<?php

declare(strict_types=1);

/**
 * N+1 Detection Model Management Integration Example
 *
 * Demonstrates how the N+1 Detection ML system integrates with
 * the ML Model Management System for versioning, monitoring, and auto-tuning.
 */

require_once __DIR__ . '/../vendor/autoload.php';

use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneModelAdapter;
use App\Framework\Database\NPlusOneDetection\QueryExecutionContext;
use App\Framework\MachineLearning\ModelManagement\ModelRegistry;
use App\Framework\MachineLearning\ModelManagement\ModelPerformanceMonitor;
use App\Framework\MachineLearning\ModelManagement\AutoTuningEngine;
use App\Framework\Core\ValueObjects\Version;
use App\Framework\Core\ValueObjects\Duration;

// ============================================================================
// Setup: Get services from DI container
// ============================================================================

$container = require __DIR__ . '/../bootstrap/container.php';

$adapter = $container->get(NPlusOneModelAdapter::class);
$registry = $container->get(ModelRegistry::class);
$performanceMonitor = $container->get(ModelPerformanceMonitor::class);
$autoTuning = $container->get(AutoTuningEngine::class);

echo "=== N+1 Detection Model Management Integration ===\n\n";

// ============================================================================
// Example 1: Model Registration
// ============================================================================

echo "1. Model Registration\n";
echo str_repeat('-', 60) . "\n";

// Register current N+1 detector model
$metadata = $adapter->registerCurrentModel();

echo "✓ Registered N+1 Detector Model\n";
echo "  Name: {$metadata->modelName}\n";
echo "  Version: {$metadata->version->toString()}\n";
echo "  Type: {$metadata->modelType->value}\n";
echo "  Configuration:\n";
foreach ($metadata->configuration as $key => $value) {
    echo "    - {$key}: " . json_encode($value) . "\n";
}
echo "\n";

// ============================================================================
// Example 2: Analysis with Automatic Tracking
// ============================================================================

echo "2. Analysis with Automatic Tracking\n";
echo str_repeat('-', 60) . "\n";

// Simulate query execution contexts
$contexts = [
    // N+1 pattern detected
    new QueryExecutionContext(
        query: 'SELECT * FROM users WHERE id = ?',
        bindParams: [1],
        executionTime: Duration::fromMilliseconds(5),
        stackTrace: ['UserRepository::find', 'UserController::show', 'index.php'],
        contextData: [
            'parent_query' => 'SELECT * FROM posts',
            'query_count_in_request' => 15,
            'similar_query_count' => 12
        ]
    ),

    // Normal query (no N+1)
    new QueryExecutionContext(
        query: 'SELECT * FROM users',
        bindParams: [],
        executionTime: Duration::fromMilliseconds(25),
        stackTrace: ['UserRepository::findAll', 'UserController::index', 'index.php'],
        contextData: [
            'query_count_in_request' => 3,
            'similar_query_count' => 0
        ]
    ),
];

foreach ($contexts as $i => $context) {
    echo "\nAnalyzing Query " . ($i + 1) . "...\n";

    // Analyze with automatic tracking (ground truth provided for training)
    $groundTruth = $i === 0; // First query is N+1, second is not

    $result = $adapter->analyzeWithTracking($context, $groundTruth);

    echo "  Query: {$context->query}\n";
    echo "  Execution Time: {$context->executionTime->toMilliseconds()}ms\n";
    echo "  Detection Result:\n";
    echo "    - Success: " . ($result['success'] ? 'Yes' : 'No') . "\n";
    echo "    - Anomalies Found: " . count($result['anomalies']) . "\n";
    echo "    - Confidence: " . sprintf('%.2f', $result['overall_confidence']) . "\n";
    echo "  Tracking Info:\n";
    echo "    - Prediction: " . ($result['tracking']['prediction'] ? 'N+1 Detected' : 'Normal') . "\n";
    echo "    - Ground Truth: " . ($groundTruth ? 'N+1 Pattern' : 'Normal Query') . "\n";
    echo "    - Tracked: " . ($result['tracking']['tracked'] ? 'Yes' : 'No') . "\n";
}

echo "\n";

// ============================================================================
// Example 3: Real-Time Performance Monitoring
// ============================================================================

echo "3. Real-Time Performance Monitoring\n";
echo str_repeat('-', 60) . "\n";

// Get current performance metrics
$metrics = $adapter->getCurrentPerformanceMetrics();

echo "Performance Metrics (last 1 hour):\n";
echo "  Total Predictions: {$metrics['total_predictions']}\n";
echo "  Correct Predictions: {$metrics['correct_predictions']}\n";
echo "  Accuracy: " . sprintf('%.4f', $metrics['accuracy']) . "\n";
echo "  Precision: " . sprintf('%.4f', $metrics['precision']) . "\n";
echo "  Recall: " . sprintf('%.4f', $metrics['recall']) . "\n";
echo "  F1-Score: " . sprintf('%.4f', $metrics['f1_score']) . "\n";
echo "  Average Confidence: " . sprintf('%.4f', $metrics['average_confidence']) . "\n";
echo "\n";

// Check for performance degradation
$degradationInfo = $adapter->checkPerformanceDegradation(thresholdPercent: 0.05);

echo "Performance Degradation Check:\n";
echo "  Has Degraded: " . ($degradationInfo['has_degraded'] ? 'Yes' : 'No') . "\n";
echo "  Baseline Accuracy: " . sprintf('%.4f', $degradationInfo['baseline_accuracy']) . "\n";
echo "  Current Accuracy: " . sprintf('%.4f', $degradationInfo['current_accuracy']) . "\n";
echo "  Degradation: " . sprintf('%.2f%%', $degradationInfo['degradation_percent']) . "\n";
echo "\nRecommendation: {$degradationInfo['recommendation']}\n\n";

// ============================================================================
// Example 4: Automatic Threshold Optimization
// ============================================================================

echo "4. Automatic Threshold Optimization\n";
echo str_repeat('-', 60) . "\n";

// Optimize confidence threshold for maximum F1-score
$optimizationResult = $autoTuning->optimizeThreshold(
    modelName: 'n1-detector',
    version: Version::fromString('1.0.0'),
    metricToOptimize: 'f1_score',
    thresholdRange: [0.5, 0.9],
    step: 0.05,
    timeWindow: Duration::fromHours(1)
);

echo "Threshold Optimization Results:\n";
echo "  Current Threshold: " . sprintf('%.2f', $optimizationResult['current_threshold']) . "\n";
echo "  Optimal Threshold: " . sprintf('%.2f', $optimizationResult['optimal_threshold']) . "\n";
echo "  Current F1-Score: " . sprintf('%.4f', $optimizationResult['current_metric_value']) . "\n";
echo "  Optimal F1-Score: " . sprintf('%.4f', $optimizationResult['optimal_metric_value']) . "\n";
echo "  Improvement: " . sprintf('%+.2f%%', $optimizationResult['improvement_percent']) . "\n";
echo "\nRecommendation: {$optimizationResult['recommendation']}\n\n";

// ============================================================================
// Example 5: Adaptive Threshold Adjustment
// ============================================================================

echo "5. Adaptive Threshold Adjustment\n";
echo str_repeat('-', 60) . "\n";

// Automatically adjust threshold based on FP/FN rates
$adaptiveResult = $autoTuning->adaptiveThresholdAdjustment(
    'n1-detector',
    Version::fromString('1.0.0')
);

echo "Adaptive Adjustment Results:\n";
echo "  Current Threshold: " . sprintf('%.2f', $adaptiveResult['current_threshold']) . "\n";
echo "  Recommended Threshold: " . sprintf('%.2f', $adaptiveResult['recommended_threshold']) . "\n";
echo "  Adjustment Reason: {$adaptiveResult['adjustment_reason']}\n";
echo "\nExpected Improvement:\n";
echo "  Accuracy: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['accuracy']) . "\n";
echo "  Precision: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['precision']) . "\n";
echo "  Recall: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['recall']) . "\n";
echo "\n";

// ============================================================================
// Example 6: Model Configuration Update
// ============================================================================

echo "6. Model Configuration Update\n";
echo str_repeat('-', 60) . "\n";

// Update model configuration based on optimization results
if ($optimizationResult['improvement_percent'] > 5.0) {
    echo "Applying optimized threshold...\n";

    $newConfiguration = $metadata->configuration;
    $newConfiguration['confidence_threshold'] = $optimizationResult['optimal_threshold'];

    $adapter->updateConfiguration($newConfiguration);

    echo "✓ Configuration Updated\n";
    echo "  New Threshold: " . sprintf('%.2f', $optimizationResult['optimal_threshold']) . "\n";
    echo "  Expected F1-Score Improvement: " . sprintf('%+.2f%%', $optimizationResult['improvement_percent']) . "\n";
} else {
    echo "Current configuration is optimal. No update needed.\n";
}

echo "\n";

// ============================================================================
// Example 7: Production Deployment
// ============================================================================

echo "7. Production Deployment\n";
echo str_repeat('-', 60) . "\n";

// Deploy to production when performance is validated
if ($metrics['accuracy'] >= 0.90 && !$degradationInfo['has_degraded']) {
    echo "Deploying N+1 Detector to production...\n";

    $adapter->deployToProduction();

    // Verify deployment
    $deployedMetadata = $adapter->getModelMetadata();

    echo "✓ Successfully deployed to production\n";
    echo "  Environment: {$deployedMetadata->environment}\n";
    echo "  Deployed At: {$deployedMetadata->deployedAt->format('Y-m-d H:i:s')}\n";
    echo "  Accuracy: " . sprintf('%.4f', $deployedMetadata->getAccuracy()) . "\n";
} else {
    echo "⚠ Deployment criteria not met:\n";
    if ($metrics['accuracy'] < 0.90) {
        echo "  - Accuracy too low: " . sprintf('%.4f', $metrics['accuracy']) . " (required: 0.90)\n";
    }
    if ($degradationInfo['has_degraded']) {
        echo "  - Performance degradation detected\n";
    }
}

echo "\n";

// ============================================================================
// Example 8: Model Versioning
// ============================================================================

echo "8. Model Versioning\n";
echo str_repeat('-', 60) . "\n";

// List all versions of N+1 detector
$allVersions = $registry->getAll('n1-detector');

echo "N+1 Detector Versions:\n";
foreach ($allVersions as $versionMetadata) {
    echo "  - v{$versionMetadata->version->toString()}\n";
    echo "    Created: {$versionMetadata->createdAt->format('Y-m-d H:i:s')}\n";
    if ($versionMetadata->deployedAt) {
        echo "    Deployed: {$versionMetadata->deployedAt->format('Y-m-d H:i:s')} ({$versionMetadata->environment})\n";
    }
    if ($versionMetadata->getAccuracy()) {
        echo "    Accuracy: " . sprintf('%.4f', $versionMetadata->getAccuracy()) . "\n";
    }
}

echo "\n=== Integration Complete ===\n";