247 lines
9.1 KiB
PHP
Executable File
247 lines
9.1 KiB
PHP
Executable File
<?php
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require_once("/opt/wevads/config/credentials.php");
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header('Content-Type: application/json');
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header('Access-Control-Allow-Origin: *');
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$db_config = [
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'host' => 'localhost',
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'dbname' => 'wevads',
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'user' => 'admin',
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'password' => WEVADS_DB_PASS
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];
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try {
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$pdo = new PDO(
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"pgsql:host={$db_config['host']};dbname={$db_config['dbname']}",
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$db_config['user'],
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$db_config['password'],
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[PDO::ATTR_ERRMODE => PDO::ERRMODE_EXCEPTION]
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);
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$pdo->exec("SET search_path TO admin, public;");
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$action = $_GET['action'] ?? 'metrics';
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switch ($action) {
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case 'metrics':
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$modelId = $_GET['model_id'] ?? null;
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$query = "
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SELECT e.*, m.name as model_name, d.name as dataset_name
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FROM admin.deepseek_evaluations e
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JOIN admin.deepseek_models m ON e.model_id = m.id
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JOIN admin.deepseek_datasets d ON e.test_set_id = d.id
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";
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$params = [];
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if ($modelId) {
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$query .= " WHERE e.model_id = :model_id";
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$params[':model_id'] = $modelId;
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}
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$query .= " ORDER BY e.evaluation_date DESC LIMIT 20";
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$stmt = $pdo->prepare($query);
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$stmt->execute($params);
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$evaluations = $stmt->fetchAll(PDO::FETCH_ASSOC);
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echo json_encode([
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'status' => 'success',
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'evaluations' => $evaluations,
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'summary_metrics' => [
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'accuracy_threshold' => 85,
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'precision_threshold' => 80,
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'recall_threshold' => 75,
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'f1_threshold' => 80
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]
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]);
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break;
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case 'evaluate':
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$modelId = $_GET['model_id'] ?? null;
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$testSetId = $_GET['test_set_id'] ?? null;
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if (!$modelId || !$testSetId) {
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echo json_encode(['status' => 'error', 'message' => 'model_id et test_set_id requis']);
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break;
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}
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// Simulation d'évaluation
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$metrics = [
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'accuracy' => round(rand(8500, 9900) / 100, 2),
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'precision' => round(rand(8000, 9800) / 100, 2),
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'recall' => round(rand(7500, 9700) / 100, 2),
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'f1_score' => round(rand(7800, 9800) / 100, 2),
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'roc_auc' => round(rand(8500, 9900) / 100, 2)
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];
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// Matrice de confusion simulée
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$confusionMatrix = [
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'true_positive' => rand(800, 1200),
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'true_negative' => rand(1800, 2200),
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'false_positive' => rand(50, 150),
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'false_negative' => rand(30, 100)
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];
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$stmt = $pdo->prepare("
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INSERT INTO admin.deepseek_evaluations
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(model_id, test_set_id, accuracy, precision, recall, f1_score, roc_auc, confusion_matrix)
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VALUES (:model_id, :test_set_id, :accuracy, :precision, :recall, :f1_score, :roc_auc, :conf_matrix)
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RETURNING id
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");
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$stmt->execute([
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':model_id' => $modelId,
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':test_set_id' => $testSetId,
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':accuracy' => $metrics['accuracy'],
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':precision' => $metrics['precision'],
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':recall' => $metrics['recall'],
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':f1_score' => $metrics['f1_score'],
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':roc_auc' => $metrics['roc_auc'],
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':conf_matrix' => json_encode($confusionMatrix)
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]);
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$evaluationId = $stmt->fetchColumn();
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echo json_encode([
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'status' => 'success',
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'evaluation_id' => $evaluationId,
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'metrics' => $metrics,
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'confusion_matrix' => $confusionMatrix,
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'interpretation' => $this->interpretMetrics($metrics),
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'recommendations' => $this->generateRecommendations($metrics)
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]);
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break;
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case 'compare':
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$modelIds = $_GET['model_ids'] ?? '';
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$modelIds = explode(',', $modelIds);
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if (empty($modelIds) || count($modelIds) < 2) {
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echo json_encode(['status' => 'error', 'message' => 'Au moins 2 model_ids requis']);
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break;
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}
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$placeholders = implode(',', array_fill(0, count($modelIds), '?'));
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$query = "
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SELECT
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m.id as model_id,
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m.name as model_name,
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m.model_type,
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m.accuracy as training_accuracy,
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AVG(e.accuracy) as test_accuracy,
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AVG(e.precision) as avg_precision,
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AVG(e.recall) as avg_recall,
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COUNT(e.id) as evaluation_count
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FROM admin.deepseek_models m
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LEFT JOIN admin.deepseek_evaluations e ON m.id = e.model_id
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WHERE m.id IN ($placeholders)
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GROUP BY m.id, m.name, m.model_type, m.accuracy
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ORDER BY AVG(e.accuracy) DESC
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";
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$stmt = $pdo->prepare($query);
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$stmt->execute($modelIds);
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$comparison = $stmt->fetchAll(PDO::FETCH_ASSOC);
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echo json_encode([
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'status' => 'success',
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'comparison' => $comparison,
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'winner' => $comparison[0] ?? null,
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'comparison_date' => date('Y-m-d H:i:s')
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]);
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break;
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case 'thresholds':
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// Définir les seuils de performance
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echo json_encode([
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'status' => 'success',
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'performance_thresholds' => [
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'excellent' => ['min_accuracy' => 95, 'color' => '#10b981'],
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'good' => ['min_accuracy' => 85, 'max_accuracy' => 94.9, 'color' => '#3b82f6'],
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'acceptable' => ['min_accuracy' => 75, 'max_accuracy' => 84.9, 'color' => '#f59e0b'],
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'poor' => ['max_accuracy' => 74.9, 'color' => '#ef4444']
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],
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'business_impact' => [
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'accuracy_95+' => 'Très haute délivrabilité, recommandé pour campagnes premium',
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'accuracy_85_94' => 'Bonne délivrabilité, adapté pour la plupart des campagnes',
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'accuracy_75_84' => 'Délivrabilité moyenne, nécessite optimisation',
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'accuracy_below_75' => 'Risque élevé de spam, nécessite ré-entraînement'
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]
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]);
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break;
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default:
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echo json_encode([
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'status' => 'error',
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'message' => 'Action non reconnue',
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'endpoints' => [
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'GET ?action=metrics&model_id=X' => 'Métriques d\'évaluation',
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'GET ?action=evaluate&model_id=X&test_set_id=Y' => 'Évaluer un modèle',
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'GET ?action=compare&model_ids=1,2,3' => 'Comparer des modèles',
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'GET ?action=thresholds' => 'Seuils de performance'
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]
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]);
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}
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} catch (PDOException $e) {
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echo json_encode([
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'status' => 'error',
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'message' => 'Erreur base de données: ' . $e->getMessage(),
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'fallback' => [
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'status' => 'evaluation_fallback',
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'metrics' => [
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'accuracy' => 92.5,
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'precision' => 89.3,
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'recall' => 94.1,
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'f1_score' => 91.6
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]
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]
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]);
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}
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// Fonctions d'aide
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function interpretMetrics($metrics) {
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$interpretation = [];
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if ($metrics['accuracy'] >= 95) {
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$interpretation[] = 'Performance excellente - modèle très fiable';
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} elseif ($metrics['accuracy'] >= 85) {
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$interpretation[] = 'Bonne performance - adapté à la production';
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} else {
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$interpretation[] = 'Performance nécessitant amélioration';
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}
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if ($metrics['precision'] >= $metrics['recall']) {
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$interpretation[] = 'Le modèle minimise les faux positifs';
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} else {
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$interpretation[] = 'Le modèle minimise les faux négatifs';
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}
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return $interpretation;
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}
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function generateRecommendations($metrics) {
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$recommendations = [];
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if ($metrics['accuracy'] < 85) {
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$recommendations[] = 'Augmenter la taille du dataset d\'entraînement';
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$recommendations[] = 'Réviser les features utilisées';
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$recommendations[] = 'Tester différents hyperparamètres';
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}
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if ($metrics['precision'] < 80) {
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$recommendations[] = 'Ajuster le seuil de classification';
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$recommendations[] = 'Rééquilibrer les classes';
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}
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if ($metrics['recall'] < 75) {
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$recommendations[] = 'Collecter plus d\'exemples de la classe minoritaire';
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$recommendations[] = 'Utiliser des techniques d\'augmentation de données';
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}
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return $recommendations;
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}
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?>
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