{ "cells": [ { "cell_type": "markdown", "id": "ac140cba", "metadata": {}, "source": [ "# 🔍 Profiling de l'API de prédiction — Guide pour débutants\n", "\n", "## 📚 Qu'est-ce que le profiling ?\n", "\n", "Le **profiling** est une technique qui permet de **mesurer le temps d'exécution** de chaque partie d'un programme. C'est comme un chronomètre ultra-précis qui enregistre combien de temps prend chaque fonction.\n", "\n", "## 🎯 Pourquoi on fait du profiling ?\n", "\n", "Imagine que ton API met **50 ms** à répondre, mais tu ne sais pas **où** elle passe tout ce temps :\n", "- Est-ce le chargement des données ? 🔄\n", "- Est-ce le modèle de prédiction ? 🤖\n", "- Est-ce la transformation des données ? 🔧\n", "\n", "Le profiling répond à cette question en te montrant **ligne par ligne** où le temps est consommé.\n", "\n", "## 🚀 Objectif de ce notebook\n", "\n", "Dans ce notebook, on va profiler la fonction `_predict()` de notre API Flask pour :\n", "1. ⏱️ Mesurer le temps d'exécution total\n", "2. 📊 Identifier les **20 fonctions les plus lentes**\n", "3. 🔴 Trouver le **goulot d'étranglement** (bottleneck)\n", "4. 💡 Proposer des **pistes d'optimisation**\n", "\n", "**Prêt ? C'est parti ! 🚀**" ] }, { "cell_type": "code", "execution_count": null, "id": "a9037598", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Imports OK — fonction _predict() prête pour le profiling\n" ] } ], "source": [ "# ═══════════════════════════════════════════════════════════════════════════\n", "# 📦 IMPORTS ET CONFIGURATION\n", "# ═══════════════════════════════════════════════════════════════════════════\n", "\n", "# ── Imports pour le profiling ──\n", "import cProfile # 🔍 Outil de profiling de Python\n", "import pstats # 📊 Pour formatter les résultats\n", "import io # Pour capturer les résultats dans un buffer\n", "from pstats import SortKey # Pour trier les résultats\n", "\n", "# ── Imports système ──\n", "import sys\n", "import os\n", "\n", "# ── Ajouter le dossier racine du projet au path ──\n", "# Ceci permet d'importer app.py depuis le notebook\n", "ROOT = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n", "if ROOT not in sys.path:\n", " sys.path.insert(0, ROOT)\n", "\n", "# ── Importer la fonction à profiler ──\n", "from app import _predict # ⚡ La fonction interne de notre API\n", "\n", "# ── Imports pour la visualisation ──\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "print(\"✅ Imports OK — fonction _predict() prête pour le profiling\")" ] }, { "cell_type": "markdown", "id": "0b6e6cfb", "metadata": {}, "source": [ "---\n", "\n", "## 🔥 2. Warm-up du modèle (comme en production)\n", "\n", "**Pourquoi un warm-up ?** 🤔\n", "\n", "Lors du **premier appel**, Python charge plein de choses en mémoire :\n", "- Le modèle LightGBM 🤖\n", "- Le preprocesseur (scaler, encodeur) 🔧\n", "- Les bibliothèques (numpy, pandas) 📚\n", "\n", "Ces chargements prennent du temps et **fausseraient nos mesures**. On fait donc 3 appels \"à blanc\" pour tout charger AVANT de lancer le profiling.\n", "\n", "**Astuce** : C'est exactement ce qui se passe en production — le 1er appel est lent, les suivants sont rapides ! ⚡" ] }, { "cell_type": "markdown", "id": "b73a89cb", "metadata": {}, "source": [ "---\n", "\n", "## 📦 1. Imports et configuration\n", "\n", "On commence par importer les outils nécessaires pour le profiling :\n", "- **`cProfile`** : le moteur de profiling de Python 🔍\n", "- **`pstats`** : pour formatter les résultats 📋\n", "- **`_predict()`** : la fonction de notre API qu'on va analyser ⚡" ] }, { "cell_type": "code", "execution_count": null, "id": "aa186960", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔄 Warm-up en cours (3 appels)...\n", "Loaded local LightGBM model from /home/valentin/Env_Python/OC_P6/models/lightgbm.txt\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:53.936394Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 513.4, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:53.973948Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 37.2, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:54.011399Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 37.0, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:54.046482Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 34.5, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "✅ Warm-up terminé — Exemple de réponse :\n", "Score: 1\n", "Probabilité de défaut: 0.0016\n", "Décision: Accordé\n" ] } ], "source": [ "# ═══════════════════════════════════════════════════════════════════════════\n", "# 🔥 WARM-UP DU MODÈLE\n", "# ═══════════════════════════════════════════════════════════════════════════\n", "\n", "import json\n", "import time\n", "\n", "# ── Définition d'un échantillon JSON réaliste ──\n", "# Ces features correspondent exactement à celles envoyées en production\n", "SAMPLE_JSON = json.dumps({\n", " \"CODE_GENDER\": 0, # 0 = Femme, 1 = Homme\n", " \"FLAG_OWN_CAR\": 0, # 0 = Pas de voiture\n", " \"FLAG_OWN_REALTY\": 1, # 1 = Propriétaire immobilier\n", " \"CNT_CHILDREN\": 0, # Nombre d'enfants\n", " \"AMT_INCOME_TOTAL\": 135000.0, # Revenu annuel\n", " \"AMT_CREDIT\": 406597.5, # Montant du crédit demandé\n", " \"AMT_ANNUITY\": 20250.0, # Annuité (remboursement annuel)\n", " \"AMT_GOODS_PRICE\": 351000.0, # Prix du bien financé\n", " \"REGION_POPULATION_RELATIVE\": 0.035792,\n", " \"DAYS_BIRTH\": -14103, # Âge (en jours négatifs)\n", " \"DAYS_EMPLOYED\": -3005, # Ancienneté emploi (en jours)\n", " \"DAYS_REGISTRATION\": -4850.0,\n", " \"DAYS_ID_PUBLISH\": -2767,\n", " \"OWN_CAR_AGE\": None, # Pas de voiture = None\n", " \"FLAG_MOBIL\": 1,\n", " \"FLAG_EMP_PHONE\": 1,\n", " \"FLAG_WORK_PHONE\": 0,\n", " \"FLAG_CONT_MOBILE\": 1,\n", " \"FLAG_PHONE\": 1,\n", " \"FLAG_EMAIL\": 0,\n", " \"CNT_FAM_MEMBERS\": 2.0,\n", " \"REGION_RATING_CLIENT\": 2,\n", " \"REGION_RATING_CLIENT_W_CITY\": 2,\n", " \"HOUR_APPR_PROCESS_START\": 10, # Heure de la demande\n", " \"REG_REGION_NOT_LIVE_REGION\": 0,\n", " \"REG_REGION_NOT_WORK_REGION\": 0,\n", " \"LIVE_REGION_NOT_WORK_REGION\": 0,\n", " \"REG_CITY_NOT_LIVE_CITY\": 0,\n", " \"EXT_SOURCE_1\": 0.502130, # Scores externes (bureaux de crédit)\n", " \"EXT_SOURCE_2\": 0.585605,\n", " \"EXT_SOURCE_3\": 0.310653,\n", " \"DAYS_LAST_PHONE_CHANGE\": -1134.0,\n", " \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0 # Nombre de demandes de crédit cette année\n", "})\n", "\n", "# ── Warm-up : 3 appels pour charger modèle + caches ──\n", "print(\"🔄 Warm-up en cours (3 appels)...\")\n", "for _ in range(3):\n", " _predict(SAMPLE_JSON) # ⚡ Appel à la fonction de prédiction\n", "\n", "# ── Vérification : est-ce que ça marche ? ──\n", "reponse_test = _predict(SAMPLE_JSON)\n", "print(f\"✅ Warm-up terminé — Exemple de réponse :\\n{reponse_test}\")" ] }, { "cell_type": "markdown", "id": "6d517ce8", "metadata": {}, "source": [ "---\n", "\n", "## ⏱️ 3. Lancement du profiling sur 50 prédictions\n", "\n", "Maintenant qu'on a fait le warm-up, on lance le **vrai profiling** ! 🔥\n", "\n", "**Comment ça marche ?**\n", "1. On active le profiler avec `pr.enable()` 🟢\n", "2. On appelle `_predict()` **50 fois** (pour avoir une moyenne fiable)\n", "3. On désactive le profiler avec `pr.disable()` 🔴\n", "4. On calcule le **temps moyen par appel** 📊\n", "\n", "**Pourquoi 50 appels ?** Pour lisser les variations et avoir une mesure précise ⚖️" ] }, { "cell_type": "code", "execution_count": null, "id": "d546ab4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔥 Lancement du profiling sur 50 prédictions...\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:54.151427Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 95.9, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:54.248424Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, 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\\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 86.8, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.258120Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 88.2, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.345444Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 86.8, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.437424Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 91.5, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.523519Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 85.4, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.608348Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 84.3, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "[LOG] {\"timestamp\": \"2026-02-25T14:43:58.697418Z\", \"input_raw\": \"{\\\"CODE_GENDER\\\": 0, \\\"FLAG_OWN_CAR\\\": 0, \\\"FLAG_OWN_REALTY\\\": 1, \\\"CNT_CHILDREN\\\": 0, \\\"AMT_INCOME_TOTAL\\\": 135000.0, \\\"AMT_CREDIT\\\": 406597.5, \\\"AMT_ANNUITY\\\": 20250.0, \\\"AMT_GOODS_PRICE\\\": 351000.0, \\\"REGION_POPULATION_RELATIVE\\\": 0.035792, \\\"DAYS_BIRTH\\\": -14103, \\\"DAYS_EMPLOYED\\\": -3005, \\\"DAYS_REGISTRATION\\\": -4850.0, \\\"DAYS_ID_PUBLISH\\\": -2767, \\\"OWN_CAR_AGE\\\": null, \\\"FLAG_MOBIL\\\": 1, \\\"FLAG_EMP_PHONE\\\": 1, \\\"FLAG_WORK_PHONE\\\": 0, \\\"FLAG_CONT_MOBILE\\\": 1, \\\"FLAG_PHONE\\\": 1, \\\"FLAG_EMAIL\\\": 0, \\\"CNT_FAM_MEMBERS\\\": 2.0, \\\"REGION_RATING_CLIENT\\\": 2, \\\"REGION_RATING_CLIENT_W_CITY\\\": 2, \\\"HOUR_APPR_PROCESS_START\\\": 10, \\\"REG_REGION_NOT_LIVE_REGION\\\": 0, \\\"REG_REGION_NOT_WORK_REGION\\\": 0, \\\"LIVE_REGION_NOT_WORK_REGION\\\": 0, \\\"REG_CITY_NOT_LIVE_CITY\\\": 0, \\\"EXT_SOURCE_1\\\": 0.50213, \\\"EXT_SOURCE_2\\\": 0.585605, \\\"EXT_SOURCE_3\\\": 0.310653, \\\"DAYS_LAST_PHONE_CHANGE\\\": -1134.0, \\\"AMT_REQ_CREDIT_BUREAU_YEAR\\\": 1.0}\", \"input_features\": {\"CODE_GENDER\": 0, \"FLAG_OWN_CAR\": 0, \"FLAG_OWN_REALTY\": 1, \"CNT_CHILDREN\": 0, \"AMT_INCOME_TOTAL\": 135000.0, \"AMT_CREDIT\": 406597.5, \"AMT_ANNUITY\": 20250.0, \"AMT_GOODS_PRICE\": 351000.0, \"REGION_POPULATION_RELATIVE\": 0.035792, \"DAYS_BIRTH\": -14103, \"DAYS_EMPLOYED\": -3005, \"DAYS_REGISTRATION\": -4850.0, \"DAYS_ID_PUBLISH\": -2767, \"OWN_CAR_AGE\": null, \"FLAG_MOBIL\": 1, \"FLAG_EMP_PHONE\": 1, \"FLAG_WORK_PHONE\": 0, \"FLAG_CONT_MOBILE\": 1, \"FLAG_PHONE\": 1, \"FLAG_EMAIL\": 0, \"CNT_FAM_MEMBERS\": 2.0, \"REGION_RATING_CLIENT\": 2, \"REGION_RATING_CLIENT_W_CITY\": 2, \"HOUR_APPR_PROCESS_START\": 10, \"REG_REGION_NOT_LIVE_REGION\": 0, \"REG_REGION_NOT_WORK_REGION\": 0, \"LIVE_REGION_NOT_WORK_REGION\": 0, \"REG_CITY_NOT_LIVE_CITY\": 0, \"EXT_SOURCE_1\": 0.50213, \"EXT_SOURCE_2\": 0.585605, \"EXT_SOURCE_3\": 0.310653, \"DAYS_LAST_PHONE_CHANGE\": -1134.0, \"AMT_REQ_CREDIT_BUREAU_YEAR\": 1.0}, \"output_proba\": 0.0016, \"output_decision\": \"Accordé\", \"execution_time_ms\": 88.6, \"error\": null, \"model_version\": \"models:/LightGBM/Production\", \"threshold\": 0.4}\n", "⏱️ Temps total profiling : 4642.7 ms\n", "📊 Temps moyen par appel : 92.85 ms\n", "✅ Profiling terminé — affichage du rapport ci-dessous\n" ] } ], "source": [ "# ═══════════════════════════════════════════════════════════════════════════\n", "# ⏱️ LANCEMENT DU PROFILING\n", "# ═══════════════════════════════════════════════════════════════════════════\n", "\n", "N_APPELS = 50 # 📊 Nombre d'appels pour la moyenne\n", "\n", "print(f\"🔥 Lancement du profiling sur {N_APPELS} prédictions...\\n\")\n", "\n", "# ── Mesure du temps mural (temps réel écoulé) ──\n", "debut = time.perf_counter() # 🕐 Démarrage du chronomètre\n", "\n", "# ── Création et activation du profiler ──\n", "pr = cProfile.Profile() # 🔍 Création du profiler\n", "pr.enable() # 🟢 Activation : on enregistre tout !\n", "\n", "# ── Boucle de prédictions (c'est ici qu'on profile) ──\n", "for _ in range(N_APPELS):\n", " _predict(SAMPLE_JSON) # ⚡ Même fonction que dans les logs de production\n", "\n", "# ── Désactivation du profiler ──\n", "pr.disable() # 🔴 Stop : on arrête l'enregistrement\n", "fin = time.perf_counter() # 🕐 Fin du chronomètre\n", "\n", "# ── Calcul des métriques globales ──\n", "temps_total_ms = (fin - debut) * 1000 # Temps total en millisecondes\n", "temps_moyen_ms = temps_total_ms / N_APPELS # Temps moyen par appel\n", "\n", "print(f\"⏱️ Temps total profiling : {temps_total_ms:.1f} ms\")\n", "print(f\"📊 Temps moyen par appel : {temps_moyen_ms:.2f} ms\")\n", "print(\"\\n✅ Profiling terminé — affichage du rapport ci-dessous\")" ] }, { "cell_type": "markdown", "id": "e4ab715b", "metadata": {}, "source": [ "---\n", "\n", "## 📋 4. Rapport textuel du profiling (Top 15)\n", "\n", "Le profiler a enregistré **toutes les fonctions appelées**. On va d'abord afficher un rapport textuel avec les **15 fonctions les plus lentes**.\n", "\n", "**Comment lire ce rapport ?**\n", "- **ncalls** : nombre de fois que la fonction a été appelée\n", "- **tottime** : temps passé **dans** la fonction (sans les sous-fonctions)\n", "- **cumtime** : temps **cumulé** (avec toutes les sous-fonctions) ⚠️ **C'EST LA COLONNE IMPORTANTE !**\n", "\n", "Plus le `cumtime` est élevé, plus la fonction est un goulot d'étranglement ! 🔴" ] }, { "cell_type": "code", "execution_count": null, "id": "50463d44", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "══════════════════════════════════════════════════════════════════════\n", " 📋 RAPPORT PROFILING — TOP 15 FONCTIONS (temps cumulé)\n", "══════════════════════════════════════════════════════════════════════\n", " 13055924 function calls (12571167 primitive calls) in 4.648 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 629 to 15 due to restriction <15>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", " 3/2 0.000 0.000 4.642 2.321 interactiveshell.py:3665(run_code)\n", " 3/2 0.001 0.000 4.642 2.321 {built-in method builtins.exec}\n", " 1 0.014 0.014 4.636 4.636 304244700.py:1()\n", " 50 0.041 0.001 4.466 0.089 app.py:307(_predict)\n", " 39950 0.090 0.000 1.985 0.000 frame.py:4282(__setitem__)\n", " 39950 0.045 0.000 1.852 0.000 frame.py:4525(_set_item)\n", " 39950 0.042 0.000 1.563 0.000 frame.py:4492(_set_item_mgr)\n", " 39950 0.042 0.000 1.434 0.000 frame.py:4481(_iset_item_mgr)\n", " 39950 0.430 0.000 1.362 0.000 managers.py:1085(iset)\n", " 39950 0.094 0.000 1.283 0.000 numeric.py:47(to_numeric)\n", " 40100 0.153 0.000 0.989 0.000 series.py:392(__init__)\n", " 76 0.003 0.000 0.827 0.011 base_events.py:1910(_run_once)\n", " 50 0.000 0.000 0.785 0.016 basic.py:4701(predict)\n", " 39950 0.064 0.000 0.744 0.000 frame.py:4073(__getitem__)\n", " 78 0.000 0.000 0.615 0.008 events.py:86(_run)\n", "\n", "\n", "\n", "══════════════════════════════════════════════════════════════════════\n", "⏱️ Rappel — Temps total : 4642.7 ms\n", "📊 Rappel — Temps moyen : 92.85 ms\n" ] } ], "source": [ "# ═══════════════════════════════════════════════════════════════════════════\n", "# 📋 RAPPORT TEXTUEL DU PROFILING\n", "# ═══════════════════════════════════════════════════════════════════════════\n", "\n", "TOP_N = 15 # 🔢 On affiche les 15 fonctions les plus lentes\n", "\n", "# ── Capture du rapport dans un buffer (pour l'afficher proprement) ──\n", "buffer = io.StringIO() # Créer un buffer en mémoire\n", "stats = pstats.Stats(pr, stream=buffer) # Créer les stats depuis le profiler\n", "\n", "# ── Formatage du rapport ──\n", "stats.strip_dirs() # Supprimer les chemins absolus (plus lisible)\n", "stats.sort_stats(SortKey.CUMULATIVE) # ⚠️ Tri par temps CUMULÉ (le plus important !)\n", "stats.print_stats(TOP_N) # Afficher les TOP_N fonctions\n", "\n", "# ── Récupération du rapport formaté ──\n", "rapport = buffer.getvalue()\n", "\n", "# ── Affichage avec un cadre joli ──\n", "print(\"═\" * 70)\n", "print(f\" 📋 RAPPORT PROFILING — TOP {TOP_N} FONCTIONS (temps cumulé)\")\n", "print(\"═\" * 70)\n", "print(rapport)\n", "print(\"═\" * 70)\n", "print(f\"⏱️ Rappel — Temps total : {temps_total_ms:.1f} ms\")\n", "print(f\"📊 Rappel — Temps moyen : {temps_moyen_ms:.2f} ms\")\n", "print(\"═\" * 70)" ] }, { "cell_type": "markdown", "id": "0e3bded3", "metadata": {}, "source": [ "---\n", "\n", "## 📊 5. Visualisation graphique — Top 20 fonctions\n", "\n", "Le rapport textuel est utile, mais un graphique est **10 fois plus clair** ! 🎨\n", "\n", "On va créer un **graphique en barres horizontales** avec :\n", "- 🔴 **Rouge** : les fonctions les plus lentes (goulots)\n", "- 🔵 **Bleu** : les fonctions rapides\n", "- ⏱️ **Temps en ms** affiché sur chaque barre\n", "\n", "**Astuce** : Regarde tout en haut du graphique — c'est là que se cache le goulot principal ! 🔍" ] }, { "cell_type": "code", "execution_count": null, "id": "1dcc497e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "📋 Top 20 fonctions les plus coûteuses (par temps cumulé)\n", " label appels t_propre (ms) t_cumulé (ms)\n", "run_code() [interactiveshell.py : L3665] · 3× 3 0.009600 4642.458555\n", " [~ : L0] · 3× 3 0.620399 4642.449655\n", " [304244700.py : L1] · 1× 1 14.056403 4636.241706\n", " _predict() [app.py : L307] · 50× 50 40.948959 4465.778651\n", " __setitem__() [frame.py : L4282] · 39950× 39950 90.093506 1984.646909\n", " _set_item() [frame.py : L4525] · 39950× 39950 45.259302 1852.004282\n", " _set_item_mgr() [frame.py : L4492] · 39950× 39950 42.053928 1563.481588\n", " _iset_item_mgr() [frame.py : L4481] · 39950× 39950 41.663529 1434.083148\n", " iset() [managers.py : L1085] · 39950× 39950 429.777454 1362.112741\n", " to_numeric() [numeric.py : L47] · 39950× 39950 93.813225 1283.155377\n", " __init__() [series.py : L392] · 40100× 40100 153.290805 988.743904\n", " _run_once() [base_events.py : L1910] · 76× 76 2.554515 826.909328\n", " predict() [basic.py : L4701] · 50× 50 0.331246 785.096873\n", " __getitem__() [frame.py : L4073] · 39950× 39950 63.938986 744.246471\n", " _run() [events.py : L86] · 78× 78 0.158139 615.032720\n", " _get_item_cache() [frame.py : L4637] · 39950× 39950 47.995600 605.837459\n", " _iset_split_block() [managers.py : L1235] · 39650× 39650 164.325402 599.903045\n", " _ixs() [frame.py : L3994] · 39950× 39950 38.530385 513.990026\n", " [~ : L0] · 78× 78 0.569004 464.256295\n", " delete() [blocks.py : L2001] · 39650× 39650 212.343146 355.302496\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_50810/1876020190.py:104: UserWarning: Glyph 9201 (\\N{STOPWATCH}) missing from font(s) DejaVu Sans.\n", " plt.tight_layout()\n", "/tmp/ipykernel_50810/1876020190.py:105: UserWarning: Glyph 9201 (\\N{STOPWATCH}) missing from font(s) DejaVu Sans.\n", " plt.savefig(\"../reports/plots/profiling_top20.png\", dpi=150, bbox_inches=\"tight\")\n", "/home/valentin/Env_Python/OC_P6/.venv/lib/python3.12/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 9201 (\\N{STOPWATCH}) missing from font(s) DejaVu Sans.\n", " fig.canvas.print_figure(bytes_io, **kw)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "💾 Graphique sauvegardé : reports/plots/profiling_top20.png\n", "\n", "🔴 Goulot principal : run_code() [interactiveshell.py : L3665] → 4642.46 ms cumulé\n" ] } ], "source": [ "# ═══════════════════════════════════════════════════════════════════════════\n", "# 📊 VISUALISATION GRAPHIQUE — TOP 20 FONCTIONS\n", "# ═══════════════════════════════════════════════════════════════════════════\n", "\n", "import re\n", "import pandas as pd\n", "\n", "TOP_N = 20 # 🔢 Limiter au top 20 pour la lisibilité\n", "\n", "# ── Extraction des données brutes du profiler ──\n", "# Le profiler stocke : {(fichier, ligne, nom): (appels, temps, ...)}\n", "stats_obj = pstats.Stats(pr)\n", "stats_obj.strip_dirs() # Retirer les chemins absolus\n", "stats_obj.sort_stats(SortKey.CUMULATIVE) # Trier par temps cumulé\n", "\n", "# ── Construction d'un DataFrame Pandas pour faciliter la manipulation ──\n", "lignes = []\n", "for (fichier, numligne, nom), (cc, nc, tt, ct, _) in stats_obj.stats.items():\n", " lignes.append({\n", " \"nom_court\": nom, # Nom de la fonction\n", " \"fichier\": fichier, # Fichier source\n", " \"numligne\": numligne, # Numéro de ligne\n", " \"appels\": nc, # Nombre d'appels\n", " \"temps_propre_ms\": tt * 1000, # Temps propre en ms\n", " \"temps_cumule_ms\": ct * 1000, # Temps cumulé en ms ⚠️\n", " })\n", "\n", "# ── Création du DataFrame et tri par temps cumulé ──\n", "df_stats = pd.DataFrame(lignes)\n", "df_stats = df_stats.sort_values(\"temps_cumule_ms\", ascending=False).reset_index(drop=True)\n", "\n", "# ── Limiter au TOP_N ──\n", "df_stats = df_stats.head(TOP_N)\n", "\n", "# ── Construction d'un label explicite pour chaque fonction ──\n", "# Format : \"nom_fonction() [fichier.py : ligne N] · K appels\"\n", "def construire_label(row):\n", " # Ajouter () sauf pour les builtins entre <>\n", " if row[\"nom_court\"].startswith(\"<\"):\n", " nom = row[\"nom_court\"]\n", " else:\n", " nom = f\"{row['nom_court']}()\"\n", " \n", " # Garder seulement le nom du fichier (sans le chemin complet)\n", " fichier = row[\"fichier\"].split(\"/\")[-1] if \"/\" in row[\"fichier\"] else row[\"fichier\"]\n", " fichier = fichier.split(\"\\\\\")[-1] if \"\\\\\" in fichier else fichier # Windows compat\n", " \n", " # Nombre d'appels lisible : \"1×\" ou \"50×\"\n", " appels = f\"{row['appels']}×\"\n", " \n", " return f\"{nom:<30} [{fichier} : L{row['numligne']}] · {appels}\"\n", "\n", "df_stats[\"label\"] = df_stats.apply(construire_label, axis=1)\n", "\n", "# ── Affichage du DataFrame récapitulatif ──\n", "print(f\"📋 Top {TOP_N} fonctions les plus coûteuses (par temps cumulé)\\n\")\n", "print(df_stats[[\"label\", \"appels\", \"temps_propre_ms\", \"temps_cumule_ms\"]]\n", " .rename(columns={\n", " \"temps_propre_ms\": \"t_propre (ms)\",\n", " \"temps_cumule_ms\": \"t_cumulé (ms) ⚠️\",\n", " })\n", " .to_string(index=False))\n", "\n", "print(\"\\n\" + \"─\" * 80 + \"\\n\")\n", "\n", "# ══════════════════════════════════════════════════════════════════════════\n", "# 🎨 CRÉATION DU GRAPHIQUE EN BARRES HORIZONTALES\n", "# ══════════════════════════════════════════════════════════════════════════\n", "\n", "n = len(df_stats)\n", "hauteur_fig = max(6, n * 0.32) # Hauteur dynamique (~0.32 inch par ligne)\n", "\n", "fig, ax = plt.subplots(figsize=(14, hauteur_fig))\n", "\n", "# ── Dégradé de couleur : rouge (lent) → bleu (rapide) ──\n", "import matplotlib.cm as cm\n", "import numpy as np\n", "\n", "valeurs = df_stats[\"temps_cumule_ms\"].values\n", "couleurs = cm.RdYlBu_r(np.linspace(0.15, 0.85, n)) # Dégradé rouge → bleu\n", "\n", "# ── Création des barres horizontales ──\n", "barres = ax.barh(\n", " df_stats[\"label\"][::-1], # Ordre décroissant : le plus lent en haut\n", " df_stats[\"temps_cumule_ms\"][::-1],\n", " color=couleurs,\n", " edgecolor=\"white\",\n", " linewidth=0.4,\n", " height=0.75,\n", ")\n", "\n", "# ── Étiquettes avec la valeur en ms à côté de chaque barre ──\n", "for barre, val in zip(barres, df_stats[\"temps_cumule_ms\"][::-1]):\n", " if val > 0.05: # Ne pas afficher les valeurs quasi nulles\n", " ax.text(\n", " val + valeurs.max() * 0.005,\n", " barre.get_y() + barre.get_height() / 2,\n", " f\"{val:.2f} ms\",\n", " va=\"center\",\n", " fontsize=8,\n", " )\n", "\n", "# ── Mise en forme du graphique ──\n", "ax.set_xlabel(\"Temps cumulé (ms)\", fontsize=11)\n", "ax.set_title(\n", " f\"⏱️ Profiling de _predict() — Top {TOP_N} fonctions \"\n", " f\"({N_APPELS} appels)\",\n", " fontsize=13,\n", " fontweight=\"bold\",\n", " pad=12,\n", ")\n", "ax.tick_params(axis=\"y\", labelsize=8)\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "\n", "# ── Sauvegarde du graphique ──\n", "plt.savefig(\"../reports/plots/profiling_top20.png\", dpi=150, bbox_inches=\"tight\")\n", "plt.show()\n", "\n", "# ── Affichage du goulot principal (fonction #1) ──\n", "print(f\"💾 Graphique sauvegardé : reports/plots/profiling_top20.png\")\n", "print(f\"\\n🔴 Goulot principal détecté : {df_stats.iloc[0]['nom_court']}() \"\n", " f\"[{df_stats.iloc[0]['fichier'].split('/')[-1]} : L{df_stats.iloc[0]['numligne']}] \"\n", " f\"→ {df_stats.iloc[0]['temps_cumule_ms']:.2f} ms cumulé\")" ] }, { "cell_type": "markdown", "id": "c2844cb8", "metadata": {}, "source": [ "---\n", "\n", "## 🎯 7. Conclusion et pistes d'optimisation\n", "\n", "### 📊 Ce qu'on a appris\n", "\n", "✅ Le profiling nous a permis de **mesurer précisément** où le temps est consommé \n", "✅ On a identifié le **goulot principal** : la manipulation des données avec Pandas \n", "✅ Le modèle LightGBM est **performant** (~15 ms) — pas besoin de l'optimiser ! \n", "\n", "### 🚀 Pistes d'optimisation pour aller plus vite\n", "\n", "| 💡 Piste | 🎯 Impact attendu | 🔧 Difficulté |\n", "|----------|-------------------|---------------|\n", "| Utiliser `numpy` au lieu de `pandas.DataFrame` | 🔥 **-30 à -40 ms** | ⭐⭐ Moyen |\n", "| Mettre les données en cache (si applicable) | 🔥 **-20 à -30 ms** | ⭐ Facile |\n", "| Paralléliser les prédictions (batch) | 🔥 **-10 à -20 ms** | ⭐⭐⭐ Difficile |\n", "| Compiler avec Cython ou Numba | 🔥 **-5 à -10 ms** | ⭐⭐⭐⭐ Très difficile |\n", "\n", "### 🎓 Ce que tu dois retenir pour ton projet MLOps\n", "\n", "1. **Le profiling est indispensable** pour optimiser une API 🔍\n", "2. **Concentre-toi sur les goulots principaux** (loi de Pareto : 80% du temps = 20% du code) 📊\n", "3. **Mesure avant et après** chaque optimisation pour vérifier l'impact réel ⚖️\n", "4. **Ne devine jamais** où est le problème — profile d'abord ! ⏱️\n", "\n", "---\n", "\n", "**Bravo ! Tu sais maintenant profiler une API comme un pro ! 🎉**" ] }, { "cell_type": "markdown", "id": "5991608f", "metadata": {}, "source": [ "## 🔍 6. Explication ligne par ligne pour un débutant\n", "\n", "Voici le **Top 20 des fonctions les plus lentes** sur 50 prédictions. \n", "Chaque ligne est expliquée **comme si tu avais 15 ans** :\n", "\n", "| 🏆 Rang | 🔧 Fonction | ⏱️ Temps total (50 appels) | Nombre d’appels | 💡 Explication simple pour débutant |\n", "|---------|-------------|-----------------------------|-----------------|-------------------------------------|\n", "| 1-3 | `run_code()`, `exec()` | ~4642 ms | 3 fois | C’est juste Jupyter qui exécute le notebook → on ignore |\n", "| **4** | **`_predict()`** | **4465 ms** | **50 fois** | **Fonction principale de ton API** (app.py ligne 307). Elle fait tout : lire le JSON + prétraitement + prédiction |\n", "| 5 | `__setitem__()` | 1984 ms | 39 950 fois | **Le grand coupable** : Pandas qui fait `df['colonne'] = valeur` pour chaque feature |\n", "| 6 | `_set_item()` | 1852 ms | 39 950 fois | Opération interne de Pandas pour ajouter une colonne |\n", "| 7 | `_set_item_mgr()` | 1563 ms | 39 950 fois | Pandas qui gère la mémoire des colonnes (copie inutile) |\n", "| 8 | `_iset_item_mgr()` | 1434 ms | 39 950 fois | Même chose, version \"indexée\" |\n", "| 9 | `iset()` | 1362 ms | 39 950 fois | Insertion de colonne dans Pandas |\n", "| 10 | `to_numeric()` | 1283 ms | 39 950 fois | Conversion texte → nombre (ex. \"M\" → 0) répétée 40 000 fois |\n", "| 11 | `__init__()` (Series) | 988 ms | 40 100 fois | Création de petites colonnes |\n", "| **13** | **`predict()` LightGBM** | **785 ms** | **50 fois** | **Le modèle lui-même** → seulement **15,7 ms par prédiction** ! Très rapide |\n", "| 14-20 | `__getitem__()`, `_get_item_cache()`, etc. | < 750 ms chacun | très nombreux | Opérations internes Pandas (lecture, cache, suppression temporaire) |\n", "\n", "### 🚨 Conclusion importante (à mettre dans ton rapport)\n", "\n", "- **80 % du temps** est perdu dans les opérations pandas `__setitem__` et `to_numeric`. \n", "- Ton **modèle LightGBM** est déjà très rapide (15,7 ms par appel). \n", "- Le vrai problème = **RawToModelTransformer** qui construit le DataFrame **colonne par colonne** (très lent).\n", "\n", "**En résumé** : Le modèle va bien, c’est le prétraitement pandas qui ralentit tout ! 🔧" ] } ], "metadata": { "kernelspec": { "display_name": "OC_P6", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }