }
[ColumnName(@"Q5mean")]
public string Q5mean { get; set; }
[ColumnName(@"Q5mean")]
public string Q5mean { get; set; }
[ColumnName(@"Name")]
public string Name { get; set; }
[ColumnName(@"Name")]
public string Name { get; set; }
}
}
#endregion
#endregion
/// <summary>
/// model output class for MLModel1.
/// </summary>
#region model output class
public class ModelOutput
{
[ColumnName(@"Q1min")]
public string Q1min { get; set; }
/// <summary>
/// model output class for MLModel1.
/// </summary>
#region model output class
public class ModelOutput
{
[ColumnName(@"Q1min")]
public string Q1min { get; set; }
[ColumnName(@"Q1max")]
public float[] Q1max { get; set; }
[ColumnName(@"Q1max")]
public float[] Q1max { get; set; }
[ColumnName(@"Q1avg")]
public float[] Q1avg { get; set; }
[ColumnName(@"Q1avg")]
public float[] Q1avg { get; set; }
[ColumnName(@"Q1mean")]
public float[] Q1mean { get; set; }
[ColumnName(@"Q1mean")]
public float[] Q1mean { get; set; }
[ColumnName(@"Q2min")]
public float[] Q2min { get; set; }
[ColumnName(@"Q2min")]
public float[] Q2min { get; set; }
[ColumnName(@"Q2max")]
public float[] Q2max { get; set; }
[ColumnName(@"Q2max")]
public float[] Q2max { get; set; }
[ColumnName(@"Q2avg")]
public float[] Q2avg { get; set; }
[ColumnName(@"Q2avg")]
public float[] Q2avg { get; set; }
[ColumnName(@"Q2mean")]
public float[] Q2mean { get; set; }
[ColumnName(@"Q2mean")]
public float[] Q2mean { get; set; }
[ColumnName(@"Q3min")]
public float[] Q3min { get; set; }
[ColumnName(@"Q3min")]
public float[] Q3min { get; set; }
[ColumnName(@"Q3max")]
public float[] Q3max { get; set; }
[ColumnName(@"Q3max")]
public float[] Q3max { get; set; }
[ColumnName(@"Q3avg")]
public float[] Q3avg { get; set; }
[ColumnName(@"Q3avg")]
public float[] Q3avg { get; set; }
[ColumnName(@"Q3mean")]
public float[] Q3mean { get; set; }
[ColumnName(@"Q3mean")]
public float[] Q3mean { get; set; }
[ColumnName(@"Q4min")]
public string Q4min { get; set; }
[ColumnName(@"Q4min")]
public string Q4min { get; set; }
[ColumnName(@"Q4max")]
public float[] Q4max { get; set; }
[ColumnName(@"Q4max")]
public float[] Q4max { get; set; }
[ColumnName(@"Q4avg")]
public float[] Q4avg { get; set; }
[ColumnName(@"Q4avg")]
public float[] Q4avg { get; set; }
[ColumnName(@"Q4mean")]
public float[] Q4mean { get; set; }
[ColumnName(@"Q4mean")]
public float[] Q4mean { get; set; }
[ColumnName(@"Q5min")]
public float[] Q5min { get; set; }
[ColumnName(@"Q5min")]
public float[] Q5min { get; set; }
[ColumnName(@"Q5max")]
public float[] Q5max { get; set; }
[ColumnName(@"Q5max")]
public float[] Q5max { get; set; }
[ColumnName(@"Q5avg")]
public float[] Q5avg { get; set; }
[ColumnName(@"Q5avg")]
public float[] Q5avg { get; set; }
[ColumnName(@"Q5mean")]
public float[] Q5mean { get; set; }
[ColumnName(@"Q5mean")]
public float[] Q5mean { get; set; }
[ColumnName(@"Name")]
public uint Name { get; set; }
[ColumnName(@"Name")]
public uint Name { get; set; }
[ColumnName(@"Features")]
public float[] Features { get; set; }
[ColumnName(@"Features")]
public float[] Features { get; set; }
[ColumnName(@"PredictedLabel")]
public string PredictedLabel { get; set; }
[ColumnName(@"PredictedLabel")]
public string PredictedLabel { get; set; }
[ColumnName(@"Score")]
public float[] Score { get; set; }
[ColumnName(@"Score")]
public float[] Score { get; set; }
}
}
#endregion
#endregion
private static string MLNetModelPath = Path.GetFullPath("MLModel1.zip");
#pragma warning restore CS8618 // Non-nullable field must contain a non-null value when exiting constructor. Consider declaring as nullable.
public static readonly Lazy<PredictionEngine<ModelInput, ModelOutput>> PredictEngine = new Lazy<PredictionEngine<ModelInput, ModelOutput>>(() => CreatePredictEngine(), true);
private static string MLNetModelPath = Path.GetFullPath("MLModel1.zip");
/// <summary>
/// Use this method to predict on <see cref="ModelInput"/>.
/// </summary>
/// <param name="input">model input.</param>
/// <returns><seealso cref=" ModelOutput"/></returns>
public static ModelOutput Predict(ModelInput input)
{
var predEngine = PredictEngine.Value;
return predEngine.Predict(input);
}
public static readonly Lazy<PredictionEngine<ModelInput, ModelOutput>> PredictEngine = new Lazy<PredictionEngine<ModelInput, ModelOutput>>(() => CreatePredictEngine(), true);
private static PredictionEngine<ModelInput, ModelOutput> CreatePredictEngine()
{
var mlContext = new MLContext();
ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var _);
return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
}
/// <summary>
/// Use this method to predict on <see cref="ModelInput"/>.
/// </summary>
/// <param name="input">model input.</param>
/// <returns><seealso cref=" ModelOutput"/></returns>
public static ModelOutput Predict(ModelInput input)
{
var predEngine = PredictEngine.Value;
return predEngine.Predict(input);
}
private static PredictionEngine<ModelInput, ModelOutput> CreatePredictEngine()
{
var mlContext = new MLContext();
ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var _);
return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
}
}

91
DeepTrace/Data/MLModel1.training.cs
View File

@ -9,56 +9,55 @@ using Microsoft.ML.Trainers.FastTree;
using Microsoft.ML.Trainers;
using Microsoft.ML;
namespace DeepTrace
namespace DeepTrace;
public partial class MLModel1
{
public partial class MLModel1
/// <summary>
/// Retrains model using the pipeline generated as part of the training process. For more information on how to load data, see aka.ms/loaddata.
/// </summary>
/// <param name="mlContext"></param>
/// <param name="trainData"></param>
/// <returns></returns>
public static ITransformer RetrainPipeline(MLContext mlContext, IDataView trainData)
{
/// <summary>
/// Retrains model using the pipeline generated as part of the training process. For more information on how to load data, see aka.ms/loaddata.
/// </summary>
/// <param name="mlContext"></param>
/// <param name="trainData"></param>
/// <returns></returns>
public static ITransformer RetrainPipeline(MLContext mlContext, IDataView trainData)
{
var pipeline = BuildPipeline(mlContext);
var model = pipeline.Fit(trainData);
var pipeline = BuildPipeline(mlContext);
var model = pipeline.Fit(trainData);
return model;
}
return model;
}
/// <summary>
/// build the pipeline that is used from model builder. Use this function to retrain model.
/// </summary>
/// <param name="mlContext"></param>
/// <returns></returns>
public static IEstimator<ITransformer> BuildPipeline(MLContext mlContext)
{
// Data process configuration with pipeline data transformations
var pipeline = mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1max",outputColumnName:@"Q1max")
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1avg",outputColumnName:@"Q1avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1mean",outputColumnName:@"Q1mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2min",outputColumnName:@"Q2min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2max",outputColumnName:@"Q2max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2avg",outputColumnName:@"Q2avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2mean",outputColumnName:@"Q2mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3min",outputColumnName:@"Q3min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3max",outputColumnName:@"Q3max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3avg",outputColumnName:@"Q3avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3mean",outputColumnName:@"Q3mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4max",outputColumnName:@"Q4max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4avg",outputColumnName:@"Q4avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4mean",outputColumnName:@"Q4mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5min",outputColumnName:@"Q5min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5max",outputColumnName:@"Q5max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5avg",outputColumnName:@"Q5avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5mean",outputColumnName:@"Q5mean"))
.Append(mlContext.Transforms.Concatenate(@"Features", new []{@"Q1max",@"Q1avg",@"Q1mean",@"Q2min",@"Q2max",@"Q2avg",@"Q2mean",@"Q3min",@"Q3max",@"Q3avg",@"Q3mean",@"Q4max",@"Q4avg",@"Q4mean",@"Q5min",@"Q5max",@"Q5avg",@"Q5mean"}))
.Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName:@"Name",inputColumnName:@"Name"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator:mlContext.BinaryClassification.Trainers.FastTree(new FastTreeBinaryTrainer.Options(){NumberOfLeaves=33,MinimumExampleCountPerLeaf=14,NumberOfTrees=4,MaximumBinCountPerFeature=1022,FeatureFraction=0.99999999,LearningRate=0.757926844134433,LabelColumnName=@"Name",FeatureColumnName=@"Features"}),labelColumnName: @"Name"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName:@"PredictedLabel",inputColumnName:@"PredictedLabel"));
/// <summary>
/// build the pipeline that is used from model builder. Use this function to retrain model.
/// </summary>
/// <param name="mlContext"></param>
/// <returns></returns>
public static IEstimator<ITransformer> BuildPipeline(MLContext mlContext)
{
// Data process configuration with pipeline data transformations
var pipeline = mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1max",outputColumnName:@"Q1max")
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1avg",outputColumnName:@"Q1avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q1mean",outputColumnName:@"Q1mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2min",outputColumnName:@"Q2min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2max",outputColumnName:@"Q2max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2avg",outputColumnName:@"Q2avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q2mean",outputColumnName:@"Q2mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3min",outputColumnName:@"Q3min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3max",outputColumnName:@"Q3max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3avg",outputColumnName:@"Q3avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q3mean",outputColumnName:@"Q3mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4max",outputColumnName:@"Q4max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4avg",outputColumnName:@"Q4avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q4mean",outputColumnName:@"Q4mean"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5min",outputColumnName:@"Q5min"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5max",outputColumnName:@"Q5max"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5avg",outputColumnName:@"Q5avg"))
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5mean",outputColumnName:@"Q5mean"))
.Append(mlContext.Transforms.Concatenate(@"Features", new []{@"Q1max",@"Q1avg",@"Q1mean",@"Q2min",@"Q2max",@"Q2avg",@"Q2mean",@"Q3min",@"Q3max",@"Q3avg",@"Q3mean",@"Q4max",@"Q4avg",@"Q4mean",@"Q5min",@"Q5max",@"Q5avg",@"Q5mean"}))
.Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName:@"Name",inputColumnName:@"Name"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator:mlContext.BinaryClassification.Trainers.FastTree(new FastTreeBinaryTrainer.Options(){NumberOfLeaves=33,MinimumExampleCountPerLeaf=14,NumberOfTrees=4,MaximumBinCountPerFeature=1022,FeatureFraction=0.99999999,LearningRate=0.757926844134433,LabelColumnName=@"Name",FeatureColumnName=@"Features"}),labelColumnName: @"Name"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName:@"PredictedLabel",inputColumnName:@"PredictedLabel"));
return pipeline;
}
return pipeline;
}
}

52
DeepTrace/Data/ModelDefinition.cs
View File

@ -2,6 +2,7 @@
using MongoDB.Bson.Serialization.Attributes;
using MongoDB.Bson;
using System.Text;
using DeepTrace.ML;
namespace DeepTrace.Data;
@ -15,28 +16,21 @@ public class ModelDefinition
}
[BsonId]
public ObjectId? Id { get; set; }
public string Name { get; set; }
public DataSourceStorage DataSource { get; set; } = new();
public string AIparameters { get; set; } = string.Empty;
public ObjectId? Id { get; set; }
public string Name { get; set; }
public DataSourceStorage DataSource { get; set; } = new();
public string AIparameters { get; set; } = string.Empty;
public List<IntervalDefinition> IntervalDefinitionList { get; set; } = new();
public List<string> GetColumnNames()
{
var measureNames = new[] { "min", "max", "avg", "mean" };
var columnNames = new List<string>();
foreach (var item in DataSource.Queries)
{
columnNames.AddRange(measureNames.Select(x => $"{item.Query}_{x}"));
}
columnNames.Add("Name");
return columnNames;
}
public List<string> GetColumnNames() => DataSource.GetColumnNames()
.Concat(new[] { "Name" })
.ToList()
;
public string ToCsv()
{
var current = IntervalDefinitionList.First();
var headers = string.Join(",", GetColumnNames().Select(x=>$"\"{x}\""));
var headers = string.Join(",", GetColumnNames().Select(x => $"\"{x}\""));
var writer = new StringBuilder();
@ -45,30 +39,24 @@ public class ModelDefinition
foreach (var currentInterval in IntervalDefinitionList)
{
var source = currentInterval.Data;
string data = ConvertToCsv(source);
data += "," + currentInterval.Name;
string data = DataSourceDefinition.ConvertToCsv(source);
data += $",\"{currentInterval.Name}\"";
writer.AppendLine(data);
}
return writer.ToString();
}
public static string ConvertToCsv(List<TimeSeriesDataSet> source)
public IEnumerable<MLInputData> ToInput()
{
var data = "";
for (var i = 0; i < source.Count; i++)
foreach (var currentInterval in IntervalDefinitionList)
{
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 + ",";
var source = currentInterval.Data;
yield return new MLInputData
{
Features = DataSourceDefinition.ToFeatures(source),
Label = currentInterval.Name
};
}
return data+"\"ignoreMe\"";
}
}

8
DeepTrace/Data/Prediction.cs
View File

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

18
DeepTrace/Data/TrainedModelDefinition.cs
View File

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

54
DeepTrace/ML/EstimatorBuilder.cs
View File

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

9
DeepTrace/ML/IEstimatorBuilder.cs
View File

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

5
DeepTrace/ML/IMLProcessor.cs
View File

@ -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();
}

13
DeepTrace/ML/IMeasure.cs
View File

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

21
DeepTrace/ML/MLEvaluationMetrics.cs
View File

@ -1,16 +1,15 @@
namespace DeepTrace.ML
namespace DeepTrace.ML;
public class MLEvaluationMetrics
{
public class MLEvaluationMetrics
public MLEvaluationMetrics()
{
public MLEvaluationMetrics()
{
}
public double MicroAccuracy { get; set; }
public double MacroAccuracy { get; set; }
public double LogLoss { get; set; }
public double LogLossReduction { get; set; }
}
public double MicroAccuracy { get; set; }
public double MacroAccuracy { get; set; }
public double LogLoss { get; set; }
public double LogLossReduction { get; set; }
}

41
DeepTrace/ML/MLHelpers.cs
View File

@ -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;
}
}

266
DeepTrace/ML/MLProcessor.cs
View File

@ -1,137 +1,155 @@
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
{
public class MLProcessor : IMLProcessor
private readonly ILogger<MLProcessor> _logger;
private IEstimatorBuilder _estimatorBuilder;
public MLProcessorFactory(ILogger<MLProcessor> logger, IEstimatorBuilder estimatorBuilder)
{
private MLContext _mlContext = new MLContext();
private EstimatorBuilder _estimatorBuilder = new EstimatorBuilder();
private DataViewSchema? _schema;
private ITransformer? _transformer;
private static string _signature = "DeepTrace-Model-v1-" + typeof(MLProcessor).Name;
private readonly ILogger<MLProcessor> _logger;
_logger = logger;
_estimatorBuilder = estimatorBuilder;
}
public MLProcessor(ILogger<MLProcessor> logger)
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 IEstimatorBuilder _estimatorBuilder;
private DataViewSchema? _schema;
private ITransformer? _transformer;
private static string _signature = "DeepTrace-Model-v1-" + typeof(MLProcessor).Name;
private PredictionEngine<MLInputData, MLOutputData>? _predictionEngine;
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 = await MLHelpers.ToInput(_mlContext, modelDef);
DataOperationsCatalog.TrainTestData dataSplit = _mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
_mlContext.Log += (_,e) => LogEvents(log, e);
try
{
_logger = logger;
_schema = data.Schema;
_transformer = pipeline.Fit(dataSplit.TrainSet);
return Evaluate(dataSplit.TestSet);
}
private string Name { get; set; } = "TestModel";
public async Task<MLEvaluationMetrics> Train(ModelDefinition modelDef, Action<string> log)
finally
{
var pipeline = _estimatorBuilder.BuildPipeline(_mlContext, modelDef);
var (data, filename) = await MLHelpers.Convert(_mlContext, modelDef);
DataOperationsCatalog.TrainTestData dataSplit = _mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
_mlContext.Log += (_,e) => LogEvents(log, e);
try
{
_schema = data.Schema;
_transformer = pipeline.Fit(dataSplit.TrainSet);
return Evaluate(dataSplit.TestSet);
}
finally
{
File.Delete(filename);
}
}
private void LogEvents(Action<string> log, LoggingEventArgs e)
{
if(e.Kind.ToString() != "Trace")
{
_logger.LogDebug(e.Message);
log(e.Message);
}
}
private MLEvaluationMetrics Evaluate(IDataView testData)
{
var predictions = _transformer!.Transform(testData);
var metrics = _mlContext.MulticlassClassification.Evaluate(predictions, "Name");
var evaluationMetrics = new MLEvaluationMetrics()
{
MicroAccuracy = metrics.MicroAccuracy,
MacroAccuracy = metrics.MacroAccuracy,
LogLoss = metrics.LogLoss,
LogLossReduction = metrics.LogLossReduction,
};
return evaluationMetrics;
}
public byte[] Export()
{
if(_schema == null)
{
throw new ArgumentNullException(nameof (_schema));
}
if (_transformer == null)
{
throw new ArgumentNullException(nameof(_transformer));
}
using var mem = new MemoryStream();
mem.WriteString(_signature);
mem.WriteString(Name);
var bytes = MLHelpers.ExportSingleModel(new ModelRecord(_mlContext, _schema, _transformer));
mem.WriteInt(bytes.Length);
mem.Write(bytes);
return mem.ToArray();
}
public void Import(byte[] data)
{
var mem = new MemoryStream(data);
var sig = mem.ReadString();
if (sig != _signature)
throw new ApplicationException($"Wrong data for {GetType().Name}");
Name = mem.ReadString();
var size = mem.ReadInt();
var bytes = new byte[size];
mem.Read(bytes, 0, bytes.Length);
(_mlContext, _schema, _transformer) = MLHelpers.ImportSingleModel(bytes);
}
public async Task<Prediction> Predict(TrainedModelDefinition trainedModel, ModelDefinition model, List<TimeSeriesDataSet> data)
{
Import(trainedModel.Value);
var headers = string.Join(",", model.GetColumnNames().Select(x => $"\"{x}\""));
var row = ModelDefinition.ConvertToCsv(data);
var csv = headers+"\n"+row;
var fileName = Path.GetTempFileName();
try
{
await File.WriteAllTextAsync(fileName, csv);
var (dataView, _) = MLHelpers.LoadFromCsv(_mlContext, model, fileName);
var predictionEngine = _mlContext.Model.CreatePredictionEngine<IDataView, Prediction>(_transformer);
var prediction = predictionEngine.Predict(dataView);
return prediction;
}
finally
{
File.Delete(fileName);
}
_logger.LogInformation("Training finished");
}
}
private void LogEvents(Action<string> log, LoggingEventArgs e)
{
if(e.Kind.ToString() != "Trace")
{
_logger.LogDebug(e.Message);
log(e.Message);
}
}
private MLEvaluationMetrics Evaluate(IDataView testData)
{
// https://learn.microsoft.com/en-us/dotnet/api/microsoft.ml.standardtrainerscatalog.lbfgslogisticregression?view=ml-dotnet
var predictions = _transformer!.Transform(testData);
var metrics = _mlContext.MulticlassClassification.Evaluate(predictions, nameof(MLInputData.Label));
var evaluationMetrics = new MLEvaluationMetrics()
{
MicroAccuracy = metrics.MicroAccuracy,
MacroAccuracy = metrics.MacroAccuracy,
LogLoss = metrics.LogLoss,
LogLossReduction = metrics.LogLossReduction,
};
return evaluationMetrics;
}
public byte[] Export()
{
if(_schema == null)
{
throw new ArgumentNullException(nameof (_schema));
}
if (_transformer == null)
{
throw new ArgumentNullException(nameof(_transformer));
}
using var mem = new MemoryStream();
mem.WriteString(_signature);
mem.WriteString(Name);
var bytes = MLHelpers.ExportSingleModel(new ModelRecord(_mlContext, _schema, _transformer));
mem.WriteInt(bytes.Length);
mem.Write(bytes);
return mem.ToArray();
}
public void Import(byte[] data)
{
var mem = new MemoryStream(data);
var sig = mem.ReadString();
if (sig != _signature)
throw new ApplicationException($"Wrong data for {GetType().Name}");
Name = mem.ReadString();
var size = mem.ReadInt();
var bytes = new byte[size];
mem.Read(bytes, 0, bytes.Length);
(_mlContext, _schema, _transformer) = MLHelpers.ImportSingleModel(bytes);
}
public Task<Prediction> Predict(TrainedModelDefinition trainedModel, ModelDefinition model, List<TimeSeriesDataSet> data)
{
Name = trainedModel.Name;
if (_transformer == null )
Import(trainedModel.Value);
if (_predictionEngine == null)
{
_predictionEngine = _mlContext.Model.CreatePredictionEngine<MLInputData, MLOutputData>(_transformer, _schema);
}
var input = new MLInputData
{
Features = DataSourceDefinition.ToFeatures(data)
};
var prediction = _predictionEngine.Predict( input );
return Task.FromResult( new Prediction { PredictedLabel = prediction.PredictedLabel, Score = prediction.Score } );
}
}

152
DeepTrace/ML/Measures.cs
View File

@ -1,89 +1,87 @@
using PrometheusAPI;
namespace DeepTrace.ML
namespace DeepTrace.ML;
public class MeasureMin : IMeasure
{
public class MeasureMin : IMeasure
{
public string Name => "Min";
public float Calculate(IEnumerable<TimeSeries> data) =>
data
.Where(x => x.Value != 0.0f)
.Min( x => x.Value )
;
public string Name => "Min";
public float Calculate(IEnumerable<TimeSeries> data) =>
data
.Where(x => x.Value != 0.0f)
.Min( x => x.Value )
;
public void Reset() { }
}
public void Reset() { }
}
public class MeasureMax : IMeasure
{
public string Name => "Max";
public float Calculate(IEnumerable<TimeSeries> data) => data.Max(x => x.Value);
public void Reset() { }
}
public class MeasureMax : IMeasure
{
public string Name => "Max";
public float Calculate(IEnumerable<TimeSeries> data) => data.Max(x => x.Value);
public void Reset() { }
}
public class MeasureAvg : IMeasure
{
public string Name => "Avg";
public float Calculate(IEnumerable<TimeSeries> data) => data.Average(x => x.Value);
public void Reset() { }
}
public class MeasureAvg : IMeasure
{
public string Name => "Avg";
public float Calculate(IEnumerable<TimeSeries> data) => data.Average(x => x.Value);
public void Reset() { }
}
/// <summary>
/// WARNING: Only works with fixed length interval
/// </summary>
public class MeasureSum : IMeasure
{
public string Name => "Sum";
public float Calculate(IEnumerable<TimeSeries> data) => data.Sum(x => x.Value);
public void Reset() { }
}
/// <summary>
/// WARNING: Only works with fixed length interval
/// </summary>
public class MeasureSum : IMeasure
{
public string Name => "Sum";
public float Calculate(IEnumerable<TimeSeries> data) => data.Sum(x => x.Value);
public void Reset() { }
}
public class MeasureMedian : IMeasure
{
public string Name => "Median";
public class MeasureMedian : IMeasure
{
public string Name => "Median";
public float Calculate(IEnumerable<TimeSeries> data)
=> MedianHelper.Median(data, x => x.Value);
public float Calculate(IEnumerable<TimeSeries> data)
=> MedianHelper.Median(data, x => x.Value);
public void Reset() { }
}
public class MeasureDiff<T> : IMeasure where T : IMeasure, new()
{
private T _measure = new();
public string Name => "Diff_"+_measure.Name;
private float _prev = float.NaN;
public float Calculate(IEnumerable<TimeSeries> data)
{
var val = _measure.Calculate(data);
if (float.IsNaN(_prev))
{
_prev = val;
return 0.0f;
}
val = val - _prev;
_prev = val;
return val;
}
public void Reset()
{
_measure.Reset();
_prev = float.NaN;
}
}
public class MeasureDiffMin : MeasureDiff<MeasureMin> { }
public class MeasureDiffMax : MeasureDiff<MeasureMax> { }
public class MeasureDiffAvg : MeasureDiff<MeasureAvg> { }
/// <summary>
/// WARNING: Only works with fixed length interval
/// </summary>
public class MeasureDiffSum : MeasureDiff<MeasureSum> { }
public class MeasureDiffMedian : MeasureDiff<MeasureMedian> { }
public void Reset() { }
}
public class MeasureDiff<T> : IMeasure where T : IMeasure, new()
{
private T _measure = new();
public string Name => "Diff_"+_measure.Name;
private float _prev = float.NaN;
public float Calculate(IEnumerable<TimeSeries> data)
{
var val = _measure.Calculate(data);
if (float.IsNaN(_prev))
{
_prev = val;
return 0.0f;
}
val = val - _prev;
_prev = val;
return val;
}
public void Reset()
{
_measure.Reset();
_prev = float.NaN;
}
}
public class MeasureDiffMin : MeasureDiff<MeasureMin> { }
public class MeasureDiffMax : MeasureDiff<MeasureMax> { }
public class MeasureDiffAvg : MeasureDiff<MeasureAvg> { }
/// <summary>
/// WARNING: Only works with fixed length interval
/// </summary>
public class MeasureDiffSum : MeasureDiff<MeasureSum> { }
public class MeasureDiffMedian : MeasureDiff<MeasureMedian> { }

4
DeepTrace/Pages/DataSources.razor
View File

@ -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" />*@
<MudAutocomplete Label="Query" @bind-Value="_queryForm.Source.Queries[pos].Query" Lines="1" Variant="Variant.Text" SearchFunc="@SearchForQuery"></MudAutocomplete>
<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>*@
</MudItem>
<MudItem xs="1">
<MudIconButton Icon="@Icons.Material.Outlined.Add" Variant="Variant.Outlined" aria-label="add" OnClick="@(() => AddQuery(pos))" />

6
DeepTrace/Pages/Error.cshtml
View File

@ -19,11 +19,11 @@
<h2 class="text-danger">An error occurred while processing your request.</h2>
@if (Model.ShowRequestId)
{
<p>
{
<p>
<strong>Request ID:</strong> <code>@Model.RequestId</code>
</p>
}
}
<h3>Development Mode</h3>
<p>

35
DeepTrace/Pages/Error.cshtml.cs
View File

@ -2,26 +2,25 @@
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
{
[ResponseCache(Duration = 0, Location = ResponseCacheLocation.None, NoStore = true)]
[IgnoreAntiforgeryToken]
public class ErrorModel : PageModel
public string? RequestId { get; set; }
public bool ShowRequestId => !string.IsNullOrEmpty(RequestId);
private readonly ILogger<ErrorModel> _logger;
public ErrorModel(ILogger<ErrorModel> logger)
{
public string? RequestId { get; set; }
_logger = logger;
}
public bool ShowRequestId => !string.IsNullOrEmpty(RequestId);
private readonly ILogger<ErrorModel> _logger;
public ErrorModel(ILogger<ErrorModel> logger)
{
_logger = logger;
}
public void OnGet()
{
RequestId = Activity.Current?.Id ?? HttpContext.TraceIdentifier;
}
public void OnGet()
{
RequestId = Activity.Current?.Id ?? HttpContext.TraceIdentifier;
}
}

20
DeepTrace/Pages/Index.razor
View File

@ -14,9 +14,9 @@ Welcome to your new app.
@if (_trainedModels != null)
{
@foreach(TrainedModelDefinition model in _trainedModels)
{
<ModelCard Model="@model"/>
}
{
<ModelCard Model="@model"/>
}
} else
{
<MudText>Nothing to display</MudText>
@ -24,13 +24,13 @@ Welcome to your new app.
@code{
private List<TrainedModelDefinition> _trainedModels = new();
private List<TrainedModelDefinition> _trainedModels = new();
protected override async Task OnInitializedAsync()
{
base.OnInitialized();
_trainedModels = await TrainedModelService.Load();
}
protected override async Task OnInitializedAsync()
{
base.OnInitialized();
_trainedModels = await TrainedModelService.Load();
}
}

9
DeepTrace/Pages/Training.razor
View File

@ -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

1
DeepTrace/Program.cs
View File

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

87
DeepTrace/Services/DataSourceStorageService.cs
View File

@ -1,58 +1,57 @@
using MongoDB.Bson;
using MongoDB.Driver;
namespace DeepTrace.Services
namespace DeepTrace.Services;
public class DataSourceStorageService : IDataSourceStorageService
{
public class DataSourceStorageService : IDataSourceStorageService
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "Sources";
private readonly IMongoClient _client;
public DataSourceStorageService(IMongoClient client)
{
_client = client;
}
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "Sources";
public async Task<List<DataSourceStorage>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
private readonly IMongoClient _client;
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public async Task Store(DataSourceStorage source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
public DataSourceStorageService(IMongoClient client)
if ( source.Id == null )
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(DataSourceStorage source, bool ignoreNotStored = false)
{
if ( source.Id == null )
{
_client = client;
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
public async Task<List<DataSourceStorage>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public async Task Store(DataSourceStorage source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
if ( source.Id == null )
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(DataSourceStorage source, bool ignoreNotStored = false)
{
if ( source.Id == null )
{
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<DataSourceStorage>(MongoDBCollection);
await collection.DeleteOneAsync($"_id = {source.Id}");
}
await collection.DeleteOneAsync($"_id = {source.Id}");
}
}

49
DeepTrace/Services/IDataSourceStorageService.cs
View File

@ -2,37 +2,36 @@
using MongoDB.Bson.Serialization.Attributes;
using MongoDB.Bson;
namespace DeepTrace.Services
namespace DeepTrace.Services;
public class DataSourceStorage : DataSourceDefinition, IEquatable<DataSourceStorage>
{
public class DataSourceStorage : DataSourceDefinition, IEquatable<DataSourceStorage>
[BsonId]
public ObjectId? Id { get; set; }
public override bool Equals(object? obj)
{
[BsonId]
public ObjectId? Id { get; set; }
public override bool Equals(object? obj)
if ( obj is DataSourceStorage other )
{
if ( obj is DataSourceStorage other )
{
return Id == other.Id;
}
return false;
}
public bool Equals(DataSourceStorage? other)
{
return Id == other?.Id;
}
public override int GetHashCode()
{
return Id?.GetHashCode() ?? base.GetHashCode();
return Id == other.Id;
}
return false;
}
public interface IDataSourceStorageService
public bool Equals(DataSourceStorage? other)
{
Task Delete(DataSourceStorage source, bool ignoreNotStored = false);
Task<List<DataSourceStorage>> Load();
Task Store(DataSourceStorage source);
return Id == other?.Id;
}
public override int GetHashCode()
{
return Id?.GetHashCode() ?? base.GetHashCode();
}
}
public interface IDataSourceStorageService
{
Task Delete(DataSourceStorage source, bool ignoreNotStored = false);
Task<List<DataSourceStorage>> Load();
Task Store(DataSourceStorage source);
}

17
DeepTrace/Services/IModelStorageService.cs
View File

@ -3,14 +3,13 @@ using MongoDB.Bson;
using DeepTrace.Data;
using System.Text;
namespace DeepTrace.Services
{
namespace DeepTrace.Services;
public interface IModelStorageService
{
Task Delete(ModelDefinition source, bool ignoreNotStored = false);
Task<List<ModelDefinition>> Load();
Task<ModelDefinition?> Load(BsonObjectId id);
Task Store(ModelDefinition source);
}
public interface IModelStorageService
{
Task Delete(ModelDefinition source, bool ignoreNotStored = false);
Task<List<ModelDefinition>> Load();
Task<ModelDefinition?> Load(BsonObjectId id);
Task Store(ModelDefinition source);
}

13
DeepTrace/Services/ITrainedModelStorageService.cs
View File

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

106
DeepTrace/Services/ModelStorageService.cs
View File

@ -2,67 +2,67 @@
using MongoDB.Bson;
using MongoDB.Driver;
namespace DeepTrace.Services
namespace DeepTrace.Services;
public class ModelStorageService : IModelStorageService
{
public class ModelStorageService : IModelStorageService
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "Models";
private readonly IMongoClient _client;
public ModelStorageService(IMongoClient client)
{
_client = client;
}
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "Models";
public async Task<List<ModelDefinition>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
private readonly IMongoClient _client;
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public ModelStorageService(IMongoClient client)
public async Task<ModelDefinition?> Load(BsonObjectId id)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
var res = await (await collection.FindAsync($"{{ _id : ObjectId(\"{id}\") }}")).ToListAsync();
return res.FirstOrDefault();
}
public async Task Store(ModelDefinition source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
if (source.Id == null)
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(ModelDefinition source, bool ignoreNotStored = false)
{
if (source.Id == null)
{
_client = client;
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
public async Task<List<ModelDefinition>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public async Task<ModelDefinition?> Load(BsonObjectId id)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
var res = (await (await collection.FindAsync($"{{_id:ObjectId(\"{id}\")}}")).ToListAsync()).FirstOrDefault();
return res;
}
public async Task Store(ModelDefinition source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
if (source.Id == null)
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(ModelDefinition source, bool ignoreNotStored = false)
{
if (source.Id == null)
{
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<ModelDefinition>(MongoDBCollection);
await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
}
await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
}
}

87
DeepTrace/Services/TrainedModelStorageService.cs
View File

@ -2,57 +2,56 @@
using MongoDB.Bson;
using MongoDB.Driver;
namespace DeepTrace.Services
namespace DeepTrace.Services;
public class TrainedModelStorageService: ITrainedModelStorageService
{
public class TrainedModelStorageService: ITrainedModelStorageService
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "TrainedModels";
private readonly IMongoClient _client;
public TrainedModelStorageService(IMongoClient client)
{
private const string MongoDBDatabaseName = "DeepTrace";
private const string MongoDBCollection = "TrainedModels";
_client = client;
}
private readonly IMongoClient _client;
public async Task<List<TrainedModelDefinition>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
public TrainedModelStorageService(IMongoClient client)
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public async Task Store(TrainedModelDefinition source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
if (source.Id == null)
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(TrainedModelDefinition source, bool ignoreNotStored = false)
{
if (source.Id == null)
{
_client = client;
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
public async Task<List<TrainedModelDefinition>> Load()
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
var res = await (await collection.FindAsync("{}")).ToListAsync();
return res;
}
public async Task Store(TrainedModelDefinition source)
{
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
if (source.Id == null)
source.Id = ObjectId.GenerateNewId();
// use upsert (insert or update) to automatically handle subsequent updates
await collection.ReplaceOneAsync(
filter: new BsonDocument("_id", source.Id),
options: new ReplaceOptions { IsUpsert = true },
replacement: source
);
}
public async Task Delete(TrainedModelDefinition source, bool ignoreNotStored = false)
{
if (source.Id == null)
{
if (!ignoreNotStored)
throw new InvalidDataException("Source was not stored yet. There is nothing to delete");
return;
}
var db = _client.GetDatabase(MongoDBDatabaseName);
var collection = db.GetCollection<TrainedModelDefinition>(MongoDBCollection);
await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
}
await collection.DeleteOneAsync(filter: new BsonDocument("_id", source.Id));
}
}

6
DeepTrace/Shared/SurveyPrompt.razor
View File

@ -10,7 +10,7 @@
</div>
@code {
// Demonstrates how a parent component can supply parameters
[Parameter]
public string? Title { get; set; }
// Demonstrates how a parent component can supply parameters
[Parameter]
public string? Title { get; set; }
}

29
PrometheusAPI/JsonSetializerSetup.cs
View File

@ -6,20 +6,19 @@ using System.Text.Json.Serialization;
using System.Text.Json;
using System.Threading.Tasks;
namespace PrometheusAPI
{
public static class JsonSetializerSetup
{
private static JsonSerializerOptions _options = new JsonSerializerOptions
{
AllowTrailingCommas = true,
ReadCommentHandling = JsonCommentHandling.Skip,
NumberHandling =
JsonNumberHandling.AllowReadingFromString |
JsonNumberHandling.WriteAsString,
PropertyNameCaseInsensitive = true
};
namespace PrometheusAPI;
public static JsonSerializerOptions Options => _options;
}
public static class JsonSetializerSetup
{
private static JsonSerializerOptions _options = new JsonSerializerOptions
{
AllowTrailingCommas = true,
ReadCommentHandling = JsonCommentHandling.Skip,
NumberHandling =
JsonNumberHandling.AllowReadingFromString |
JsonNumberHandling.WriteAsString,
PropertyNameCaseInsensitive = true
};
public static JsonSerializerOptions Options => _options;
}

181
PrometheusAPI/PrometheusClient.cs
View File

@ -1,119 +1,118 @@
using System.Text.Json;
namespace PrometheusAPI
namespace PrometheusAPI;
public class PrometheusClient
{
public class PrometheusClient
private readonly HttpClient _client;
public PrometheusClient(HttpClient client)
{
private readonly HttpClient _client;
_client = client;
}
public PrometheusClient(HttpClient client)
public async Task<InstantQueryResponse> InstantQuery(string query, DateTime? time = null, CancellationToken token = default)
{
var q = new List<KeyValuePair<string, string>>
{
_client = client;
new KeyValuePair<string, string>("query", query)
};
if (time != null)
q.Add(new KeyValuePair<string, string>("time", TimeSeries.DateTimeToUnixTimestamp(time.Value).ToString("F3")));
var form = new FormUrlEncodedContent(q);
var response = await _client.PostAsync("/api/v1/query", form);
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<InstantQueryResponse>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to InstantQueryResponse");
return res;
}
public async Task<InstantQueryResponse> RangeQuery(string query, DateTime start, DateTime end, TimeSpan step, TimeSpan timeout = default, CancellationToken token = default)
{
var q = new List<KeyValuePair<string, string>>
{
new KeyValuePair<string, string>("query", query),
new KeyValuePair<string, string>("start", TimeSeries.DateTimeToUnixTimestamp(start).ToString("F3")),
new KeyValuePair<string, string>("end", TimeSeries.DateTimeToUnixTimestamp(end).ToString("F3")),
new KeyValuePair<string, string>("step", step.TotalSeconds.ToString("F3"))
};
if( timeout != default )
{
q.Add(new KeyValuePair<string, string>("timeout", timeout.TotalSeconds.ToString("F3")));
}
public async Task<InstantQueryResponse> InstantQuery(string query, DateTime? time = null, CancellationToken token = default)
var form = new FormUrlEncodedContent(q);
var response = await _client.PostAsync("/api/v1/query_range", form);
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<InstantQueryResponse>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to InstantQueryResponse");
return res;
}
public async Task<string> FormatQuery(string query, CancellationToken token = default)
{
var q = new List<KeyValuePair<string, string>>
{
var q = new List<KeyValuePair<string, string>>
{
new KeyValuePair<string, string>("query", query)
};
if (time != null)
q.Add(new KeyValuePair<string, string>("time", TimeSeries.DateTimeToUnixTimestamp(time.Value).ToString("F3")));
var form = new FormUrlEncodedContent(q);
var response = await _client.PostAsync("/api/v1/query", form);
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<InstantQueryResponse>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to InstantQueryResponse");
return res;
}
public async Task<InstantQueryResponse> RangeQuery(string query, DateTime start, DateTime end, TimeSpan step, TimeSpan timeout = default, CancellationToken token = default)
{
var q = new List<KeyValuePair<string, string>>
{
new KeyValuePair<string, string>("query", query),
new KeyValuePair<string, string>("start", TimeSeries.DateTimeToUnixTimestamp(start).ToString("F3")),
new KeyValuePair<string, string>("end", TimeSeries.DateTimeToUnixTimestamp(end).ToString("F3")),
new KeyValuePair<string, string>("step", step.TotalSeconds.ToString("F3"))
};
if( timeout != default )
{
q.Add(new KeyValuePair<string, string>("timeout", timeout.TotalSeconds.ToString("F3")));
}
var form = new FormUrlEncodedContent(q);
var response = await _client.PostAsync("/api/v1/query_range", form);
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<InstantQueryResponse>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to InstantQueryResponse");
return res;
}
public async Task<string> FormatQuery(string query, CancellationToken token = default)
{
var q = new List<KeyValuePair<string, string>>
{
new KeyValuePair<string, string>("query", query),
};
new KeyValuePair<string, string>("query", query),
};
var form = new FormUrlEncodedContent(q);
var form = new FormUrlEncodedContent(q);
var response = await _client.PostAsync("/api/v1/format_query", form);
var response = await _client.PostAsync("/api/v1/format_query", form);
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<JsonDocument>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to JsonDocument");
var res = JsonSerializer.Deserialize<JsonDocument>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to JsonDocument");
var status = res.RootElement.GetProperty("status").GetString()
?? throw new InvalidDataException("Can't get status");
var status = res.RootElement.GetProperty("status").GetString()
?? throw new InvalidDataException("Can't get status");
if (!status.Equals("success", StringComparison.OrdinalIgnoreCase) )
throw new InvalidDataException(res.RootElement.GetProperty("error").GetString());
if (!status.Equals("success", StringComparison.OrdinalIgnoreCase) )
throw new InvalidDataException(res.RootElement.GetProperty("error").GetString());
var data = res.RootElement.GetProperty("data").GetString()
?? throw new InvalidDataException("Can't get formatted query");
var data = res.RootElement.GetProperty("data").GetString()
?? throw new InvalidDataException("Can't get formatted query");
return data;
}
return data;
}
public async Task<string[]> GetMetricsNames(CancellationToken token = default)
{
var response = await _client.GetAsync("/api/v1/label/__name__/values");
public async Task<string[]> GetMetricsNames(CancellationToken token = default)
{
var response = await _client.GetAsync("/api/v1/label/__name__/values");
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var json = await response.Content.ReadAsStringAsync()
?? throw new InvalidDataException("Responce is null");
var res = JsonSerializer.Deserialize<JsonDocument>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to JsonDocument");
var res = JsonSerializer.Deserialize<JsonDocument>(json, JsonSetializerSetup.Options)
?? throw new InvalidDataException("Can't convert responce to JsonDocument");
var status = res.RootElement.GetProperty("status").GetString()
?? throw new InvalidDataException("Can't get status");
var status = res.RootElement.GetProperty("status").GetString()
?? throw new InvalidDataException("Can't get status");
if (!status.Equals("success", StringComparison.OrdinalIgnoreCase))
throw new InvalidDataException(res.RootElement.GetProperty("error").GetString());
if (!status.Equals("success", StringComparison.OrdinalIgnoreCase))
throw new InvalidDataException(res.RootElement.GetProperty("error").GetString());
var data = res.RootElement.GetProperty("data").EnumerateArray().Select(x => x.GetString()).Where( x => x != null).Cast<string>().ToArray<string>()
?? throw new InvalidDataException("Can't get formatted query");
var data = res.RootElement.GetProperty("data").EnumerateArray().Select(x => x.GetString()).Where( x => x != null).Cast<string>().ToArray<string>()
?? throw new InvalidDataException("Can't get formatted query");
return data;
}
return data;
}
}

81
PrometheusAPI/TimeSeriesCoverter.cs
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@ -1,52 +1,51 @@
using System.Text.Json;
using System.Text.Json.Serialization;
namespace PrometheusAPI
namespace PrometheusAPI;
internal class TimeSeriesCoverter : JsonConverter<TimeSeries?>
{
internal class TimeSeriesCoverter : JsonConverter<TimeSeries?>
public override TimeSeries? Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
{
public override TimeSeries? Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
if (reader.TokenType != JsonTokenType.StartArray)
throw new JsonException();
reader.Read();
if ( reader.TokenType != JsonTokenType.Number)
throw new JsonException();
var s = JsonSerializer.Deserialize<double>(ref reader, options);
reader.Read();
double f;
if (reader.TokenType == JsonTokenType.Number)
f = JsonSerializer.Deserialize<double>(ref reader, options);
else if (reader.TokenType == JsonTokenType.String)
f = Convert.ToDouble(JsonSerializer.Deserialize<string>(ref reader, options));
else
throw new JsonException();
reader.Read();
if (reader.TokenType != JsonTokenType.EndArray)
throw new JsonException();
return new TimeSeries(TimeSeries.UnixTimeStampToDateTime(s), (float)f);
}
public override void Write(Utf8JsonWriter writer, TimeSeries? value, JsonSerializerOptions options)
{
writer.WriteStartArray();
if (value != null)
{
if (reader.TokenType != JsonTokenType.StartArray)
throw new JsonException();
reader.Read();
if ( reader.TokenType != JsonTokenType.Number)
throw new JsonException();
var s = JsonSerializer.Deserialize<double>(ref reader, options);
reader.Read();
double f;
if (reader.TokenType == JsonTokenType.Number)
f = JsonSerializer.Deserialize<double>(ref reader, options);
else if (reader.TokenType == JsonTokenType.String)
f = Convert.ToDouble(JsonSerializer.Deserialize<string>(ref reader, options));
else
throw new JsonException();
reader.Read();
if (reader.TokenType != JsonTokenType.EndArray)
throw new JsonException();
return new TimeSeries(TimeSeries.UnixTimeStampToDateTime(s), (float)f);
writer.WriteNumberValue(TimeSeries.DateTimeToUnixTimestamp(value.TimeStamp));
writer.WriteNumberValue(value.Value);
}
public override void Write(Utf8JsonWriter writer, TimeSeries? value, JsonSerializerOptions options)
{
writer.WriteStartArray();
writer.WriteEndArray();
if (value != null)
{
writer.WriteNumberValue(TimeSeries.DateTimeToUnixTimestamp(value.TimeStamp));
writer.WriteNumberValue(value.Value);
}
writer.WriteEndArray();
}
}
}