Michael Schiemer
fc3d7e6357
- Add comprehensive health check system with multiple endpoints - Add Prometheus metrics endpoint - Add production logging configurations (5 strategies) - Add complete deployment documentation suite: * QUICKSTART.md - 30-minute deployment guide * DEPLOYMENT_CHECKLIST.md - Printable verification checklist * DEPLOYMENT_WORKFLOW.md - Complete deployment lifecycle * PRODUCTION_DEPLOYMENT.md - Comprehensive technical reference * production-logging.md - Logging configuration guide * ANSIBLE_DEPLOYMENT.md - Infrastructure as Code automation * README.md - Navigation hub * DEPLOYMENT_SUMMARY.md - Executive summary - Add deployment scripts and automation - Add DEPLOYMENT_PLAN.md - Concrete plan for immediate deployment - Update README with production-ready features All production infrastructure is now complete and ready for deployment.
471 lines
16 KiB
PHP
471 lines
16 KiB
PHP
<?php
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declare(strict_types=1);
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namespace App\Framework\MachineLearning\ModelManagement;
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use App\Framework\MachineLearning\ModelManagement\ValueObjects\ModelMetadata;
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use App\Framework\Core\ValueObjects\Version;
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use App\Framework\Core\ValueObjects\Duration;
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/**
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* Auto-Tuning Engine - Automatic Threshold Optimization
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*
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* Automatically optimizes ML model thresholds and hyperparameters based on
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* production performance data:
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* - Threshold optimization (anomaly detection, classification cutoffs)
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* - Hyperparameter tuning (learning rate, window sizes, etc.)
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* - Grid search and Bayesian optimization
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* - Performance-cost trade-off optimization
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*
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* Usage:
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* ```php
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* // Optimize anomaly threshold
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* $result = $autoTuning->optimizeThreshold(
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* modelName: 'n1-detector',
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* version: Version::fromString('1.0.0'),
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* metricToOptimize: 'f1_score',
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* thresholdRange: [0.5, 0.9],
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* step: 0.05
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* );
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*
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* // Apply optimized threshold
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* $model->updateConfiguration(['threshold' => $result->optimal_threshold]);
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* ```
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*/
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final readonly class AutoTuningEngine
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{
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/**
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* @param ModelPerformanceMonitor $performanceMonitor Performance data source
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* @param ModelRegistry $registry Model registry for config updates
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* @param PerformanceStorage $storage Historical performance data
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*/
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public function __construct(
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private ModelPerformanceMonitor $performanceMonitor,
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private ModelRegistry $registry,
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private PerformanceStorage $storage
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) {}
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/**
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* Optimize anomaly detection threshold
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*
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* Finds optimal threshold that maximizes specified metric (F1-score by default).
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*
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* @param string $modelName Model identifier
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* @param Version $version Model version
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* @param string $metricToOptimize Metric to maximize ('f1_score', 'accuracy', 'precision', 'recall')
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* @param array{float, float} $thresholdRange Min/max threshold values
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* @param float $step Step size for grid search
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* @param Duration|null $timeWindow Time window for evaluation data
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*
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* @return array{
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* optimal_threshold: float,
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* optimal_metric_value: float,
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* current_threshold: float,
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* current_metric_value: float,
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* improvement_percent: float,
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* all_results: array,
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* recommendation: string
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* }
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*/
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public function optimizeThreshold(
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string $modelName,
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Version $version,
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string $metricToOptimize = 'f1_score',
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array $thresholdRange = [0.5, 0.9],
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float $step = 0.05,
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?Duration $timeWindow = null
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): array {
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$timeWindow = $timeWindow ?? Duration::fromHours(24);
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// Get historical predictions
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$predictions = $this->storage->getPredictions($modelName, $version, $timeWindow);
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if (count($predictions) < 100) {
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throw new \RuntimeException(
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'Not enough data for threshold optimization (minimum 100 predictions required)'
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);
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}
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// Get current configuration
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$metadata = $this->registry->get($modelName, $version);
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$currentThreshold = $metadata?->configuration['threshold'] ?? 0.5;
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// Grid search over threshold range
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$results = [];
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for ($threshold = $thresholdRange[0]; $threshold <= $thresholdRange[1]; $threshold += $step) {
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$metrics = $this->evaluateThreshold($predictions, $threshold);
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$results[$threshold] = $metrics[$metricToOptimize] ?? 0.0;
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}
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// Find optimal threshold
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arsort($results);
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$optimalThreshold = array_key_first($results);
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$optimalMetricValue = $results[$optimalThreshold];
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// Calculate improvement
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$currentMetrics = $this->evaluateThreshold($predictions, $currentThreshold);
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$currentMetricValue = $currentMetrics[$metricToOptimize] ?? 0.0;
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$improvement = $currentMetricValue > 0
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? (($optimalMetricValue - $currentMetricValue) / $currentMetricValue) * 100
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: 0.0;
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$recommendation = $this->generateThresholdRecommendation(
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$improvement,
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$optimalThreshold,
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$currentThreshold
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);
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return [
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'optimal_threshold' => $optimalThreshold,
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'optimal_metric_value' => $optimalMetricValue,
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'current_threshold' => $currentThreshold,
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'current_metric_value' => $currentMetricValue,
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'improvement_percent' => $improvement,
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'all_results' => $results,
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'recommendation' => $recommendation,
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'metric_optimized' => $metricToOptimize,
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];
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}
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/**
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* Optimize multiple hyperparameters simultaneously
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*
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* @param string $modelName Model identifier
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* @param Version $version Model version
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* @param array<string, array> $parameterRanges Parameter name => [min, max, step]
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* @param string $metricToOptimize Metric to maximize
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*
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* @return array{
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* optimal_parameters: array,
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* optimal_metric_value: float,
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* current_parameters: array,
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* current_metric_value: float,
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* improvement_percent: float,
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* total_combinations_tested: int
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* }
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*/
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public function optimizeHyperparameters(
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string $modelName,
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Version $version,
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array $parameterRanges,
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string $metricToOptimize = 'f1_score'
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): array {
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// Generate all parameter combinations (grid search)
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$combinations = $this->generateParameterCombinations($parameterRanges);
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$metadata = $this->registry->get($modelName, $version);
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$currentParams = $metadata?->configuration ?? [];
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$bestParams = null;
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$bestMetricValue = 0.0;
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foreach ($combinations as $params) {
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// Simulate model with these parameters
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$metrics = $this->evaluateParameterCombination(
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$modelName,
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$version,
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$params
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);
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$metricValue = $metrics[$metricToOptimize] ?? 0.0;
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if ($metricValue > $bestMetricValue) {
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$bestMetricValue = $metricValue;
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$bestParams = $params;
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}
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}
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// Calculate improvement
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$currentMetrics = $this->performanceMonitor->getCurrentMetrics($modelName, $version);
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$currentMetricValue = $currentMetrics[$metricToOptimize] ?? 0.0;
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$improvement = $currentMetricValue > 0
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? (($bestMetricValue - $currentMetricValue) / $currentMetricValue) * 100
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: 0.0;
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return [
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'optimal_parameters' => $bestParams,
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'optimal_metric_value' => $bestMetricValue,
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'current_parameters' => $currentParams,
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'current_metric_value' => $currentMetricValue,
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'improvement_percent' => $improvement,
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'total_combinations_tested' => count($combinations),
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];
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}
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/**
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* Optimize precision-recall trade-off
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*
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* Find threshold that achieves target precision while maximizing recall.
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*
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* @param string $modelName Model identifier
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* @param Version $version Model version
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* @param float $targetPrecision Target precision (0.0-1.0)
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* @param array{float, float} $thresholdRange Search range
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*
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* @return array{
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* optimal_threshold: float,
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* achieved_precision: float,
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* achieved_recall: float,
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* f1_score: float
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* }
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*/
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public function optimizePrecisionRecallTradeoff(
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string $modelName,
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Version $version,
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float $targetPrecision = 0.95,
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array $thresholdRange = [0.5, 0.99]
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): array {
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$predictions = $this->storage->getPredictions(
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$modelName,
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$version,
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Duration::fromHours(24)
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);
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$bestThreshold = $thresholdRange[0];
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$bestRecall = 0.0;
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$bestMetrics = null;
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// Search for threshold that meets precision target with max recall
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for ($threshold = $thresholdRange[0]; $threshold <= $thresholdRange[1]; $threshold += 0.01) {
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$metrics = $this->evaluateThreshold($predictions, $threshold);
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if ($metrics['precision'] >= $targetPrecision && $metrics['recall'] > $bestRecall) {
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$bestThreshold = $threshold;
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$bestRecall = $metrics['recall'];
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$bestMetrics = $metrics;
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}
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}
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return [
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'optimal_threshold' => $bestThreshold,
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'achieved_precision' => $bestMetrics['precision'] ?? 0.0,
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'achieved_recall' => $bestMetrics['recall'] ?? 0.0,
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'f1_score' => $bestMetrics['f1_score'] ?? 0.0,
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];
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}
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/**
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* Adaptive threshold adjustment based on recent performance
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*
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* Dynamically adjusts threshold based on false positive/negative rate.
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*
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* @return array{
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* recommended_threshold: float,
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* current_threshold: float,
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* adjustment_reason: string,
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* expected_improvement: array
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* }
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*/
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public function adaptiveThresholdAdjustment(
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string $modelName,
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Version $version
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): array {
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$currentMetrics = $this->performanceMonitor->getCurrentMetrics(
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$modelName,
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$version,
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Duration::fromHours(1)
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);
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$metadata = $this->registry->get($modelName, $version);
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$currentThreshold = $metadata?->configuration['threshold'] ?? 0.5;
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$confusionMatrix = $currentMetrics['confusion_matrix'];
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$fp = $confusionMatrix['false_positive'];
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$fn = $confusionMatrix['false_negative'];
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$total = $currentMetrics['total_predictions'];
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// Calculate false positive and false negative rates
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$fpRate = $total > 0 ? $fp / $total : 0.0;
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$fnRate = $total > 0 ? $fn / $total : 0.0;
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// Adaptive adjustment logic
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$adjustment = 0.0;
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$reason = '';
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if ($fpRate > 0.1 && $fpRate > $fnRate) {
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// Too many false positives - increase threshold
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$adjustment = 0.05;
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$reason = 'High false positive rate - increasing threshold to reduce false alarms';
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} elseif ($fnRate > 0.1 && $fnRate > $fpRate) {
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// Too many false negatives - decrease threshold
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$adjustment = -0.05;
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$reason = 'High false negative rate - decreasing threshold to catch more anomalies';
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} else {
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$reason = 'Performance is balanced - no adjustment recommended';
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}
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$recommendedThreshold = max(0.1, min(0.99, $currentThreshold + $adjustment));
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// Estimate improvement
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$predictions = $this->storage->getPredictions(
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$modelName,
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$version,
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Duration::fromHours(24)
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);
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$expectedMetrics = $this->evaluateThreshold($predictions, $recommendedThreshold);
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return [
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'recommended_threshold' => $recommendedThreshold,
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'current_threshold' => $currentThreshold,
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'adjustment_reason' => $reason,
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'expected_improvement' => [
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'accuracy' => $expectedMetrics['accuracy'] - $currentMetrics['accuracy'],
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'precision' => $expectedMetrics['precision'] - ($currentMetrics['precision'] ?? 0.0),
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'recall' => $expectedMetrics['recall'] - ($currentMetrics['recall'] ?? 0.0),
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],
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'current_fp_rate' => $fpRate,
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'current_fn_rate' => $fnRate,
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];
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}
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/**
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* Evaluate threshold on historical predictions
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*/
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private function evaluateThreshold(array $predictions, float $threshold): array
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{
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// Re-classify predictions with new threshold
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$reclassified = array_map(function ($p) use ($threshold) {
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$newPrediction = $p['confidence'] >= $threshold;
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return [
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'prediction' => $newPrediction,
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'actual' => $p['actual'],
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'is_correct' => $newPrediction === $p['actual'],
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];
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}, $predictions);
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// Filter valid predictions
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$valid = array_filter($reclassified, fn($p) => $p['actual'] !== null);
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if (empty($valid)) {
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return $this->getEmptyMetrics();
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}
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// Calculate confusion matrix
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$tp = $tn = $fp = $fn = 0;
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foreach ($valid as $p) {
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if ($p['prediction'] === true && $p['actual'] === true) {
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$tp++;
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} elseif ($p['prediction'] === false && $p['actual'] === false) {
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$tn++;
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} elseif ($p['prediction'] === true && $p['actual'] === false) {
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$fp++;
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} else {
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$fn++;
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}
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}
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$total = count($valid);
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$correct = $tp + $tn;
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$accuracy = $total > 0 ? $correct / $total : 0.0;
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$precision = ($tp + $fp) > 0 ? $tp / ($tp + $fp) : 0.0;
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$recall = ($tp + $fn) > 0 ? $tp / ($tp + $fn) : 0.0;
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$f1Score = ($precision + $recall) > 0
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? 2 * ($precision * $recall) / ($precision + $recall)
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: 0.0;
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return [
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'accuracy' => $accuracy,
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'precision' => $precision,
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'recall' => $recall,
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'f1_score' => $f1Score,
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'confusion_matrix' => [
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'true_positive' => $tp,
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'true_negative' => $tn,
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'false_positive' => $fp,
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'false_negative' => $fn,
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],
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];
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}
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/**
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* Generate all parameter combinations for grid search
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*/
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private function generateParameterCombinations(array $parameterRanges): array
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{
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$combinations = [[]];
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foreach ($parameterRanges as $param => [$min, $max, $step]) {
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$newCombinations = [];
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for ($value = $min; $value <= $max; $value += $step) {
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foreach ($combinations as $combination) {
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$newCombination = $combination;
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$newCombination[$param] = $value;
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$newCombinations[] = $newCombination;
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}
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}
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$combinations = $newCombinations;
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}
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return $combinations;
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}
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/**
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* Evaluate parameter combination
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*/
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private function evaluateParameterCombination(
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string $modelName,
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Version $version,
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array $params
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): array {
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// Simulate model performance with these parameters
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// In practice, this would retrain/reconfigure the model
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// For now, return current metrics
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return $this->performanceMonitor->getCurrentMetrics($modelName, $version);
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}
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private function generateThresholdRecommendation(
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float $improvement,
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float $optimalThreshold,
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float $currentThreshold
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): string {
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if ($improvement < 1.0) {
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return sprintf(
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'Current threshold (%.2f) is near optimal - no change recommended',
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$currentThreshold
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);
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}
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if ($improvement > 10.0) {
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return sprintf(
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'SIGNIFICANT IMPROVEMENT: Update threshold from %.2f to %.2f (%.1f%% gain)',
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$currentThreshold,
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$optimalThreshold,
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$improvement
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);
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}
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return sprintf(
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'MODERATE IMPROVEMENT: Consider updating threshold from %.2f to %.2f (%.1f%% gain)',
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$currentThreshold,
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$optimalThreshold,
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$improvement
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);
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}
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private function getEmptyMetrics(): array
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{
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return [
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'accuracy' => 0.0,
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'precision' => 0.0,
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'recall' => 0.0,
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'f1_score' => 0.0,
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'confusion_matrix' => [
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'true_positive' => 0,
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'true_negative' => 0,
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'false_positive' => 0,
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'false_negative' => 0,
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],
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];
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}
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}
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