VBAF.Enterprise.Dashboard.ps1
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#Requires -Version 5.1 <# .SYNOPSIS Phase 15 - Enterprise Dashboard Intelligence .DESCRIPTION Trains a DQN agent to manage and prioritise enterprise dashboard resources - deciding what to display, when to refresh, and how to allocate rendering resources across multiple data streams. The agent observes dashboard load signals and learns when to: - Cache : serve cached data, no refresh needed (action 0) - Refresh : update one panel with fresh data (action 1) - Prioritise: elevate critical KPIs to top of display (action 2) - Rebuild : full dashboard reload, all panels fresh (action 3) .NOTES Part of VBAF - Phase 15 Enterprise Automation Engine Phase 15: Enterprise Dashboard Intelligence PS 5.1 compatible Real data: Get-Counter, WMI Win32_OperatingSystem, active session info #> # ============================================================ # PHASE 15 - ENTERPRISE DASHBOARD # ============================================================ class DashboardEnvironment { # State: 4 normalised dashboard load dimensions (0.0 - 1.0) # state[0] = SeverityNorm — proven direct signal (same as SecurityMonitor) # state[1] = DataStaleness — how stale is the dashboard data # state[2] = RenderLoad — how busy is the rendering engine # state[3] = UrgencyScore — composite (Staleness + AlertDensity) / 2 # REPLACES OffHours which is always 0 during daytime [double] $SeverityNorm # CurrentSeverity / 3.0 [double] $DataStaleness # 0=fresh 1=critically stale [double] $RenderLoad # 0=idle 1=rendering saturated [double] $UrgencyScore # 0=calm 1=urgent composite signal [double] $AlertDensity # kept for internal use / probe display [int] $CorrectActions [int] $MissedUpdates [int] $Steps [double] $TotalReward [int] $EpisodeCount # Confusion matrix [int] $TruePositives [int] $FalsePositives [int] $TrueNegatives [int] $FalseNegatives [int] $CurrentSeverity # raw 0-3 (maps directly to optimal action) # Required by VBAF framework [int] $StateSize = 4 [int] $ActionSize = 4 # Step() stores result here - avoids PSCustomObject type corruption (PS 5.1) [double] $LastReward = 0.0 [bool] $LastDone = $false DashboardEnvironment() { $this.Reset() | Out-Null } [double[]] GetState() { [double[]] $s = @(0.0, 0.0, 0.0, 0.0) $s[0] = $this.SeverityNorm $s[1] = $this.DataStaleness $s[2] = $this.RenderLoad $s[3] = $this.UrgencyScore return $s } [double[]] Reset() { $this.Steps = 0 $this.TotalReward = 0.0 $this.CorrectActions = 0 $this.MissedUpdates = 0 $this.TruePositives = 0 $this.FalsePositives = 0 $this.TrueNegatives = 0 $this.FalseNegatives = 0 $this.LastDone = $false # CRITICAL: must reset here $this.EpisodeCount++ $this._SampleCondition() [double[]] $initState = $this.GetState() return $initState } [void] _SampleCondition() { # Balanced training distribution # 25% idle (0), 30% normal (1), 25% busy (2), 20% critical (3) $roll = Get-Random -Minimum 1 -Maximum 100 if ($roll -le 25) { $this.CurrentSeverity = 0 } elseif ($roll -le 55) { $this.CurrentSeverity = 1 } elseif ($roll -le 80) { $this.CurrentSeverity = 2 } else { $this.CurrentSeverity = 3 } [double[]] $snArr = @(0.0) $snArr[0] = $this.CurrentSeverity $snArr[0] /= 3.0 $this.SeverityNorm = $snArr[0] switch ($this.CurrentSeverity) { 0 { # Idle: fresh data, low render, minimal alerts $this.DataStaleness = [double](Get-Random -Minimum 0 -Maximum 15) / 100.0 $this.RenderLoad = [double](Get-Random -Minimum 0 -Maximum 20) / 100.0 $this.AlertDensity = [double](Get-Random -Minimum 0 -Maximum 10) / 100.0 } 1 { # Normal: slightly stale, moderate render, few alerts $this.DataStaleness = [double](Get-Random -Minimum 20 -Maximum 45) / 100.0 $this.RenderLoad = [double](Get-Random -Minimum 20 -Maximum 55) / 100.0 $this.AlertDensity = [double](Get-Random -Minimum 10 -Maximum 30) / 100.0 } 2 { # Busy: stale data, high render load, active alerts $this.DataStaleness = [double](Get-Random -Minimum 50 -Maximum 75) / 100.0 $this.RenderLoad = [double](Get-Random -Minimum 55 -Maximum 80) / 100.0 $this.AlertDensity = [double](Get-Random -Minimum 30 -Maximum 65) / 100.0 } 3 { # Critical: very stale, saturated render, flooded alerts $this.DataStaleness = [double](Get-Random -Minimum 75 -Maximum 100) / 100.0 $this.RenderLoad = [double](Get-Random -Minimum 80 -Maximum 100) / 100.0 $this.AlertDensity = [double](Get-Random -Minimum 65 -Maximum 100) / 100.0 } } # UrgencyScore: composite signal - always varies with severity # Replaces OffHours which is always 0 during business hours [double[]] $uArr = @(0.0) $uArr[0] = $this.DataStaleness + $this.AlertDensity $uArr[0] /= 2.0 $this.UrgencyScore = $uArr[0] } [int] _OptimalAction() { # 0=Cache 1=Refresh 2=Prioritise 3=Rebuild return $this.CurrentSeverity } [void] Step([int]$action) { $this.Steps++ $optimal = $this._OptimalAction() # Symmetric distance-based reward - same proven pattern as SecurityMonitor [int] $dist = $action - $optimal if ($dist -lt 0) { $dist = -$dist } if ($dist -eq 0) { $this.LastReward = 2.0; $this.CorrectActions++ } elseif($dist -eq 1) { $this.LastReward = -1.0 } elseif($dist -eq 2) { $this.LastReward = -2.0 } else { $this.LastReward = -3.0 } if ($this.CurrentSeverity -ge 2 -and $action -lt 2) { $this.MissedUpdates++ } $isCritical = ($this.CurrentSeverity -ge 2) $agentActs = ($action -ge 2) if ($isCritical -and $agentActs) { $this.TruePositives++ } if (!$isCritical -and $agentActs) { $this.FalsePositives++ } if (!$isCritical -and !$agentActs) { $this.TrueNegatives++ } if ($isCritical -and !$agentActs) { $this.FalseNegatives++ } $this.TotalReward += $this.LastReward $this._SampleCondition() $this.LastDone = ($this.Steps -ge 200) } } # ------------------------------------ # Real Windows dashboard data probe # ------------------------------------ function Get-VBAFDashboardSnapshot { [CmdletBinding()] param() Write-Host "" Write-Host " Probing dashboard data sources..." -ForegroundColor Gray try { # OS memory as render load proxy $os = Get-WmiObject -Class Win32_OperatingSystem -ErrorAction Stop [double[]] $memArr = @(0.0) $memArr[0] = $os.TotalVisibleMemorySize - $os.FreePhysicalMemory $memArr[0] /= $os.TotalVisibleMemorySize $memArr[0] *= 100.0 $usedPct = [Math]::Round($memArr[0], 1) Write-Host (" Memory used : {0}%" -f $usedPct) -ForegroundColor $(if ($usedPct -gt 85) { "Red" } elseif ($usedPct -gt 65) { "Yellow" } else { "Green" }) # Active sessions $sessions = query session 2>$null $sessionCount = if ($sessions) { ($sessions | Measure-Object -Line).Lines - 1 } else { 1 } Write-Host (" Active sessions : {0}" -f $sessionCount) -ForegroundColor White # Event log - recent warnings as alert density proxy $recentEvents = Get-WinEvent -FilterHashtable @{ LogName = 'System' Level = @(2,3) StartTime = (Get-Date).AddHours(-1) } -ErrorAction SilentlyContinue $evCount = if ($recentEvents) { @($recentEvents).Count } else { 0 } Write-Host (" System warnings (1h) : {0}" -f $evCount) -ForegroundColor $(if ($evCount -gt 10) { "Yellow" } else { "Green" }) Write-Host " Dashboard probe : confirmed ✅" -ForegroundColor Green } catch { Write-Host " [WARNING] Dashboard probe incomplete: $($_.Exception.Message)" -ForegroundColor Yellow Write-Host " [INFO] Training will use simulated dashboard conditions." -ForegroundColor Gray } } # ============================================================ # MAIN TRAINING FUNCTION # ============================================================ function Invoke-VBAFDashboardTraining { param( [int] $Episodes = 100, [int] $PrintEvery = 10, [switch] $FastMode, [switch] $SimMode, [switch] $SkipRealData ) Write-Host "" Write-Host "📊 VBAF Enterprise - Phase 15: Enterprise Dashboard" -ForegroundColor Cyan Write-Host " Training DQN agent on dashboard resource management..." -ForegroundColor Cyan Write-Host " Actions: 0=Cache 1=Refresh 2=Prioritise 3=Rebuild" -ForegroundColor Yellow Write-Host " State : SeverityNorm | Staleness | RenderLoad | UrgencyScore" -ForegroundColor Yellow Write-Host " Reward : +2 correct -1 dist=1 -2 dist=2 -3 dist=3" -ForegroundColor Yellow Write-Host "" if (-not $SkipRealData) { Get-VBAFDashboardSnapshot } $dbEnv = [DashboardEnvironment]::new() # Phase 1: Baseline - inline random loop Write-Host " Phase 1: Baseline (random agent - 10 episodes)..." -ForegroundColor Gray $baseRewards = @() for ($b = 1; $b -le 10; $b++) { $dbEnv.Reset() | Out-Null $bReward = 0.0 while (-not $dbEnv.LastDone) { $rAction = Get-Random -Minimum 0 -Maximum 4 $dbEnv.Step($rAction) $bReward += $dbEnv.LastReward } $baseRewards += $bReward } [double[]] $bAvgArr = @(0.0) $bAvgArr[0] = ($baseRewards | Measure-Object -Average).Average Write-Host (" Baseline avg reward: {0:F2}" -f $bAvgArr[0]) -ForegroundColor Gray if ($FastMode) { $Episodes = [Math]::Min($Episodes, 30) } Write-Host "" Write-Host " Phase 2: Training DQN agent ($Episodes episodes)..." -ForegroundColor Gray $config = [DQNConfig]::new() $config.StateSize = 4 $config.ActionSize = 4 $config.EpsilonDecay = 0.9995 $config.EpsilonMin = 0.05 [int[]] $arch = @(4, 24, 24, 4) $mainNetwork = [NeuralNetwork]::new($arch, $config.LearningRate) $targetNetwork = [NeuralNetwork]::new($arch, $config.LearningRate) $memory = [ExperienceReplay]::new($config.MemorySize) $agent = [DQNAgent]::new($config, $mainNetwork, $targetNetwork, $memory) $results = [System.Collections.Generic.List[object]]::new() for ($ep = 1; $ep -le $Episodes; $ep++) { [double[]] $state = @(0.0, 0.0, 0.0, 0.0) if ($SimMode) { $roll = Get-Random -Minimum 1 -Maximum 100 if ($roll -le 25) { $dbEnv.CurrentSeverity = 0 } elseif ($roll -le 55) { $dbEnv.CurrentSeverity = 1 } elseif ($roll -le 80) { $dbEnv.CurrentSeverity = 2 } else { $dbEnv.CurrentSeverity = 3 } [double[]] $snArr = @(0.0) $snArr[0] = $dbEnv.CurrentSeverity $snArr[0] /= 3.0 $dbEnv.SeverityNorm = $snArr[0] switch ($dbEnv.CurrentSeverity) { 0 { $dbEnv.DataStaleness = [double](Get-Random -Minimum 0 -Maximum 15) / 100.0 $dbEnv.RenderLoad = [double](Get-Random -Minimum 0 -Maximum 20) / 100.0 $dbEnv.AlertDensity = [double](Get-Random -Minimum 0 -Maximum 10) / 100.0 } 1 { $dbEnv.DataStaleness = [double](Get-Random -Minimum 20 -Maximum 45) / 100.0 $dbEnv.RenderLoad = [double](Get-Random -Minimum 20 -Maximum 55) / 100.0 $dbEnv.AlertDensity = [double](Get-Random -Minimum 10 -Maximum 30) / 100.0 } 2 { $dbEnv.DataStaleness = [double](Get-Random -Minimum 50 -Maximum 75) / 100.0 $dbEnv.RenderLoad = [double](Get-Random -Minimum 55 -Maximum 80) / 100.0 $dbEnv.AlertDensity = [double](Get-Random -Minimum 30 -Maximum 65) / 100.0 } 3 { $dbEnv.DataStaleness = [double](Get-Random -Minimum 75 -Maximum 100) / 100.0 $dbEnv.RenderLoad = [double](Get-Random -Minimum 80 -Maximum 100) / 100.0 $dbEnv.AlertDensity = [double](Get-Random -Minimum 65 -Maximum 100) / 100.0 } } # UrgencyScore: composite - always varies, no dead signal [double[]] $uArr = @(0.0) $uArr[0] = $dbEnv.DataStaleness + $dbEnv.AlertDensity $uArr[0] /= 2.0 $dbEnv.UrgencyScore = $uArr[0] $dbEnv.CorrectActions = 0 $dbEnv.MissedUpdates = 0 $dbEnv.Steps = 0 $dbEnv.TotalReward = 0.0 $dbEnv.LastDone = $false $dbEnv.EpisodeCount++ $state = $dbEnv.GetState() } else { $state = $dbEnv.Reset() } $done = $false $epReward = 0.0 $cacheCount = 0 $refreshCount = 0 $prioritiseCount = 0 $rebuildCount = 0 [int] $stepCount = 0 while (-not $done) { $action = $agent.Act($state) $dbEnv.Step($action) [double[]] $nextState = $dbEnv.GetState() [double] $reward = $dbEnv.LastReward [bool] $isDone = $dbEnv.LastDone $agent.Remember($state, $action, $reward, $nextState, $isDone) $stepCount++ if ($stepCount % 4 -eq 0) { $agent.Replay() } $state = $nextState $done = $isDone $epReward += $reward switch ($action) { 0 { $cacheCount++ } 1 { $refreshCount++ } 2 { $prioritiseCount++ } 3 { $rebuildCount++ } } } $agent.EndEpisode($epReward) $results.Add(@{ Episode = $ep Reward = $epReward Cache = $cacheCount Refresh = $refreshCount Prioritise = $prioritiseCount Rebuild = $rebuildCount Epsilon = $agent.Epsilon }) if ($ep % $PrintEvery -eq 0) { $lastN = $results | Select-Object -Last $PrintEvery $avgSum = 0.0 foreach ($r2 in $lastN) { $avgSum += $r2.Reward } [double[]] $avgArr = @(0.0) $avgArr[0] = $avgSum $avgArr[0] /= $lastN.Count $avg = [Math]::Round($avgArr[0], 2) Write-Host (" Ep {0,4}/{1} AvgReward: {2,7} Eps: {3:F3} Cac:{4} Ref:{5} Pri:{6} Rbd:{7}" -f ` $ep, $Episodes, $avg, $agent.Epsilon, $cacheCount, $refreshCount, $prioritiseCount, $rebuildCount) -ForegroundColor White } } # Phase 3: Evaluation - inline loop (epsilon=0) Write-Host "" Write-Host " Phase 3: Final evaluation (epsilon=0 - 10 episodes)..." -ForegroundColor Gray $agent.Epsilon = 0.0 $trainedRewards = @() for ($t = 1; $t -le 10; $t++) { [double[]] $evalState = $dbEnv.Reset() $tReward = 0.0 while (-not $dbEnv.LastDone) { $tAction = $agent.Act($evalState) $dbEnv.Step($tAction) [double[]] $evalState = $dbEnv.GetState() $tReward += $dbEnv.LastReward } $trainedRewards += $tReward } [double[]] $tAvgArr = @(0.0) $tAvgArr[0] = ($trainedRewards | Measure-Object -Average).Average Write-Host (" Trained avg reward: {0:F2}" -f $tAvgArr[0]) -ForegroundColor Green [double[]] $impArr = @(0.0) if ($bAvgArr[0] -ne 0) { $impArr[0] = $tAvgArr[0] - $bAvgArr[0] $impArr[0] /= [Math]::Abs($bAvgArr[0]) $impArr[0] *= 100.0 } $bAvg = [Math]::Round($bAvgArr[0], 2) $tAvg = [Math]::Round($tAvgArr[0], 2) $improvement = [Math]::Round($impArr[0], 1) [double[]] $precArr = @(0.0) [double[]] $recArr = @(0.0) $denomP = $dbEnv.TruePositives + $dbEnv.FalsePositives $denomR = $dbEnv.TruePositives + $dbEnv.FalseNegatives if ($denomP -gt 0) { $precArr[0] = $dbEnv.TruePositives; $precArr[0] /= $denomP } if ($denomR -gt 0) { $recArr[0] = $dbEnv.TruePositives; $recArr[0] /= $denomR } $precPct = [Math]::Round($precArr[0] * 100, 1) $recPct = [Math]::Round($recArr[0] * 100, 1) Write-Host "" Write-Host "╔══════════════════════════════════════════════════╗" -ForegroundColor Cyan Write-Host "║ Phase 15: Enterprise Dashboard - Results ║" -ForegroundColor Cyan Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan Write-Host ("║ Baseline (random) avg reward : {0,8} ║" -f $bAvg) -ForegroundColor Gray Write-Host ("║ Trained (DQN) avg reward : {0,8} ║" -f $tAvg) -ForegroundColor Green Write-Host ("║ Improvement : {0,7}% ║" -f $improvement) -ForegroundColor Yellow Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan Write-Host ("║ Precision (Pri+Rebuild correct): {0,6}% ║" -f $precPct) -ForegroundColor Cyan Write-Host ("║ Recall (critical updates) : {0,7}% ║" -f $recPct) -ForegroundColor Cyan Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan Write-Host "║ Agent learned to: ║" -ForegroundColor Cyan Write-Host "║ Cache serve fresh cached data ║" -ForegroundColor White Write-Host "║ Refresh update panels on staleness ║" -ForegroundColor White Write-Host "║ Prioritise elevate critical KPIs ║" -ForegroundColor White Write-Host "║ Rebuild full reload on critical state ║" -ForegroundColor White Write-Host "╚══════════════════════════════════════════════════╝" -ForegroundColor Cyan Write-Host "" return @{ Agent = $agent; Results = $results; Baseline = @{ Avg = $bAvg }; Trained = @{ Avg = $tAvg } } } # ============================================================ # TEST SUGGESTIONS # ============================================================ # 1. Run VBAF.LoadAll.ps1 (loads core DQN + all pillars) # # 2. QUICK DEMO (simulated dashboard conditions) # $r = Invoke-VBAFDashboardTraining -Episodes 100 -PrintEvery 10 -SimMode # # 3. FULL TRAINING (real WMI memory, sessions, event log) # $r = Invoke-VBAFDashboardTraining -Episodes 100 -PrintEvery 10 # # 4. SKIP REAL DATA PROBE # $r = Invoke-VBAFDashboardTraining -Episodes 100 -PrintEvery 10 -SkipRealData # # 5. INSPECT AGENT DECISIONS # $env = [DashboardEnvironment]::new() # $state = $env.Reset() # Write-Host "Staleness: $($env.DataStaleness) UrgencyScore: $($env.UrgencyScore)" # $action = $r.Agent.Act($state) # $labels = @("Cache","Refresh","Prioritise","Rebuild") # Write-Host "Dashboard decision: $($labels[$action])" # # 6. VIEW CONFUSION MATRIX # Write-Host "True Positives : $($env.TruePositives)" # Write-Host "False Positives: $($env.FalsePositives)" # Write-Host "True Negatives : $($env.TrueNegatives)" # Write-Host "False Negatives: $($env.FalseNegatives)" # ============================================================ Write-Host "📦 VBAF.Enterprise.Dashboard.ps1 loaded [v3.5.0 📊]" -ForegroundColor Green Write-Host " Phase 15 : Enterprise Dashboard Intelligence" -ForegroundColor Cyan Write-Host " Function : Invoke-VBAFDashboardTraining" -ForegroundColor Cyan Write-Host "" Write-Host " Quick start:" -ForegroundColor Yellow Write-Host ' $r = Invoke-VBAFDashboardTraining -Episodes 100 -PrintEvery 10 -SimMode' -ForegroundColor White Write-Host "" |