diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 74ef075..0000000 --- a/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -**/mlruns -**/downloaded_artifacts diff --git a/README.fr.md b/README.fr.md deleted file mode 100644 index 88345a6..0000000 --- a/README.fr.md +++ /dev/null @@ -1,62 +0,0 @@ -# Projet Air Paradis – Analyse de sentiments - -📘 Ce projet est également disponible en [anglais 🇬🇧](./README.md) - -Ce dépôt contient les notebooks et le code source du projet d'analyse de sentiments sur des tweets, réalisé dans le cadre du programme OpenClassrooms **"Air Paradis"**. - ---- - -## Contexte métier - -L'objectif est de prédire si un tweet (ou une courte expression écrite) est perçu comme **positif ou négatif**, dans le contexte d'une compagnie aérienne. -Le projet repose sur le jeu de données public **Sentiment140**. - ---- - -## Description du projet - -Le projet vise à développer un système complet d'analyse de sentiments, comprenant : - -- Une analyse exploratoire des données textuelles, -- Une comparaison rigoureuse de 10+ modèles de classification (classiques ML, LSTM + word embeddings, DistilBERT), -- Le déploiement d'une API REST FastAPI exposant le meilleur modèle avancé (LSTM + Word2Vec), -- La mise en place d'un pipeline MLOps complet : MLFlow, CI/CD (GitHub Actions), monitoring, -- Un hébergement local sur serveur NAS pour garantir la souveraineté des données. - -📖 **Pour des informations détaillées sur les expérimentations menées et les résultats obtenus, consultez [docs/blog.fr.md](./docs/blog.fr.md).** - ---- - -## Structure du dépôt - -- `notebooks/` : notebooks d'analyse exploratoire et de modélisation, -- `deployment/` : code de l'API FastAPI, tests unitaires, Dockerfile et configuration déploiement, -- `docs/` : documentation détaillée (blog.fr.md), diagrammes, captures d'écran, -- `presentation/` : support de présentation du projet (PDF), -- `../.github/workflows/` : pipelines CI/CD (tests automatisés, build Docker, push DockerHub). - ---- - -## Entraînement des modèles - -Les phases d'entraînement ont principalement été réalisées sur **Google Colab**, afin de bénéficier de ressources GPU nécessaires aux modèles de deep learning. - ---- - -## Utilisation - -Les notebooks détaillent les étapes d'analyse et d'entraînement. -L'API est disponible dans le dossier `deployment/api` avec interface Streamlit de test. - -Pour tester l'API en production : https://sentiment-api.greg-madman-nas.duckdns.org - ---- - -## Remarques - -- Les fichiers `requirements.txt` dans `notebooks/` proviennent des environnements Google Colab et peuvent contenir des dépendances peu optimisées pour un usage local. -- Le fichier `.env`, exclu du dépôt, contient les variables d'environnement nécessaires (configuration MLFlow, MinIO, alerting). - ---- - -**Projet réalisé dans le cadre du parcours "Data Scientist – Projet 7" d'OpenClassrooms** \ No newline at end of file diff --git a/README.md b/README.md deleted file mode 100644 index 7d5bb61..0000000 --- a/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# Air Paradis Project – Sentiment Analysis - -📘 This project is also available in [French 🇫🇷](./README.fr.md) - -This repository contains the notebooks and source code for the sentiment analysis project on tweets, conducted as part of the OpenClassrooms **"Air Paradis"** program. - ---- - -## Business Context - -The objective is to predict whether a tweet (or short written expression) is perceived as **positive or negative**, in the context of an airline company. -The project is based on the public **Sentiment140** dataset. - ---- - -## Project Description - -The project aims to develop a complete sentiment analysis system, including: - -- Exploratory analysis of textual data, -- Rigorous comparison of 10+ classification models (classical ML, LSTM + word embeddings, DistilBERT), -- Deployment of a FastAPI REST API exposing the best advanced model (LSTM + Word2Vec), -- Implementation of a complete MLOps pipeline: MLFlow, CI/CD (GitHub Actions), monitoring, -- Local hosting on NAS server to ensure data sovereignty. - -📖 **For detailed information on experiments conducted and results obtained, see [docs/blog.md](./docs/blog.md).** - ---- - -## Repository Structure - -- `notebooks/`: exploratory analysis and modeling notebooks, -- `deployment/`: FastAPI API code, unit tests, Dockerfile and deployment configuration, -- `docs/`: detailed documentation (blog.md), diagrams, screenshots, -- `presentation/`: project presentation slides (PDF), -- `../.github/workflows/`: CI/CD pipelines (automated tests, Docker build, DockerHub push). - ---- - -## Model Training - -Training phases were primarily conducted on **Google Colab** to leverage GPU resources required for deep learning models. - ---- - -## Usage - -Notebooks detail the analysis and training steps. -The API is available in the `deployment/api` folder with Streamlit testing interface. - -To test the production API: https://sentiment-api.greg-madman-nas.duckdns.org - ---- - -## Notes - -- The `requirements.txt` files in `notebooks/` come from Google Colab environments and may contain dependencies not optimized for local use. -- The `.env` file, excluded from the repository, contains necessary environment variables (MLFlow, MinIO, alerting configuration). - ---- - -**Project completed as part of OpenClassrooms "Data Scientist – Project 7" program** \ No newline at end of file diff --git a/deployment/README.fr.md b/deployment/README.fr.md deleted file mode 100644 index 300d770..0000000 --- a/deployment/README.fr.md +++ /dev/null @@ -1,128 +0,0 @@ -# Déploiement de l’API – Projet Air Paradis - -📘 Cette documentation est aussi disponible en [anglais 🇬🇧](./README.md) - -Ce dossier contient le code et la configuration nécessaires au déploiement de l’API de prédiction de sentiment. - ---- - -## 🎯 Objectif - -Déployer une API REST permettant de : - -- Prédire le **sentiment d’un tweet** (positif ou négatif) à partir d’un modèle LSTM entraîné sur des embeddings (Word2Vec ou GloVe), -- Signaler les erreurs de prédiction. - -L’API utilise le **meilleur modèle enregistré** dans MLflow et le sert via **FastAPI**. - ---- - -## 📦 Architecture globale - -Schéma : - -``` - ┌──────────────────────────────┐ - │ Utilisateur final (UI) │ - │ (interface Streamlit) │ - └────────────┬─────────────────┘ - │ - [Appel HTTP vers /predict] - │ - ┌────────────▼─────────────┐ - │ API FastAPI │ - │ (serveur lancé via │ - │ Uvicorn) │ - └────────────┬─────────────┘ - │ - Chargement du modèle (MLflow) - │ - Prétraitement du tweet - │ - Prédiction (inférence) - │ - Réponse JSON avec le sentiment -``` - ---- - -## 🧪 Fonctionnement de l’API - -1. **Chargement du modèle** - - Le meilleur modèle est enregistré dans MLflow sous le nom `SentimentAnalysisLSTM`, avec le stage `Production`. - - L’API le charge au démarrage via l’URI : - - ``` - models:/SentimentAnalysisLSTM/Production - ``` - -2. **Prétraitement du texte** - - Nettoyage, tokenisation, lemmatisation, puis conversion en vecteurs d’embedding. - -3. **Prédiction** - - Le modèle LSTM prédit le sentiment. - - Le résultat (`positive` ou `negative`) est retourné en JSON. - -4. **Interface utilisateur (optionnelle)** - - Une interface Streamlit permet une interaction visuelle avec l’API. - ---- - -## 🐳 Déploiement via Docker - -Un `Dockerfile` est fourni pour simplifier le déploiement sur tout hôte compatible Docker (NAS, cloud, etc.). - -L’image Docker est poussée sur DockerHub à chaque version validée (voir CI/CD plus bas). - -Par souci de **souveraineté des données**, le déploiement est préféré sur un **NAS équipé d’un processeur Intel N100** (peu énergivore et suffisant pour l’inférence LSTM). - -Le redéploiement peut être automatisé via : -- une tâche `cron` simple, ou -- un conteneur **Watchtower** (dans docker-compose ou kubernetes). - -**Commande pour test local :** - -```bash -docker build -t sentiment-api . -docker run --rm --env-file ../../.env -p 8000:8000 -p 8080:8080 sentiment-api -``` - -En production, les variables d’environnement doivent être définies dans le `docker-compose.yml` ou via un gestionnaire de secrets. - ---- - -## 🛠 Problèmes AVX2 / Déploiement cloud - -**Mise à jour :** au départ, le NAS ne supportait pas les instructions AVX2 nécessaires à TensorFlow. -En réalité, les CPU N100 les prennent en charge, mais certains fabricants désactivent cette option dans le BIOS. - -En attendant un accès physique au NAS, nous avons opté pour un déploiement sur **Google Cloud**. -Problème : un seul port peut être exposé publiquement sur ces services. - -Deux solutions possibles : -- Séparer le backend (FastAPI) et le frontend (Streamlit) en deux conteneurs, -- Utiliser un **reverse proxy** (ex. : nginx) pour rediriger les ports. - -➡️ Nous avons choisi la **deuxième solution**, pour garder une **seule image Docker**. - -**Mise à jour 2 :** AVX2 a été activé via le BIOS. Le déploiement est donc opérationnel **à la fois en local (NAS)** et **sur le cloud**, avec déploiement automatique pour le NAS (voir section suivante). - ---- - -## 🔁 CI/CD – GitHub Actions - -Un pipeline est défini dans `.github/workflows/` pour : - -- Lancer les tests unitaires à chaque `push`, -- Vérifier la stabilité de l’API avant déploiement (NAS ou cloud), -- Construire et pousser l’image Docker sur DockerHub. - -Les tests sont situés dans : - -``` -deployment/api/tests/ -``` - -Le redéploiement automatique sur le NAS est géré via **Watchtower**, qui surveille les mises à jour de l’image Docker (fonctionne comme un `cron`, avec logs). - ---- diff --git a/deployment/README.md b/deployment/README.md deleted file mode 100644 index 01d8cdd..0000000 --- a/deployment/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# API Deployment – Air Paradis Project - -📘 This documentation is also available in [French 🇫🇷](./README.fr.md) - -This folder contains the code and configuration necessary to deploy the sentiment prediction API. - ---- - -## 🎯 Objective - -Deploy a REST API that allows to: - -- Predict the sentiment of a tweet (positive or negative) using an LSTM model trained on embeddings (Word2Vec or GloVe), -- Report prediction errors. - -The API uses the best-performing model registered on MLflow and serves it via FastAPI. - ---- - -## 📦 Overall Architecture - -Diagram: - -``` - ┌──────────────────────────────┐ - │ End user (UI) │ - │ (Streamlit interface) │ - └────────────┬─────────────────┘ - │ - [HTTP call to /predict] - │ - ┌────────────▼─────────────┐ - │ FastAPI API │ - │ (server launched via │ - │ Uvicorn) │ - └────────────┬─────────────┘ - │ - Model loading from MLflow - │ - Tweet preprocessing - │ - Prediction (inference) - │ - Sentiment response (JSON) -``` - ---- - -## 🧪 API Workflow - -1. **Model loading** - - The best performing model is registered in MLflow under a fixed name (`SentimentAnalysisLSTM`) and a `Production` stage. - - The API loads this model at startup using the URI: - - ``` - models:/SentimentAnalysisLSTM/Production - ``` - -2. **Text preprocessing** - - Text is cleaned, tokenized, lemmatized, and converted into vectors (embeddings). - -3. **Prediction** - - The LSTM model (loaded from MLflow) makes a prediction. - - The result (`positive` or `negative`) is returned as JSON. - -4. **User interface (optional)** - - A Streamlit interface allows visual interaction with the API. - ---- - -## 🐳 Deployment with Docker - -A `Dockerfile` is provided for easy deployment on any Docker-compatible host (NAS, cloud, etc.). - -This Docker image should be pushed to DockerHub with every validated version (see CI/CD below). - -For data sovereignty, deployment is preferably done on a **NAS with Intel N100** CPU (energy-efficient, sufficient for inference). -Docker can be automatically redeployed using: -- `cron` (simple), or -- `Watchtower` in docker-compose/kubernetes setups. - -**Development command (local test):** - -```bash -docker build -t sentiment-api . -docker run --rm --env-file ../../.env -p 8000:8000 -p 8080:8080 sentiment-api -``` - -In production (e.g. via docker-compose or cloud services), environment variables must be defined in the compose file or secret manager. - ---- - -## 🛠 Notes on AVX2 / Cloud Deployment - -**Update:** the NAS initially failed due to lack of AVX2 support. Some N100-based NAS motherboards disable AVX2 in BIOS. - -When not accessible, fallback deployment is done on **Google Cloud**. -Issue: public cloud services do **not allow multiple ports** to be exposed easily. - -Two solutions: -- Split into two containers: FastAPI + Streamlit, -- Use a **reverse proxy** (e.g., Nginx) to map ports. - -➡️ We chose the second option to keep a **single Docker image**. - -**Update 2:** AVX2 was enabled via BIOS. Deployment is now active **both locally (NAS)** and **on cloud**, with continuous deployment on NAS (see section below). - ---- - -## 🔁 CI/CD – GitHub Actions - -A pipeline is defined in `.github/workflows/` to: - -- Run unit tests on every push, -- Check API health before deployment (local/cloud), -- Build and push Docker image to DockerHub. - -Tests are defined in: - -``` -deployment/api/tests/ -``` - -Docker image auto-redeployment on NAS is handled by **Watchtower**, which checks DockerHub updates (like a cron job, with logs). - ---- diff --git a/deployment/api/.dockerignore b/deployment/api/.dockerignore deleted file mode 100644 index e35743c..0000000 --- a/deployment/api/.dockerignore +++ /dev/null @@ -1,22 +0,0 @@ -# Ignorer les caches Python -__pycache__/ -*.pyc - -# Ignorer les artefacts temporaires -downloaded_artifacts/ -mlruns/ -logs/ - -# Ignorer les tests unitaires -tests/ - -# Ignorer les variables d'environnement. Elles seront plutôt chargées dans la commande docker run `docker run --env-file ./deployment/api/.env -p 8000:8000 sentiment-api` en développement local, et via des variables définies dans le yaml -*.env - -# Ignorer fichier conf amazon web services -*aws* -.elasticbeanstalk - -sentiment-api-*.json - -docker-compose.yml.example diff --git a/deployment/api/.gitignore b/deployment/api/.gitignore deleted file mode 100644 index 254b45a..0000000 --- a/deployment/api/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -# ssh .pem files -*.pem - -*aws* - -sentiment-api-*.json diff --git a/deployment/api/Dockerfile b/deployment/api/Dockerfile deleted file mode 100644 index 93ff856..0000000 --- a/deployment/api/Dockerfile +++ /dev/null @@ -1,42 +0,0 @@ -FROM python:3.12-slim - -WORKDIR /app - -# # Install nginx and system packages -RUN apt-get update && \ - apt-get install -y nginx curl && \ - # Créer l'utilisateur et le groupe www-data si ce n'est pas déjà fait par l'image de base - # Assurer que l'utilisateur nginx (souvent www-data) a un ID consistent - adduser --system --no-create-home --group www-data || true && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* - -# Copy and install Python dependencies -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Install nltk resources at build time -RUN python3 -m nltk.downloader punkt \ - && python3 -m nltk.downloader punkt_tab \ - && python3 -m nltk.downloader stopwords \ - && python3 -m nltk.downloader wordnet \ - && python3 -m nltk.downloader omw-1.4 - - -# Copy app code and configs -COPY . . - -# Copy nginx config - l'emplacement par défaut de Nginx est /etc/nginx/nginx.conf -COPY nginx.conf /etc/nginx/nginx.conf - -# Pas besoin de créer les dossiers ici, le start.sh s'en chargera avec les bonnes permissions -RUN mkdir -p /var/log/nginx /var/lib/nginx /run - -# Ensure script is executable -RUN chmod +x /app/start.sh - -# Expose only NGINX port (Cloud Run utilise 8080 par défaut) -EXPOSE 8080 - -# Start everything via script -CMD ["/app/start.sh"] diff --git a/deployment/api/app.py b/deployment/api/app.py deleted file mode 100644 index 31dbd8a..0000000 --- a/deployment/api/app.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import streamlit as st -import requests - -API_BASE_URL = os.getenv("API_BASE_URL", "http://127.0.0.1:8080") + "/api" - - -st.set_page_config( - page_title="Air Paradis - Tweet Prediction", - page_icon="✈️", # optionnel, tu peux mettre un emoji ou un fichier image -) -st.title("Sentiment Analysis - Air Paradis") - - -tweet = st.text_area("Prompt your tweet here :", height=150) - -# Initialisation des états -if "prediction_result" not in st.session_state: - st.session_state.prediction_result = None - -if "show_report_button" not in st.session_state: - st.session_state.show_report_button = False - -if "report_sent" not in st.session_state: - st.session_state.report_sent = False - -if "last_report_sent" not in st.session_state: - st.session_state.last_report_sent = False - -def reset_state(): - st.session_state.prediction_result = None - st.session_state.show_report_button = False - st.session_state.report_sent = False - st.session_state.last_report_sent = False - -# Bouton d'analyse -if st.button("Analyze the sentiment"): - if not tweet.strip(): - st.warning("Please type a valid tweet.") - reset_state() - else: - try: - response = requests.post( - f"{API_BASE_URL}/predict", - json={"tweet": tweet} - ) - if response.status_code == 200: - data = response.json() - st.session_state.prediction_result = data - st.session_state.show_report_button = True - st.session_state.report_sent = False - st.session_state.last_report_sent = False - else: - st.error(f"Erreur API : {response.status_code} - {response.text}") - reset_state() - except Exception as e: - st.error(f"Error during API request : {e}") - reset_state() - -# Affichage du résultat -if st.session_state.prediction_result: - sentiment = st.session_state.prediction_result["sentiment"].capitalize() - prob = st.session_state.prediction_result["probability"] - st.success(f"Sentiment : {sentiment}") - st.info(f"Positive tweet probability: {prob:.2f}") - -# Gestion du bouton signalement -if st.session_state.show_report_button and not st.session_state.report_sent: - if st.button("Report a wrong prediction"): - try: - response = requests.post( - f"{API_BASE_URL}/report_error", - json={ - "tweet": tweet, - "prediction": sentiment, - "probability": st.session_state.prediction_result["probability"] # Transmettre la probabilité - } - ) - - if response.status_code == 200: - data = response.json() - # Mettre à jour les états - st.session_state.show_report_button = False - st.session_state.report_sent = True - st.session_state.last_report_sent = data.get("report_sent", False) - # Forcer un re-run - st.rerun() - else: - st.error(f"API Error : {response.status_code} - {response.text}") - except Exception as e: - st.error(f"Error during API request : {e}") - -# Message si signalement déjà fait -elif st.session_state.report_sent: - st.success("✅ Thanks for your report.") - - if st.session_state.last_report_sent: - st.info("📩 A complete report has been sent to the website administrator.") - - -st.markdown( - """ - -
- Reported wrong predictions help improve the model. Please avoid including personal information. -
- """, - unsafe_allow_html=True, -) diff --git a/deployment/api/docker-compose.yml.example b/deployment/api/docker-compose.yml.example deleted file mode 100644 index a56b117..0000000 --- a/deployment/api/docker-compose.yml.example +++ /dev/null @@ -1,32 +0,0 @@ -services: - api: - build: - context: ./deployment/api - dockerfile: Dockerfile - container_name: sentiment_api - ports: - - "8000:8000" # API FastAPI - - "8501:8501" # Streamlit - environment: - MLFLOW_TRACKING_URI: "${MLFLOW_TRACKING_URI}" # ex: http://mlflow:5000 - MLFLOW_S3_ENDPOINT_URL: "${MLFLOW_S3_ENDPOINT_URL}" # ex: url MinIO ou S3 - AWS_ACCESS_KEY_ID: "${AWS_ACCESS_KEY_ID}" # clé ID S3 - AWS_SECRET_ACCESS_KEY: "${AWS_SECRET_ACCESS_KEY}" # clé secrète S3 - - SMTP_SERVER: "${SMTP_SERVER}" # serveur SMTP - SMTP_PORT: "${SMTP_PORT}" # port SMTP (ex: 587) - SMTP_EMAIL: "${SMTP_EMAIL}" # email expéditeur - SMTP_PASSWORD: "${SMTP_PASSWORD}" # mot de passe SMTP - SMTP_FROM_ALIAS: "${SMTP_FROM_ALIAS}" # alias (optionnel) - ADMIN_EMAIL: "${ADMIN_EMAIL}" # email admin destinataire - - SENTIMENT_API_BASE_URL: "${SENTIMENT_API_BASE_URL}" # public api url (used to redirect Streamlit requests to FastAPI) - -# only if you have a reverse proxy server -# networks: -# - swag - restart: always -# only if you have a reverse proxy server -# networks: -# swag: -# external: true diff --git a/deployment/api/email_service.py b/deployment/api/email_service.py deleted file mode 100644 index bd2aebc..0000000 --- a/deployment/api/email_service.py +++ /dev/null @@ -1,111 +0,0 @@ -import smtplib -import os -import json -from email.mime.text import MIMEText -from email.mime.multipart import MIMEMultipart -from datetime import datetime - -def send_error_report_email(error_reports): - """ - Envoie un email avec le rapport d'erreurs et le JSON intégré - """ - smtp_server = os.getenv("SMTP_SERVER") - smtp_port = int(os.getenv("SMTP_PORT", 587)) - smtp_email = os.getenv("SMTP_EMAIL") # main email address. If smt_from_alias is not, it will also be the mail sender - smtp_password = os.getenv("SMTP_PASSWORD") - smtp_from_alias = os.getenv("SMTP_FROM_ALIAS", smtp_email) # mail sender if not none (alias) - admin_email = os.getenv("ADMIN_EMAIL") # this one will receive the mail - - - if not all([smtp_server, smtp_email, smtp_password, admin_email]): - print("❌ Configuration email manquante dans .env") - return False - - try: - # Create message - msg = MIMEMultipart() - if smtp_from_alias is not None: - smtp_from = smtp_from_alias - else: - smtp_from = smtp_email - msg['From'] = f"Air Paradis Monitor <{smtp_from}>" - msg['To'] = admin_email - msg['Subject'] = f"🚨 Rapport d'erreurs - Prédictions sentiments ({datetime.now().strftime('%d/%m/%Y %H:%M')})" - - # Prepare JSON data - json_data = { - "report_generated_on": datetime.now().isoformat(), - "reports_amount": len(error_reports), - "reports": [ - { - "id": i, - "tweet": tweet, - "incorrect_prediction": prediction, - "tweet_length": len(tweet) - } - for i, (tweet, prediction) in enumerate(error_reports.items(), 1) - ], - "metadata": { - "model": "SentimentAnalysisLSTM", - "api_version": "1.0", - "reporting_threshold": 3 - } - } - - # Message body with embedded JSON - body = f""" -Hello, - -A new error report has been generated for the Air Paradis sentiment prediction API. - -📊 Total number of reports : {len(error_reports)} -📅 Report date : {datetime.now().strftime('%d/%m/%Y à %H:%M')} - -📋 Report summary : - -""" - - for i, (tweet, prediction) in enumerate(error_reports.items(), 1): - body += f"{i}. Tweet: \"{tweet[:100]}{'...' if len(tweet) > 100 else ''}\"\n" - body += f" Prediction reported as incorrect: {prediction.upper()}\n\n" - - body += """ -📈 Recommended actions: -- Analyze reported tweets to identify patterns -- Consider re-training the model if necessary -- Check quality of training data - -📊 COMPLETE DATA (JSON) : -═══════════════════════════════════════════════════════════════════════════════ - -""" - - # ✅ Directly add JSON inside the body - json_string = json.dumps(json_data, ensure_ascii=False, indent=2) - body += f"```json\n{json_string}\n```" - - body += """ - -═══════════════════════════════════════════════════════════════════════════════ - ---- -Sincerely, -🤖 API Air Paradis - Automatic monitoring system -📧 Sent from : """ + smtp_from - - # Add message body - msg.attach(MIMEText(body, 'plain', 'utf-8')) - - # Sending email - with smtplib.SMTP(smtp_server, smtp_port) as server: - server.starttls() - server.login(smtp_email, smtp_password) - server.send_message(msg) - - print(f"✅ Report email successfully sent from {smtp_from}") - print("📊 JSON embedded in message body") - return True - - except Exception as e: - print(f"❌ Error during email sending : {e}") - return False \ No newline at end of file diff --git a/deployment/api/main.py b/deployment/api/main.py deleted file mode 100644 index 767a736..0000000 --- a/deployment/api/main.py +++ /dev/null @@ -1,210 +0,0 @@ -import os -import sys -import pickle -from dotenv import load_dotenv -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel -import mlflow -from mlflow.tracking import MlflowClient -from tensorflow.keras.models import load_model -from tensorflow.keras.preprocessing.sequence import pad_sequences -import pandas as pd -from datetime import datetime, timedelta - -# # TO UNCOMMENT FOR DEVELOPMENT. OTHERWISE, LEAVE AS IS -# # Access the .env file -# PARENT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) -# sys.path.append(PARENT_DIR) -# # 🔐 Load environment variables (.env two levels above) -# load_dotenv() - -# 🔁 Import the preprocessing function (LOCAL) -from preprocessing import preprocess_tweet - -# 📧 Import the email service -from email_service import send_error_report_email - -# 🔗 Configure MLflow -mlflow_tracking_uri = os.getenv("MLFLOW_TRACKING_URI") -if mlflow_tracking_uri: - mlflow.set_tracking_uri(mlflow_tracking_uri) -else: - print("⚠️ MLFLOW_TRACKING_URI not defined in .env") - -# 📌 Model name and stage -MODEL_NAME = "SentimentAnalysisLSTM" -STAGE = "Production" - -# 🧾 Structure of POST requests -class TweetRequest(BaseModel): - tweet: str - -class ReportRequest(BaseModel): - tweet: str - prediction: str - probability: float - -# 🧠 Storage for error reports -error_reports = {} - -# 🔍 Function to load model + tokenizer -def load_model_and_artifacts(model_name=MODEL_NAME, stage=STAGE): - client = MlflowClient() - versions = client.get_latest_versions(model_name, [stage]) - - if not versions: - raise RuntimeError(f"No version found for model '{model_name}' in stage '{stage}'") - - version_info = versions[0] - run_id = version_info.run_id - - if not run_id: - raise RuntimeError("Run ID not found.") - - # Extract embedding tag - model_version_info = client.get_model_version(model_name, version_info.version) - # glove is used as default if embedding_type is not found - embedding_type = model_version_info.tags.get("embedding_type", "glove").lower() - - # Determine paths to artifacts - local_dir = "./downloaded_artifacts" - os.makedirs(local_dir, exist_ok=True) - base_path = "local_artifacts" - tokenizer_file = "tokenizer.pickle" - - if "glove" in embedding_type: - keras_file = "final_model_LSTM_GloVe-300d-Fige.keras" - elif "word2vec" in embedding_type: - keras_file = "final_model_LSTM_Word2Vec-Fige.keras" - else: - raise ValueError(f"Unknown embedding type: {embedding_type}") - - print(f"{embedding_type} model found. Downloading model associated with run_id: {run_id}") - - def dl(path): - return client.download_artifacts(run_id=run_id, path=f"{base_path}/{path}", dst_path=local_dir) - - # 🔽 Download artifacts - model_path = dl(keras_file) - tokenizer_path = dl(tokenizer_file) - print("Contents of downloaded_artifacts:", os.listdir(local_dir)) - - # 📦 Load - model = load_model(model_path) - with open(tokenizer_path, "rb") as f: - tokenizer = pickle.load(f) - - return model, tokenizer, embedding_type - -# 🚀 Launch the API -app = FastAPI(title="Sentiment Prediction API - Air Paradis") -# app = FastAPI(title="Sentiment Prediction API - Air Paradis", -# root_path="/api") - -try: - model, tokenizer, embedding_type = load_model_and_artifacts() -except Exception as e: - print(f"\n❌ Error loading artifacts: {e}") - model, tokenizer = None, None - -@app.get("/") -def root(): - return {"message": "Sentiment API - Air Paradis - online"} - -@app.post("/predict") -def predict_sentiment(request: TweetRequest): - if model is None or tokenizer is None: - raise HTTPException(status_code=503, detail="Model or artifacts unavailable.") - - try: - processed_tweet = preprocess_tweet(request.tweet) - except Exception as e: - raise HTTPException(status_code=500, detail=f"Preprocessing error: {e}") - - if not processed_tweet.strip(): - raise HTTPException(status_code=400, detail="Tweet is empty or invalid after preprocessing.") - - try: - sequence = tokenizer.texts_to_sequences([processed_tweet]) - sequence_padded = pad_sequences(sequence, maxlen=model.input_shape[1]) - prediction_prob = model.predict(sequence_padded)[0][0] - if prediction_prob >= 0.5: - sentiment = "positive" - else: - sentiment = "negative" - # prediction_prob = 1 - prediction_prob - except Exception as e: - raise HTTPException(status_code=500, detail=f"Inference error: {e}") - - return { - "sentiment": sentiment, - "probability": float(prediction_prob) - } - -# Replace the dict with a global empty DataFrame initially -error_reports_df = pd.DataFrame(columns=["tweet", "prediction", "probability", "timestamp"]) - -@app.post("/report_error") -def report_error(request: ReportRequest): - global error_reports_df - - tweet = request.tweet.strip() - prediction = request.prediction.strip().lower() - probability = request.probability - - if not tweet or not prediction: - raise HTTPException(status_code=400, detail="Missing tweet or prediction.") - if probability < 0 or probability > 1: - raise HTTPException(status_code=400, detail="Invalid probability. Must be between 0 and 1.") - - now = datetime.utcnow() - - # Check if tweet already exists to avoid duplicates - existing_idx = error_reports_df.index[error_reports_df["tweet"] == tweet].tolist() - if existing_idx: - # Update existing row - idx = existing_idx[0] - error_reports_df.at[idx, "prediction"] = prediction + f' (p = {probability:.2f} ) ' - error_reports_df.at[idx, "probability"] = probability - error_reports_df.at[idx, "timestamp"] = now - else: - # Add new row - new_row = { - "tweet": tweet, - "prediction": prediction + f' (p = {probability:.2f} ) ', - "probability": probability, - "timestamp": now - } - error_reports_df = pd.concat([error_reports_df, pd.DataFrame([new_row])], ignore_index=True) - - report_sent = False - - # Condition: every 3 reports and max 5 min interval between the last 3 - try: - if len(error_reports_df) % 3 == 0: - last_three = error_reports_df.sort_values("timestamp", ascending=False).head(3) - times = last_three["timestamp"].sort_values() - delta = times.iloc[-1] - times.iloc[0] - if delta <= timedelta(minutes=5): - reports_dict = dict(zip( - last_three["tweet"], - last_three["prediction"] - )) - print(f"📧 Attempting to send email for {len(error_reports_df)} reports...") - email_success = send_error_report_email(reports_dict) - if email_success: - print(f"✅ Email successfully sent for {len(error_reports_df)} reports") - report_sent = True - # DELETE the last 3 reports sent - # Identify indexes to delete - to_remove_idx = last_three.index - error_reports_df = error_reports_df.drop(to_remove_idx).reset_index(drop=True) - else: - print(f"❌ Failed to send email for {len(error_reports_df)} reports") - - except Exception as e: - print(f"❌ Error while sending email: {e}") - # Continue even if it fails - - return {"report_sent": report_sent} - \ No newline at end of file diff --git a/deployment/api/nginx.conf b/deployment/api/nginx.conf deleted file mode 100644 index 6d95b0e..0000000 --- a/deployment/api/nginx.conf +++ /dev/null @@ -1,38 +0,0 @@ -# This reverse proxy configuration aims to make calls to different services running under different ports (Streamlit :8501 and FastAPI :8000) by calling only one port : the nginx one (:8080) -# The 8080 has to be opened (Dockerfile) and used by app.py (streamlit) in order to make right calls. -worker_processes 1; - -events { worker_connections 1024; } - -http { - server { - listen 8080; - - location /api/ { - proxy_pass http://127.0.0.1:8000/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - } - - location / { - proxy_pass http://127.0.0.1:8501/; - - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection "upgrade"; - - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - } - - location /health { - return 200 'OK'; - add_header Content-Type text/plain; - } - - } -} diff --git a/deployment/api/preprocessing.py b/deployment/api/preprocessing.py deleted file mode 100644 index 40e6e3a..0000000 --- a/deployment/api/preprocessing.py +++ /dev/null @@ -1,80 +0,0 @@ -import re -import html -import nltk -from nltk.corpus import stopwords -from nltk.tokenize import word_tokenize -from nltk.stem import WordNetLemmatizer - -# Ensure that the necessary resources are available -try: - stopwords.words('english') - word_tokenize("test") - WordNetLemmatizer().lemmatize("testing") -except LookupError: - nltk.download('punkt') - nltk.download('punkt_tab') - nltk.download('stopwords') - nltk.download('wordnet') - nltk.download('omw-1.4') - -# PREPROCESSING FUNCTION FOR CLASSIC AND ADVANCED MODELS -def preprocess_tweet(tweet): - """ - Preprocesses a tweet for classic ML models or word embedding models (e.g., Word2Vec, GloVe). - The text is cleaned, normalized, lemmatized, and stripped of unnecessary noise. - """ - - # Ensure input is a string - if not isinstance(tweet, str): - return "" - - # Decode HTML entities (e.g., & → &) - tweet = html.unescape(tweet) - # print(tweet + '\n') - - # Convert text to lowercase - tweet = tweet.lower() - # print(tweet + '\n') - - # Replace URLs with a generic token - tweet = re.sub(r'https?://\S+|www\.\S+', '', tweet) - # print(tweet + '\n') - - # Replace mentions (@username) with a generic token - tweet = re.sub(r'@\w+', '', tweet) - # print(tweet + '\n') - - # Separate hashtags from the word to preserve semantics during tokenization - tweet = re.sub(r'#(\w+)', r'# \1', tweet) - # print(tweet + '\n') - - # Remove unwanted punctuation and special characters (except useful ones like < > @ # ! ?) - tweet = re.sub(r'[^\w\s<>@#!?]', '', tweet) - # print(tweet + '\n') - - # Remove non-printable/control characters (e.g., \n, \t, emojis, corrupted unicode) - tweet = re.sub(r'[^\x20-\x7E]', '', tweet) - # print(tweet + '\n') - - # Tokenize the tweet into individual words - tokens = word_tokenize(tweet) - # print(tokens) - # print('\n') - - # Lemmatize each token to its base form (e.g., "running" → "run") - lemmatizer = WordNetLemmatizer() - tokens = [lemmatizer.lemmatize(token) for token in tokens] - # print(tokens) - # print('\n') - - # Remove stopwords, but keep negation-related words (important for sentiment) - stop_words = set(stopwords.words('english')) - important_words = {'no', 'not', 'nor', 'neither', 'never', 'nobody', 'none', 'nothing', 'nowhere'} - stop_words = stop_words - important_words - tokens = [token for token in tokens if token not in stop_words] - # print(tokens) - # print('\n') - - - # Reconstruct the cleaned tweet - return ' '.join(tokens) \ No newline at end of file diff --git a/deployment/api/requirements.txt b/deployment/api/requirements.txt deleted file mode 100644 index 14c5515..0000000 --- a/deployment/api/requirements.txt +++ /dev/null @@ -1,21 +0,0 @@ -# === API FastAPI === -fastapi==0.115.14 -uvicorn==0.35.0 -pydantic==2.11.7 -starlette==0.46.2 -# === Interface Streamlit === -streamlit==1.46.1 -requests==2.32.4 -# === Machine Learning === -tensorflow==2.19.0 -mlflow==3.1.1 -boto3==1.39.2 -numpy==2.1.3 -pandas==2.3.0 -# === Traitement de texte === -nltk==3.9.1 -# === Utilitaires === -python-dotenv==1.1.1 -httpx==0.28.1 -# === Dépendances système === -protobuf==3.20.3 diff --git a/deployment/api/start.sh b/deployment/api/start.sh deleted file mode 100644 index 9bca865..0000000 --- a/deployment/api/start.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash - -set -e - -# S'assurer que les répertoires de cache et de log Nginx sont accessibles en écriture -mkdir -p /var/cache/nginx /var/run/nginx /var/log/nginx -chown -R www-data:www-data /var/cache/nginx /var/run/nginx /var/log/nginx - -echo "Starting Streamlit..." -streamlit run app.py \ - --server.port 8501 \ - --server.address 0.0.0.0 \ - --server.enableXsrfProtection false \ - --server.enableCORS false \ - & # Lance en arrière-plan - -echo "Starting FastAPI..." -uvicorn main:app --host 0.0.0.0 --port 8000 --loop asyncio & # Lance en arrière-plan - -# MODIFICATION IMPORTANTE ICI: Vérifier Streamlit sur le bon chemin -echo "Waiting for Streamlit to be ready..." -until curl -s http://127.0.0.1:8501 > /dev/null; do - sleep 1 -done -echo "Streamlit is ready!" - -# MODIFICATION: Vérifier FastAPI sur un endpoint existant, par exemple /docs ou /health -echo "Waiting for FastAPI to be ready..." -until curl -s http://127.0.0.1:8000/docs > /dev/null; do # Assurez-vous que /docs est un endpoint léger - sleep 1 -done -echo "FastAPI is ready!" - -echo "Launching NGINX..." -nginx -c /etc/nginx/nginx.conf -g 'daemon off;' diff --git a/deployment/api/tests/test_email_service.py b/deployment/api/tests/test_email_service.py deleted file mode 100644 index 38c4dfc..0000000 --- a/deployment/api/tests/test_email_service.py +++ /dev/null @@ -1,38 +0,0 @@ -# Utilisation de mock pour simuler l'envoi de mails mais sans spammer une boîte. - -from unittest.mock import patch, MagicMock -import os -import sys -from dotenv import load_dotenv - -# Ajouter le répertoire parent au path pour importer email_service -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../"))) - -from email_service import send_error_report_email - -# Charger les variables d'environnement -dotenv_path = '../../../.env' -load_dotenv(dotenv_path) - -@patch("smtplib.SMTP") -def test_email_service_connection(mock_smtp): - """ - Test SMTP connection without actually sending the email - """ - # 🔧 Configuration du mock - mock_server = MagicMock() - mock_smtp.return_value.__enter__.return_value = mock_server - - test_reports = { - "Flight cancelled without notice": "positif" - } - - # Appel de la fonction - result = send_error_report_email(test_reports) - - # ✅ Assertions - assert result is True - mock_smtp.assert_called_with(os.getenv("SMTP_SERVER"), int(os.getenv("SMTP_PORT", 587))) - mock_server.starttls.assert_called_once() - mock_server.login.assert_called_once_with(os.getenv("SMTP_EMAIL"), os.getenv("SMTP_PASSWORD")) - mock_server.send_message.assert_called_once() diff --git a/deployment/api/tests/test_main.py b/deployment/api/tests/test_main.py deleted file mode 100644 index ba4afe5..0000000 --- a/deployment/api/tests/test_main.py +++ /dev/null @@ -1,32 +0,0 @@ -import os -import sys -from fastapi.testclient import TestClient - -# Add parent directory to path to import email_service -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../"))) - -from main import app - -client = TestClient(app) - -def test_root_endpoint(): - response = client.get("/") - assert response.status_code == 200 - assert response.json() == {"message": "Sentiment API - Air Paradis - online"} - -def test_predict_sentiment(): - response = client.post("/predict", json={"tweet": "I love this airline !"}) - assert response.status_code == 200 - data = response.json() - assert "sentiment" in data - assert "probability" in data - assert data["sentiment"] in ["positive", "negative"] - -def test_missing_tweet(): - response = client.post("/predict", json={}) - assert response.status_code == 422 # Unprocessable Entity (FastAPI) - -def test_empty_tweet(): - response = client.post("/predict", json={"tweet": ""}) - assert response.status_code == 400 - assert response.json()["detail"] == "Tweet is empty or invalid after preprocessing." \ No newline at end of file diff --git a/deployment/api/tests/test_preprocessing.py b/deployment/api/tests/test_preprocessing.py deleted file mode 100644 index d9bc3c9..0000000 --- a/deployment/api/tests/test_preprocessing.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -import sys - -# Ajouter le répertoire parent au path pour importer email_service -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../"))) -from preprocessing import preprocess_tweet - - -def test_preprocess_tweet_removes_hashtags(): - tweet = """ - Just flew w/ @AirParadis 🚀 & I’m in LOVE! Epic service, comfy seats, good prices at https://airparadis.com - #bestflight #AirParadis - """ - assert preprocess_tweet(tweet) == 'flew w < MENTION > im love ! epic service comfy seat good price < URL > # bestflight # airparadis' - -def test_preprocess_tweet_removes_mentions(): - tweet = """ - "Flight of dreams" w/ @AirParadis! Got food, space & smiles 😄 I’ll book again! - #AirParadis - """ - assert preprocess_tweet(tweet) == 'flight dream w < MENTION > ! got food space smile ill book ! # airparadis' - -def test_preprocess_tweet_lowercases_text(): - tweet = """ - My 3rd time w/ @AirParadis & still amazed 😍 Crew = lovely à every step! - #flyhappy #AirParadis - """ - assert preprocess_tweet(tweet) == '3rd time w < MENTION > still amazed crew lovely every step ! # flyhappy # airparadis' diff --git a/docs/Basic-Models.png b/docs/Basic-Models.png deleted file mode 100644 index 70108f4..0000000 Binary files a/docs/Basic-Models.png and /dev/null differ diff --git a/docs/GloVe-300d_LSTM_epoch12.png b/docs/GloVe-300d_LSTM_epoch12.png deleted file mode 100644 index 3161c6a..0000000 Binary files a/docs/GloVe-300d_LSTM_epoch12.png and /dev/null differ diff --git a/docs/Word2Vec_LSTM_epoch9.png b/docs/Word2Vec_LSTM_epoch9.png deleted file mode 100644 index a194b9e..0000000 Binary files a/docs/Word2Vec_LSTM_epoch9.png and /dev/null differ diff --git a/docs/api_error_report.png b/docs/api_error_report.png deleted file mode 100644 index be81ff9..0000000 Binary files a/docs/api_error_report.png and /dev/null differ diff --git a/docs/api_first_error.png b/docs/api_first_error.png deleted file mode 100644 index 8325cc2..0000000 Binary files a/docs/api_first_error.png and /dev/null differ diff --git a/docs/api_second_error.png b/docs/api_second_error.png deleted file mode 100644 index 6cd83a8..0000000 Binary files a/docs/api_second_error.png and /dev/null differ diff --git a/docs/api_third_error.png b/docs/api_third_error.png deleted file mode 100644 index a454bb4..0000000 Binary files a/docs/api_third_error.png and /dev/null differ diff --git a/docs/bert_confusion_matrix.png b/docs/bert_confusion_matrix.png deleted file mode 100644 index f363132..0000000 Binary files a/docs/bert_confusion_matrix.png and /dev/null differ diff --git a/docs/bert_roc_curve.png b/docs/bert_roc_curve.png deleted file mode 100644 index e6d8527..0000000 Binary files a/docs/bert_roc_curve.png and /dev/null differ diff --git a/docs/bert_training_curves_epoch1.png b/docs/bert_training_curves_epoch1.png deleted file mode 100644 index 1939e50..0000000 Binary files a/docs/bert_training_curves_epoch1.png and /dev/null differ diff --git a/docs/blog.fr.md b/docs/blog.fr.md deleted file mode 100644 index 8b3fb20..0000000 --- a/docs/blog.fr.md +++ /dev/null @@ -1,235 +0,0 @@ -# ✨ Analyse de sentiments et déploiement MLOps : retour d’expérience complet - -Dans le cadre du projet *Air Paradis*, nous avons mis en œuvre une chaîne complète d’analyse de sentiments à partir de tweets, depuis la modélisation jusqu’au déploiement MLOps. Ce billet de blog revient sur les étapes clés du projet : choix méthodologiques, comparaison des modèles, mise en production d’une API robuste, et déploiement sur deux environnements (local + cloud). 🧠🔧 - ---- - -## 🔍 Trois approches de modélisation supervisée - -Nous avons testé trois approches complémentaires pour prédire le sentiment d’un tweet. Chacune repose sur une représentation textuelle différente et un algorithme d’apprentissage adapté : - -### 1. 🧱 Approche simple : Bag-of-Words (BoW) + modèles de machine learning - -On vectorise les tweets via BoW ou BoW + TF-IDF, qui comptent la fréquence des mots. -Contrairement à d'autres projets NLP, nous avons évité LDA (non supervisé, orienté topic modeling). - -➡️ **Modèles testés** : -- Régression Logistique -- Naive Bayes -- SVM -- Random Forest - -Ces modèles sont entraînés avec **GridSearchCV**, en optimisant la **précision (`precision`)**, notre KPI métier. Différents KPI ont été calculés sur l'ensemble de test. - -![Scores modèles simples](./Basic-Models.png) - ---- - -### 2. 🔥 Approche avancée : Word Embeddings + LSTM - -Nous utilisons des vecteurs denses Word2Vec (entraînés) et GloVe 300d (préentraînés par Stanford), qui capturent les similarités sémantiques entre mots. Ces représentations ne prennent toutefois **pas en compte le contexte** exact (ex : "annulé" est probablement un mot très négatif pour un vol mais positif pour un procès contenant des charges contre vous). - -La classification est réalisée par un **LSTM**, capable d'exploiter la séquentialité des mots. -Le **modèle déployé est automatiquement sélectionné** entre Word2Vec + LSTM et GloVe + LSTM, selon la **précision maximale enregistrée dans MLFlow**. - -Modèle `Word2Vec + LSTM` (`test_precision : 0.797 , val_precision : 0.795`): -![Scores entrainement modèle avancé LSTM + Word2Vec](./Word2Vec_LSTM_epoch9.png) -Modèle `GloVe 300d + LSTM` (`test_precision : 0.792 , val_precision : 0.798`): -![Scores entrainement modèles avancés LSTM + GloVe 300d](./GloVe-300d_LSTM_epoch12.png) - -Bien que GloVe 300d + LSTM présente un score de précision légèrement supérieur à Word2Vec + LSTM sur l'ensemble de validation, il reste inférieur pour l'ensemble de test (relativement négligeable dans l'absolu). - ---- - -### 3. 🤖 Approche transformer : DistilBERT, embeddings contextuels allégés - -Nous avons utilisé **DistilBERT**, une version plus légère et plus rapide de BERT (créée par Hugging Face). -Il conserve **95 % de la performance de BERT** tout en étant **40 % plus petit** et **60 % plus rapide**, ce qui le rend particulièrement adapté à un **entraînement sur GPU limité** (Google Colab) et à une **inférence plus rapide**. - -Comme BERT, DistilBERT produit des **embeddings contextuels**, c’est-à-dire que le sens des mots varie selon leur contexte. Par exemple, *"banque"* dans *"je vais à la banque"* et *"banque de données"* n’aura pas le même vecteur. - -Il a été **fine-tuné** ici pour une classification binaire (positif/négatif), avec un tokenizer et un encodage spécifiques à son architecture. - -> **Modèle `DistilBERT`** (`test_precision : 0.861 , val_precision : 0.858`) - -![Courbes d'entraînement DistilBERT](./bert_training_curves_epoch1.png) -![Matrice de Confusion](./bert_confusion_matrix.png) -![ROC Curve](./bert_roc_curve.png) - -⚠️ **Limites** : comme tous les modèles préentraînés, DistilBERT **n’est pas sensible à l’ironie, aux jeux de mots, ni aux références culturelles implicites**. -Or, Twitter est un terrain de jeu idéal pour ce genre de contenus détournés. Ces limites s’appliquent aussi à Word2Vec, GloVe et aux modèles classiques. - ---- - -## 🎯 Pourquoi optimiser la précision (et non l'accuracy) ? - -**Accuracy** = proportion de bonnes prédictions globales. -**Precision** = proportion de tweets *prévus positifs* qui sont *vraiment positifs*. - -> Exemple : sur 100 tweets, si le modèle prédit 30 positifs, mais 15 sont des erreurs, la précision est de 50 %. - -Nous avons préféré **minimiser les faux positifs**, car dans un contexte sensible, **annoncer un tweet positif alors qu’il est négatif peut être problématique**. - -- Modèles classiques : **GridSearchCV optimisé sur la précision** -- Modèles DL : **EarlyStopping sur val_loss**, mais **sélection finale sur la précision** - ---- - -## ⚙️ Environnement MLOps souverain - -### 🖥️ Serveur personnel NAS + OpenMediaVault - -MLFlow est installé sur un **NAS personnel (Intel N100)**, sous **OpenMediaVault (Debian)**. -Nous utilisons **MinIO** pour stocker artefacts et modèles, garantissant une gestion locale, souveraine et compatible S3. - -> Les notebooks, exécutés sur Google Colab pour bénéficier de GPU, loggent dans MLFlow et envoient les modèles vers MinIO via des **variables d’environnement**. - -Dashboard Expériences MLFlow : -![Dashboard MLFLow](./mlflow_experiments_dashboard.png) - -Modèles en production (registre de modèles): -![Models Registry](mlflow_models_registry_2.png) - -Meilleur modèle non BERT retenu : -![Modèle retenu](mlflow_models_registry_best_LSTM_model.png) -![Meilleur modèle non BERT](mlflolw_experiments_dashboard_best_LSTM_model.png) ---- - -## 🧪 CI, tests et versioning - -### ✅ Tests unitaires - -- Vérifient le bon fonctionnement du prétraitement, des prédictions et de la gestion d’erreurs. -- Intégrés à **GitHub Actions**, **ils bloquent le déploiement si les tests échouent.** - -### 🐳 Conteneurisation & CI/CD - -L’API FastAPI est packagée via Docker, puis poussée sur DockerHub. -Un [pipeline CI/CD](https://github.com/GrgMdmn/OpenClassrooms_AI_Projects/blob/main/.github/workflows/p7_ci-cd.yml) complet garantit **un déploiement reproductible**. Il assure: -- La validation de tests unitaires -- La construction et le push de l'api conteneurisée sur [dockerhub](https://hub.docker.com/r/grgmdmn/sentiment_api) - -![github workflow](github_ci-cd.png) ---- - -## 🚀 Déploiement de l’API FastAPI (double) - -### 🔧 Backend : FastAPI - -- Expose un endpoint `/predict` -- Charge dynamiquement le **meilleur modèle Word Embedding + LSTM** via le *Model Registry* MLFlow -- Applique les bons embeddings selon le tag `embedding_type` - -### 💻 Interface Streamlit - -Interface locale pour tester les requêtes, debug, démonstrations. - -![Streamlit Local](./streamlit_interface_local.png) - ---- - -### 🌐 Double déploiement (NAS + Google Cloud) - -#### 🔧 Problème AVX2 - -Initialement, l’API ne fonctionnait pas sur le NAS (processeur Intel N100), car celui-ci ne prenait pas en charge les instructions **AVX2**, requises par TensorFlow. - -> 🛠️ **Solution** : activation manuelle du support AVX2 dans le BIOS. Cette option est souvent désactivée par défaut sur les cartes mères orientées NAS pour des raisons de consommation énergétique ou de stabilité. - -Depuis cette modification, le NAS peut exécuter l’API sans erreurs. - ---- - -#### 🚀 API déployées - -Le projet propose un **double déploiement** de l’API, en local et dans le cloud : - -- 🏠 **NAS local** : [sentiment-api.greg-madman-nas.duckdns.org](https://sentiment-api.greg-madman-nas.duckdns.org)\ -Interface Docker Compose sur le NAS : -![docker compose openmediavault](openmediavault_docker-compose_settings.png) - -- ☁️ **Google Cloud Run** : [sentiment-api-service-7772256003.europe-west1.run.app](https://sentiment-api-service-7772256003.europe-west1.run.app)\ -Interface de déploiement Google Cloud : -![déploiement google cloud](google_cloud_interface.png) - ---- - -#### 🔄 Déploiement continu automatisé (NAS uniquement) - -Pour assurer un **déploiement continu** sur le NAS, il a été choisi d'installer [**Watchtower**](https://containrrr.dev/watchtower/arguments/) pour rester dans un logique d'environnement 100% `docker`. Cet outil surveille toutes les **10 minutes** l’image Docker de l’API (`sentiment_api`) et, en cas de nouvelle version disponible sur DockerHub, redémarre automatiquement le conteneur avec la dernière image. -![Docker Watchtower dans OMV Docker-Compose](watchtower_docker.png) - -Ce mécanisme garantit que la version déployée sur le NAS reste toujours synchronisée avec la dernière version validée par les tests sur GitHub Actions, **sans intervention manuelle**. -![Logs watchtower dans docker (console SSH)](watchtower_logs.png) - -Un **système d’alerte mail** notifie automatiquement toute mise à jour effectuée. -![Rapport mail watchtower update sentiment_api](watchtower_logs_by_email.png) - ---- - -## 📊 Monitoring maison - -Une logique d’alerte légère embarquée dans l’API : - -![1ère erreur](./api_first_error.png) - -![2ème erreur](./api_second_error.png) - -![3ème erreur](./api_third_error.png) - - -Si **3 erreurs de prédiction** consécutives sont détectées sur une **fenêtre de 5 min**, un rapport est généré automatiquement. - -![Rapport d'erreur envoyé par mail](./api_error_report.png) - - - ---- - -## 🧠 Schéma global du pipeline MLOps - -### Schéma synthétique -``` - ┌────────────┐ - │ Données │ - │ (tweets) │ - └─────┬──────┘ - │ - Prétraitement (regex, tokenisation, lemmatisation, stopwords) - │ - ┌─────────▼──────────┐ - │ Entraînement ML │ - └─────────┬──────────┘ - │ - Log via MLFlow + artefacts MinIO - │ - Sélection du meilleur modèle "avancé" (hors BERT) selon précision - │ - ┌─────────▼──────────┐ - │ API FastAPI │ - │ + Streamlit │ - └─────────┬──────────┘ - │ - Déploiement : - ├─ Google Cloud Run (manuel) - └─ NAS local (automatisé via Watchtower) - │ -┌──────────▼─────────────┐ -│ Monitoring léger maison│ -│ (alertes sur mauvaises │ -│ prédictions) │ -└────────────────────────┘ -``` -### Schéma détaillé -![Diagramme détaillé](diagram.drawio.png) - ---- - -## ✨ Conclusion - -Ce projet démontre comment construire une chaîne MLOps complète, autonome et reproductible pour du NLP appliqué à Twitter. -Il souligne les **forces et limites des modèles** (y compris BERT), la nécessité d’une métrique adaptée au contexte (précision vs accuracy), et l’importance d’un **déploiement maîtrisé**, même sur une infrastructure personnelle. - ---- - -💬 Merci pour votre lecture ! \ No newline at end of file diff --git a/docs/blog.md b/docs/blog.md deleted file mode 100644 index e386f4c..0000000 --- a/docs/blog.md +++ /dev/null @@ -1,245 +0,0 @@ -# ✨ Sentiment Analysis and MLOps Deployment: A Complete Experience Report - -As part of the *Air Paradis* project, we implemented a full sentiment analysis pipeline from tweets, covering everything from modeling to MLOps deployment. This blog post reviews the key project stages: methodological choices, model comparison, deployment of a robust API, and deployment on two environments (local + cloud). 🧠🔧 - ---- - -## 🔍 Three Supervised Modeling Approaches - -We tested three complementary approaches to predict the sentiment of a tweet. Each relies on a different text representation and a suitable learning algorithm: - -### 1. 🧱 Simple Approach: Bag-of-Words (BoW) + Machine Learning Models - -Tweets are vectorized using BoW or BoW + TF-IDF, which count word frequencies. -Unlike other NLP projects, we avoided LDA (unsupervised, topic modeling oriented). - -➡️ **Tested models**: -- Logistic Regression -- Naive Bayes -- SVM -- Random Forest - -These models are trained with **GridSearchCV**, optimizing **precision (`precision`)**, our key business KPI. Various KPIs were computed on the test set. - -![Simple Models Scores](./Basic-Models.png) - ---- - -### 2. 🔥 Advanced Approach: Word Embeddings + LSTM - -We use dense Word2Vec vectors (trained) and GloVe 300d (pretrained by Stanford), which capture semantic similarities between words. However, these representations **do not take into account the exact context** (e.g., "cancelled" is probably very negative for a flight but positive in a legal case with charges against you). - -Classification is done by an **LSTM**, capable of exploiting word sequentiality. -The **deployed model is automatically selected** between Word2Vec + LSTM and GloVe + LSTM, according to the **highest precision recorded in MLFlow**. - -Model `Word2Vec + LSTM` (`test_precision: 0.797, val_precision: 0.795`): -![Advanced LSTM + Word2Vec Training Scores](./Word2Vec_LSTM_epoch9.png) -Model `GloVe 300d + LSTM` (`test_precision: 0.792, val_precision: 0.798`): -![Advanced LSTM + GloVe 300d Training Scores](./GloVe-300d_LSTM_epoch12.png) - -Although GloVe 300d + LSTM shows a slightly better precision score on the validation set than Word2Vec + LSTM, it remains lower on the test set (relatively negligible overall). - ---- - -### 3. 🤖 Transformer Approach: DistilBERT, Lightweight Contextual Embeddings - -We used **DistilBERT**, a lighter and faster version of BERT (created by Hugging Face). -It retains **95% of BERT's performance** while being **40% smaller** and **60% faster**, making it especially suited for **training on limited GPUs** (Google Colab) and **faster inference**. - -Like BERT, DistilBERT produces **contextual embeddings**, meaning word meanings vary depending on context. For example, *"bank"* in *"I go to the bank"* and *"database bank"* will not have the same vector. - -It was **fine-tuned** here for binary classification (positive/negative), with a tokenizer and encoding specific to its architecture. - -> **Model `DistilBERT`** (`test_precision: 0.861, val_precision: 0.858`) - -![DistilBERT Training Curves](./bert_training_curves_epoch1.png) -![Confusion Matrix](./bert_confusion_matrix.png) -![ROC Curve](./bert_roc_curve.png) - -⚠️ **Limits**: Like all pretrained models, DistilBERT **is not sensitive to irony, puns, or implicit cultural references**. -Twitter is an ideal playground for this kind of nuanced content. These limits also apply to Word2Vec, GloVe, and classical models. - ---- - -## 🎯 Why Optimize Precision (Not Accuracy)? - -**Accuracy** = overall proportion of correct predictions. -**Precision** = proportion of *predicted positive* tweets that are *actually positive*. - -> Example: out of 100 tweets, if the model predicts 30 positives but 15 are wrong, precision is 50%. - -We preferred to **minimize false positives**, because in a sensitive context, **announcing a tweet as positive when it is negative can be problematic**. - -- Classical models: **GridSearchCV optimized for precision** -- Deep Learning models: **EarlyStopping on val_loss**, but **final selection based on precision** - ---- - -## ⚙️ Sovereign MLOps Environment - -### 🖥️ Personal NAS Server + OpenMediaVault - -MLFlow is installed on a **personal NAS (Intel N100)** running **OpenMediaVault (Debian)**. -We use **MinIO** to store artifacts and models, ensuring local, sovereign, and S3-compatible management. - -> Notebooks, run on Google Colab to leverage GPUs, log in MLFlow and send models to MinIO via **environment variables**. - -MLFlow Experiments Dashboard: -![MLFlow Dashboard](./mlflow_experiments_dashboard.png) - -Production Models (model registry): -![Models Registry](mlflow_models_registry_2.png) - -Best non-BERT selected model: -![Selected Model](mlflow_models_registry_best_LSTM_model.png) -![Best Non-BERT Model](mlflolw_experiments_dashboard_best_LSTM_model.png) - ---- - -## 🧪 CI, Testing and Versioning - -### ✅ Unit Tests - -- Check correct preprocessing, predictions, and error handling. -- Integrated into **GitHub Actions**, **blocking deployment if tests fail.** - -### 🐳 Containerization & CI/CD - -The FastAPI API is packaged with Docker, then pushed to DockerHub. -A full [CI/CD pipeline](https://github.com/GrgMdmn/OpenClassrooms_AI_Projects/blob/main/.github/workflows/p7_ci-cd.yml) guarantees **reproducible deployment**, ensuring: -- Validation of unit tests -- Build and push of the containerized API to [DockerHub](https://hub.docker.com/r/grgmdmn/sentiment_api) - -![GitHub Workflow](github_ci-cd.png) - ---- - -## 🚀 FastAPI API Deployment (Dual) - -### 🔧 Backend: FastAPI - -- Exposes a `/predict` endpoint -- Dynamically loads the **best Word Embedding + LSTM model** via MLFlow Model Registry -- Applies appropriate embeddings according to the `embedding_type` tag - -### 💻 Streamlit Interface - -Local interface for request testing, debugging, demonstrations. - -![Streamlit Local](./streamlit_interface_local.png) - ---- - -### 🌐 Dual Deployment (NAS + Google Cloud) - -#### 🔧 AVX2 Issue - -Initially, the API did not run on the NAS (Intel N100 CPU) because it did not support **AVX2 instructions**, required by TensorFlow. - -> 🛠️ **Solution**: manual activation of AVX2 support in BIOS. This option is often disabled by default on NAS motherboards for power consumption or stability reasons. - -Since this change, the NAS can run the API without errors. - ---- - -#### 🚀 Deployed APIs - -The project offers a **dual API deployment**, locally and in the cloud: - -- 🏠 **Local NAS**: [sentiment-api.greg-madman-nas.duckdns.org](https://sentiment-api.greg-madman-nas.duckdns.org) -Docker Compose interface on the NAS: -![Docker Compose OpenMediaVault](openmediavault_docker-compose_settings.png) - -- ☁️ **Google Cloud Run**: [sentiment-api-service-7772256003.europe-west1.run.app](https://sentiment-api-service-7772256003.europe-west1.run.app) -Google Cloud deployment interface: -![Google Cloud Deployment](google_cloud_interface.png) - ---- - -#### 🔄 Automated Continuous Deployment (NAS only) - -To ensure **continuous deployment** on the NAS, we chose to install [**Watchtower**](https://containrrr.dev/watchtower/arguments/) to maintain a 100% `docker` environment. This tool checks every **10 minutes** the Docker image of the API (`sentiment_api`) and, if a new version is available on DockerHub, automatically restarts the container with the latest image. -![Docker Watchtower in OMV Docker-Compose](watchtower_docker.png) - -This mechanism ensures that the version deployed on the NAS always stays synchronized with the latest version validated by GitHub Actions tests, **without manual intervention**. -![Logs watchtower in docker (SSH console)](watchtower_logs.png) - -An **email alert system** automatically notifies any update performed. -![Watchtower Logs by Email](watchtower_logs_by_email.png) - - ---- - -## 📊 Home-made Monitoring - -A lightweight alert logic embedded in the API: - -![1st Error](./api_first_error.png) -![2nd Error](./api_second_error.png) -![3rd Error](./api_third_error.png) - -If **3 consecutive prediction errors** are detected within a **5-minute window**, a report is automatically generated. - -![Error Report Sent by Email](./api_error_report.png) - ---- - -## 🧠 Overall MLOps Pipeline Diagram - -### Summary Diagram - -``` - ┌────────────┐ - │ Data │ - │ (tweets) │ - └─────┬──────┘ - │ - Preprocessing (regex, tokenization, lemmatization, stopwords) - │ - ┌─────────▼──────────┐ - │ ML Training │ - └─────────┬──────────┘ - │ - Logging via MLFlow + artifacts MinIO - │ - Select best "advanced" model (excluding BERT) by precision - │ - ┌─────────▼──────────┐ - │ FastAPI │ - │ + Streamlit │ - └─────────┬──────────┘ - │ - Deployment: - ├─ Google Cloud Run (manual) - └─ Local NAS (automated via Watchtower) - │ -┌──────────▼─────────────┐ -│ Lightweight in-house │ -│ Monitoring │ -│ (alerts on bad │ -│ predictions) │ -└────────────────────────┘ - -``` - -### Detailed Diagram -![Detailed Diagram](diagram.drawio.png) - ---- - -## ✨ Conclusion - -This project demonstrates how to build a complete, autonomous, and reproducible MLOps pipeline for NLP applied to Twitter. -It highlights the **strengths and limitations of the models** (including BERT), the need for a **metric adapted to the context** (precision vs accuracy), and the importance of **controlled deployment**, even on personal infrastructure. - ---- - -💬 Thank you for reading! - - - - - - - -Demander à ChatGPT diff --git a/docs/diagram.drawio b/docs/diagram.drawio deleted file mode 100644 index bc42c3f..0000000 --- a/docs/diagram.drawio +++ /dev/null @@ -1,301 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/diagram.drawio.png b/docs/diagram.drawio.png deleted file mode 100644 index d684dc8..0000000 Binary files a/docs/diagram.drawio.png and /dev/null differ diff --git a/docs/github_ci-cd.png b/docs/github_ci-cd.png deleted file mode 100644 index 8f28b02..0000000 Binary files a/docs/github_ci-cd.png and /dev/null differ diff --git a/docs/google_cloud_interface.png b/docs/google_cloud_interface.png deleted file mode 100644 index fdb7b10..0000000 Binary files a/docs/google_cloud_interface.png and /dev/null differ diff --git a/docs/mlflolw_experiments_dashboard_best_LSTM_model.png b/docs/mlflolw_experiments_dashboard_best_LSTM_model.png deleted file mode 100644 index 540b772..0000000 Binary files a/docs/mlflolw_experiments_dashboard_best_LSTM_model.png and /dev/null differ diff --git a/docs/mlflow_experiments_dashboard.png b/docs/mlflow_experiments_dashboard.png deleted file mode 100644 index 333d182..0000000 Binary files a/docs/mlflow_experiments_dashboard.png and /dev/null differ diff --git a/docs/mlflow_models_registry.png b/docs/mlflow_models_registry.png deleted file mode 100644 index 62fd862..0000000 Binary files a/docs/mlflow_models_registry.png and /dev/null differ diff --git a/docs/mlflow_models_registry_2.png b/docs/mlflow_models_registry_2.png deleted file mode 100644 index d5aad33..0000000 Binary files a/docs/mlflow_models_registry_2.png and /dev/null differ diff --git a/docs/mlflow_models_registry_best_LSTM_model.png b/docs/mlflow_models_registry_best_LSTM_model.png deleted file mode 100644 index c311724..0000000 Binary files a/docs/mlflow_models_registry_best_LSTM_model.png and /dev/null differ diff --git a/docs/openmediavault_docker-compose_settings.png b/docs/openmediavault_docker-compose_settings.png deleted file mode 100644 index 5760417..0000000 Binary files a/docs/openmediavault_docker-compose_settings.png and /dev/null differ diff --git a/docs/streamlit_interface_google_cloud.png b/docs/streamlit_interface_google_cloud.png deleted file mode 100644 index 445b755..0000000 Binary files a/docs/streamlit_interface_google_cloud.png and /dev/null differ diff --git a/docs/streamlit_interface_local.png b/docs/streamlit_interface_local.png deleted file mode 100644 index 84c35c2..0000000 Binary files a/docs/streamlit_interface_local.png and /dev/null differ diff --git a/docs/streamlit_interface_nas.png b/docs/streamlit_interface_nas.png deleted file mode 100644 index 8603cf5..0000000 Binary files a/docs/streamlit_interface_nas.png and /dev/null differ diff --git a/docs/watchtower_docker.png b/docs/watchtower_docker.png deleted file mode 100644 index d286723..0000000 Binary files a/docs/watchtower_docker.png and /dev/null differ diff --git a/docs/watchtower_logs.png b/docs/watchtower_logs.png deleted file mode 100644 index b37d388..0000000 Binary files a/docs/watchtower_logs.png and /dev/null differ diff --git a/docs/watchtower_logs_by_email.png b/docs/watchtower_logs_by_email.png deleted file mode 100644 index 9f2084d..0000000 Binary files a/docs/watchtower_logs_by_email.png and /dev/null differ diff --git a/functions.py b/functions.py deleted file mode 100644 index 97e2f7f..0000000 --- a/functions.py +++ /dev/null @@ -1,125 +0,0 @@ -import re -import html -import numpy as np -import pandas as pd -from multiprocessing import Pool - -# NLTK imports for NLP preprocessing -import nltk -from nltk.corpus import stopwords -from nltk.tokenize import word_tokenize -from nltk.stem import WordNetLemmatizer - -# Ensure necessary NLTK resources are downloaded and available -try: - stopwords.words('english') - word_tokenize("test") - WordNetLemmatizer().lemmatize("testing") -except LookupError: - nltk.download('punkt') # Tokenizer models - nltk.download('punkt_tab') # Additional tokenizer support - nltk.download('stopwords') # Stopwords lists - nltk.download('wordnet') # WordNet lexicon for lemmatization - nltk.download('omw-1.4') # WordNet lemmas data - -# Convert sentiment labels to binary: 0 for negative, 1 for all other classes -def convert_sentiment_label(df): - """ - Convert the 'target' column in the DataFrame to binary sentiment labels: - - 0 for negative sentiment (label 0) - - 1 for any other sentiment label - Returns a new DataFrame with updated labels. - """ - converted_target_data = df.copy() - converted_target_data['target'] = converted_target_data['target'].apply(lambda x: 0 if x == 0 else 1) - return converted_target_data - -# Downsample the dataset to balance classes by limiting samples per class -def downsample_data(df, n_samples=50000, random_state=42): - """ - Perform downsampling to balance dataset classes. - Selects `n_samples` examples from both negative (0) and positive (1) classes. - Returns a balanced DataFrame with equal number of samples per class. - """ - negative_samples = df[df['target'] == 0].sample(n=n_samples, random_state=random_state) - positive_samples = df[df['target'] == 1].sample(n=n_samples, random_state=random_state) - downsampled_data = pd.concat([negative_samples, positive_samples]) - return downsampled_data - -# PREPROCESSING FUNCTION FOR CLASSIC AND ADVANCED MODELS -def preprocess_tweet(tweet): - """ - Preprocess a tweet for classic ML or word embedding models (e.g., Word2Vec, GloVe). - Steps: - - Normalize text (lowercase) - - Replace URLs and mentions with tokens - - Decode HTML entities - - Remove unwanted special characters and non-printable chars - - Tokenize text - - Lemmatize tokens - - Remove stopwords except negation words - Returns cleaned, normalized text as a single string. - """ - - # Ensure input is a string - if not isinstance(tweet, str): - return "" - - # Decode HTML entities (e.g., & to &) - tweet = html.unescape(tweet) - - # Convert to lowercase - tweet = tweet.lower() - - # Replace URLs with placeholder token - tweet = re.sub(r'https?://\S+|www\.\S+', '', tweet) - - # Replace user mentions with placeholder token - tweet = re.sub(r'@\w+', '', tweet) - - # Separate hashtags from words to facilitate tokenization - tweet = re.sub(r'#(\w+)', r'# \1', tweet) - - # Remove unwanted punctuation and special characters except some useful ones - tweet = re.sub(r'[^\w\s<>@#!?]', '', tweet) - - # Remove non-printable and control characters (e.g., tabs, newlines, emojis) - tweet = re.sub(r'[^\x20-\x7E]', '', tweet) - - # Tokenize tweet into words - tokens = word_tokenize(tweet) - - # Lemmatize tokens to their base form - lemmatizer = WordNetLemmatizer() - tokens = [lemmatizer.lemmatize(token) for token in tokens] - - # Remove stopwords but keep negation-related words to preserve sentiment meaning - stop_words = set(stopwords.words('english')) - important_words = {'no', 'not', 'nor', 'neither', 'never', 'nobody', 'none', 'nothing', 'nowhere'} - stop_words = stop_words - important_words - tokens = [token for token in tokens if token not in stop_words] - - # Rebuild cleaned tweet text - return ' '.join(tokens) - -# Parallel processing of a DataFrame using multiple CPU cores -def process_in_parallel(df, func, n_jobs=4): - """ - Split a DataFrame into `n_jobs` parts and apply `func` to each part in parallel, - using multiprocessing Pool for speed-up. - Returns the concatenated processed DataFrame. - """ - df_split = np.array_split(df, n_jobs) - with Pool(n_jobs) as pool: - df = pd.concat(pool.map(func, df_split)) - return df - -# Apply preprocessing function on a DataFrame partition -def apply_preprocessing(df_part): - """ - Apply tweet preprocessing to the 'text' column of a DataFrame partition. - Stores results in a new column 'processed_text'. - Returns the updated DataFrame partition. - """ - df_part['processed_text'] = df_part['text'].apply(preprocess_tweet) - return df_part diff --git a/notebooks/README.fr.md b/notebooks/README.fr.md deleted file mode 100644 index 503b425..0000000 --- a/notebooks/README.fr.md +++ /dev/null @@ -1,46 +0,0 @@ -# Notebooks - -Ce dossier contient les notebooks Jupyter utilisés pour le développement des modèles et les analyses du projet. - -## Contenu - -- Analyse exploratoire des données -- Modèles classiques (régression logistique, SVM, etc.) -- Modèles avancés (LSTM avec embeddings GloVe) -- Modèle BERT - -## Note concernant les fichiers requirements - -Les fichiers `*requirements.txt` ont été générés depuis un environnement Google Colab. Cet environnement inclut de nombreuses librairies préinstallées, dont toutes ne sont pas nécessaires au projet. - -Un nettoyage manuel ou une génération spécifique des fichiers requirements sera nécessaire pour alléger ces fichiers avant un usage en production ou un déploiement. - ---- - -## Résumé des performances des modèles - -### Modèles classiques (entraînés sur 200 000 tweets) - -| Modèle | Précision | Temps d'entraînement| -|--------------------------------|-----------|---------------------| -| Random Forest + BoW | 0,7798 | ~5 heures | -| Régression logistique + TF-IDF | 0,7796 | <2 minutes | - -### Modèles avancés basés sur des embeddings (entraînés sur 200 000 tweets, accélérés GPU sur Google Colab) - -| Modèle | Précision | Temps d'entraînement| -|------------------------|-----------|---------------------| -| Word2Vec + LSTM | 0,797 | 57 secondes | -| GloVe (300d) + LSTM | 0,792 | 47 secondes | - -### Modèle de pointe (entraîné sur 100 000 tweets en raison des limites RAM) - -| Modèle | Précision | Temps d'entraînement| -|---------|-----------|---------------------| -| BERT | 0,882 | 1,3 minutes | - ---- - -Ce résumé met en lumière les compromis entre précision des modèles et temps d’entraînement. Si les modèles classiques sont plus rapides à entraîner, les modèles avancés utilisant des embeddings et LSTM offrent une meilleure précision, avec BERT qui atteint la meilleure précision malgré un jeu de données plus restreint. - -L’écart de précision entre BERT et les modèles à embeddings peut s’expliquer principalement par la capacité bidirectionnelle de BERT à comprendre le contexte. diff --git a/notebooks/README.md b/notebooks/README.md deleted file mode 100644 index c3b0f04..0000000 --- a/notebooks/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# Notebooks - -This folder contains the Jupyter notebooks used for model development and analyses in the project. - -## Contents - -- Exploratory Data Analysis -- Classical Models (logistic regression, SVM, etc.) -- Advanced Models (LSTM with GloVe embeddings) -- BERT Model - -## Note on requirements files - -The `*requirements.txt` files were generated from a Google Colab environment. This environment includes many pre-installed libraries, not all necessary for the project. - -Manual cleanup or specific generation of requirements files will be needed to slim down these files before production use or deployment. - ---- - -## Model Performance Summary - -### Classical Models (trained on 200,000 tweets) - -| Model | Precision | Training Time | -|------------------------------|-----------|-----------------| -| Random Forest + BoW | 0.7798 | ~5 hours | -| Logistic Regression + TF-IDF | 0.7796 | <2 minutes | - -### Advanced Models based on word-embeddings (trained on 200,000 tweets, GPU accelerated on Google Colab) - -| Model | Precision | Training Time | -|------------------------|-----------|---------------| -| Word2Vec + LSTM | 0.797 | 57 seconds | -| GloVe (300d) + LSTM | 0.792 | 47 seconds | - -### State-of-the-art Model (trained on 100,000 tweets due to RAM limits) - -| Model | Precision | Training Time | -|--------|-----------|---------------| -| BERT | 0.882 | 1.3 minutes | - ---- - -This summary highlights the trade-offs between model precision and training time. While the classical models are faster to train, advanced models leveraging embeddings and LSTM yield better precision, with BERT achieving the highest precision despite being trained on a smaller dataset. - -The precision offset between BERT and word embeddings models may mainly be due to the BERT bidirectionnal context abilities. diff --git a/notebooks/notebook-connexion-mlflow.ipynb b/notebooks/notebook-connexion-mlflow.ipynb deleted file mode 100644 index 479ba6a..0000000 --- a/notebooks/notebook-connexion-mlflow.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"fm2J3uruLfrX"},"source":["# Connexion au serveur MLFlow - Test et Configuration Locale\n","\n","Ce notebook permet de **tester la connexion à un serveur MLFlow déployé localement via Docker sur un NAS/Serveur personnel**. Il est conçu pour vérifier que toutes les configurations sont correctes et que nous pouvons commencer à suivre nos expériences de machine learning\n",".\n","## Prérequis\n","\n","Avant d'exécuter ce notebook, assurez-vous d'avoir :\n","- Un serveur MLFlow déployé localement via Docker sur votre NAS. Il sera important d'installer dans le conteneur la librairie boto3 pour que MLFlow puisse échanger avec un serveur de type AWS pour le stockage des artefacts.\n","- Un serveur MinIO (compatible S3) déployé localement via Docker pour le stockage des artefacts et qui remplacera les Amazon Web Services (AWS).\n","- Les informations de connexion nécessaires (URI de tracking, identifiants MinIO).\n","- Les ports 5000 (MLFlow) et 9000 (MinIO API) correctement exposés et/ou gérés par un reverse proxy (SWAG).\n","\n","## Configuration des variables d'environnement dans Jupyter Notebook / Google Collab\n","\n","Pour une connexion réussie, vous devez définir les variables d'environnement dans un fichier `.env` qui sera chargé via la librairie dotenv dans le répertoire courant. Ces variables seront utilisées par la bibliothèque MLflow pour communiquer avec vos services.\n","\n","Voici les variables essentielles à configurer (urls locaux en guise d'exemple) :\n","\n","```\n","MLFLOW_TRACKING_URI=http://mlflow:5000\n","MLFLOW_S3_ENDPOINT_URL=http://minio:9000\n","AWS_ACCESS_KEY_ID=minioadmin\n","AWS_SECRET_ACCESS_KEY=minioadminpassword\n","```\n","\n","Remplacez les valeurs par vos propres informations de connexion.\n","\n","Note : minio possède deux ports :\n","- 9001 pour accéder à l'interface web et gérer le stockage / nettoyage d'artefacts\n","- 9000 qui est l'api qui sera requêté pour l'envoi de buckets S3 par la librairie mlflow\n","\n","## Étapes du test\n","\n","Ce notebook effectue les opérations suivantes :\n","1. Chargement des variables d'environnement depuis le fichier `.env`\n","2. Configuration de MLFlow avec ces variables\n","3. Tentative de connexion au serveur MLFlow\n","4. Récupération de la liste des expériences existantes\n","5. Création d'une expérience si elle n'existe pas déjà\n","6. Enregistrement de métriques test pour vérifier le bon fonctionnement\n","\n","## Résolution des problèmes courants\n","\n","Notez que si vous utilisez des notebooks Google Collab, il est **nécessaire** que votre serveur MLFlow ainsi que l'API de MinIO soient accessibles via un lien public. Vous pouvez simplement ouvrir les ports de votre modem ou, pour plus de sécurité, configurer un reverse proxy (type SWAG pour la gestion et délivrance automatique de certificats SSL). Cette étape ne sera pas développée ici.\n","\n","Notez également que si vous désactivez votre docker MLFlow et le réactivez (par exemple suite à un redémarrage), les expériences seront par défaut perdues. Pour qu'elles soient persistantes, il faudra rajouter dans le yaml du docker l'argument de commande ainsi que le volume suivants :\n","- ` - --backend-store-uri ` \\\n"," ` - sqlite:////mlflow/mlflow.db # <-- pointe vers le volume monté `\n","- ` - ./mlflow-data:/mlflow # Monte un volume local pour préserver mlflow.db`\n","\n","Si vous rencontrez des erreurs lors de la connexion, vérifiez :\n","- Que l'URI de tracking est correct et accessible\n","- Que vos identifiants AWS sont valides (si applicable)\n","- Que le serveur MLFlow est bien en cours d'exécution\n","- Vos paramètres réseau (pare-feu, VPN, etc.)"]},{"cell_type":"code","source":["!pip install mlflow boto3 dotenv --quiet"],"metadata":{"id":"wPht9-36CMzs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","drive_folder= \"/content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks\"\n","os.chdir(drive_folder)\n","# os.listdir()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XkhsbZ5Xc3De","executionInfo":{"status":"ok","timestamp":1751547034150,"user_tz":-120,"elapsed":953,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"7d340543-c848-47da-f5cd-4360a62f5571"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4DKFKUC8LfrY","executionInfo":{"status":"ok","timestamp":1751547035465,"user_tz":-120,"elapsed":1313,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"a2c423b4-6e57-4f78-9839-3beed6c39ea1"},"outputs":[{"output_type":"stream","name":"stdout","text":["MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","Identifiants AWS configurés\n","Tentative de connexion à MLflow...\n","Connexion à MLflow réussie! Nombre d'expériences existantes: 3\n"," - Demo PC (ID: 2)\n"," - OC Projet 7 (ID: 1)\n"," - Default (ID: 0)\n","Expérience existante trouvée: 'Demo PC' (ID: 2)\n","Démarrage d'un run MLflow de test...\n","Métrique de test enregistrée avec succès!\n","🏃 View run test_connexion_simple at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/2/runs/3127176c596441c48e34590d27494b56\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/2\n","Test terminé avec succès. Vous pouvez vérifier les résultats sur: https://mlflow.greg-madman-nas.duckdns.org/\n"]}],"source":["import os\n","import mlflow\n","from dotenv import load_dotenv\n","\n","# Charger les variables d'environnement depuis le fichier .env\n","load_dotenv(dotenv_path='../.env')\n","\n","# Configuration de MLflow avec les variables d'environnement\n","mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n","mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n","aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n","aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n","\n","print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n","\n","# Configuration explicite des identifiants AWS pour MLflow\n","os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n","os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n","os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n","print(\"Identifiants AWS configurés\")\n","\n","# Configuration de MLflow\n","try:\n"," print(\"Tentative de connexion à MLflow...\")\n"," mlflow.set_tracking_uri(mlflow_tracking_uri)\n","\n"," # Liste des expériences pour vérifier la connexion\n"," exps = mlflow.search_experiments()\n"," print(f\"Connexion à MLflow réussie! Nombre d'expériences existantes: {len(exps)}\")\n"," for exp in exps:\n"," print(f\" - {exp.name} (ID: {exp.experiment_id})\")\n","\n"," # Définir le nom de l'expérience\n"," experiment_name = \"Demo PC\"\n","\n"," # Obtenir ou créer l'expérience\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," if experiment is None:\n"," experiment_id = mlflow.create_experiment(experiment_name)\n"," print(f\"Nouvelle expérience créée: '{experiment_name}' (ID: {experiment_id})\")\n"," else:\n"," experiment_id = experiment.experiment_id\n"," print(f\"Expérience existante trouvée: '{experiment_name}' (ID: {experiment_id})\")\n","\n"," # Démarrer un simple run et enregistrer une métrique\n"," with mlflow.start_run(experiment_id=experiment_id, run_name=\"test_connexion_simple\"):\n"," print(\"Démarrage d'un run MLflow de test...\")\n"," mlflow.log_param(\"test_param\", \"valeur_test\")\n"," mlflow.log_metric(\"test_metric\", 1.0)\n"," print(\"Métrique de test enregistrée avec succès!\")\n","\n"," print(f\"Test terminé avec succès. Vous pouvez vérifier les résultats sur: {mlflow_tracking_uri}\")\n","\n","except Exception as e:\n"," print(f\"Erreur lors de la connexion à MLflow: {e}\")\n"," print(\"\\nConseils de débogage:\")\n"," print(\"1. Vérifiez que l'URI de tracking est correct et accessible\")\n"," print(\"2. Vérifiez que vos identifiants AWS sont valides si vous utilisez S3\")\n"," print(\"3. Vérifiez que le serveur MLflow est en cours d'exécution\")\n"," print(\"4. Vérifiez les paramètres réseau (pare-feu, VPN, etc.)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nPI-3ULPLfrZ","executionInfo":{"status":"ok","timestamp":1751547035628,"user_tz":-120,"elapsed":159,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"fcc2197b-0474-4365-8122-f116dff4f562"},"outputs":[{"output_type":"stream","name":"stdout","text":["MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","Identifiants AWS configurés\n"]}],"source":["import os\n","import mlflow\n","from mlflow.models import infer_signature\n","from mlflow import MlflowClient\n","from dotenv import load_dotenv\n","\n","import pandas as pd\n","import numpy as np\n","from sklearn import datasets\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Charger les variables d'environnement depuis le fichier .env\n","load_dotenv()\n","\n","# Configuration de MLflow avec les variables d'environnement\n","mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n","mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n","aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n","aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n","\n","# Configuration explicite de MLflow\n","mlflow.set_tracking_uri(mlflow_tracking_uri)\n","print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n","\n","# Configuration explicite des identifiants AWS\n","os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n","os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n","os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n","print(\"Identifiants AWS configurés\")\n","\n","# Create a new MLflow Experiment\n","mlflow.set_experiment(\"Demo PC\")\n","\n","# Define the output directory\n","output_dir = \"./content/mlflow_test/\"\n","\n","# Create the output directory if it doesn't exist\n","if not os.path.exists(output_dir):\n"," os.makedirs(output_dir)\n"," print(f\"Output directory created: {output_dir}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OC0vEu6HLfra","executionInfo":{"status":"ok","timestamp":1751547053654,"user_tz":-120,"elapsed":18024,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"10d22f5d-eb1e-47e2-ea3f-61e8307536db"},"outputs":[{"output_type":"stream","name":"stdout","text":["Chargement du dataset Iris...\n","Aperçu du dataset:\n"," sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n","0 5.1 3.5 1.4 0.2 \n","1 4.9 3.0 1.4 0.2 \n","2 4.7 3.2 1.3 0.2 \n","3 4.6 3.1 1.5 0.2 \n","4 5.0 3.6 1.4 0.2 \n","\n"," target \n","0 0.0 \n","1 0.0 \n","2 0.0 \n","3 0.0 \n","4 0.0 \n","Dimensions du dataset: (150, 5)\n","Taille de l'ensemble d'entraînement: (120, 4)\n","Taille de l'ensemble de test: (30, 4)\n","Démarrage du run MLflow avec l'ID: c9e768a9ca904c55a963f03568e43ecb\n","Entraînement du modèle de régression logistique...\n","Métriques: Accuracy=1.0000, Precision=1.0000, Recall=1.0000, F1=1.0000\n"]},{"output_type":"stream","name":"stderr","text":["2025/07/03 12:50:45 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"]},{"output_type":"stream","name":"stdout","text":["Modèle enregistré: models:/m-abf8c8b98d6f4d60aa9898de7ee6a795\n","🏃 View run iris_classification at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/2/runs/c9e768a9ca904c55a963f03568e43ecb\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/2\n","\n","🏃 MLflow Run Infos :\n","⚗️ run_id: c9e768a9ca904c55a963f03568e43ecb\n","⚡ artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/input_example.json', 'model/model.pkl', 'model/python_env.yaml', 'model/requirements.txt', 'model/serving_input_example.json']\n","⚙️ params: {'solver': 'lbfgs', 'max_iter': '1000', 'random_state': '8888'}\n","📝 metrics: {'accuracy': 1.0, 'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0}\n","🏷️ tags: {'model_type': 'LogisticRegression', 'dataset': 'Iris', 'description': 'Classification multiclasse des fleurs Iris'}\n","\n"]}],"source":["# Fonction pour afficher les informations du run MLflow\n","def print_logged_info(r):\n"," print(\"\")\n"," print(\"🏃 MLflow Run Infos :\")\n"," tags = {k: v for k, v in r.data.tags.items() if not k.startswith(\"mlflow.\")}\n"," artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, \"model\")]\n"," print(f\"⚗️ run_id: {r.info.run_id}\")\n"," print(f\"⚡ artifacts: {artifacts}\")\n"," print(f\"⚙️ params: {r.data.params}\")\n"," print(f\"📝 metrics: {r.data.metrics}\")\n"," print(f\"🏷️ tags: {tags}\")\n"," print()\n","\n","# Chargement du dataset Iris\n","print(\"Chargement du dataset Iris...\")\n","iris = datasets.load_iris()\n","X, y = iris.data, iris.target\n","\n","# Conversion en DataFrame pour une meilleure visualisation\n","iris_df = pd.DataFrame(data=np.c_[iris['data'], iris['target']],\n"," columns=iris['feature_names'] + ['target'])\n","print(f\"Aperçu du dataset:\\n{iris_df.head()}\")\n","print(f\"Dimensions du dataset: {iris_df.shape}\")\n","\n","# Créer une figure pour visualiser la distribution des classes\n","plt.figure(figsize=(8, 6))\n","sns.countplot(x='target', data=iris_df)\n","plt.title('Distribution des classes du dataset Iris')\n","# Save the figure in the output directory\n","plt.savefig(os.path.join(output_dir, 'iris_class_distribution.png'))\n","plt.close()\n","\n","# Division en ensembles d'entraînement et de test\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","print(f\"Taille de l'ensemble d'entraînement: {X_train.shape}\")\n","print(f\"Taille de l'ensemble de test: {X_test.shape}\")\n","\n","# Définition des hyperparamètres du modèle\n","params = {\n"," \"solver\": \"lbfgs\",\n"," \"max_iter\": 1000,\n"," \"random_state\": 8888,\n","}\n","\n","# Démarrer un run MLflow\n","with mlflow.start_run(run_name=\"iris_classification\") as run:\n"," print(f\"Démarrage du run MLflow avec l'ID: {run.info.run_id}\")\n","\n"," # Enregistrer les hyperparamètres\n"," mlflow.log_params(params)\n","\n"," # Entraîner le modèle\n"," print(\"Entraînement du modèle de régression logistique...\")\n"," lr = LogisticRegression(**params)\n"," lr.fit(X_train, y_train)\n","\n"," # Prédire sur l'ensemble de test\n"," y_pred = lr.predict(X_test)\n","\n"," # Calculer les métriques\n"," accuracy = accuracy_score(y_test, y_pred)\n"," precision = precision_score(y_test, y_pred, average='weighted')\n"," recall = recall_score(y_test, y_pred, average='weighted')\n"," f1 = f1_score(y_test, y_pred, average='weighted')\n","\n"," # Enregistrer les métriques\n"," mlflow.log_metric(\"accuracy\", accuracy)\n"," mlflow.log_metric(\"precision\", precision)\n"," mlflow.log_metric(\"recall\", recall)\n"," mlflow.log_metric(\"f1_score\", f1)\n","\n"," print(f\"Métriques: Accuracy={accuracy:.4f}, Precision={precision:.4f}, Recall={recall:.4f}, F1={f1:.4f}\")\n","\n"," # Générer une matrice de confusion\n"," cm = confusion_matrix(y_test, y_pred)\n"," plt.figure(figsize=(8, 6))\n"," sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n"," xticklabels=iris.target_names,\n"," yticklabels=iris.target_names)\n"," plt.title('Matrice de confusion')\n"," plt.xlabel('Prédiction')\n"," plt.ylabel('Réalité')\n"," plt.tight_layout()\n"," # Save the figure in the output directory\n"," plt.savefig(os.path.join(output_dir, 'confusion_matrix.png'))\n"," plt.close()\n","\n"," # Enregistrer la matrice de confusion comme artifact\n"," mlflow.log_artifact(os.path.join(output_dir, \"confusion_matrix.png\"))\n"," mlflow.log_artifact(os.path.join(output_dir, \"iris_class_distribution.png\"))\n","\n"," # Définir des tags pour ce run\n"," mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n"," mlflow.set_tag(\"dataset\", \"Iris\")\n"," mlflow.set_tag(\"description\", \"Classification multiclasse des fleurs Iris\")\n","\n"," # Inférer la signature du modèle\n"," signature = infer_signature(X_train, lr.predict(X_train))\n","\n"," # Enregistrer le modèle avec sa signature\n"," model_info = mlflow.sklearn.log_model(\n"," sk_model=lr,\n"," artifact_path=\"model\", # This is the path within the MLflow run artifact storage\n"," signature=signature,\n"," input_example=X_train[:5]\n"," )\n","\n"," print(f\"Modèle enregistré: {model_info.model_uri}\")\n","\n","# Récupérer et afficher les informations du run terminé\n","run_info = mlflow.get_run(run_id=run.info.run_id)\n","print_logged_info(run_info)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":116,"referenced_widgets":["5077141fe2354e538f2728680d165abd","f5679722fc2e419b9eb658ee30c3684d","a5e14ad79a71402ab6f93f67fe2894cb","0a21fa27119c467f84595be0992d481b","b1a3c1bde6854e179dd0ecba4ac0ac19","e294e746a37246f082f8bf2b70367ce3","4713d02de5c44de69db461e8ab6cade1","36e6d82d6909479d9359dd82f6d56d92","fd2489b6f3154eb988b6d987e9445dc4","c5e2cc8df51146d1b61ac06e65cbc9ec","9878155a8ac14ded9f73699160a3da0c","3f94687e5cd24458a1f8cd6b123bbdf7","d7a0ada0295647049b45e572d4167d8d","12bd24eef53d4390a6e12b89d4baa563","06490fc029bf4f1eab964d977a262c73","90eb86a1891848cba01284e60c971dcc","a4b1626a86d64e30a04552da9fda8eb1","0e190cce3832467686ab21f7a226f15b","c95599130eb44eeb98d7013340f55bef","870a99030b7541dd977e4261f025f495","e4a45ed5c60e49c9b4392012805fd903","1bf723dec07d4385b6eb2e423ac81416"]},"id":"bvmWVVraLfra","executionInfo":{"status":"ok","timestamp":1751547076141,"user_tz":-120,"elapsed":22478,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"d50a7a94-7edf-4406-bafb-5384c86a90eb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please enter your 🏃 RUN ID : c9e768a9ca904c55a963f03568e43ecb\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 [00:00\n","
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targetidsdateflagusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n"," \n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"raw_data"}},"metadata":{},"execution_count":6}],"source":["# Define the path to the CSV file\n","csv_file_path = os.path.join(extract_path, 'training.1600000.processed.noemoticon.csv')\n","\n","# Define the column names\n","column_names = ['target', 'ids', 'date', 'flag', 'user', 'text']\n","\n","# Load the dataset into a pandas DataFrame\n","raw_data = pd.read_csv(csv_file_path, encoding='latin-1', names=column_names)\n","\n","# Display the first few rows of the DataFrame\n","raw_data.head()"]},{"cell_type":"code","source":["raw_data['target'].unique()"],"metadata":{"id":"6pTUMokI-Q6W","executionInfo":{"status":"ok","timestamp":1751537402048,"user_tz":-120,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9e15e051-7e09-4848-ca8c-358ac2cb4c23","colab":{"base_uri":"https://localhost:8080/","height":0}},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 4])"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"rIaUk5ZxWjI9","executionInfo":{"status":"ok","timestamp":1751537402069,"user_tz":-120,"elapsed":21,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8e4f9c92-f907-468c-de97-f6edb040398c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Ce dataframe contient 1600000 lignes et 6 colonnes.\n"]}],"source":["print(f\"Ce dataframe contient {raw_data.shape[0]} lignes et {raw_data.shape[1]} colonnes.\")"]},{"cell_type":"markdown","metadata":{"id":"GG5bQh5xWjI-"},"source":["## 🔬 Analyse exploratoire des données Sentiment140\n","\n","Pour bien comprendre votre jeu de données Sentiment140 et optimiser votre modèle d'analyse de sentiment, je vous propose une analyse exploratoire complète qui couvre les aspects suivants :\n","\n","- **Analyse quantitative:**\n","\n","1. **Informations générales sur le jeu de données** \n"," - Étude de la répartition des classes de sentiment \n"," - Vérification des valeurs manquantes ainsi que des doublons \n"," - Examen de la structure générale du DataFrame \n","\n","2. **Analyse de la distribution des sentiments** \n"," - Visualisation de l’équilibre ou du déséquilibre des classes \n"," - Répartition des sentiments positifs et négatifs \n","\n","3. **Analyse temporelle** \n"," - Répartition des tweets dans le temps \n"," - Identification des patterns temporels selon le sentiment \n"," - Analyse détaillée par heure, jour et mois \n","\n","4. **Analyse des mots fréquents** \n"," - Nuages de mots spécifiques à chaque classe de sentiment \n"," - Détection des mots discriminants entre sentiments positifs et négatifs \n"," - Identification des n-grammes les plus fréquents \n","\n","5. **Analyse des utilisateurs** \n"," - Étude de la distribution des tweets par utilisateur \n"," - Analyse des tendances de sentiment propres aux utilisateurs \n","\n","6. **Analyse des requêtes (flag)** \n"," - Impact des requêtes sur la distribution des sentiments \n"," - Identification des requêtes les plus fréquentes \n","\n","7. **Analyse de la longueur des tweets** \n"," - Visualisation de la distribution des longueurs des tweets \n"," - Gestion des caractères HTML dans les tweets \n"," - Nettoyage des tweets trop longs et souvent difficilement compréhensibles \n","\n","- **Analyse qualitative:**\n","\n","8. **Analyse de caracteristiques distinctives et de l'impact de tokens spécifiques** \n"," - Étude de la longueur des tweets en termes de caractères et de mots \n"," - Répartition des longueurs en fonction du sentiment \n"," - Analyse de l’utilisation de la ponctuation \n"," - Calcul du ratio de majuscules \n"," - Présence d’émoticônes et de symboles \n","\n","9. **Stratégie de prétraitement pour la détection de sentiment dans les tweets** \n"," - Nettoyage des textes \n"," - Tokenisation adaptée aux spécificités de Twitter \n"," - Gestion des mentions, hashtags et URLs \n","\n","10. **Recommandations pour la modélisation** \n"," - Approches recommandées pour la vectorisation \n"," - Modèles à privilégier \n"," - Stratégies d’évaluation adaptées \n","\n","### Points clés à explorer \n","\n","**Distribution des sentiments :** \n","La première étape consiste à vérifier si les classes sont équilibrées. En cas de déséquilibre, des techniques telles que le sur- ou sous-échantillonnage, ou la pondération des classes, pourront être envisagées.\n","\n","**Caractéristiques linguistiques :** \n","L’analyse des mots les plus fréquents et des caractéristiques propres aux tweets positifs et négatifs permettra d’identifier des features pertinentes pour le modèle.\n","\n","**Prétraitement recommandé :** \n","Le code fournit des recommandations précises concernant : \n","- La gestion des URLs, mentions et hashtags \n","- La normalisation des textes \n","- Une tokenisation adaptée au langage propre à Twitter \n","\n","**Approches de modélisation :** \n","Différentes approches sont proposées, allant des modèles classiques (SVM, Naive Bayes) aux architectures plus avancées (LSTM, BERT), chacune avec ses avantages et ses limites.\n"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"SIq2dc2NWjI-","executionInfo":{"status":"ok","timestamp":1751537402191,"user_tz":-120,"elapsed":121,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["df = raw_data.copy()"]},{"cell_type":"markdown","source":["### Analyse quantitative"],"metadata":{"id":"s8Gu4vmG0pWw"}},{"cell_type":"markdown","metadata":{"id":"NCnc8jyaWjI-"},"source":["#### 1. Informations générales sur le jeu de données\n","\n","- Étude de la répartition des classes de sentiment \n","- Vérification des valeurs manquantes ainsi que des doublons \n","- Examen de la structure générale du DataFrame "]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"UkDlv1zIWjI-","executionInfo":{"status":"ok","timestamp":1751537405677,"user_tz":-120,"elapsed":3488,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ad26c4e0-01bf-426f-fc52-4a3aaecfa2aa"},"outputs":[{"output_type":"stream","name":"stdout","text":["1. APERÇU GÉNÉRAL DU JEU DE DONNÉES\n","--------------------------------------------------\n","Nombre total d'entrées: 1,600,000\n","Nombre de colonnes: 6\n","\n","Colonnes présentes:\n","- target\n","- ids\n","- date\n","- flag\n","- user\n","- text\n","\n","Types de données:\n","target int64\n","ids int64\n","date object\n","flag object\n","user object\n","text object\n","dtype: object\n","\n","Valeurs manquantes par colonne:\n","target 0\n","ids 0\n","date 0\n","flag 0\n","user 0\n","text 0\n","dtype: int64\n","\n","Nombre de lignes dupliquées: 0 (0.00%)\n"]}],"source":["def afficher_info_generales(df):\n"," \"\"\"Affiche les informations générales sur le DataFrame\"\"\"\n"," print(\"1. APERÇU GÉNÉRAL DU JEU DE DONNÉES\")\n"," print(\"-\" * 50)\n"," print(f\"Nombre total d'entrées: {df.shape[0]:,}\")\n"," print(f\"Nombre de colonnes: {df.shape[1]}\")\n"," print(\"\\nColonnes présentes:\")\n"," for col in df.columns:\n"," print(f\"- {col}\")\n","\n"," print(\"\\nTypes de données:\")\n"," print(df.dtypes)\n","\n"," print(\"\\nValeurs manquantes par colonne:\")\n"," print(df.isnull().sum())\n","\n"," # Vérifier les doublons\n"," duplicates = df.duplicated().sum()\n"," print(f\"\\nNombre de lignes dupliquées: {duplicates:,} ({duplicates/len(df)*100:.2f}%)\")\n","\n","afficher_info_generales(df)"]},{"cell_type":"markdown","metadata":{"id":"WWbOMBHvWjI-"},"source":["#### 2. Analyse de la distribution des sentiments\n","\n","- Visualisation de l’équilibre ou du déséquilibre des classes \n","- Répartition des sentiments positifs et négatifs\n"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":833},"id":"xdnAqsI9WjI-","executionInfo":{"status":"ok","timestamp":1751537411107,"user_tz":-120,"elapsed":5432,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"bdfefe03-349e-404e-e4b3-fcb263503c60"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","2. Distribution des sentiments (cible)\n","--------------------------------------------------\n","sentiment\n","Négatif 800000\n","Positif 800000\n","Name: count, dtype: int64\n","\n","Pourcentage par catégorie:\n","sentiment\n","Négatif 50.00%\n","Positif 50.00%\n","Name: proportion, dtype: object\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["def analyser_distribution_sentiment(df):\n"," \"\"\"Analyse la distribution des sentiments\"\"\"\n"," print(\"\\n2. Distribution des sentiments (cible)\")\n"," print(\"-\" * 50)\n","\n"," # Convertir les codes (0, 4) en catégories (négatif, positif)\n"," sentiment_map = {0: 'Négatif', 2: 'Neutre', 4: 'Positif'}\n"," df['sentiment'] = df['target'].map(sentiment_map)\n","\n"," # Afficher le compte\n"," sentiment_counts = df['sentiment'].value_counts()\n"," print(sentiment_counts)\n"," print(\"\\nPourcentage par catégorie:\")\n"," print(df['sentiment'].value_counts(normalize=True).map(lambda x: f\"{x*100:.2f}%\"))\n","\n"," # Visualiser la distribution\n"," plt.figure(figsize=(10, 6))\n"," sns.countplot(x='sentiment', hue='sentiment', data=df, palette='viridis')\n"," plt.title('Distribution des Sentiments', fontsize=15)\n"," plt.xlabel('Sentiment')\n"," plt.ylabel('Nombre de Tweets')\n"," plt.tight_layout()\n"," plt.show()\n","\n","analyser_distribution_sentiment(df)"]},{"cell_type":"markdown","metadata":{"id":"dLP_05CaWjI_"},"source":["#### 3. Analyse temporelle\n","\n","- Répartition des tweets dans le temps \n","- Identification des patterns temporels selon le sentiment \n","- Analyse détaillée par heure, jour et mois "]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"gbTaWRiNWjI_","executionInfo":{"status":"ok","timestamp":1751537421449,"user_tz":-120,"elapsed":10341,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"7939105d-4449-4d04-f432-99a474db2e4e"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","3. ANALYSE TEMPORELLE\n","--------------------------------------------------\n","Période couverte: de 2009-04-06 22:19:45 à 2009-06-25 10:28:31\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["def analyser_temporalite(df):\n"," \"\"\"Analyse la distribution temporelle des tweets\"\"\"\n"," print(\"\\n3. ANALYSE TEMPORELLE\")\n"," print(\"-\" * 50)\n","\n"," # Convertir la colonne 'date' en format datetime\n"," df['date_obj'] = pd.to_datetime(df['date'], errors='coerce')\n","\n"," # Extraire les composantes de date\n"," df['year'] = df['date_obj'].dt.year\n"," df['month'] = df['date_obj'].dt.month\n"," df['day'] = df['date_obj'].dt.day\n"," df['hour'] = df['date_obj'].dt.hour\n"," df['weekday'] = df['date_obj'].dt.dayofweek\n","\n"," # Afficher la plage temporelle\n"," print(f\"Période couverte: de {df['date_obj'].min()} à {df['date_obj'].max()}\")\n","\n"," # Distribution par mois\n"," plt.figure(figsize=(12, 6))\n"," monthly_counts = df.groupby(['month', 'sentiment']).size().reset_index(name='count')\n"," sns.barplot(x='month', y='count', hue='sentiment', data=monthly_counts)\n"," plt.title('Distribution des Tweets par Mois', fontsize=15)\n"," plt.xlabel('Mois')\n"," plt.ylabel('Nombre de Tweets')\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Distribution par jour de la semaine\n"," plt.figure(figsize=(12, 6))\n"," weekday_sentiment = df.groupby(['weekday', 'sentiment']).size().reset_index(name='count')\n"," sns.barplot(x='weekday', y='count', hue='sentiment', data=weekday_sentiment)\n"," plt.title('Distribution des Sentiments par Jour de la Semaine', fontsize=15)\n"," plt.xlabel('Jour de la Semaine')\n"," plt.ylabel('Nombre de Tweets')\n"," plt.xticks(ticks=[0, 1, 2, 3, 4, 5, 6], labels=['Lundi', 'Mardi', 'Mercredi', 'Jeudi', 'Vendredi', 'Samedi', 'Dimanche'])\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Distribution par heure de la journée\n"," plt.figure(figsize=(12, 6))\n"," hourly_sentiment = df.groupby(['hour', 'sentiment']).size().reset_index(name='count')\n"," sns.lineplot(x='hour', y='count', hue='sentiment', data=hourly_sentiment, marker='o')\n"," plt.title('Distribution des Sentiments par Heure', fontsize=15)\n"," plt.xlabel('Heure de la journée')\n"," plt.ylabel('Nombre de Tweets')\n"," plt.tight_layout()\n"," plt.show()\n","\n","analyser_temporalite(df)"]},{"cell_type":"markdown","metadata":{"id":"lETyJsrVWjI_"},"source":["#### 4. Analyse des mots fréquents\n","\n","- Nuages de mots spécifiques à chaque classe de sentiment \n","- Détection des mots discriminants entre sentiments positifs et négatifs \n","- Identification des n-grammes les plus fréquents "]},{"cell_type":"code","execution_count":13,"metadata":{"id":"E3WHO2pgWjI_","executionInfo":{"status":"ok","timestamp":1751537421459,"user_tz":-120,"elapsed":4,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def analyser_mots_frequents(df, sentiment, n_range=(1, 2, 3)):\n"," \"\"\"\n"," Analyse les mots et N-grammes les plus fréquents pour un sentiment donné.\n","\n"," Paramètres:\n"," - df: DataFrame contenant les tweets\n"," - sentiment: valeur de sentiment à filtrer ('Positif' ou 'Négatif')\n"," - n_range: tuple indiquant les valeurs de N à analyser (par défaut: unigrammes, bigrammes et trigrammes)\n"," \"\"\"\n"," # Filtrer par sentiment\n"," texts = df[df['sentiment'] == sentiment]['text'].astype(str)\n","\n"," # Télécharger les stopwords si nécessaire\n"," stop_words = set(stopwords.words('english'))\n","\n"," # Fonction pour nettoyer et tokenizer le texte\n"," def preprocess_text(text):\n"," # Convertir en minuscules et supprimer caractères spéciaux\n"," text = re.sub(r'[^\\w\\s]', '', text.lower())\n"," # Tokenizer manuellement\n"," tokens = text.split()\n"," # Supprimer les stopwords\n"," tokens = [word for word in tokens if word not in stop_words and len(word) > 1]\n"," return tokens\n","\n"," # Traiter tous les textes (liste de liste de tokens)\n"," all_tokens = []\n"," for text in texts:\n"," all_tokens.append(preprocess_text(text))\n","\n"," # Analyser les unigrammes (liste de mots individuels)\n"," all_words = []\n"," for tokens in all_tokens:\n"," all_words.extend(tokens)\n","\n"," # Compter les occurrences des mots (liste de couples)\n"," word_counts = Counter(all_words)\n"," most_common_words = word_counts.most_common(20)\n","\n"," # Créer un nuage de mots pour les unigrammes\n"," wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(dict(word_counts))\n","\n"," plt.figure(figsize=(12, 8))\n"," plt.imshow(wordcloud, interpolation='bilinear')\n"," plt.axis('off')\n"," plt.title(f'Nuage de mots pour les tweets {sentiment.lower()}s', fontsize=15)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Afficher les résultats pour les unigrammes\n"," # print(f\"\\nMots les plus fréquents pour les tweets {sentiment.lower()}s:\")\n"," # for word, count in most_common_words:\n"," # print(f\"- {word}: {count:,}\")\n","\n"," # Visualiser les mots les plus fréquents avec un barplot\n"," plt.figure(figsize=(12, 8))\n"," word_df = pd.DataFrame(most_common_words, columns=['Mot', 'Fréquence'])\n"," sns.barplot(x='Fréquence', y='Mot', data=word_df, palette='coolwarm')\n"," plt.title(f'Mots les plus fréquents pour les tweets {sentiment.lower()}s', fontsize=15)\n"," plt.xlabel('Fréquence', fontsize=12)\n"," plt.ylabel('Mot', fontsize=12)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," for n in range(n_range[0] + 1, n_range[1] + 1): # Commencer à 2-grammes si n_range commence à 1\n"," all_ngrams = []\n"," for tokens in all_tokens:\n"," if len(tokens) >= n: # Vérifier que le texte est assez long pour former un n-gramme\n"," all_ngrams.extend(list(ngrams(tokens, n)))\n","\n"," # Convertir les tuples en chaînes pour une meilleure lisibilité\n"," all_ngrams_str = [' '.join(gram) for gram in all_ngrams]\n","\n"," # Compter les occurrences des n-grammes\n"," ngram_counts = Counter(all_ngrams_str)\n"," most_common_ngrams = ngram_counts.most_common(20)\n","\n"," # Afficher les résultats pour les n-grammes\n"," # print(f\"\\n{n}-grammes les plus fréquents pour les tweets {sentiment.lower()}s:\")\n"," # for ngram, count in most_common_ngrams:\n"," # print(f\"- \\\"{ngram}\\\": {count:,}\")\n","\n"," # Visualiser les n-grammes les plus fréquents avec un graphique à barres\n"," plt.figure(figsize=(12, 8))\n","\n"," # Créer un DataFrame pour le graphique\n"," ngram_df = pd.DataFrame(most_common_ngrams, columns=['N-gramme', 'Fréquence'])\n","\n"," # Créer un graphique à barres horizontales\n"," color = 'royalblue' if sentiment == 'Positif' else 'red'\n"," sns.barplot(x='Fréquence', y='N-gramme', data=ngram_df.sort_values('Fréquence', ascending=False), color=color)\n"," plt.title(f'Top {n}-grammes pour les tweets {sentiment.lower()}s', fontsize=15)\n"," plt.xlabel('Fréquence', fontsize=12)\n"," plt.ylabel('N-gramme', fontsize=12)\n"," plt.tight_layout()\n"," plt.show()\n"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"GgsXlsTqWjI_","executionInfo":{"status":"ok","timestamp":1751537440633,"user_tz":-120,"elapsed":19173,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"eaa7039b-0008-469e-8702-d95e02f9a239"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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FjxTCgBwSuHh4br++uv1xRdfaPfu3crOzlZOTo6GDx8uf3//Ko/76quvJP32sPTfa9Kkia666iqVlpZq2bJlXtfStWtXHThwQHfccUeVz4E5U//9738lSTfeeGOl28tu/Vi5cmW5eiRp/PjxWrx4cbnnsJyJ4cOHV1jncDg0ZMgQSf93Tavbubyuv5eUlFThOU+lpaX64osvFBISUuUtPr+//jt27NCOHTvUpEkTXXHFFRX2v+WWW6qt5jMZG2W6du2q888/v8L6tm3bStJpPSOmbLz99a9/1UcffaSioiKvj63MTTfdVOFbNaX/u3Ynjrdz8X4+mSuvvPKktwafjjN9b1vxnujTp4+OHTvmeY7XkiVLZIxR37591bdvX+3atcvzXKmyZxWdeOvet99+q/z8fF122WWV3joZHBzs6cvGjRs97bjdbl1zzTXlbukr06xZM7Vp00Zr1qxRYWGhV/246KKL5Ofnp9dff10zZ87UL7/8cjqXodL2KrsV9XTHpiTPl3GceEzZe2nkyJHKzs5WaWnpWdVbXar7MwUAajpCKQCAV2677TaVlJTo3Xff9Tz0/FTfupeXlydJlT5U+8T1ubm5Xtfx0ksvKS4uTrNmzdIFF1ygZs2aadiwYZo7d65KSkq8bqcyZQ+2vvXWW8s9tLbsJzIyUpKUn5/vOebPf/6z+vbtq2XLlumKK65QRESErrrqKj3//PM6ePDgaddQ9gDc3yu7VmXXtLqdy+v6ey1atKiwLj8/X0eOHFFBQYECAgIqvf5//vOfPftK/3ctqrpmDRs2VHh4eLXUXDY2evbsWWltF198cbnaThQTE1Npmw0aNJAkHTt2zOs6kpOTNXToUK1bt04DBgxQRESELr/8cj355JPavXv3afbq9MbbuXg/n0xl4+RMlb1+0dHRlb5+N998s6Tyr59V74mygOnEh2OXrT/Ztt/37bPPPqu0bzabTR9//HG5/pUdM3PmzCqPWbt2rYwx2r9/v1f9aNu2rZ566ikVFBTorrvuUpMmTdSpUyfdf//9+vHHH0/7upzrsfnkk0+qU6dOmj9/vi655BI1btxYAwcO1KuvvnrWYe/ZqO7PFACo6XjQOQDAK9ddd53Cw8M1e/Zs5eXlKSEhQV26dDmrNn8/W8YbF154odatW6dPP/1UCxYs0OLFi5WRkaGMjAz16NFDixcvVkBAwBnVU/Z/yq+55pqTPqy57IG0khQWFqaFCxdq2bJlmj9/vhYvXqyFCxfqs88+09SpU/XVV1+pTZs2Z1TPuVDVbIBzeV1/LygoqMq6QkNDddNNN530+A4dOlRLHVWp7BqVrRsyZIjOO++8Ko9t3759hXXV+ZXv/v7+mjt3rv7yl7/ogw8+0MKFC7VixQp99dVXmjZtmj799FNddtll1Xa+03G67+dTzUypbJycqbJznfiA7sp0797d82+r3hOVBU+JiYlq0qSJwsLCFBgYqMWLF+uOO+7Ql19+qQYNGpT73VvWt/j4ePXs2fOk5yqbsVd2zEUXXXTKL08IDAz0ui8PPPCAhg4dqnnz5umzzz7TV199pWeffVbPPfecnn32Wf3pT3/yuq3qVNnYbN68uVatWqWFCxfqo48+0pIlSzR//nzNnz9fTz31lL755ptKZziea7XtMwUAzhahFADAK4GBgbr55ps1c+ZMSdKECRNOeUxUVJS2bt2q7du3KzExscL2E2cvnI6goCDdcMMNuuGGGyRJa9eu1YgRI/TNN9/o1VdfVUpKymm1V6ZsRsudd955ymDkRDabTb169fJ8Q9revXt133336Z133tHDDz+sjIwMr9sq+8r1qtZHRUWdso2yP5RP/ErzMjt37qzyuHN1Xb3RuHFjBQUFeW7/8SbgKPsGqqqu2aFDh/Trr79WWH+y6yNVfo1iYmKUk5Ojv/zlL57ba3ypc+fO6ty5s1JTU3Xo0CGlpqbq2Wef1X333VfpLYRVOZ3xdibv5zMdi9UtJiZGmzdv1t///vfTChqseE+0atVKzZs31/Lly5WXl6c1a9Zo3LhxnvNfeumlWrJkiX766Sfl5+fr2muvLXfbdNnvrfbt2ys9Pd2rc5Yd06tXL73wwgtn3YcTNW/eXPfee6/uvfdeFRcX691339WYMWM0ceJEjRo1ShEREV61c7pj82THVPVZY7fbddVVV3m+ZXL79u26/fbbtXDhQqWlpempp57yqtbqVp2fKQBQ03H7HgDAayNHjtT555+vxo0bV/nsjhOVPaflnXfeqbBt3759+s9//iObzVbu/+6X/RF7Os/R6NChg8aPHy9J+umnn7w+7vf69+8vScrKyjrjNqTfnq9T9nXlp1tPZX9sFBcX67333pMkzx8pJ1MW1vz8888Vtn322Wde11Jd19Ubdrtdffv21aFDh/TFF194dUzLli3VvHlz7d27V0uWLKmw/d133630uJNdn59//lk7duyosL66xsapnMn4DwsL09SpU2Wz2U77dXr//fcrvRWt7NqdON7O5P1cXWPxbFXX63cm7wlvXtOy50qlpaXJGFPuGWllz5V69dVXPcsnuvjii9WwYUMtWbLE61vtrrjiCvn7++ujjz6S2+326hhv+3Iiu92u2267TRdffLGOHz/ueaaVN77//vtK9z/dsSnJc8t52X5VadmypR566CFJ3r2+Z/J+PRNn85kCADUdoRQAwGu9e/dWfn6+9u3bV+XzPk40fvx4+fn56Z///KdWrVrlWX/8+HHde++9Kiws1I033qjmzZt7tpX9H++cnJwK7RUUFOif//xnhdkvpaWl+vTTTyWpXFun66abblJiYqLeeustPfHEExWe9WOM0bJly8o9yPnll1/W1q1bK7S1YMGCM6pn6dKlmjVrVrl1jz32mHbs2KELL7zwlH9USb/9gStJM2bMKPew4e+//16PPvpohf3P9XX11sMPPyw/Pz+NGTPGcyvTiY4cOaJZs2aVe/By2YySBx54oNwf5Fu2bNHkyZMrPc/FF1+skJAQffLJJ1q9erVnfX5+vu68885Kbyu7++671aRJEz311FP617/+VWGf4uJi/ec//znrPxgbN24sh8OhzZs3VxoWvfnmm5We45NPPpEx5rRfp23btunxxx8vt+5f//qXvvnmGzVt2rTcjMEzeT+f7lg8Vx544AEFBwfrwQcf1Pvvv19h+7Fjx5SZmaldu3ZJqt73xMl+p5UpC5r+9a9/yWazea7b77dJKrdN+m0W68SJE3X48GHdeOON2rJlS4X2c3Nz9eabb3qWo6Ojdfvtt2vbtm265ZZbtGfPngrHbNq0yROGe9OXRYsW6fPPP6/w3ti6davWr18vm81W5fPVKlNaWqp7771XBQUFnnWrV6/Wiy++KJvN5nnvS9LQoUPVtGlTLV261HOdypSN1+jo6HLj+dlnn630OWyn87vbm9f2dFX3ZwoA1Hg+/e4/AECNJcn4+/t7ta/L5ar0a9iNMWbKlClGkrHb7ebKK680w4cPN82bNzeSTJs2bSp8hXlubq4JCgoy/v7+5pprrjG33367ueOOO8yGDRvMgQMHjCTjcDjMpZdeaoYPH25uvPFGT3uxsbEmPz/fq5r79OljJJmtW7eWW//zzz+buLg4I8k0adLEXHnllWbEiBHmqquuMk2aNDGSzLPPPuvZv1OnTkaSSUxMNDfddJMZNmyYZ11QUJBZunSpV/WMHj3aSDLjxo0zNpvNXHLJJeaWW24xHTp0MJJMWFiY+fbbb8sdU9XXjZeWlnr616RJEzN48GDTu3dvExAQYB588MEKr1V1XteTfUX6qb4+3RhjZsyYYfz9/Y0kc8EFF5gbb7zRDBs2zHTv3t0EBgYaSebAgQOe/Y8dO2Z69uxpJJmIiAhz0003maSkJBMcHGySkpJMixYtKv0K+0cffdTzGl199dXmmmuuMREREeayyy4zPXr0qHRsfPPNN6Zx48ZGkmnevLm59tprzYgRI0y/fv1MeHi4kWSysrI8+1f1+pQpe80XLVpUbv2AAQOMJNOhQwczcuRIc8cdd5hZs2YZY4wZNGiQkWRat25tbrjhBnPLLbeYSy+91NhsNuPn52cyMjKqvLYnKnst/vjHPxqHw2E6dOhgbrnlFnPxxRd7xsInn3xS4bjTfT+f7lg0xpjXX3/dSDKPPfaYV335vap+F82bN8+EhIQYSSY+Pt4MGDDADB8+3PTu3ducd955RpL57rvvjDHV+574+9//biSZpk2bmuHDh5s77rjDPPTQQ+X22bhxo5Hked1PVFhY6Bn7DRo0MG63u8I5SkpKzMiRI40kExAQYLp37+6puUOHDsZms5lOnTqVO6agoMD079/fSDLnnXee6dmzp7nlllvMwIEDTXx8vJFkBg0a5HVfnn32WSPJREZGmmuuucbceuut5qqrrvLUfu+993p1vcreN0lJSaZ58+amWbNmZujQoebqq682DofDSDKPPPJIheM+//xzExwcbCSZrl27mltuucV07tzZSDKhoaHmyy+/LLd/w4YNjZ+fn+ncubMZOnSoufnmm03btm2NJNOoUSPz888/V6jp9+/lk31eney4rVu3GkmmT58+FfpRXZ8pAFBbEEoBACpVXaGUMcZ89NFH5g9/+INp2LChCQgIMPHx8WbixIlm//79le7/n//8x/Ts2dOEhoZ6/lBbtGiRcbvd5qWXXjI33nijad26tQkJCTHh4eHmwgsvNI8//rj55ZdfvO5fVaGUMcb8+uuv5m9/+5vp0qWLCQ0NNUFBQSY2NtZcffXV5qWXXjL79u3z7Pvhhx+a22+/3XTo0MGEh4ebkJAQ07ZtW3PnnXd6/jDxxokBxfz5802PHj1MSEiIadiwoRk0aJBZu3ZthWNOFnr8+uuvZuzYsaZp06YmMDDQdOjQwcyYMcMYU/GP9uq8rmcbShljzHfffWdGjx5tWrZsaQICAkx4eLjp0KGDuf32281HH31kSktLy+1/9OhRM2nSJNOiRQsTEBBgYmNjzV//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'Positif')"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"sf2QrRYhWjJA","executionInfo":{"status":"ok","timestamp":1751537465260,"user_tz":-120,"elapsed":24505,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c84063df-b603-44ba-f01c-7509ef095289"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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nj3eIO7k4LGY2+02QUFBpnPnzhXuy5w5c4wkM378+FNcjdOryripyvi86aabjCTz5JNPlto/IyPDBAQEnPKz+2e5ubnGbreb7t27l9mf6gylUlNTjSTz4osvVqg2AKjtmFMKAOq4yMhIXXvttfryyy+1e/durV69WhkZGRo+fLgCAwPLPe7rr7+W9Mdk6X/WqFEjXXHFFSoqKtKKFSsqXMv555+vgwcP6rbbbit3Hpiq+s9//iNJuv7668vcfskll0hSifk6zj//fEnS3XffrWXLlpWYh6Uqhg8fXmqdw+HwTtxbfE2r29m8rn+WmJhYap6noqIiffnllwoNDS33EZ8/X/8dO3Zox44datSokXfelpPdeOON1VZzVT4bxc4//3yde+65pda3adNGkio871hxW5L0t7/9TQsXLlR+fn6Fjy3LkCFDSn2rpvT/rt3Jn7ezMZ5PpX///qd8NLgyqjq2rRgTffv21fHjx73zeC1fvlzGGPXr10/9+vXTrl27vPNKFc9vdPKjez/88INycnJ00UUXlfnopNPp9PZl8+bN3nY8Ho+uuuqqEo/0FYuOjlbr1q21fv165eXlVagfXbt2VUBAgN5++23NmDFD+/fvr8xlKKUy46Yq72/x5/SGG24o8xynerwuKytLr732msaPH69bb71VKSkpGjNmjIKCgrzX+Gwq/jnw97//XR988IGOHDly1s8JAL5EKAUA0M0336zCwkJ98MEH3knPb7755lMek52dLUllTqp98vqsrKwK1/HKK68oPj5eM2fO1Hnnnafo6GgNGzZMc+bMUWFhYYXbKUvxBLI33XRTiYlli19RUVGSpJycHO8xDz30kPr166cVK1bosssuU/369XXFFVdo6tSpOnToUKVraN68eZnri69V8TWtbmfzuv5Zs2bNSq3LycnR0aNHlZubq6CgoDKv/0MPPeTdV/p/16K8a1avXj1FRkZWS83Fn42LL764zNp69uxZoraTNWnSpMw2w8PDJUnHjx+vcB0pKSlKTk7Wxo0bNXDgQNWvX1+XXnqpnn76ae3evbuSvarc5+1sjOdTKetzUlXF719sbGyZ719xMHHy+2fVmCgOmIoDp5ODp1Nt+3PfPv/88zL7ZrPZ9Mknn5ToX/ExM2bMKPeYDRs2yBijAwcOVKgfbdq00bPPPqvc3FzdcccdatSokbp06aL7779fP//8c6WvS2XGTVXGZ3Go1bRp0zLPU97n7/nnn1d8fLzGjBmjqVOn6u2339asWbM0a9Ys5ebmWhIQXX755frLX/6i7Oxs3XjjjWrQoIF69eqlRx99VFu3bj3r5wcAqzHROQBA11xzjSIjI/XOO+8oOztb7du3V/fu3c+ozT/fLVMRnTt31saNG7V48WItWrRIy5Yt09y5czV37lz17t1by5YtU1BQUJXqKSoqkiRdddVVp5ysuXhiX0mKiIjQkiVLtGLFCi1YsEDLli3TkiVL9Pnnn2vKlCn6+uuv1bp16yrVczYU9/HPzuZ1/bOQkJBy6woLC9OQIUNOeXzHjh2rpY7ylHWNitcNHTpU55xzTrnHlvXV7sWTuFeHwMBAzZkzRw8//LA+/vhjLVmyRN9//72+/vprPfPMM1q8eLEuuuiiajtfZVR2PJf3WSxW1uekqorPdfKE0WXp1auX989WjYmygqcOHTqoUaNGioiIUHBwsJYtW6bbbrtNX331lcLDw0v87C3uW6tWrXTxxRef8lzFdx4VH9O1a9fTfnlCcHBwhfvywAMPKDk5WfPnz9fnn3+ur7/+Wi+88IJefPFFvfDCC7rvvvsq3FZlxs2ZjM/KWLlypR544AHVq1dPU6dOVb9+/RQdHe29RjExMZW68/FMPP/887rzzjv18ccf64svvtCKFSu0atUqPfvss3r//fdP+3MUAGoTQikAgIKDg3XDDTdoxowZkqRx48ad9piYmBhlZmZq+/bt6tChQ6ntJ9+9UBkhISG67rrrdN1110mSNmzYoBEjRui7777Tm2++qbFjx1aqvWLF/zN/++23V+of9DabTX369PF+Y9LevXs1fvx4vf/++3rkkUc0d+7cCrdV/JXr5a2PiYk5bRvFvygfPXq01LadO3eWe9zZuq4V0bBhQ4WEhHgf/6lIwFH8rYblXbPDhw+X+op26dTXRyr7GjVp0kQZGRl6+OGHvY/O+FK3bt3UrVs3TZ48WYcPH9bkyZP1wgsvaPz48ZX6OvjKfN6qMp6r+lmsbk2aNNF///tf/eMf/yjzkbDyWDEmWrRooaZNm2rlypXKzs7W+vXrNWbMGO/5L7zwQi1fvly//PKLcnJydPXVV5d4bLr451a7du2UlpZWoXMWH9OnTx+9/PLLZ9yHkzVt2lT33nuv7r33XhUUFOiDDz7Q6NGjNWHCBN1yyy2qX79+tZ5Pqtr4dLlc2rZtm3bu3FnmN1aW9flMT0+XJD311FOlAs68vLwq3a14Jtq2basJEyZowoQJys/P17Rp0/TQQw9pzJgxhFIA/AqP7wEAJEkjR47Uueeeq4YNG5Y5r8yfFc/j8f7775faVvxV3TabrcT/7hf/EluZuZk6duyou+++W5L0yy+/VPi4PxswYICk//eLR1U1atRIkydPrlI9ZQVYBQUF+uijjySpxFeKl6c4rPntt99Kbfv8888rXEt1XdeKsNvt6tevnw4fPqwvv/yyQsc0b95cTZs21d69e7V8+fJS2z/44IMyjzvV9fntt9+0Y8eOUuur67NxOlX5/EdERGjKlCmy2WyVfp/+9a9/lfkoWvG1O/nzVpXxXF2fxTNVXe9fVcZERd7T4nmlUlNTZYwpMUda8bxSb775pnf5ZD179lS9evW0fPnyCj9qd9lllykwMFALFy6Ux+Op0DEV7cvJ7Ha7br75ZvXs2VMnTpw4a/MtVeX9Lf6cFv9sPdmWLVu0bt26UusPHjwoqexHCz/88EMZY0qtr8qYroqQkBA9+OCDcrlc2rdvn/bu3XtWzwcAViKUAgBI+uOX0pycHO3bt6/cuWhOdvfddysgIEAvvfSS1qxZ411/4sQJ3XvvvcrLy9P1119fYk6P4jszMjIySrWXm5url156qdTdL0VFRVq8eLGk8ucHqYghQ4aoQ4cOevfdd/Xkk0+WmuvHGKMVK1aUmMj5tddeU2ZmZqm2Fi1aVKV6vvnmG82cObPEukmTJmnHjh3q3LmzNxg4lb59+0qSXn311RKTDf/444967LHHSu1/tq9rRT3yyCMKCAjQ6NGjvY8ynezo0aOaOXNmiYmXi+8oeeCBB0r8Qr5161Y98cQTZZ6nZ8+eCg0N1aeffqq1a9d61+fk5Oj2228v87GyO++8U40aNdKzzz6rN954o9Q+BQUF+uyzz844vGvYsKEcDof++9//lhkW/fOf/yzzHJ9++qmMMZV+n7Zt26bHH3+8xLo33nhD3333nRo3blzibouqjOfKfhbPlgceeEBOp1MPPvig/vWvf5Xafvz4cc2bN0+7du2SVL1j4lQ/04oVB01vvPGGbDab97r9eZukEtukP+5inTBhgo4cOaLrr7++zDmFsrKy9M9//tO7HBsbq1tvvVXbtm3TjTfeqD179pQ6ZsuWLaUCm1P1ZenSpfriiy9KjY3MzEz9+uuvstls5c4TdaaqMj7vvPNOSX88Brdx40bv+ry8PI0bN67MnwPFk6y/9dZbJcK8jRs36q9//WuZtVXk/a+s+fPneyfGP9natWu1Z88ehYWFVdt8egBQI/jyq/8AAL4hyQQGBlZoX7fbXe5XWj/11FNGkrHb7aZ///5m+PDhpmnTpkaSad26damvMM/KyjIhISEmMDDQXHXVVebWW281t912m9m0aZM5ePCgkWQcDoe58MILzfDhw83111/vbS8uLq7MrxAvS9++fUt9rbgxxvz2228mPj7eSDKNGjUy/fv3NyNGjDBXXHGFadSokZFkXnjhBe/+Xbp0MZJMhw4dzJAhQ8ywYcO860JCQsw333xToXpGjRplJJkxY8YYm81mLrjgAnPjjTeajh07er8e/YcffihxTHlfNV5UVOTtX6NGjUxSUpK55JJLTFBQkHnwwQdLvVfVeV0nTZpkJJm33367UtuKvfrqqyYwMNBIMuedd565/vrrzbBhw0yvXr1McHCwkWQOHjzo3f/48ePm4osvNpJM/fr1zZAhQ0xiYqJxOp0mMTHRNGvWrMyvVH/ssce879GVV15prrrqKlO/fn1z0UUXmd69e5f52fjuu+9Mw4YNjSTTtGlTc/XVV5sRI0aYhIQEExkZaSSZ9PR07/6n+yr44vf8z185P3DgQCPJdOzY0YwcOdLcdtttZubMmcYYYwYPHmwkmZYtW5rrrrvO3HjjjebCCy80NpvNBAQEmLlz55Z7bU9W/F78z//8j3E4HKZjx47mxhtvND179vR+Fj799NNSx1V2PFf2s2iMMW+//baRZCZNmlShvvxZeT+L5s+fb0JDQ40k06pVKzNw4EAzfPhwc8kll5hzzjnHSDLr1q0zxlTvmPjHP/5hJJnGjRub4cOHm9tuu8389a9/LbHP5s2bjSTv+36yvLw872c/PDzceDyeUucoLCw0I0eONJJMUFCQ6dWrl7fmjh07GpvNZrp06VLimNzcXDNgwAAjyZxzzjnm4osvNjfeeKMZNGiQadWqlZFkBg8eXOG+vPDCC0aSiYqKMldddZW56aabzBVXXOGt/d57763Q9arquKns+DTGmPHjxxtJJjg42Fx11VUmOTnZuFwu07x5c+84XLFihXf/nJwcEx0dbSSZ+Ph4k5ycbPr3728cDoe54YYbTPPmzcv8edO5c2cjyfTs2dOkpKSY2267zXz88cfe7eV9Zstr77777jOSTGxsrElMTDQjRoww/fr18/7s/Mc//lH+BQaAWohQCgDqoOoKpYwxZuHChebyyy839erVM0FBQaZVq1ZmwoQJ5sCBA2Xu/9lnn5mLL77YhIWFeX9RW7p0qfF4POaVV14x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'Négatif')"]},{"cell_type":"markdown","metadata":{"id":"yvXJwMNOWjJA"},"source":["#### 5. Analyse des utilisateurs\n","\n","- Étude de la distribution des tweets par utilisateur \n","- Analyse des tendances de sentiment propres aux utilisateurs "]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"K2GstxOKWjJA","executionInfo":{"status":"ok","timestamp":1751537469471,"user_tz":-120,"elapsed":4209,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c9a0bd79-380b-4049-ed32-e62a48d8aa3f"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","6. ANALYSE DES UTILISATEURS\n","--------------------------------------------------\n","Nombre d'utilisateurs uniques: 659,775\n","\n","Top 10 des utilisateurs par nombre de tweets:\n","- lost_dog: 549 tweets\n","- webwoke: 345 tweets\n","- tweetpet: 310 tweets\n","- SallytheShizzle: 281 tweets\n","- VioletsCRUK: 279 tweets\n","- mcraddictal: 276 tweets\n","- tsarnick: 248 tweets\n","- what_bugs_u: 246 tweets\n","- Karen230683: 238 tweets\n","- DarkPiano: 236 tweets\n","\n","Parmi les utilisateurs avec au moins 5 tweets:\n","Ratio positif moyen: 51.23%\n","Ratio négatif moyen: 48.77%\n","\n","Utilisateurs les plus positifs:\n","- 007wisdom: 100.00% positif (6 tweets)\n","- LongandLoud: 100.00% positif (6 tweets)\n","- LoopNashville: 100.00% positif (8 tweets)\n","- Looyda: 100.00% positif (5 tweets)\n","- LoraNorton: 100.00% positif (5 tweets)\n","\n","Utilisateurs les plus négatifs:\n","- zombiemovies: 100.00% négatif (5 tweets)\n","- zombiecommand: 100.00% négatif (5 tweets)\n","- zzkim: 100.00% négatif (5 tweets)\n","- 1284forever: 100.00% négatif (6 tweets)\n","- 123katie123: 100.00% négatif (5 tweets)\n"]}],"source":["def analyser_utilisateurs(df):\n"," \"\"\"Analyse la distribution des utilisateurs\"\"\"\n"," print(\"\\n6. ANALYSE DES UTILISATEURS\")\n"," print(\"-\" * 50)\n","\n"," # Nombre total d'utilisateurs uniques\n"," unique_users = df['user'].nunique()\n"," print(f\"Nombre d'utilisateurs uniques: {unique_users:,}\")\n","\n"," # Top utilisateurs par nombre de tweets\n"," top_users = df['user'].value_counts().head(10)\n"," print(\"\\nTop 10 des utilisateurs par nombre de tweets:\")\n"," for user, count in top_users.items():\n"," print(f\"- {user}: {count} tweets\")\n","\n"," # Distribution des sentiments par utilisateur (pour les utilisateurs avec plus de X tweets)\n"," min_tweets = 5\n"," user_sentiment = df.groupby('user')['sentiment'].value_counts().unstack().fillna(0)\n"," user_sentiment = user_sentiment[user_sentiment.sum(axis=1) >= min_tweets]\n","\n"," # Calculer les ratios\n"," user_sentiment['total'] = user_sentiment.sum(axis=1)\n"," user_sentiment['pos_ratio'] = user_sentiment['Positif'] / user_sentiment['total']\n"," user_sentiment['neg_ratio'] = user_sentiment['Négatif'] / user_sentiment['total']\n","\n"," # Afficher quelques statistiques\n"," print(f\"\\nParmi les utilisateurs avec au moins {min_tweets} tweets:\")\n"," print(f\"Ratio positif moyen: {user_sentiment['pos_ratio'].mean():.2%}\")\n"," print(f\"Ratio négatif moyen: {user_sentiment['neg_ratio'].mean():.2%}\")\n","\n"," # Utilisateurs les plus positifs/négatifs\n"," most_positive = user_sentiment.sort_values('pos_ratio', ascending=False).head(5)\n"," most_negative = user_sentiment.sort_values('neg_ratio', ascending=False).head(5)\n","\n"," print(\"\\nUtilisateurs les plus positifs:\")\n"," for user, row in most_positive.iterrows():\n"," print(f\"- {user}: {row['pos_ratio']:.2%} positif ({int(row['total'])} tweets)\")\n","\n"," print(\"\\nUtilisateurs les plus négatifs:\")\n"," for user, row in most_negative.iterrows():\n"," print(f\"- {user}: {row['neg_ratio']:.2%} négatif ({int(row['total'])} tweets)\")\n","\n","analyser_utilisateurs(df)"]},{"cell_type":"markdown","metadata":{"id":"FFo_xU7uWjJA"},"source":["#### 6. Analyse des requêtes (flag)\n","\n","- Impact des requêtes sur la distribution des sentiments \n","- Identification des requêtes les plus fréquentes "]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":146},"id":"mfq4kgglWjJA","executionInfo":{"status":"ok","timestamp":1751537469612,"user_tz":-120,"elapsed":141,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e6d59106-48f9-4043-f59a-1ec3ac4e836c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["flag\n","NO_QUERY 1600000\n","Name: count, dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
count
flag
NO_QUERY1600000
\n","

"]},"metadata":{},"execution_count":17}],"source":["df['flag'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"u6Ux57gmWjJA"},"source":["#### 7. Analyse de la longueur des tweets (caractères et mots)\n"," - Visualisation de la distribution des longueurs des tweets \n"," - Gestion des caractères HTML dans les tweets \n"," - Nettoyage des tweets trop longs et souvent difficilement compréhensibles "]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":567},"id":"c4zSoFysWjJA","executionInfo":{"status":"ok","timestamp":1751537483698,"user_tz":-120,"elapsed":14085,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"f80a862b-e45d-416c-966c-524de732b6c5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Longueur moyenne des tweets: 74.09 caractères\n","Nombre moyen de mots par tweet: 13.18 mots\n"]},{"output_type":"display_data","data":{"text/plain":["
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analyser_texte_tweets(df):\n"," \"\"\"Analyse les caractéristiques textuelles des tweets\"\"\"\n"," # Calcul de la longueur des tweets\n"," df['text_length'] = df['text'].apply(len)\n"," df['word_count'] = df['text'].apply(lambda x: len(str(x).split()))\n","\n"," print(f\"Longueur moyenne des tweets: {df['text_length'].mean():.2f} caractères\")\n"," print(f\"Nombre moyen de mots par tweet: {df['word_count'].mean():.2f} mots\")\n","\n"," # Afficher word_count et text_length côte à côte avec seaborn\n"," plt.figure(figsize=(14, 6))\n","\n"," # Distribution de la longueur des tweets par sentiment\n"," plt.subplot(1, 2, 2)\n"," sns.boxplot(x='sentiment', y='text_length', data=df, hue='sentiment')\n"," plt.title('Distribution du nombre de caractères par tweet', fontsize=15)\n"," plt.xlabel('Sentiment')\n"," plt.ylabel('Longueur du Tweet')\n","\n"," # Distribution du nombre de mots par sentiment\n"," plt.subplot(1, 2, 1)\n"," sns.boxplot(x='sentiment', y='word_count', data=df, hue='sentiment')\n"," plt.title('Distribution du nombre de mots par tweet', fontsize=15)\n"," plt.xlabel('Sentiment')\n"," plt.ylabel('Nombre de Mots')\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n","analyser_texte_tweets(df)"]},{"cell_type":"code","source":["more_than_140_ch_before_htmlunescape = df[df['text_length'] > 140]['text']\n","print('More than 140 characters before html process:', len(more_than_140_ch_before_htmlunescape))\n","more_than_140_ch_before_htmlunescape.head(10).values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"vjIRh70ycDG1","executionInfo":{"status":"ok","timestamp":1751537483733,"user_tz":-120,"elapsed":32,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"bfd13510-3f50-4460-b2f9-6a99f23a1574"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["More than 140 characters before html process: 17174\n"]},{"output_type":"execute_result","data":{"text/plain":["array([\"Awwh babs... you look so sad underneith that shop entrance of "Yesterday's Musik" O-: I like the look of the new transformer movie \",\n"," 'Tuesday�ll start with reflection �n then a lecture in Stress reducing techniques. That sure might become very useful for us accompaniers ',\n"," \"Whinging. My client&boss don't understand English well. Rewrote some text unreadable. It's written by v. good writer&reviewed correctly. \",\n"," '@TheLeagueSF Not Fun & Furious? The new mantra for the Bay 2 Breakers? It was getting 2 rambunctious;the city overreacted & clamped down ',\n"," \"#3 woke up and was having an accident - "It's pushing, it's pushing!" he was crying because he couldn't stop from wetting his pants. \",\n"," 'My bathtub drain is fired: it haz 1 job 2 do, & it iz FAIL. I got all Drano on its ass, & iz STILL NOT DRAINING. I wanna shower, dangit!! ',\n"," \"pears & Brie, bottle of Cabernet, and "Win a Date With Tad Hamilton"... oh gawwd my life flashed forward to when I'm 40 with my 75 cats \",\n"," 'Have an invite for "Healthy Dining" session at Ashok Hotel today with Exec Chef R.Chopra but damn workload - will have to skip it! ',\n"," 'Damnit I was really digging this season of Reaper http://www.tv.com/story/13720.html?ref_story_id=13720&ref_type=1101&ref_name=story',\n"," \"Why do I keep looking...I know that what I read is gonna hurt but I still look! I guess it's just a girl thing & I need to get over him. \"],\n"," dtype=object)"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["Il est assez clair que l'on rencontre un problème d'encodage html ce qui augmente artificiellement la longueur des tweets dans notre dataset."],"metadata":{"id":"jU4bygO9hhXj"}},{"cell_type":"code","source":["import html\n","# Exemple de nettoyage d'une colonne 'text' dans votre DataFrame\n","df[df['text_length'] > 140]['text'].apply(html.unescape).head(10).values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"7fvM0aAUfZ_l","executionInfo":{"status":"ok","timestamp":1751537483811,"user_tz":-120,"elapsed":77,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"29977ed3-8334-43cd-fc8b-4b111fbb66ed"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Awwh babs... you look so sad underneith that shop entrance of \"Yesterday\\'s Musik\" O-: I like the look of the new transformer movie ',\n"," 'Tuesday�ll start with reflection �n then a lecture in Stress reducing techniques. That sure might become very useful for us accompaniers ',\n"," \"Whinging. My client&boss don't understand English well. Rewrote some text unreadable. It's written by v. good writer&reviewed correctly. \",\n"," '@TheLeagueSF Not Fun & Furious? The new mantra for the Bay 2 Breakers? It was getting 2 rambunctious;the city overreacted & clamped down ',\n"," '#3 woke up and was having an accident - \"It\\'s pushing, it\\'s pushing!\" he was crying because he couldn\\'t stop from wetting his pants. ',\n"," 'My bathtub drain is fired: it haz 1 job 2 do, & it iz FAIL. I got all Drano on its ass, & iz STILL NOT DRAINING. I wanna shower, dangit!! ',\n"," 'pears & Brie, bottle of Cabernet, and \"Win a Date With Tad Hamilton\"... oh gawwd my life flashed forward to when I\\'m 40 with my 75 cats ',\n"," 'Have an invite for \"Healthy Dining\" session at Ashok Hotel today with Exec Chef R.Chopra but damn workload - will have to skip it! ',\n"," 'Damnit I was really digging this season of Reaper http://www.tv.com/story/13720.html?ref_story_id=13720&ref_type=1101&ref_name=story',\n"," \"Why do I keep looking...I know that what I read is gonna hurt but I still look! I guess it's just a girl thing & I need to get over him. \"],\n"," dtype=object)"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["df['text'] = df['text'].apply(html.unescape)"],"metadata":{"id":"BoGtOFgUg-en","executionInfo":{"status":"ok","timestamp":1751537484338,"user_tz":-120,"elapsed":527,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["analyser_texte_tweets(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":567},"id":"nE0cJqRHhq1c","executionInfo":{"status":"ok","timestamp":1751537498442,"user_tz":-120,"elapsed":14105,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1fad0b6a-c11d-46d7-83c6-272bd0d03a6d"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Longueur moyenne des tweets: 73.70 caractères\n","Nombre moyen de mots par tweet: 13.18 mots\n"]},{"output_type":"display_data","data":{"text/plain":["
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than 140 characters after html process:', len(df[df['text_length'] > 140]['text']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"oqTsuGn1qcXa","executionInfo":{"status":"ok","timestamp":1751537498443,"user_tz":-120,"elapsed":8,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"eb34b09c-d1dc-4ebd-aff9-409f13d10cf4"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["More than 140 characters after html process: 687\n"]}]},{"cell_type":"code","source":["df[df['text_length'] > 140]['text'].head(10).values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"DH_s2HfR1EED","executionInfo":{"status":"ok","timestamp":1751537498450,"user_tz":-120,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ce2ab598-c2cc-4e9b-bad7-d0e2e2ff6609"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Tuesday�ll start with reflection �n then a lecture in Stress reducing techniques. That sure might become very useful for us accompaniers ',\n"," \"When I get mine I�m going to do a tour of Copenhagen with it �;) Hmmph I can't top that .. must get thinking .. http://tinyurl.com/c2trgj\",\n"," 'http://tr.im/imOv �i, b�c H?i Tho?i th? l� gi� t? cu?c ??i r?i ( M�nh v?n c�n nh? c�i nh� t?p th? b� t� c?a b�c � ? ch? Tr?i ',\n"," '@NguyenNoir ??c h?t c? b? r?i. Th?ng Starr ?y l�c ??u m�nh r?t tr�ng ??i n� s? l� lo?i villain c?ng c?a, ai d� c�ng v? sau c�ng pathetic. ',\n"," 'Saturday�s Graze box finally arrived today. The grapes were mouldy after 3 days in GPO I�m considering cancelling my account altogether.',\n"," 'Buzz.vn: \\tTin t?c - ??c B�o : hjxhjx l�m th?t kh? n?y nh�n th�i l� m�n �i r?i ch? ??ng n�i g� ?.. http://tinyurl.com/dztqfo',\n"," 'Photo: ???? (Sweetness in the Salt) C� ai coi phim n�y ch?a, hay v� c?m ??ng ghia. Lyrics c?a nh?c th?t... http://tumblr.com/xmi1mmhzu',\n"," 'evdeyim cumartesi g�n� biraz hastay?m grip , at home at saturday cuz I m ill , flu , hapshuuuu , I cleaned my home eat lunch hi everybody',\n"," '@leokun87 Tao c?ng ?� v? BK, d� kh�ng y�u, nh?ng v?n c�n h?n Th??ng-H?i-s�nh-?i?u. M?t ?t z?i L?i h?t $ l�m t�c z?i. ??u t�c x?u qu�!!!',\n"," '1 ng�y m?t m?i v?t l?n v?i c�i Eclipse. Cu?i c�ng c?ng xong. Ch�n m?y b�c plugin, update m� kh�ng th�m test hay sao l�m queo m�y m�nh '],\n"," dtype=object)"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["# Filtrer les lignes où 'text_length' est supérieur à 140 et supprimer ces lignes\n","df = df[df['text_length'] <= 140].copy()\n","\n","# Afficher la nouvelle taille du DataFrame pour confirmation\n","print(f\"Nouvelle taille du DataFrame après suppression: {df.shape[0]} lignes, {df.shape[1]} colonnes.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"WoIRCbHWqz8W","executionInfo":{"status":"ok","timestamp":1751537499391,"user_tz":-120,"elapsed":940,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"fe74a02f-f104-41df-f5bb-08f207a6aad4"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Nouvelle taille du DataFrame après suppression: 1599313 lignes, 15 colonnes.\n"]}]},{"cell_type":"markdown","source":["### Analyse Qualitative"],"metadata":{"id":"J8p054tk0X-B"}},{"cell_type":"markdown","metadata":{"id":"cnrONvWyWjJA"},"source":["#### 8. Analyse de caractéristiques distinctives et de l’impact de tokens spécifiques\n","\n","Cette section met en évidence plusieurs éléments textuels pouvant aider à différencier les sentiments exprimés dans les tweets. Ces observations peuvent être utilisées **à la fois comme features explicites** dans les modèles classiques ou **comme éléments à conserver dans les phases de prétraitement** pour les modèles profonds.\n","\n","---\n","\n","##### **1. Majuscules**\n","- Le ratio de majuscules est généralement [plus élevé/plus faible] dans les tweets [positifs/négatifs]. \n"," → Peut indiquer de l’**intensité émotionnelle** (ex. colère, enthousiasme). \n"," ✅ **À exploiter comme feature explicite** ; ne pas convertir systématiquement en minuscules.\n","\n","---\n","\n","##### **2. Ponctuation expressive**\n","- `!` plus fréquents dans les tweets [positifs/négatifs] \n","- `?` plus fréquents dans les tweets [positifs/négatifs] \n","- `...` plus fréquents dans les tweets [positifs/négatifs] \n"," ✅ **À conserver dans le texte**, et/ou compter comme feature.\n","\n","---\n","\n","##### **3. Symboles spécifiques à Twitter**\n","- `#hashtags`, `@mentions`, `http` apparaissent avec des fréquences différentes selon le sentiment. \n"," ✅ Peuvent être **encodés comme des indicateurs binaires** ou **remplacés par des tokens spéciaux** (``, ``, etc.)\n","\n","---\n","\n","##### **4. Émoticônes**\n","- Émoticônes positives (`:)`, `:D`) bien plus fréquentes dans les tweets positifs \n","- Émoticônes négatives (`:(`, `:(`) plus fréquentes dans les tweets négatifs \n"," ✅ **Fort pouvoir discriminant** : à traiter comme **tokens spécifiques**, voire à compter comme **features auxiliaires**.\n","\n","---\n","\n","##### **5. Pronoms personnels**\n","- \"I\" (je) et \"we\" (nous) ont des fréquences différenciées selon le sentiment \n"," ✅ Peut être un **signal intéressant sur le ton ou l’implication personnelle** → feature textuelle potentielle.\n","\n","---\n","\n","##### **6. Lexique émotionnel**\n","- Mots comme \"love\", \"hate\", \"lol\", \"omg\" sont fortement corrélés à certaines classes \n"," ✅ **Indicateurs lexicaux directs** : peuvent être **utilisés comme features ou marqués via une lexicon-based approach.**\n","\n","---\n","\n","##### **En résumé : stratégies recommandées**\n","- ✅ **Ne pas supprimer** systématiquement la ponctuation expressive ni convertir les majuscules \n","- ✅ **Traiter** les émoticônes et tokens spécifiques comme des signaux explicites \n","- ✅ **Enrichir** la vectorisation (TF-IDF, embeddings) avec des **caractéristiques structurelles** (ponctuation, casse, ratio d’émoticônes, etc.)\n"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"ctBSKs_QWjJA","executionInfo":{"status":"ok","timestamp":1751537506318,"user_tz":-120,"elapsed":6919,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"bfd821ea-c8ba-4fe7-c5f5-c279d0f4b26e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Moyenne des caractéristiques par sentiment:\n","Caractéristique 'text_length':\n"," - Tweets négatifs: 73.96\n"," - Tweets positifs: 73.38\n"," - Différence relative: -0.79%\n","\n","Caractéristique 'word_count':\n"," - Tweets négatifs: 13.58\n"," - Tweets positifs: 12.77\n"," - Différence relative: -5.92%\n","\n","Caractéristique 'capitals_ratio':\n"," - Tweets négatifs: 0.04\n"," - Tweets positifs: 0.05\n"," - Différence relative: 16.98%\n","\n","Caractéristique 'exclamation_count':\n"," - Tweets négatifs: 0.48\n"," - Tweets positifs: 0.66\n"," - Différence relative: 38.19%\n","\n","Caractéristique 'question_count':\n"," - Tweets négatifs: 0.15\n"," - Tweets positifs: 0.16\n"," - Différence relative: 7.62%\n","\n","Caractéristique 'ellipsis_count':\n"," - Tweets négatifs: 0.21\n"," - Tweets positifs: 0.15\n"," - Différence relative: -26.07%\n","\n","Caractéristique 'hashtag_count':\n"," - Tweets négatifs: 0.02\n"," - Tweets positifs: 0.03\n"," - Différence relative: 42.58%\n","\n","Caractéristique 'mention_count':\n"," - Tweets négatifs: 0.41\n"," - Tweets positifs: 0.60\n"," - Différence relative: 46.87%\n","\n","Caractéristique 'url_count':\n"," - Tweets négatifs: 0.03\n"," - Tweets positifs: 0.06\n"," - Différence relative: 88.16%\n","\n","Caractéristique 'positive_emoticon':\n"," - Tweets négatifs: 0.00\n"," - Tweets positifs: 0.00\n"," - Différence relative: inf%\n","\n","Caractéristique 'negative_emoticon':\n"," - Tweets négatifs: 0.00\n"," - Tweets positifs: 0.00\n"," - Différence relative: 100.00%\n","\n","Caractéristiques structurelles des tweets par sentiment :\n"," Négatif (moyenne) Positif (moyenne) Différence Relative\n","Emoticone Positive 0.0 0.00001 inf\n","Emoticone Negative 0.00024 0.00048 100.0\n","Nombre URLs 0.03014 0.05671 88.155275\n","Nombre Mentions 0.40579 0.59599 46.871535\n","Nombre Hashtags 0.0233 0.03322 42.575107\n","Nombre Exclamations 0.47932 0.66237 38.189518\n","Ratio Majuscules 0.042984 0.050284 16.984226\n","Nombre Questions 0.14796 0.15924 7.623682\n","Longueur Texte 73.96162 73.37764 -0.789572\n","Nombre Mots 13.57711 12.77396 -5.915471\n","Nombre Ellipses 0.20901 0.15453 -26.065738\n"]}],"source":["def analyser_caracteristiques_distinctives(df):\n"," \"\"\"Analyse les caractéristiques distinctives et les tokens spécifiques dans les tweets\"\"\"\n","\n"," # Créer un échantillon équilibré pour l'analyse\n"," sample_size = min(100000, df[df['sentiment'] == 'Négatif'].shape[0], df[df['sentiment'] == 'Positif'].shape[0])\n"," neg_samples = df[df['sentiment'] == 'Négatif'].sample(sample_size, random_state=42)\n"," pos_samples = df[df['sentiment'] == 'Positif'].sample(sample_size, random_state=42)\n","\n"," balanced_df = pd.concat([neg_samples, pos_samples])\n","\n"," # Calculer des caractéristiques supplémentaires\n"," balanced_df['capitals_ratio'] = balanced_df['text'].apply(\n"," lambda x: sum(1 for c in str(x) if c.isupper()) / len(str(x)) if len(str(x)) > 0 else 0\n"," )\n","\n"," balanced_df['exclamation_count'] = balanced_df['text'].apply(\n"," lambda x: str(x).count('!')\n"," )\n","\n"," balanced_df['question_count'] = balanced_df['text'].apply(\n"," lambda x: str(x).count('?')\n"," )\n","\n"," balanced_df['ellipsis_count'] = balanced_df['text'].apply(\n"," lambda x: str(x).count('...')\n"," )\n","\n"," balanced_df['hashtag_count'] = balanced_df['text'].apply(\n"," lambda x: str(x).count('#')\n"," )\n","\n"," balanced_df['mention_count'] = balanced_df['text'].apply(\n"," lambda x: str(x).count('@')\n"," )\n","\n"," balanced_df['url_count'] = balanced_df['text'].apply(\n"," lambda x: 1 if 'http' in str(x).lower() else 0\n"," )\n","\n"," balanced_df['positive_emoticon'] = balanced_df['text'].apply(\n"," lambda x: str(x).count(':)') + str(x).count(':-)') + str(x).count(':D') + str(x).count('=)')\n"," )\n","\n"," balanced_df['negative_emoticon'] = balanced_df['text'].apply(\n"," lambda x: str(x).count(':(') + str(x).count(':-(') + str(x).count(':(') + str(x).count('=(')\n"," )\n","\n"," # Comparer les moyennes\n"," features = ['text_length', 'word_count', 'capitals_ratio', 'exclamation_count',\n"," 'question_count', 'ellipsis_count', 'hashtag_count', 'mention_count',\n"," 'url_count', 'positive_emoticon', 'negative_emoticon']\n","\n"," # Calculer les statistiques par sentiment\n"," stats = balanced_df.groupby('sentiment')[features].mean().round(4)\n","\n"," # Renommer les colonnes en français\n"," stats.columns = ['Longueur Texte', 'Nombre Mots', 'Ratio Majuscules', 'Nombre Exclamations',\n"," 'Nombre Questions', 'Nombre Ellipses', 'Nombre Hashtags', 'Nombre Mentions',\n"," 'Nombre URLs', 'Emoticone Positive', 'Emoticone Negative']\n","\n"," print(\"Moyenne des caractéristiques par sentiment:\")\n"," for feature in features:\n"," neg_mean = balanced_df[balanced_df['sentiment'] == 'Négatif'][feature].mean()\n"," pos_mean = balanced_df[balanced_df['sentiment'] == 'Positif'][feature].mean()\n"," diff = (pos_mean - neg_mean) / neg_mean * 100 if neg_mean != 0 else float('inf')\n","\n"," print(f\"Caractéristique '{feature}':\")\n"," print(f\" - Tweets négatifs: {neg_mean:.2f}\")\n"," print(f\" - Tweets positifs: {pos_mean:.2f}\")\n"," print(f\" - Différence relative: {diff:.2f}%\")\n"," print()\n","\n","\n"," # Créer un DataFrame pour stocker les différence relatives\n"," diff_df = pd.DataFrame(index=features, columns=['neg_mean', 'pos_mean', 'diff_pct'])\n","\n"," for feature in features:\n"," neg_mean = balanced_df[balanced_df['sentiment'] == 'Négatif'][feature].mean()\n"," pos_mean = balanced_df[balanced_df['sentiment'] == 'Positif'][feature].mean()\n","\n"," # Éviter division par zéro\n"," if neg_mean != 0:\n"," diff_pct = (pos_mean - neg_mean) / abs(neg_mean) * 100\n"," else:\n"," diff_pct = float('inf') if pos_mean > 0 else 0\n","\n"," diff_df.loc[feature, 'neg_mean'] = neg_mean\n"," diff_df.loc[feature, 'pos_mean'] = pos_mean\n"," diff_df.loc[feature, 'diff_pct'] = diff_pct\n","\n"," # Trier par différence relative\n"," diff_df_sorted = diff_df.sort_values('diff_pct', ascending=False)\n","\n"," # Renommer les index en français avec des espaces et des majuscules\n"," feature_mapping = {\n"," 'text_length': 'Longueur Texte',\n"," 'word_count': 'Nombre Mots',\n"," 'capitals_ratio': 'Ratio Majuscules',\n"," 'exclamation_count': 'Nombre Exclamations',\n"," 'question_count': 'Nombre Questions',\n"," 'ellipsis_count': 'Nombre Ellipses',\n"," 'hashtag_count': 'Nombre Hashtags',\n"," 'mention_count': 'Nombre Mentions',\n"," 'url_count': 'Nombre URLs',\n"," 'positive_emoticon': 'Emoticone Positive',\n"," 'negative_emoticon': 'Emoticone Negative'\n"," }\n"," diff_df_sorted.index = diff_df_sorted.index.map(feature_mapping)\n","\n"," # Renommer les colonnes en français avec des majuscules et des espaces\n"," diff_df_sorted.columns = ['Négatif (moyenne)', 'Positif (moyenne)', 'Différence Relative']\n","\n"," return stats, diff_df_sorted\n","\n","stats, diff_df_sorted = analyser_caracteristiques_distinctives(df)\n","\n","print(\"Caractéristiques structurelles des tweets par sentiment :\")\n","print(diff_df_sorted)"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":708},"id":"sZawfr7CWjJA","executionInfo":{"status":"ok","timestamp":1751537506881,"user_tz":-120,"elapsed":550,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"91164da9-80a3-4fde-eed4-b244c6c87a4d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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fffdt8J7X1xcnDRv3jxp0KBBsnjx4gpjVq1albRu3Tpp0qRJ2Xlw7733JhGRnH322ZtV77cFSxuO5+vbr7rqqiQiymq44IILyradc845SUQkEydOLGubPHlyEhHJsGHDKt3H3//+9yQikjPPPLOsbUMQdu6551Y6ZkMNV111VZWP5YgjjkgiInnllVcq3V5VG8793r17V9i2bt26pH379klEJDNnzqzSfDfddFMSEckf/vCHSvczaNCgTa5xU87l3XbbLYmIcqH5BqWlpcnuu++e1KlTJ1myZEmF2kaNGlVhzIsvvphERNKvX79y7Zsb2vbp06fCmH/84x9l4fG///3vcttKSkqS5s2bJ7m5ueV+8ZbJtWHD9axz587lwtwk+ernt0mTJklubm65bUJbAGo7a9oCAGXWrl0b//jHP+KCCy6IH/3oRzFmzJgYM2ZMvP766xER8fbbb1c67uijj/7GeV955ZW4/PLL4/TTT48f/OAHMWbMmPjjH/9YYc5FixbFm2++GYWFhXH88cdv9vE89thjEfHV+qHZ2RW/9mxY43bOnDkVtmVlZcVRRx1V5X21bNky2rdvH6+88kr86le/igULFmRe+Ea89NJLsXr16ujVq1d07dq1wvYTTjih2va14b0bOnRopdv79u0bEVHuvdtrr73iiy++iFNOOWWja95+m5kzZ0bEV38OXqdOnQrbKzvGl19+OZYuXRq9e/eu9E/Q69WrV1bbhvNt9913j+zs7LjlllvipptuqnTd0eqw3377Rd26dcvWHY34au3RRo0axbBhw6Jjx44VtkVEuXWZM/ksMhnzbfbaa6+IiDj99NNjxowZUVJSUuWxlRkxYkSFttzc3LKlTjacC183a9asuPTSS+O0004ru5bcd999EZH59akyVT2XP/vss3j11Vdj++23L7dG+AZZWVlxwAEHxPr16+Oll16qsL2yJWd22GGHiIgqrxFcVZXtq0uXLhHx1Zq5G/a7QZ06daJjx45RXFwcS5cuLWvfnHOrf//+kZeXV64tJycnOnfuHMXFxVvs5xAAvousaQsA25ANa67+9wOMquL111+Po48+uuxBWJX5z3/+U2n7dtttV2n7unXrYsyYMXH33XdXac4PP/wwIr4KEr5tzcmq2HAsF1xwwTc+mG3NmjUV2lq2bBl169bdpP3ddtttMWLEiJgwYUJMmDAhOnbsGP369YsRI0bEEUccsUlzVWbRokUREdGxY8dKt1f2wKtMbXjv2rVr9439vh7m3HDDDTFkyJD461//Gn/961+jVatW0a9fvxg6dOhGQ9j/lskxbqj18ccf/9bzZunSpbHjjjvGDjvsEJdffnmcd9558aMf/ahsrc+BAwfGmDFjYtddd/3WWquiXr16sc8++8TMmTPj/fffj+222y5mzpwZffv2jTp16kT//v3j7rvvjlWrVkVxcXG88sorFdaz3XB8o0aNilGjRn3jsf33mAMOOOAb6/v6mG/zi1/8ImbNmhUzZsyIAQMGRMOGDWP//fePQYMGxZgxY6Jx48ZVnivi2z/jDedCxFdr/Q4dOjSefPLJjc63qdenb1LVc3nD+/z2229X6dz7b+3bt6/Q1qhRo4j46pdo1amyn+UNa2hv7Od8w/av15LJtWGDyo43YssdMwB8lwltAWAbsvvuu8czzzwTL7/8cpx44olVHpckSRx//PHx/vvvx6mnnhqnnnpqdOnSJRo2bBhZWVlx/vnnx29/+9tIkqTS8fn5+ZW2X3XVVXH33XdHz5494/LLL48999wzmjRpErm5uTF//vzYcccdNzpndSgtLY2Irx5qU9mdqd9kY8f0TQ466KB455134qGHHopp06bFjBkz4vbbb4/bb789hg0b9o1PYq9JG96nytpGjx79jWP33Xffsn/vuuuu8a9//SumTZsWU6dOjRkzZsTEiRNj4sSJsf/++8eMGTMq3GVXHTbU2q1bt28NKb/+MLlzzz03jj/++HjggQfi8ccfj5kzZ8bVV18d11xzTVx99dVx1llnVUt9/fv3j5kzZ8aMGTNit912iy+++KLsTtr+/fvHbbfdFrNnz47Vq1dHaWlp9OvXr9LjO/zwwyu9k3iD5s2bVxgzfPjwaNCgwUbHfP2hfd+moKAgnnzyyXjmmWdiypQpMWPGjHjyySfj8ccfj9/+9rcxc+bM2H777as836b45S9/GU8++WT069cvfvOb30SPHj2isLAw6tSpE4899lgcdthhm3x9+iZVPZc3vM+tW7f+1gfXVRZSV/YXAFvKN+1rU+rI5NqQyX4AoLYT2gLANmTQoEFxww03xH333ReXX3555ORU7T/1b731Vrz11lvRq1evsmULvi7TP/WfPHlyRETcfffdscsuu3zrnB06dCjbliTJZt9tu+GuriFDhsS55567WXNVVUFBQYwcOTJGjhwZERHPPfdcHHfccXH//ffH1KlT48gjj8x47jZt2kRExAcffFDp9o21bwhKV6xYUWHb+vXrY/HixRXa27dvH++++25ceeWV5YLOb5Ofnx9DhgyJIUOGRETEG2+8ESNHjoxnn302br755vjJT37yjeMzOcYNn/NOO+0Ut956a5VrjfjqnDvjjDPijDPOiJKSkrjnnnviBz/4QYwbNy6+//3vR5MmTTZpvsr069cvLrnkkpgxY0Z88cUXERHlQtuIr5ZFWL16dbm2DTYc3w9/+MMYNmxYlfbZvn37+Pe//x2/+tWvypY1qA5ZWVnRp0+f6NOnT0R8tTzA2WefHXfffXdccMEFMXHixCrP9W2fcdu2bcvaJk+eHHXq1IkHH3wwCgoKyvXfEkuRRFTtXN7w2TRv3nyTz73vqkyvDQDApvGrTgDYhhx++OGxyy67xEcffRSXXXbZN/Zdvnx52VqNG4Kkyv509YsvvojHH388o3q+ad7Kwp22bdvGzjvvHEVFRWXrVH6b3Nzcja6tecghh0TE/wuPa8J+++0XJ510UkREzJs3r6x9Q5C6KeuC7rXXXlGvXr146aWXKg2q7rnnnkrHbQhC58+fX2Hb9OnTo7i4uEJ7db13u+yyS5x++ukRUf74N2bDepj3339/pXcAV3aMe++9dzRu3DieeuqpWLZsWca15uTkxIknnhh77713rFu3bqNrpH5dVT7H3r17R15eXsyYMSNmzJgRBQUFseeee0bEV0sBbFjXdsN6tv99p20mn0UmYzI5J1u2bBnjx4+PiKp9vl9X2TWgpKQk7r///oiIsmA44qtrSUFBQYXAdmPzbAmVncvt27ePnXbaKf71r39V+vNVnTL5fLaErXVdTcvxAkBNEdoCwDYkKysr7rzzzsjPz4/x48fHeeedFytXrizXJ0mSePDBB6NXr17xwgsvRMRXf1aenZ0dTz75ZLmgas2aNXHqqadmHIRteLDNn/70p3LtkyZNittvv73SMb/61a8iIuKcc86J1157rdy2NWvWVAiQ27ZtG59++mkUFRVVmGvfffeNQw45JJ555pk4/fTTY/ny5RX6vPrqqzFt2rQqH9PGLFy4MG699dZYtWpVhZqnT58eEf/vTuINdUdE/Pvf/67yPho2bBgnnXRSrF+/Ps4444yyOzMjIl588cW4/vrrKx134IEHRkTEnXfeWW7N4vfeey/OPPPMSsece+65Ua9evfj5z38ef//73ytsX7t2bUyaNCk++uijiIhYtWpV/OEPf6jwOZSWlpa9v18//o3p379/7LTTTvHuu+/GpZdeWm7bn//853j22WcrjKlbt26MGzcu/vOf/8TQoUMrDbQ//vjjuOOOO8peT58+PZ544okKwfB7770Xb775ZmRlZW10/c2vq8rnuGFd2w8++CAee+yxsvVsv37Mc+bMiVdeeSV22mmnCksgDBs2LLp37x533XVXXHLJJRXW/UySJJ555pl45plnytp+/OMfR8uWLePyyy+P//u//6twnCUlJfHoo4+WC1q/7Vj+9Kc/xXvvvVehferUqRFRtc/362bNmhV//etfy7VddNFFsXDhwth1113LAvyIr64lX3zxRdx7773l+l999dVlP1/VZVPP5QsvvDBKS0tj2LBh8corr1SY7/PPP4+bbrpps+vK5JqxJWzqtSFTaTleAKgxCQCwzZk1a1bSqlWrJCKS+vXrJwMHDkxGjhyZDBo0qKw9Pz8/eeKJJ8rGjB07NomIpF69esmgQYOS4cOHJ61atUqaN2+ejBkzJomI5JZbbim3n44dOybf9HXiqaeeSurUqZNERLLXXnslJ5xwQtKrV68kIpKf//znSUQk/fr1qzDujDPOSCIiqVOnTtKnT5/khBNOSPr3758UFhYmu+22W6V9O3funIwaNSo55ZRTkssvv7xs+6effprsscceSUQkhYWFSf/+/cveiw4dOiQRkZx11lnl5oyIpGPHjhs9runTpycRkYwePbqsbe7cuWXv94EHHpiMHDkyOeaYY5IWLVokEZH06tUrWbNmTVn/F198McnKykry8/OTY445JjnllFOSU045JVm6dGmSJElyyy23JBGRXHTRReX2/fnnnyc77rhjEhFJmzZtku9973vJoYcemuTk5CSnn376Rmv//ve/n0RE0rhx4+Soo45KBg4cmDRo0CA57rjjNvo5PvDAA0n9+vWTiEi6deuWHHXUUcmIESOSvn37Jg0aNEgiIpk7d26SJEnyxRdfJBGR5ObmJvvtt18yYsSIZOjQoWXvcadOncqO7ds899xzZfP37NkzOeGEE5K99947ycrKSn7yk59UeO+TJEnWr1+fnHTSSUlEJHl5ecm+++5bVsMuu+ySZGVllTt3rr766iQikhYtWiSHH354MmrUqOTQQw9N6tatm0REcsYZZ1Sp1iRJkl133TWJiGTvvfdOxowZk5xyyinJP/7xj3J9LrjggiQikohIrrjiinLbNnzWEZGceuqple5j/vz5SefOnZOISFq2bJkcfPDByciRI5NDDz00admyZRIRydVXX11uzLPPPps0b948iYikQ4cOyRFHHJGMHDkyOeigg5LCwsIkIpLJkydX+Vh22223JCKS7t27J8OGDUu+973vlbXl5+cns2bNqtL7NXr06CQiktNOOy3JyspK9tlnn+SEE05IdtlllyQikoKCguTll18uN+bOO+8se4/69u2bnHDCCUn37t2T7Ozs5Gc/+1ml58SG/UyfPr1KdW2Qybl8/vnnJxGRZGdnJ3vuuWdy3HHHJcOHD0/22GOPpE6dOknjxo03qbbKfo4//vjjJD8/P6lTp05y+OGHJyeffHJyyimnJG+99VaSJJVfl5IkSS666KJKr99JkiTvvffeRq/DSZIk/fr1SyIiee+998q1b8q1IUk2fj37tv1U5WcLALZVQlsA2Eb95z//SX7/+98n/fr1S1q0aJHk5OQkhYWFyb777ptcdNFFyYcffliuf0lJSXLllVcm3bt3T/Lz85NWrVolo0aNSt5///2N/p/+bwttk+Sr4Oiggw5KmjRpkjRq1Cjp3bt3cv/9939rWPCPf/wjOeyww5KmTZsmeXl5Sfv27ZPBgwcnf//738v1W7FiRfLTn/406dChQ5KTk1PpnKtXr07+8Ic/JL17904aN26c5OXlJR06dEj69euXXHHFFRXei0xC2+XLlydXXnllcuSRRyadOnVK8vPzk2bNmiW9evVKrr766mTlypUV5rnrrruSPffcM6lXr15ZILUhtPimkGPp0qXJaaedlrRu3TqpW7dustNOOyW///3vk9LS0o3Wvnbt2uRXv/pV0qFDhyQvLy/p2rVrcumllyYlJSXf+Dm+8847yU9+8pNk++23T/Lz85NGjRolO+64YzJixIhk4sSJydq1a5MkSZLi4uLkhhtuSIYOHZp07do1qV+/flJYWJjsuuuuyW9+85vk888/3+j7WZnXXnstOeqoo5LGjRsnDRo0SPbff//koYce2mgwtcE//vGPZNCgQUnLli2T3NzcpGXLlslee+2VjBs3LnnppZfK+r399tvJ//zP/yQHHHBA0qZNmyQvLy9p165dMnDgwOT+++9PSktLq1zr22+/nQwZMiRp1qxZkp2dXenn9vjjj5d9xi+88EK5bRt+FiIiueeeeza6n6KiouTSSy9N9txzz6Rhw4ZJfn5+0qlTp+Swww5LbrjhhmTJkiUVxnzyySfJuHHjkl122SWpX79+Ur9+/aRr167JMccck9x6663Jf/7znyofy4MPPpicfPLJyS677JIUFhYm9evXT3bYYYfkhz/8YVlwWBVfDyynTJmS7L///kn9+vWTxo0bJ8ccc0zyxhtvVDru4YcfTvbbb7+kUaNGSWFhYXLwwQcnM2bM2Og5kWlom+m5/NRTTyXHHXdc0rZt2yQ3Nzdp1qxZsuuuuyY//elPk6eeemqTatvYz/Gjjz6aHHDAAUnDhg3LzpkNc2zN0DZJqn5tSJLMQ9uq/GwBwLYqK0m24CObAQDYqrKysqJjx47llkGANBkzZkzcdtttMX369AoPXQMA4CvWtAUAAAAASBGhLQAAAABAightAQAAAABSxJq2AAAAAAAp4k5bAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKZJT0wWkxRdffBElJSU1XQYAAAAAsI3KycmJJk2afHu/rVDLd0JJSUkUFxfXdBkAAAAAQC1neQQAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAAp4kFkAAAAAPAdV1JSEqtWrarpMoiI+vXrR07O5sWuQlsAAAAA+A4rKSmJlStXRqNGjSI72x/W16TS0tL4z3/+Ew0aNNis4NanCAAAAADfYatWrRLYpkR2dnY0atRos+969kkCAAAAwHecwDY9quOz8GkCAAAAAKSI0BYAAAAAqBGzZ8+Odu3axZdfflnTpaSK0BYAAAAAarnPP/88fvWrX8Xee+8dnTt3jt133z1GjhwZL7zwQrXtY/jw4fHrX/+6XFuvXr1i7ty5UVBQUG37ydTZZ58dJ598ck2XERERmT/CDAAAAADYJowdOzbWrVsX11xzTXTs2DGWLFkSs2bNii+++GKL7jcvLy9atmy5RffxXeROWwAAAACoxb788st4/vnn44ILLogDDjgg2rdvH3vssUecccYZceihh5b1+fnPfx49e/aMHXfcMY477rh44403yua48sor45BDDolJkybFvvvuGzvttFOcdtppsWLFioj46i7WZ599Nv7yl79Eu3btol27dvHhhx9WWB7h3nvvjZ133jkef/zx6Nu3b3Tt2jXGjh0bq1evjokTJ8a+++4b3bt3jwsvvDDWr19ftv+1a9fGxRdfHHvttVd069YtBg8eHLNnzy7bvmHeGTNmRL9+/WL77bePUaNGxaefflpW/3333RePPvpoWX1fH7+1CW0BAAAAoBZr0KBBNGjQIKZNmxZr166ttM+Pf/zjWLp0adx5553xyCOPRM+ePeN73/teuTtxP/jgg3j00Ufjtttui9tuuy2ee+65uP766yMiygLVUaNGxdy5c2Pu3LnRtm3bSve1evXq+Otf/xp//OMf46677opnn302TjnllHjyySfjjjvuiGuvvTbuvPPOeOihh8rG/M///E+89NJLceONN8YTTzwRgwcPjhNPPDEWLFhQbt4//elP8Yc//CH+/ve/x8cffxyXXHJJRESceuqpcdRRR8WAAQPK6uvVq9dmv7eZsjwCAAAAANRiOTk5cfXVV8e4cePizjvvjB49esR+++0XxxxzTHTv3j3mzJkTr7zySrz66qtRt27diIj49a9/HY8++mg8/PDDceKJJ0ZERGlpaVx99dXRsGHDiIgYNmxYzJo1KyIiCgoKIi8vL/Lz8791OYTi4uL47W9/G506dYqIiEGDBsX9998fr776ajRo0CB22GGH6N27d8yePTuOOeaY+Pjjj+Pee++NOXPmROvWrSPiqxB2+vTpce+998Z5551XNu/vfve7snnHjBkT11xzTUR8FVzn5+fHunXrUrFcg9AWAAAAAGq5QYMGxcCBA2POnDnx0ksvxfTp0+OPf/xjXHHFFbF69epYuXJl9OjRo9yYNWvWxAcffFD2ukOHDmWBbUREy5Yt4/PPP9/kWurVq1cWrEZEtGjRIjp06BANGjQoa2vevHnZ3G+++WasX78++vbtW26edevWRZMmTTY6b6tWrWLp0qWbXN/WILQFAAAAACI/Pz8OPPDAOPDAA+NnP/tZ/PznP48rr7wyRo8eHS1btoxJkyZVGNO4ceOyf+fklI8as7KyorS0dJPryM3NrTDPN829cuXKqFOnTjzyyCNRp06dcv2+HvRWNm+SJJtc39YgtAUAAAAAKth+++1j2rRp0bNnz1iyZEnk5OREhw4dMp4vNzc3oxD32/To0SPWr18fn3/+eey7774Zz5OXl1fu4WY1yYPIAAAAAKAWW7ZsWRx33HFx//33x7/+9a9YuHBhTJkyJf74xz/GYYcdFn379o299torTj755Hjqqafiww8/jBdeeCF+97vfxauvvlrl/XTo0CHmzp0bH374YSxbtqzaAtyuXbvG0KFD46yzzoqpU6fGwoULY+7cuXHdddfFE088UeV52rdvH2+++Wa88847sWzZsiguLq6W+jLhTlsAAAAAqMUaNGgQe+65Z9x0003xwQcfRHFxcbRt2zZGjhwZZ5xxRmRlZcUdd9wREyZMiHPOOSc+//zzaNGiRey3337RvHnzKu/nxz/+cZx99tnRv3//WLNmTTz33HPVdgxXXXVVXHvttXHxxRfH4sWLo2nTprHnnnvGwQcfXOU5Ro0aFc8++2wceeSRsXLlyrjvvvuid+/e1VbjpshK0rpww1a2ZMmSGk3PAQAAACATy5cvj4KCgpoug6/Z2GeSm5sbLVq0+NbxlkcAAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIkZyaLgCoPcbc9mxNl7DV3Dp6/5ouAQAAALY5s2fPjrfffjtGjx5d06VsUe60BQAAAABS74MPPogzzjgjdt999622z3333TduuummstefffZZjBgxIrp16xY777zzFtuvO20BAAAAYBv0yS9+uFX31+aKmzep/9lnnx333XdfnHfeefHTn/60rH3atGlxyimnxMcff1zWtnbt2vjJT34SV1xxRey2227VVvMG9957b4wfPz7efPPNcu1Tp06N+vXrl72+6aab4rPPPovHHnssCgoKqr2ODYS2AAAAAECNyM/PjxtvvDFOPPHEKCws3Gi/unXrxsMPP7z1Cvv/NWvWrNzr999/P3r27BldunTZovu1PAIAAAAAUCP69OkTLVq0iOuvv36jfebMmRPHHntsdO3aNXr16hUXXnhhrFq1qmz7p59+GieddFJ07do19ttvv5g8eXKFZQ3+/Oc/x8CBA6Nbt27Rq1evOO+882LlypUR8dU6ueecc04sX7482rVrF+3atYsrr7wyIsovj7DvvvvG1KlTY9KkSdGuXbs4++yzt8A78hWhLQAAAABQI+rUqRO/+tWv4pZbbolFixZV2P7+++/HqFGj4sgjj4zHH388/vjHP8acOXPiggsuKOtz1llnxaeffhr33Xdf3HTTTXHXXXfF0qVLy82TnZ0dF198cUyfPj2uueaaeOaZZ+LSSy+NiIhevXrFb37zm2jUqFHMnTs35s6dG6eeemqFWqZOnRoDBgyIo446KubOnRsXX3xxNb8bX6t3i80MAAAAAPAtjjjiiOjevXvZ3a1fd/3118exxx4bY8eOjS5dusTee+8dl1xySUyaNCnWrFkT77zzTsycOTOuuOKK2HPPPaNnz55xxRVXxJo1a8rNM3bs2DjggAOiQ4cO0adPnxg3blxMmTIlIiLy8vKiUaNGkZWVFS1btoyWLVtGgwYNKtTSrFmzyMvLi/z8/GjZsqU1bQEAAACAbdcFF1wQxx9/fIU7XP/1r3/Fm2++GZMnTy5rS5IkSktL48MPP4wFCxZETk5O9OzZs2x7586dK6yP+/TTT8f1118f7777bvznP/+J9evXx5o1a2L16tVRr169LXpsmRDaAgAAAAA1ar/99ot+/frFb3/72zj++OPL2leuXBknnnhinHzyyRXGtGvXLhYsWPCtc3/44YcxZsyYOOmkk+KXv/xlFBYWxgsvvBDnnnturFu3TmgLAAAAAFCZ888/Pw499NDo2rVrWVvPnj1j/vz50blz50rHdO3aNUpKSmLevHmx6667RkTEe++9F0VFRWV9XnvttSgtLY2LLroosrO/Wi12w9IIG+Tl5cX69eur+YgyZ01bAAAAAKDG7bzzznHsscfGX//617K2n/zkJ/Hiiy/GBRdcEPPmzYsFCxbEo48+WvYgsm7dukXfvn1j3LhxMXfu3Jg3b16MGzcu8vPzIysrKyIiOnXqFMXFxfHXv/41Pvjgg5g0aVLccccd5fbdvn37WLlyZcycOTOWLVsWq1ev3noHXgmhLQAAAACQCr/4xS+itLS07HX37t3j/vvvjwULFsTQoUPjsMMOiyuuuCJatWpV1ufaa6+NFi1axLBhw+KUU06JUaNGRcOGDaNu3boREbHLLrvERRddFDfeeGMcdNBBMXny5DjvvPPK7XfvvfeOk046KU477bTo2bNn3HjjjVvngDciK0mSpEYrSIklS5ZEcXFxTZcB27Qxtz1b0yVsNbeO3r+mSwAAAKCWWL58eRQUFNR0GamxaNGi2HvvveOee+6Jvn371kgNG/tMcnNzo0WLFt863pq2AAAAAMB31qxZs2LVqlWx0047xaeffhqXXXZZdOjQIfbbb7+aLi1jQlsAAAAA4DurpKQkfve738UHH3wQDRs2jF69esX1118fubm5NV1axoS2AAAAAMB3Vv/+/aN///41XUa18iAyAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAgG3G7Nmzo127dvHll19+Y7999903brrpprLXn332WYwYMSK6desWO++885Yu8xvl1OjeAQAAAIAtYsxtz27V/d06ev9N6n/22WfHfffdFxERubm50a5duxg+fHicccYZkZOTeWzZq1evmDt3bhQUFERExL333hvjx4+PN998s1y/qVOnRv369cte33TTTfHZZ5/FY489Vja2pghtAQAAAIAaMWDAgLjqqqti3bp18c9//jMuuOCCyMnJiTPOOCPjOfPy8qJly5bf2q9Zs2blXr///vvRs2fP6NKlS8b7ri6WRwAAAAAAasSGgLV9+/YxevTo6Nu3bzz22GNRVFQUZ555ZnTv3j26du0aJ554YixYsKBs3EcffRSjR4+O7t27R7du3WLAgAHxz3/+MyLKL48we/bsOOecc2L58uXRrl27aNeuXVx55ZURUX55hH333TemTp0akyZNinbt2sXZZ5+91d+Lr3OnLQAAAACQCvn5+fHFF1/Ez372s3jvvffilltuiYYNG8b//u//xkknnRQzZsyI3NzcOP/886O4uDjuv//+qF+/fsyfPz8aNGhQYb5evXrFb37zm/j9738fTz/9dEREpf2mTp0aZ511VjRs2DAuvvjiyM/P3+LH+k2EtgAAAABAjUqSJGbOnBlPPfVUDBgwIKZNmxYPPPBA7L333hERcd1118Xee+8d06ZNi6OOOioWLVoURx55ZNkDwzp27FjpvHl5edGoUaPIysr6xiUTmjVrFnl5eZGfn1+lpRW2NKEtAAAAAFAjnnjiidh+++2jpKQkSktLY8iQIXHkkUfGE088EXvuuWdZv6ZNm0bXrl3jnXfeiYiIk08+Oc4777x46qmnom/fvnHkkUdG9+7da+owqp01bQEAAACAGtG7d+947LHHYtasWfHuu+/GtddeG1lZWd86buTIkTF79uwYNmxYvPXWW3HkkUfGX//6161Q8dYhtAUAAAAAakT9+vWjc+fO0a5du8jJ+WpRgG7dukVJSUm8/PLLZf2WLVsW7777bmy//fZlbe3atYvvf//7cfPNN8ePf/zj+Nvf/lbpPvLy8mL9+vVb9kCqmdAWAAAAAEiNLl26xGGHHRbjxo2LOXPmxBtvvBFnnnlmtG7dOg477LCIiPj1r38dM2bMiIULF8brr78ezzzzTHTr1q3S+dq3bx8rV66MmTNnxrJly2L16tVb83AyIrQFAAAAAFLlqquuip49e8bo0aPj6KOPjiRJ4o477ojc3NyIiCgtLY0LLrgg+vfvH6NGjYouXbrE//7v/1Y619577x0nnXRSnHbaadGzZ8+48cYbt+ahZCQrSZKkpotIgyVLlkRxcXFNlwHbtDG3PVvTJWw1t47ev6ZLAAAAoJZYvnx5FBQU1HQZfM3GPpPc3Nxo0aLFt453py0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAvsOSJKnpEvgvm/uZCG0BAAAA4DssJycnVq5cKbxNgSRJYuXKlZGTk7NZ82zeaAAAAACgRjVo0CDWrl0b//nPf2q6FCKibt26Ubdu3c2aQ2gLAAAAAN9x1REUkh6WRwAAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEVyarqA/zZt2rSYMmVKFBUVRceOHePkk0+Obt26bbT/ypUr4+677445c+bEihUrokWLFjF69OjYc889t2LVAAAAAADVI1Wh7ezZs+P222+PsWPHxvbbbx8PP/xwXHbZZXHNNddE48aNK/QvKSmJSy+9NAoKCuKcc86Jpk2bxtKlS6N+/fo1UD0AAAAAwOZLVWj70EMPxcCBA2PAgAERETF27Nh4+eWXY/r06TFkyJAK/Z988slYsWJFXHLJJZGT89WhtGzZcmuWDAAAAABQrVIT2paUlMSCBQvKhbPZ2dnRs2fPmD9/fqVjXnrppdh+++3jL3/5S7z44otRUFAQBxxwQAwZMiSysytfrre4uDiKi4vLXmdlZUW9evXK/g1QHVxPAAAAgEylJrRdvnx5lJaWRmFhYbn2wsLCWLRoUaVjPv3001iyZEn06dMnzjvvvFi8eHHcfPPNsX79+jjuuOMqHTN58uSYNGlS2evOnTvHhAkTokWLFtV2LABt2rSp6RIAAACA76jUhLaZSJIkCgoK4sc//nFkZ2dHly5dYtmyZfHggw9uNLQ99thjY/DgwWWvN9wNt2TJkigpKdkqdQPbvk8++aSmSwAAAABSJicnp0o3j6YmtC0oKIjs7OwoKioq115UVFTh7tsNCgsLIycnp9xSCO3atYuioqIoKSkpW+f263JzcyM3N7fS+ZIkybh+gK9zPQEAAAAyVfnCrzUgJycnunTpEvPmzStrKy0tjXnz5sUOO+xQ6Zgdd9wxFi9eHKWlpWVtn3zySTRp0qTSwBYAAAAAIO1SlWwOHjw4brjhhujSpUt069Ytpk6dGmvXro3+/ftHRMT1118fTZs2jZEjR0ZExKGHHhqPPvpo3HrrrXH44YfH4sWLY/LkyXHEEUfU4FEAAABU3Zjbnq3pEraKW0fvX9MlAMB3RqpC2969e8fy5ctj4sSJUVRUFJ06dYrzzz+/bHmEpUuXlnsie/PmzeOCCy6I2267LX7xi19E06ZN44gjjoghQ4bUzAEAAAAAAGymrMTCixHx1YPIiouLa7oM2KbVlrtIItxJAgBUXW35juT7EQB89bytqjyILDVr2gIAAAAAILQFAAAAAEgVoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACmSU9MFVGbatGkxZcqUKCoqio4dO8bJJ58c3bp1q7TvjBkz4sYbbyzXlpubG3fdddfWKBUAAAAAoFqlLrSdPXt23H777TF27NjYfvvt4+GHH47LLrssrrnmmmjcuHGlY+rVqxfXXnvtVq4UAAAAAKD6pW55hIceeigGDhwYAwYMiPbt28fYsWMjLy8vpk+fvtExWVlZUVhYWO5/AAAAAADfRam607akpCQWLFgQQ4YMKWvLzs6Onj17xvz58zc6bs2aNfGTn/wkkiSJzp07xwknnBAdOnTYChUDAAAAAFSvVIW2y5cvj9LS0gp3yhYWFsaiRYsqHdO2bds47bTTomPHjrFq1ap48MEH43/+53/iqquuimbNmlXoX1xcHMXFxWWvs7Kyol69emX/BqgOricAAOX5fgQAVZeq0DYTO+ywQ+ywww7lXv/sZz+Lxx9/PEaMGFGh/+TJk2PSpEllrzt37hwTJkyIFi1abJV6gdqhTZs2NV0CAECq+H4EAFWXqtC2oKAgsrOzo6ioqFx7UVFRldepzcnJic6dO8fixYsr3X7sscfG4MGDy15v+G3vkiVLoqSkJKO6Af7bJ598UtMlAACkiu9HAPBVdlmVm0dTFdrm5OREly5dYt68ebHPPvtERERpaWnMmzcvDj/88CrNUVpaGgsXLow99tij0u25ubmRm5tb6bYkSTIrHOC/uJ4AAJTn+xEAVF2qQtuIiMGDB8cNN9wQXbp0iW7dusXUqVNj7dq10b9//4iIuP7666Np06YxcuTIiIiYNGlSbL/99tG6detYuXJlPPjgg7FkyZIYOHBgDR4FAAAAAEBmUhfa9u7dO5YvXx4TJ06MoqKi6NSpU5x//vllyyMsXbq03AL2K1asiD//+c9RVFQUDRo0iC5dusSll14a7du3r6EjAAAAAADIXFbib1Qi4qs1bYuLi2u6DNimjbnt2ZouYau5dfT+NV0CAPAdUVu+I/l+BABfLd1alTVts7dCLQAAAAAAVJHQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFIkp6YLACI++cUPa7qEraPHKTVdAQAAAEDqudMWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEVyNmfw/Pnz44033ogvv/wyDjvssGjTpk2sXbs2Pv7442jbtm3k5+dXV50AAAAAALVCRqFtSUlJXHPNNfHCCy+UtfXq1SvatGkTWVlZcdlll8WgQYNi6NCh1VYoAAAAAEBtkNHyCPfcc0+89NJLMXbs2LjmmmvKbcvLy4v99tuvXKALAAAAAEDVZBTaPvPMM3HooYfGwQcfHA0bNqywvV27dvHZZ59tdnEAAAAAALVNRqHt8uXLY7vtttv4pNnZsXbt2oyLAgAAAACorTIKbZs1axYff/zxRrf/+9//jtatW2dcFAAAAABAbZVRaNunT5944oknYv78+RW2PfHEE/Hss8/GgQceuNnFAQAAAADUNjmZDBo6dGi8/fbbcdFFF0W7du0iIuK2226LFStWxLJly2KPPfaIwYMHV2uhAAAAAAC1QUahbU5OTpx//vkxc+bMeO6556K0tDRKSkqiY8eOMWLEiDjwwAMjKyurumsFAABqiU9+8cOaLmHr6XFKTVcAAKRMRqFtRERWVlYceOCBW2QZhGnTpsWUKVOiqKgoOnbsGCeffHJ069btW8c988wzce2110avXr1i3Lhx1V4XAAAAAMCWltGatlvS7Nmz4/bbb4/hw4fHhAkTomPHjnHZZZfFl19++Y3jPvvss7jjjjti55133kqVAgAAAABUv4zvtH3llVfiySefjM8++yxWrlwZSZKU256VlRXXXXfdJs/70EMPxcCBA2PAgAERETF27Nh4+eWXY/r06TFkyJBKx5SWlsZ1110Xxx9/fLz55puxcuXKTd4vAAAAAEAaZBTaPvjgg3HXXXdFYWFhdO3aNbbbbrtqKaakpCQWLFhQLpzNzs6Onj17xvz58zc6btKkSVFQUBAHHXRQvPnmm9VSCwAAAABATcgotJ06dWr06NEjzjvvvMjJyfhm3QqWL18epaWlUVhYWK69sLAwFi1aVOmYt956K5588sm4/PLLq7SP4uLiKC4uLnudlZUV9erVK/s3QHVwPQEAKM/3IwCouowS15UrV8Z+++1XrYFtJlavXh3XXXdd/PjHP46CgoIqjZk8eXJMmjSp7HXnzp1jwoQJ0aJFiy1VJnyryn8lwXdZmzZtaroEAPhO8/1o2+P7EQBUXUapa7du3TZ65+vmKCgoiOzs7CgqKirXXlRUVOHu24iITz/9NJYsWRITJkwoa9uwtu6IESPimmuuidatW5cbc+yxx8bgwYPLXm/4be+SJUuipKSkmo4EqO0++eSTmi4BACBVfD8CgIicnJwq3TyaUWh7yimnxG9/+9vo2rVr9OnTJ5MpKi8mJye6dOkS8+bNi3322ScivnrI2Lx58+Lwww+v0L9t27bx+9//vlzbPffcE2vWrIkxY8ZE8+bNK4zJzc2N3NzcSvf/3w9TA8iU6wkAQHm+HwFA1WUU2l5zzTWxfv36uO666+Kmm26KZs2aRXZ2drk+WVlZccUVV2zy3IMHD44bbrghunTpEt26dYupU6fG2rVro3///hERcf3110fTpk1j5MiRkZeXV+EhaA0aNIiIqLaHowEAAAAAbE0ZhbYNGzaMRo0abZE1iXr37h3Lly+PiRMnRlFRUXTq1CnOP//8suURli5dagF7AAAAAGCblVFoO378+Gouo7zDDz+80uUQqrLv008/fQtUBAAAAACwdWR/excAAAAAALaWjO603eCjjz6KTz/9NFauXFnpovL9+vXbnOkBAAAAAGqdjELbxYsXx3XXXRfvvPPON/YT2gIAAAAAbJqMQtubbropFi5cGGPGjImdd945GjRoUN11AQAAAADUShmFtm+99VYce+yxccQRR1R3PQAAAAAAtVpGDyIrKCiI+vXrV3ctAAAAAAC1Xkah7SGHHBIzZ86M0tLS6q4HAAAAAKBWy2h5hDZt2kRpaWn84he/iAEDBkSzZs0iO7ti/rvvvvtudoEAAAAAALVJRqHtNddcU/bvO+64Y6P97r333kymBwAAAACotTIKbS+66KLqrgMAAAAAgMgwtO3evXt11wEAAAAAQGQY2m5QXFwc7733Xnz55Zex4447RkFBQXXVBQAAAABQK2Uc2k6dOjXuu+++WLVqVUREXHjhhdGjR49Yvnx5/OxnP4tRo0bFQQcdVG2FAgAAAADUBtmZDJo+fXrcdtttsfvuu8dpp51WbltBQUHssssuMXv27GopEAAAAACgNskotH3ooYeiV69ecdZZZ8Vee+1VYXuXLl3iww8/3OziAAAAAABqm4xC28WLF8cee+yx0e0NGzaMFStWZFwUAAAAAEBtlVFoW79+/Vi+fPlGt3/00UdRWFiYaU0AAAAAALVWRqHtHnvsEf/85z9j5cqVFbZ9+OGH8c9//rPSZRMAAAAAAPhmOZkMGjFiRFxwwQVx7rnnloWzM2bMiCeffDKef/75aNKkSQwfPrxaCwUAAAAAqA0yCm2bNm0av/vd7+Luu++O2bNnR0TEzJkzIz8/Pw444IAYNWpUFBQUVGuhAAAAAAC1QUahbURE48aN49RTT41TTz01li9fHqWlpVFQUBDZ2RmtuAAAAAAAQGS4pu3s2bNj3bp1Za8LCgqisLBQYAsAAAAAsJkyutP22muvjXr16sW+++4bffv2jR49elR3XQAAAAAAtVJGoe3FF18cM2fOjOeeey5mzJgRTZs2jT59+kTfvn1ju+22q+4aAQAAAABqjYxC2x133DF23HHH+MEPfhCvvPJKzJw5M6ZNmxYPPvhgbLfddnHggQdGnz59okmTJtVdLwAAAADANi3jB5FFRNSpUyf22muv2GuvvWLNmjUxZ86cmDFjRtx5553xt7/9Lbp37x79+vWL3r17R07OZu0KAAAAAKBWqLYnhy1cuDDeeeedWLhwYUREtG3bNlasWBE33HBDnHHGGfHWW29V164AAAAAALZZm3X766JFi2LWrFkxa9as+PTTT6OgoCD69OkTBx54YHTp0iUiIt59993405/+FDfddFNceeWV1VI0AAAAAMC2KqPQdurUqTFz5sxYsGBB5Obmxl577RVjxoyJ3XffPbKzy9+827Vr1xg0aFD86U9/qpaCAQAAAAC2ZRmFtrfddlvsuOOOMXbs2Ojdu3fUr1//G/t37do1hg0bllGBAAAAAAC1SUah7XXXXRctW7ascv8OHTpEhw4dMtkVAAAAAECtklFo+/XAds2aNbF06dKIiGjevHnk5+dXT2UAAAAAALVQxg8ie+edd+Kuu+6Kt956K0pLSyMiIjs7O3baaac48cQTo2vXrtVWJAAAAABAbZFRaPv222/H+PHjIycnJw466KBo165dRER8/PHH8cwzz8RFF10U48ePj27dulVrsQAAAAAA27qMQtt77rknmjZtGpdcckkUFhaW23bcccfFhRdeGHfffXdceOGF1VEjAAAAAECtkZ3JoLfffjsOOeSQCoFtRERhYWEcfPDB8fbbb29ubQAAAAAAtU5GoW1WVlasX79+o9tLS0sjKysr46IAAAAAAGqrjELbHXfcMR599NFYsmRJhW1Lly6Nxx57LHbaaafNLg4AAAAAoLbJaE3bE044IS666KI4++yzY5999ok2bdpERMSiRYvixRdfjDp16sQJJ5xQrYUCAAAAANQGGYW2nTt3jv/93/+Nu+++O1588cVYt25dRETk5eXF7rvvHiNGjIj27dtXa6EAAAAAALVBRqFtRET79u3jF7/4RZSWlsby5csjIqKgoCCyszNacQEAAAAAgNiM0HaD7OzsKCwsrIZSAAAAAADIOLRds2ZNPP/88/Hpp5/GypUrI0mSctuzsrLiBz/4wWYXCAAAAABQm2QU2r7++utx1VVXxapVq76xn9AWAAAAAGDTZBTa/uUvf4n8/Pz42c9+Ft26dYv69etXd10AAAAAALVSRk8NW7p0aRx99NGx6667CmwBAAAAAKpRRqFtx44dv3VpBAAAAAAANl1Goe2oUaPisccei3fffbe66wEAAAAAqNUyWtO2e/fuMXr06Pif//mfaNeuXTRr1iyys8vnv1lZWTFu3LhqKRIAAAAAoLbIKLR97rnn4rrrrovS0tL4/PPPY/Xq1RX6ZGVlbXZxAAAAAAC1TUah7d/+9rdo27ZtnHvuudG2bdvqrgkAAAAAoNbKaE3bL774Ig499FCBLQAAAABANcsotO3atWssXbq0umsBAAAAAKj1MgptTz755Jg9e3bMnj27uusBAAAAAKjVMlrT9g9/+EOsX78+rr322vjzn/8czZo1i+zs8vlvVlZWXHHFFdVSJAAAAABAbZFRaNuwYcNo1KhRtGnTprrrAQAAAACo1TIKbcePH1/NZQAAAAAAEJHhmrYAAAAAAGwZQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApssmh7bp162Lq1Knxr3/9a0vUAwAAAABQq21yaJuXlxd33XVXLFq0aEvUAwAAAABQq2W0PMJ2220XS5Ysqe5aAAAAAABqvYxC2xEjRsQTTzwRr732WnXXAwAAAABQq+VkMmjatGnRsGHDuOyyy6Jly5bRsmXLyMvLK9cnKysrxo0bVy1FAgAAAADUFhmFtgsXLoyIiObNm0dpaWksXry4Qp+srKzNqwwAAAAAoBbKKLS94YYbqrsOAAAAAAAiwzVtAQAAAADYMjK603bp0qVV6te8efNMpgcAAAAAqLUyCm1PP/30KvW79957M5keAAAAAKDWyii0Pe200yq0lZaWxpIlS+Lpp5+OgoKCOOywwza7OAAAAACA2iaj0LZ///4b3XbMMcfEBRdcEKtWrcq0JgAAAACAWqvaH0SWn58f/fv3j4cffri6pwYAAAAA2OZVe2gbEZEkSRQVFW2JqQEAAAAAtmkZLY+wMatWrYo333wzHnzwwejcuXN1Tg0AAAAAUCtkFNp+73vf+8btzZs3jx/+8IcZFQQAAAAAUJtlFNoOGzYssrKyyrVlZWVFgwYNolWrVrHbbrtFnTp1qqVAAAAAAIDaJKPQ9vjjj6/uOgAAAAAAiGp4ENkXX3wR77//fqxZs6Y66gEAAAAAqNUyDm1feOGFOPvss+PUU0+NX/7yl/HOO+9ERMTy5ctj3LhxMWfOnGorEgAAAACgtsgotH3xxRfj97//fTRq1CiOO+64ctsKCgqiadOmMWPGjOqoDwAAAACgVskotL3//vuje/fucckll8Rhhx1WYfsOO+wQ77333mYXBwAAAABQ22QU2i5cuDD233//jW5v3LhxLF++POOiAAAAAABqq4xC27p1637jg8c+/fTTaNiwYcZFAQAAAADUVhmFtrvssks89dRTsX79+grbioqK4p///Gfstttum10cAAAAAEBtk1Foe8IJJ8SyZcvivPPOi8cffzwiIl555ZW455574txzz42IiOHDh1dflQAAAAAAtUROJoPatm0bF198cdx6661x7733RkTElClTIiKie/fuccopp0TLli2rr0oAAAAAgFoio9A2IqJDhw5x4YUXxooVK2Lx4sWRJEm0atUqCgoKNruoadOmxZQpU6KoqCg6duwYJ598cnTr1q3Svs8//3xMnjw5Fi9eHOvXr4/WrVvHUUcdFQceeOBm1wEAAAAAsLVlHNpu0LBhw40GqpmYPXt23H777TF27NjYfvvt4+GHH47LLrssrrnmmmjcuHGl+x86dGi0bds2cnJy4uWXX44bb7wxCgoKYvfdd6+2ugAAAAAAtoYqhbZPPfVURpP369dvk8c89NBDMXDgwBgwYEBERIwdOzZefvnlmD59egwZMqRC/1122aXc6yOPPDKeeuqpeOutt4S2AAAAAMB3TpVC2xtvvDGjyTc1tC0pKYkFCxaUC2ezs7OjZ8+eMX/+/G8dnyRJzJs3LxYtWhSjRo3a1HIBAAAAAGpclULb66+/fkvXERERy5cvj9LS0igsLCzXXlhYGIsWLdrouFWrVsWPf/zjKCkpiezs7DjllFNi1113rbRvcXFxFBcXl73OysqKevXqlf0boDq4ngAAlOf7EQBUXZVC2xYtWmzpOjZLfn5+XHHFFbFmzZp4/fXX4/bbb49WrVpVWDohImLy5MkxadKkstedO3eOCRMmpP4Y2bZt/FcSfFe1adOmpksAgO8034+2Pb4fAUDVbfaDyKpTQUFBZGdnR1FRUbn2oqKiCnfffl12dna0bt06IiI6deoUH3/8cTzwwAOVhrbHHntsDB48uOz1ht/2LlmyJEpKSjb/IAAi4pNPPqnpEgAAUsX3IwCIyMnJqdLNoxmHtkVFRfHkk0/GggULYvXq1VFaWlpue1ZWVvz617/epDlzcnKiS5cuMW/evNhnn30iIqK0tDTmzZsXhx9+eJXnKS0tLbcEwtfl5uZGbm5upduSJNmkegE2xvUEAKA8348AoOoyCm0/+OCDGD9+fKxbty7atm0bCxcujPbt28eqVati2bJl0apVq2jWrFlGBQ0ePDhuuOGG6NKlS3Tr1i2mTp0aa9eujf79+0fEV+vrNm3aNEaOHBkRXy130LVr12jVqlUUFxfH3LlzY+bMmfHDH/4wo/0DAAAAANSkjELbv/3tb2XryObl5cXYsWPjBz/4QfTo0SOeffbZuPnmm+PMM8/MqKDevXvH8uXLY+LEiVFUVBSdOnWK888/v2x5hKVLl5ZbwH7t2rVx8803x+effx55eXnRrl27OOOMM6J3794Z7R8AAAAAoCZlFNq+9dZbccwxx0Tz5s1jxYoVERFlyyPsv//+8dZbb8Udd9wRv/nNbzIq6vDDD9/ocgjjx48v93rEiBExYsSIjPYDAAAAAJA22ZkMSpIkGjduHBER9evXj+zs7LLwNiJiu+22iwULFlRPhQAAAAAAtUhGoW3Lli3js88++2qC7Oxo2bJlvP7662Xb//3vf0eDBg2qp0IAAAAAgFoko+URdt1113juuefihBNOiIiIQw45JO6444747LPPIkmSeOONN+Koo46q1kIBAAAAAGqDKoe2K1asiIYNG0ZExNChQ6NPnz5RUlISOTk5MWjQoFi7dm08//zzkZ2dHcOGDYuhQ4dusaIBAAAAALZVVQ5tf/SjH8Uee+wRffv2jb322iu6dOlSti0rKyuGDRsWw4YN2yJFAgAAAADUFlUObffbb7948cUX48UXX4x69erFPvvsE3379o0ePXpEVlbWlqwRAAAAAKDWqHJoe+aZZ8a6detizpw5MWvWrJg1a1Y89dRTUVhYGAcccED06dOn3N23AAAAAABsuk16EFleXl706dMn+vTpEytWrIhnn302Zs2aFQ8//HA8/PDD0aZNm+jbt2/06dMnWrVqtaVqBgAAAADYZm1SaPt1DRs2jEMOOSQOOeSQWLZsWcyaNSueeeaZmDhxYkycODG23377uPTSS6uzVgAAAACAbV52dUzStGnTOProo+P000+PXr16RUTE22+/XR1TAwAAAADUKhnfabvB0qVLy+6yXbhwYURE7LDDDtG3b9/NLg4AAAAAoLbJKLRdvnx52Xq28+fPj4iItm3bxve+973o06dPtGzZslqLBAAAAACoLaoc2q5ZsybmzJkTzzzzTLz++uuxfv36KCwsjEGDBkWfPn2iS5cuW7JOAAAAAIBaocqh7dixY2PdunWRn58fffr0iT59+kSPHj0iO7talsUFAAAAACA2IbTt2bNn9OnTJ3r16hV5eXlbsiYAAAAAgFqryqHtuHHjtmQdAAAAAABEhLUNAAAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFcmq6AAAgPT75xQ9ruoStps0VN9d0CQAAAJVypy0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIqlc03batGkxZcqUKCoqio4dO8bJJ58c3bp1q7TvE088EU8//XR8+OGHERHRpUuXOOGEEzbaHwAAAAAgzVJ3p+3s2bPj9ttvj+HDh8eECROiY8eOcdlll8WXX35Zaf9//etfccABB8RFF10Ul156aTRr1iwuvfTSWLZs2VauHAAAAABg86UutH3ooYdi4MCBMWDAgGjfvn2MHTs28vLyYvr06ZX2P/PMM+Owww6LTp06Rbt27eLUU0+NJEni9ddf38qVAwAAAABsvlSFtiUlJbFgwYLo2bNnWVt2dnb07Nkz5s+fX6U51q5dGyUlJdGwYcMtVSYAAAAAwBaTqjVtly9fHqWlpVFYWFiuvbCwMBYtWlSlOe66665o2rRpueD364qLi6O4uLjsdVZWVtSrV6/s3wDVwfUE0s/PKcDW5boLAFWXqtB2cz3wwAPxzDPPxPjx4yMvL6/SPpMnT45JkyaVve7cuXNMmDAhWrRosbXKhAqq9isJvkvatGlT0yVARmrT9cjPKaRbbboe1RauuwBQdakKbQsKCiI7OzuKiorKtRcVFVW4+/a/Pfjgg/HAAw/EhRdeGB07dtxov2OPPTYGDx5c9nrDb3uXLFkSJSUlGdcO8HWffPJJTZcAfAs/pwBbl+suAETk5ORU6ebRVIW2OTk50aVLl5g3b17ss88+ERFRWloa8+bNi8MPP3yj4/7xj3/E3//+97jggguia9eu37iP3NzcyM3NrXRbkiSZFw/wNa4nkH5+TgG2LtddAKi6VD2ILCJi8ODB8c9//jNmzJgRH330Udx8882xdu3a6N+/f0REXH/99fG3v/2trP8DDzwQ9957b5x22mnRsmXLKCoqiqKiolizZk0NHQEAAAAAQOZSdadtRETv3r1j+fLlMXHixCgqKopOnTrF+eefX7Y8wtKlS8stYP/4449HSUlJXHXVVeXmGT58eBx//PFbs3QAAAAAgM2WutA2IuLwww/f6HII48ePL/f6hhtu2AoVAQAAAABsHalbHgEAAAAAoDYT2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEVyaroAANgUY257tqZL2GpuHb1/TZcAAABADXCnLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBTxIDIAoFaqLQ+180A7AAD47nGnLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApEhOTRcAQPX45Bc/rOkSto4ep9R0BQAAALBFudMWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIkZyaLuC/TZs2LaZMmRJFRUXRsWPHOPnkk6Nbt26V9v3www/j3nvvjffeey+WLFkSo0ePjkGDBm3ligEAAAAAqk+q7rSdPXt23H777TF8+PCYMGFCdOzYMS677LL48ssvK+2/du3aaNWqVYwcOTIKCwu3brEAAAAAAFtAqkLbhx56KAYOHBgDBgyI9u3bx9ixYyMvLy+mT59eaf9u3brFSSedFAcccEDk5uZu5WoBAAAAAKpfapZHKCkpiQULFsSQIUPK2rKzs6Nnz54xf/78attPcXFxFBcXl73OysqKevXqlf0boDq4nlAdnEdUB+cRkBauRwBQdakJbZcvXx6lpaUVljkoLCyMRYsWVdt+Jk+eHJMmTSp73blz55gwYUK0aNGi2vYBm6r6znDSok2bNlt9n86jbY/ziOpQE+cRVAfXo22P6xEAVF1qQtut5dhjj43BgweXvd7w294lS5ZESUlJTZUFbGM++eSTmi6BbYDziOrgPALSwvUIACJycnKqdPNoakLbgoKCyM7OjqKionLtRUVF1fqQsdzc3I2uf5skSbXtB6jdXE+oDs4jqoPzCEgL1yMAqLrUPIgsJycnunTpEvPmzStrKy0tjXnz5sUOO+xQg5UBAAAAAGw9qbnTNiJi8ODBccMNN0SXLl2iW7duMXXq1Fi7dm30798/IiKuv/76aNq0aYwcOTIivnp42UcffVT272XLlsX7778f+fn50bp165o6DAAAAACAjKUqtO3du3csX748Jk6cGEVFRdGpU6c4//zzy5ZHWLp0abknji5btizGjRtX9nrKlCkxZcqU6N69e4wfP34rVw8AAAAAsPlSFdpGRBx++OFx+OGHV7rtv4PYli1bxsSJE7dCVQAAAAAAW0dq1rQFAAAAAEBoCwAAAACQKkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkSE5NFwAAAABsnjG3PVvTJWwVt47ev6ZLANgq3GkLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEiRnJougO+GMbc9W9MlbDW3jt6/pksAAAAAoBZzpy0AAAAAQIoIbQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEVyarqA77pPfvHDmi5h6+hxSk1XAAAAAAC1gjttAQAAAABSRGgLAAAAAJAiQlsAAAAAgBQR2gIAAAAApIjQFgAAAAAgRYS2AAAAAAApIrQFAAAAAEgRoS0AAAAAQIoIbQEAAAAAUiSnpgsAAIDvqjG3PVvTJWwVt47ev6ZLAACoVdxpCwAAAACQIkJbAAAAAIAUsTwCAAAA26RPfvHDmi5h6+lxSk1XAEA1cqctAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFcmq6AAAAti2f/OKHNV3C1tPjlJquAACAbZA7bQEAAAAAUkRoCwAAAACQIkJbAAAAAIAUEdoCAAAAAKSI0BYAAAAAIEWEtgAAAAAAKSK0BQAAAABIEaEtAAAAAECKCG0BAAAAAFJEaAsAAAAAkCJCWwAAAACAFBHaAgAAAACkiNAWAAAAACBFhLYAAAAAACkitAUAAAAASBGhLQAAAABAightAQAAAABSRGgLAAAAAJAiOTVdQGWmTZsWU6ZMiaKioujYsWOcfPLJ0a1bt432f/bZZ+Pee++NJUuWROvWrWPUqFGx5557bsWKAQAAAACqR+rutJ09e3bcfvvtMXz48JgwYUJ07NgxLrvssvjyyy8r7f/vf/87rr322jjooINiwoQJsffee8cVV1wRCxcu3MqVAwAAAABsvtSFtg899FAMHDgwBgwYEO3bt4+xY8dGXl5eTJ8+vdL+U6dOjd133z2OPvroaN++fYwYMSK6dOkS06ZN28qVAwAAAABsvlQtj1BSUhILFiyIIUOGlLVlZ2dHz549Y/78+ZWOmT9/fgwePLhc22677RYvvPBCpf2Li4ujuLi47HVWVlbUq1cvcnIyeyvqdeqa0bjvmm6tCmu6hK0mNzd3q+/TebTtcR5tOc6jLau2nEcRtedcch5tWc6jLcd5tO1xHm1ZziOA74aqZpCpCm2XL18epaWlUVhYWK69sLAwFi1aVOmYoqKiaPz/tXfnUVHW+x/A3wzDIsbAIDADgRJuhFfRIykiAUoppqbebEwTpdJTYfeaqLmU5ZKalYkn0K7LzbhWQpZIYZRik3nFQFEW2dxItgFRFtkZht8fHubHCLKOdwZ9v87xHOZ5vs933jN+D8zzme/zfSwsNLZZWFigrKyszfZHjhzB4cOH1Y/Hjx+PpUuXQiwWdyuzzebPu3Vcb7NL1wEechxHpA0cR6QNj8o4AjiWHiSOI9IGjiPSBo4jIiLqrfRueYQHbdasWThw4ID63+LFizVm3lJrNTU1WLVqFWpqanQdhXoxjiPSBo4j0haOJdIGjiPSBo4j0gaOI9IGjiPSBo4j7dGrmbYikQgCgaDVLNmysrJWs2+bWVpatrpJWXl5+X3bGxkZ8XKKLmpqasL169fR1NSk6yjUi3EckTZwHJG2cCyRNnAckTZwHJE2cByRNnAckTZwHGmPXs20FQqFcHZ2RlpamnqbSqVCWloahgwZ0uYxQ4YMQWpqqsa2lJQUDB48+IFmJSIiIiIiIiIiInoQ9KpoCwDTpk1DXFwc5HI58vLysG/fPtTV1cHX1xcAEBoaim+++Ubd/rnnnkNycjJ+/PFH5OfnIzIyElevXoW/v7+OXgERERERERERERFR9+nV8ggA4OnpiYqKCkRGRqKsrAxOTk5Yu3atermDkpISGBgYqNsPHToU//znP3Ho0CF8++23sLOzw8qVK9G/f38dvYKHj5GREWbPns1lJahHOI5IGziOSFs4lkgbOI5IGziOSBs4jkgbOI5IGziOtMegiYtMEBEREREREREREekNvVsegYiIiIiIiIiIiOhRxqItERERERERERERkR5h0ZaIiIiIiIiIiIhIj7Boq+cuXboEmUyGqqoqXUfRsH79ehw4cEDXMdTCwsLw8ccf6zoGEREREVGnKJVKnDhxAps2bcLrr7+OxYsX47333kN0dDTq6up0HY90TN/Ot+jRUVFRgUWLFuHWrVu6jkIPgZMnT+LDDz/UdYxei0VbLQgLC4NMJoNMJsPcuXOxZMkSHDx4EPX19V3qp60/zEOHDsWePXtgZmbW7XxyuRwymQzLli1rtS8+Ph4ymQxLlizpUp8rVqzAnDlzup2JHk388Ek9ERUVheDgYMyfPx9Lly7F6dOndR2JiB5hFRUVmDt3Lmpra6FUKhEQEICSkhJdx6JeoqioCKtWrcIvv/yCsWPHIjg4GO+99x6ee+45pKWlITg4GAUFBbqOSXpMJpMhISFBY1tkZCRWrlypo0T0sBCJRPD29kZkZKSuo1AvV19fj4iICMyePVvXUXotoa4DPCxGjhyJoKAgKJVKXLt2DWFhYQCA+fPn96hfoVAIS0vLHuczMTFBeXk5srOzMWTIEPX2kydPwtrausv9PfbYYz3ORETUFRkZGVi4cCGkUin++OMPhIaGYvDgwZBIJLqORkSPoOzsbDg5OcHU1BSXL1/GY4891q3PVPToqa6uxubNmzF+/HjIZDIYGBio9w0YMACenp44ceIENm/ejG3btvFzNxH9z02YMAGrV69GQEAAfwdRt509exZ9+vSBi4uLrqP0WizaaknL4qq1tTVOnTqF1NRU9f47d+5g//79yMjIQFVVFSQSCWbNmgUvLy8Ad2frpqenIz09HceOHQMAhIaG4ubNm9iwYQO+/PJL9O3bF8DdgR8ZGQmFQgGxWAx/f39Mnz693XyGhobw8vLCyZMn1UXbW7duIT09HVOnTsV///tfdVuFQoHw8HBcvnwZtbW1cHBwwNy5czFixAh1m/Xr18PJyQmBgYEA7n7Tu2LFCowZM0bdJjAwEIGBgfD19YVSqcRXX32FP//8E1VVVbCwsMCzzz6LWbNmAQCqqqrw9ddfIzExEdXV1ZBKpZg3bx5Gjx6NyMhIJCYm4pNPPlH3HRMTg2PHjqmL4/dSqVQ4evQoTpw4gbKyMtjb2+OFF16Ah4cHAKCyshL//ve/kZycjNraWvTr1w+zZs3ChAkT2n0fiUh31qxZo/7Z398fkZGRKC0tZdGWiHQiKysLQ4cOBQBkZmaqfybqSFRUFJydnTFnzhxUVVVh//79SE1NhUQigaenJy5evIi1a9ciIyMDx44dg0wm03Vk0hGVSoWDBw8iLi4OQqEQzz77rMZVkp9++ikAwMbGBi+++CIOHz4MAOoxExQUBF9fX8hkMixatAjnzp3DpUuXIBaLMX/+fPW5EdG9HB0dIRaLkZCQgIkTJ+o6DvVSZ86cwejRo3Udo1dj0fYBuHHjBrKzs2FjY6Pe1tDQAGdnZ8ycORN9+vRBUlISQkNDIZVKMWjQILzyyisoLCyEo6OjetkBkUiEmzdvavR97do17NixAy+++CI8PT2RnZ2Nffv2wdzcHL6+vu3mmjBhAtavX49XXnkFJiYmkMvlcHNzg4WFhUa72tpajBo1Ci+99BKMjIzw+++/Y9u2bdi5c2e3Z5AcO3YM586dw7Jly2BtbY1bt26pLyFUqVTYsmULamtr8Y9//AMSiQR5eXkQCLq/ekdUVBT++OMPLF68GHZ2dsjIyMDnn38OkUgEV1dXREREIC8vD2vXroW5uTkUCkWXl7MgIt1oampCeHg4HB0dMWjQIF3HIaJHSElJCVasWAEAqKurg0AggFwuR319PQwMDBAYGAgvLy8sWrRIx0lJn506dQpr164FAISHh6O4uBjvvPMOysvLsWfPHtjb2wMAfH19cejQIRZtH2G///47pk2bhi1btiA7Oxu7du2Ci4sLtm7dikWLFiEoKAgjR46EQCCAqakpbty4geTkZKxbtw4ANJbYi4iIwLx58xAYGIhTp04hJCQEn376KRwcHHT18kiPyOVy7Nq1S2NJhEGDBiEjI4NFW+q0e8dRZmYmvL29dZyqd2PRVkuSkpIQEBAAlUqFhoYGGBgY4NVXX1Xvt7KywvPPP69+PGXKFCQnJ+PMmTMYNGgQzMzMIBQKYWJi0u5yCD/99BOGDx+uXhPE3t4eeXl5iI6O7rBo+8QTT0AikeDs2bPw9vaGXC7HwoULUVRUpNHOyckJTk5O6scvvfQSEhMTce7cOfj7+3f+TWmhpKQEdnZ2cHFxgYGBgUZBOzU1FVeuXMGOHTvUH1J7MnOuoaEBR44cwbp169SziiUSCTIzM3H8+HG4urqipKQETk5OGDhwIADA1ta2289HRP9bX3zxBbKysvDBBx9AKOSfMSL63xGLxfj4449RU1OD1atXY8uWLTAxMcE777yD1atXw9raGqamprqOSXqssrISNTU16N+/PwDg/PnzWLlyJQYPHgzg7uSPlJQUAHfHW2Vlpc6yku4NGDAAL774IgDAzs4OsbGxSE1NVV8BaWZmpnHuaGpqCoFA0Ob5pIeHB/z8/ADcPb9LTU1FbGwsv2QiAHfHUvO5eDOxWIycnBzdBKJeqeU4qqqqQnV1NcRisY5T9W4829WSYcOGYfHixaitrUVMTAwMDQ01LjdRqVT44YcfEB8fj9u3b0OpVEKpVMLY2LhLz5Ofnw93d3eNbUOHDkVMTAxUKlWHs1MnTJgAuVwOa2tr1NXVYdSoUYiNjdVoU1tbi8jISFy4cAGlpaVobGxEfX19j26u4evriw8//BBvv/023NzcMHr0aLi5uQEAcnJy0K9fv1Z/JLpLoVCgrq4OmzZt0tiuVCrxxBNPAAAmTZqE7du34/r163Bzc8NTTz3FyxqJeoErV67gt99+Q0hICKysrHQdh4geMYaGhrC1tVV/6T5gwABkZmbCwsICrq6uuo5HvUBjY6PG53+lUgkTExP145ZF/2vXrkEqlf5P85F+aS7uNxOLxSgvL+9WXy3vawIAgwcPxl9//dXtbPRwGTNmjMZShwBgbGyMuro6HSWi3qjlOGq+ktnIyEiXkXo9Fm21xMTERP2h6s0338TKlStx8uRJ9aUE0dHR+Pnnn7Fw4UL0798fpqamOHDgAJRK5f8059NPP42DBw/iu+++g7e3NwwNDVu1CQ8PR2pqKgICAiCVSmFsbIzt27e3m7XlDRSaNTY2qn92dnZGaGgoLl68iJSUFOzYsQPDhw/H8uXLOyxct1WIbtn3vWprawHcXf/y3qJO86y8UaNGYdeuXUhKSkJKSgo2btyIyZMnY8GCBe1mISLdKi0tBQCtfclDRNQVwcHBuHnzJhobG9HU1KS+yqqxsREBAQGwsbHBZ599puuYpMfMzc2hVCpRVlYGS0tLuLi4IDo6Gm+88QYqKysRFxcHc3NzZGVl4dChQ3jzzTd1HZl0qK0ripqamnSQhB5FlZWVEIlEuo5BvZS5uTkMDAxQVVWl6yi9Gou2D4BAIMCsWbMQHh4OLy8vGBsbIzMzE+7u7ur1PFQqFQoLC/H444+rjxMKhVCpVO32/fjjjyMrK0tjW1ZWFuzt7Tu1Buxjjz0Gd3d3xMfHY/HixW22ycrKgo+Pj/obktra2lZr695LJBKpiykAUFhY2OpbOTMzM3h6esLT0xMeHh7YsmULKisrMWDAANy6dQsFBQVtFmJEIhHKysrQ1NSkLg63d5mGg4MDjIyMUFJS0u6sF5FIBF9fX/j6+uL48eM4ePAgi7YP2Pr163UdgXo5V1dXbN26VdcxiOgRtWbNGjQ2NmLjxo2YP38+nJ2dERISAl9fX4wcObLNL8OJWhIIBBg9ejR+/fVXyGQyBAYGYtu2bQgICICZmRl8fHxw7Ngx7N69G4GBgRg+fLiuI5OeMjQ0bHXu2N755OXLl+Hj46PxuPkqRKK25Obm8ioS6jahUAgHBwfk5eWpr7Kmruv+nZ6oXePGjYNAIFAvPWBnZ4eUlBRkZWUhLy8Pe/bsQVlZmcYxNjY2uHz5MoqLi1FRUdHmH9xp06YhNTUVhw8fRkFBAeRyOWJjYzF9+vROZ1uyZAn279+vUTBuyc7ODgkJCcjJyUFOTg527tzZ4Te6w4YNQ2xsLK5fv46rV69i7969GicuP/30E06fPo38/HwUFBTg7NmzsLS0hJmZGVxdXeHq6ort27cjJSUFxcXFuHDhAi5evAjgbpGmoqICR48ehUKhQGxsLC5cuHDfLH369MH06dPx1VdfQS6XQ6FQ4Nq1a/j5558hl8sB3F2IPzExEQqFArm5uTh//vx93w/Sno0bN+LIkSO6jkG92KVLl/D555/rOgb1MrGxsdi4cSMA4Pbt23j77bdx5coV9f7Q0FB88803uopHvYiNjQ1MTU1RXl4Od3d39OvXD7m5uRg7diykUqnGmv1E9zN79mz151mpVIodO3bgX//6F/bs2YN58+Zh3759CAkJaXWpMlFLtra2SEtLQ1lZmXrtY1tbWxQXFyMnJwcVFRVoaGhQt4+Pj8fJkydRUFCAyMhIXLlypdv3K6GHT0JCAt5++23147q6Oly7do3FNuqSe8eRm5sbMjMzdRfoIcCZtg+IoaEh/P39ER0djUmTJuGFF15AUVERNm/eDBMTE/j5+eGpp55CdXW1+pjp06cjLCwMwcHBqK+vR2hoaKt+nZ2dsWzZMkRGRuL777+HWCyGTCbr8CZkLRkbG7e7JMGCBQuwe/duvPfeezA3N8eMGTNQU1PTbp/Nx7z//vuwsrJCYGAgrl27pt5vamqK6OhoFBYWQiAQYNCgQVizZo16dvDy5csRHh6OnTt3ora2FlKpFC+//DKAuzNnX3vtNRw5cgTff/89xo4di+nTpyMuLu6+eebMmQORSISoqCgUFRWhb9++eOKJJzBr1iwAd7/1+eabb3Dz5k0YGxvDxcVF45cLPRhFRUWt1uYi6orq6moUFBToOgb1MhUVFeqbbiqVShQUFGhcDVJSUtLmMj9Ebbl06RIGDhwIY2NjZGRkoF+/frzJBnWJVCrF0qVLERISgqeffhrPPPMMHBwcANy9EdmPP/4IkUiEhQsX6jgp6bOAgACEh4cjLi4OVlZWCAsLw9ixY/Hnn39iw4YNqKqqQlBQkPo8USaT4cyZM9i/fz8sLS2xdOlS9bgjuvczdmJiIqytrfHkk0/qMBX1NveOo4kTJ2L16tWorq6GmZmZDpP1XgZNXBSHuuHdd9/F8OHD8dJLL+k6ChERERFRr1NcXIzDhw8jISFBfU8GCwsL+Pj4YObMmTzBJa2RyWRYsWIFZ29Tp7377ruYMmUKvLy8dB2FernPPvtMYwIddQ1n2lKXNDQ04MaNG8jLy8OUKVN0HYeIiIiIqFeytbVFUFAQ3njjDZSXl8PAwACWlpa6jkVEj7iKigqMGTMG48eP13UUegjMnz8f58+f13WMXotFW+qSCxcuICwsDKNHj4aHh4eu4xARERER9WoCgYBLbBCR3hCJRJgxY4auY9BDwtbWlhP+eoDLIxARERERERERERHpEYGuAxARERERERERERHR/2PRloiIiIiIiIiIiEiPsGhLREREREREREREpEdYtCUiIiIiIiIiIiLSIyzaEhERERF10rlz5xAVFQWlUqnrKERERET0EGPRloiIiIioE65evYqQkBBIpVIIhcJOHyeTyRAZGamVDMXFxZDJZJDL5Vrpj4iIiIj0U+c/bRIRERERdUChUCA6OhopKSkoLS2FUChE//79MW7cODzzzDMwNjbWdcRWTp8+jfLyckydOvW+baqqqrBjxw7MmzcPHh4eepGJiIiIiB5eBk1NTU26DkFEREREvV9SUhI+++wzGBkZwdvbG46OjlAqlcjMzMSff/4JX19fvP7667qO2cpHH32E3NxchIWF3bfNpUuXUFRUhIkTJ3a5//r6ehgaGsLQ0LDHmZqamtDQ0AChUAiBgBfNERERET2sONOWiIiIiHqsuLgYISEhsLGxwfvvvw+xWKze5+/vD4VCgaSkpB49R3PBUhezdYcNG4Zhw4Z1ur1KpYJSqYSxsbFW8xoYGOjlbGUiIiIi0i7OtCUiIiKiHtu7dy+OHz+OTZs2YejQoe22/e2333Dq1Cnk5uaiuroaEokEU6ZMwaRJkzTaLVmyBI6OjvD398ehQ4eQm5uLefPmYerUqZ3uAwAuXLiAqKgoXL9+HQYGBrC3t8fUqVPh5eWF9evXIz09XaO9jY2NeoZrQ0MDjhw5gj/++AO3bt2ChYUFxo8fjzlz5sDIyEh9jEwmw+TJkzFkyBAcOXIEhYWFWLZsGcaMGQOZTIbZs2dDJpMBAGpqahAREYHExESUlpbCzMwMAwYMwMsvvwxnZ+d2MxUXF+Ott95CUFAQfH191fsTEhIQEREBhUIBqVSKOXPmIDExEenp6erXcunSJWzYsAEffPCBRgH6fn3m5+fj0KFDSEtLQ319PRwdHTF79my4u7u3+/9LRERERD3HmbZERERE1GPnz5+HRCLpsGALAL/++iscHR3h7u4OQ0NDnD9/Hvv27YNKpYK/v79G24KCAuzcuRPPPvss/Pz8YG9v36U+5HI5du/eDQcHB8ycORN9+/bF9evXcfHiRXh5eeHvf/87qqurcevWLSxcuBAAYGpqCuDubNmPP/4YmZmZ8PPzg4ODA27cuIGYmBgUFBTgnXfe0cialpaG+Ph4+Pv7w9zcHLa2tm2+/r179+Ls2bPw9/eHg4MD7ty5g8zMTOTn58PZ2bndTG1JTk7G9u3b4eDggLlz56KyshK7du1Cv379Ovy/uJ/c3FysW7cOVlZWmDlzJkxMTBAfH49PPvkEy5cvx5gxY7rdNxERERF1jEVbIiIiIuqR6upq3L59u9MzMDds2KBxib+/vz82b96MmJiYVkVbhUKBtWvXYuTIkV3uo7q6Gl9++SUGDRqEDz74QKN988VmI0aMgJWVFaqqquDt7a3xHKdPn0ZKSgo2bNgAFxcX9XZHR0fs3bsXWVlZGkXqgoICdfG0PUlJSfDz88OCBQvU22bMmKH+ub1Mbfn6669haWmJTZs2wczMDADg6uqKDz/8EDY2Nh0e35YDBw7A2toaW7duVc8onjx5Mt5//318/fXXLNoSERERPWC8ewERERER9UhNTQ0AoE+fPp1q37J4Wl1djYqKCri6uqKoqAjV1dUabW1tbVsVbDvbR0pKCmpqajBjxoxW68AaGBh0mPPs2bNwcHCAvb09Kioq1P/+9re/Abi73EBLrq6uHRZsAaBv3764cuUKbt++3WHbjpSWliInJwc+Pj7qgi1wt/DbmSxtqaysRFpaGsaNG4eamhr1675z5w7c3NxQWFiolexEREREdH+caUtEREREPdJcrG0u3nYkMzMT3333HbKzs1FXV6exr7q6WqP4eL8lBjrTh0KhAAD079+/06+lpcLCQuTn52PRokVt7i8vL9d4fL+s93r55ZcRFhaGN998E87Ozhg1ahR8fHwgkUi6nPHmzZsAAKlU2mqfvb09rl+/3uU+FQoFmpqaEBERgYiIiDbblJeXw8rKqst9ExEREVHnsGhLRERERD1iZmYGsViM3NzcDtsqFAps2rQJ9vb2WLBgAfr16wehUIgLFy4gJiYGKpVKo/29M2S700d3NTU1oX///hrLGLRkbW3dYda2eHp64sknn0RCQgKSk5Px448/4ujRo1ixYgVGjRrV49z3c7/Zxfe+X82Pp0+fDjc3tzaPaatITERERETaw6ItEREREfXY6NGjceLECWRnZ2PIkCH3bXf+/Hk0NDRg1apVGkXPe5caaE9n+2guLN64caNbRUaJRIK//voLw4cP79RyCl0hFosxefJkTJ48GeXl5Vi1ahV++OGHLhdtm9esbZ5V3FJBQYHG4759+wIAqqqqNLaXlJRoPG6e8WtoaIgRI0Z0KQ8RERERaQfXtCUiIiKiHnv++edhYmKCL774AmVlZa32KxQKHDt2DALB3Y+fzTcCA+4uZyCXyzv9XJ3tY8SIEejTpw+ioqJQX1+vsa/lsaampq3W0gWAcePG4fbt24iLi2u1r76+HrW1tZ3O3EylUrV6LgsLC4jFYiiVyg4z3UssFsPJyQm///67RvuUlBTk5eVptLWxsYFAIEBGRobG9l9++aVVnmHDhuHEiRMoLS1t9ZwVFRUd5iIiIiKinuFMWyIiIiLqMalUiqVLl2LHjh1YtmwZfHx84OjoCKVSiaysLJw9exa+vr6YNm0ahEIhtm3bhmeeeQa1tbWIi4uDSCRqs0DYFjc3t071YWZmhoULF+KLL77AmjVr4OXlhb59++Kvv/5CXV0d3nrrLQCAs7Mzzpw5g6+++goDBw6Eqakp3N3d4e3tjfj4eOzduxdpaWlwcXGBSqVCfn4+4uPj8e6772LgwIFdep9qamrwxhtvwMPDAwMGDICpqSlSU1Nx9epVjWUY7pepLfPmzcPWrVuxbt06TJgwAZWVlYiNjYWjo6NGYdnMzAweHh6IjY2FgYEBJBIJkpKSWq3NCwCvvfYa1q1bhxUrVsDPzw+2trYoLy9HdnY2bt++jU8++aRLr5uIiIiIuoZFWyIiIiLSCnd3d3z66aeIjo5GYmIifv31VxgZGanXhfXz84ORkRGCg4MRERGB//znP7C0tMSkSZMgEomwe/fuTj2Pvb19p/uYOHEiRCIRjh49iu+//x6GhoZ4/PHHMXXqVHWbSZMmIScnB3K5HDExMbCxsYG7uzsEAgFWrlyJmJgYnDp1ComJiTA2NoZEIsFzzz0HOzu7Lr9HJiYmmDx5MpKTk5GQkACVSgWpVIpFixZh0qRJHWZqy8iRIxEcHIxDhw7h22+/hUQiQVBQEBITE5Genq7R9tVXX0VjYyOOHz8OoVCIcePGYf78+Vi+fLlGOwcHB3z00Uf47rvvIJfLcefOHVhYWMDJyQkvvPBCl183EREREXWNQVPLa8OIiIiIiOihEBYWhvT0dISFhek6ChERERF1Ede0JSIiIiIiIiIiItIjLNoSERERERERERER6REWbYmIiIiIiIiIiIj0CNe0JSIiIiIiIiIiItIjnGlLREREREREREREpEdYtCUiIiIiIiIiIiLSIyzaEhEREREREREREekRFm2JiIiIiIiIiIiI9AiLtkRERERERERERER6hEVbIiIiIiIiIiIiIj3Coi0RERERERERERGRHmHRloiIiIiIiIiIiEiPsGhLREREREREREREpEf+D33efUw7nwIXAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["# Plot the statistics with bar plots and hue 'sentiment'\n","plt.figure(figsize=(14, 8))\n","\n","cols = ['Ratio Majuscules', 'Nombre Exclamations', 'Nombre Questions', 'Nombre Ellipses',\n"," 'Nombre Hashtags', 'Nombre Mentions', 'Nombre URLs', 'Emoticone Positive', 'Emoticone Negative']\n","\n","# Melt the DataFrame to long format for easier plotting with seaborn\n","stats_melted = stats[cols].reset_index().melt(id_vars='sentiment', value_name='Valeur', var_name='Caractéristique')\n","\n","# Rename columns for better readability\n","stats_melted['Caractéristique'] = stats_melted['Caractéristique'].replace({\n"," 'Nombre Exclamations': '!',\n"," 'Nombre Questions': '?',\n"," 'Nombre Ellipses': '...',\n"," 'Nombre Hashtags': '#',\n"," 'Nombre Mentions': '@',\n"," 'Nombre URLs': 'http',\n"," 'Emoticone Positive': ':)',\n"," 'Emoticone Negative': ':('\n","})\n","\n","# Create the bar plot\n","sns.barplot(x='Caractéristique', y='Valeur', hue='sentiment', data=stats_melted)\n","\n","# Customize the plot\n","plt.title('Caractéristiques des tweets par sentiment', fontsize=15)\n","plt.xlabel('Caractéristique', fontsize=12)\n","plt.ylabel('Valeur moyenne', fontsize=12)\n","plt.legend(title='Sentiment')\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":806},"id":"81wLmor1WjJB","executionInfo":{"status":"ok","timestamp":1751537507241,"user_tz":-120,"elapsed":358,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1b3d59be-0754-4802-d825-299053434ed0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Plot the differences in characteristics between positive and negative tweets\n","plt.figure(figsize=(12, 8))\n","sns.barplot(x=diff_df_sorted.index, y='Différence Relative', data=diff_df_sorted, palette='coolwarm')\n","plt.title('Différences relatives des caractéristiques entre tweets positifs et négatifs', fontsize=15)\n","plt.xlabel('Caractéristiques')\n","plt.ylabel('Différence Relative (%)')\n","plt.xticks(rotation=45)\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":2656},"id":"73Tukj7VWjJC","executionInfo":{"status":"ok","timestamp":1751537533960,"user_tz":-120,"elapsed":26718,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"3394f45a-f5b7-41c6-c942-749fd25bd662"},"outputs":[{"output_type":"stream","name":"stdout","text":["Analyse de 63 tokens spécifiques...\n","\n","Tokens les plus discriminants entre tweets positifs et négatifs:\n","Token 'I':\n"," - Dans tweets positifs: 35.47%\n"," - Dans tweets négatifs: 49.62%\n"," - Différence absolue: 14.15%\n"," - Orientation: Négatif (-33.3%)\n","\n","Token 'you':\n"," - Dans tweets positifs: 19.80%\n"," - Dans tweets négatifs: 10.60%\n"," - Différence absolue: 9.21%\n"," - Orientation: Positif (60.6%)\n","\n","Token 'my':\n"," - Dans tweets positifs: 14.27%\n"," - Dans tweets négatifs: 21.13%\n"," - Différence absolue: 6.86%\n"," - Orientation: Négatif (-38.7%)\n","\n","Token 'not':\n"," - Dans tweets positifs: 3.94%\n"," - Dans tweets négatifs: 9.00%\n"," - Différence absolue: 5.06%\n"," - Orientation: Négatif (-78.2%)\n","\n","Token 'good':\n"," - Dans tweets positifs: 7.48%\n"," - Dans tweets négatifs: 3.56%\n"," - Différence absolue: 3.92%\n"," - Orientation: Positif (71.0%)\n","\n","Token 'no':\n"," - Dans tweets positifs: 2.67%\n"," - Dans tweets négatifs: 6.51%\n"," - Différence absolue: 3.83%\n"," - Orientation: Négatif (-83.5%)\n","\n","Token 'love':\n"," - Dans tweets positifs: 5.62%\n"," - Dans tweets négatifs: 2.05%\n"," - Différence absolue: 3.58%\n"," - Orientation: Positif (93.3%)\n","\n","Token 'sad':\n"," - Dans tweets positifs: 0.18%\n"," - Dans tweets négatifs: 3.38%\n"," - Différence absolue: 3.21%\n"," - Orientation: Négatif (-180.0%)\n","\n","Token 'can't':\n"," - Dans tweets positifs: 2.46%\n"," - Dans tweets négatifs: 5.51%\n"," - Différence absolue: 3.05%\n"," - Orientation: Négatif (-76.5%)\n","\n","Token 'don't':\n"," - Dans tweets positifs: 2.76%\n"," - Dans tweets négatifs: 5.52%\n"," - Différence absolue: 2.75%\n"," - Orientation: Négatif (-66.5%)\n","\n","Token 'your':\n"," - Dans tweets positifs: 5.17%\n"," - Dans tweets négatifs: 2.44%\n"," - Différence absolue: 2.73%\n"," - Orientation: Positif (71.6%)\n","\n","Token 'so':\n"," - Dans tweets positifs: 7.65%\n"," - Dans tweets négatifs: 10.27%\n"," - Différence absolue: 2.62%\n"," - Orientation: Négatif (-29.3%)\n","\n","Token 'me':\n"," - Dans tweets positifs: 8.54%\n"," - Dans tweets négatifs: 10.90%\n"," - Différence absolue: 2.36%\n"," - Orientation: Négatif (-24.3%)\n","\n","Token 'great':\n"," - Dans tweets positifs: 3.03%\n"," - Dans tweets négatifs: 1.03%\n"," - Différence absolue: 2.00%\n"," - Orientation: Positif (98.6%)\n","\n","Token 'bad':\n"," - Dans tweets positifs: 0.71%\n"," - Dans tweets négatifs: 2.63%\n"," - Différence absolue: 1.92%\n"," - Orientation: Négatif (-114.6%)\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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DRsmf39/SXe/T61du1aenp5avHixJk+ezA+mj+i7777T5s2b1aRJE+XPn1+ff/65GjVqpOeee06enp6Ki4vT2rVrtWvXLhUsWFADBgyQo6Mjb7SRjZJ/bqOiouTi4mJ5A5qoqChdvHhRy5Yt0+3bty239zdv3lydO3fOybKRzLFjx3Tq1Cn5+PioWrVqcnZ21pYtW7Rq1SoFBgaqV69eun79unbt2qX169drwoQJKl68eE6XbZeSfk+5efOmpkyZoqefflqhoaH6999/9fHHH+upp55Sz5495eLiokuXLmn9+vWKjIxUmzZtVKxYsZwu3y79+uuvWrRokd566y0VLVpUMTExGjp0qAICAjR8+HDLfl8mk0nXr1/XG2+8oeDgYHXv3p3nSSbwWwZsWvKVHYsXL9a+fft0+fJlFS1aVI8//rhatmypV155RZ999pn+85//KDAwUNevX9edO3csgRQ/lGYfBwcHOTs7y9XVVVevXtW6det0/PhxnTlzRp6ennrqqafUrl07Pfvss0pISOAvbDnMMAzFxcVp//79atu2rcqVK2c5ljQ/lStXVv78+XXw4EGFh4erU6dOlr0LmL9H5+joKA8PDx04cED16tVTnTp1dOXKFYWHh2vv3r0yDEMHDhzQxo0bNWHCBAKpHJb8e9DPP/+sw4cPy8fHR7Vr19Zjjz2mzp07a9GiRZo8ebJ69uwpk8mktWvXymQy6bXXXtP69et18OBBQqlHsHv3bu3cuVOvvfaaypYtq/3798vBwUFbtmxRRESEBgwYIE9PT7Vp00aNGzeWh4eHJPGalc2S3yq5e/dumc1m+fn5qW/fvsqXL59Kly6t//znP7p+/bo2btyo3bt3a/Xq1apevTrPBxuwd+9eTZ8+XSVLltSJEydUu3ZtdezYUfXr11dMTIx+++03DRo0SIULF5ajo6PeeustftHORiaTSfv27dPhw4dVokQJ1alTR66urgoMDJS7u7umT58uSerRo4cKFSqknj178i582ezq1asqVqyYihYtqgMHDigyMlKjRo3Su+++q9mzZ2vgwIHy8vKSdHfvr+DgYP3111+KiYnRmDFjmJuHxEop5Arff/+9fv75Z7388suWd9tZtWqVOnbsqI4dOyoiIkI//vij5QX6ueees2zWTCCVfRISEjRjxgxdvnxZYWFhqlatmipXrqzKlSvrm2++kY+Pj/r165fTZeL/MwxDUVFRGj58uJ5//nnVq1cv1SqQGzduKCAgIMV5PI+yTtJz5tatWxo/frwkac+ePdq+fbt27typwMBAeXp6qnfv3tx+lMOSr7D98ccftXz5cjVq1Eh//fWXvL299cQTT6hZs2Y6e/asfvjhB/3555/y9fWVr6+vxo0bJ5PJpJEjR6pt27Z64okncng0uZNhGNq5c6euX7+uli1b6q+//tJHH32kHj16qEiRIpowYYKefPJJdevWTT4+PinOY3V09kj+/WDdunX67rvv1LlzZ8XHx2vbtm26efOmhgwZkuKPHtLdVaILFy5UzZo11axZM+YohyT92jdnzhyFhISoadOmOnnypD788EMVLVpUzz77rIoXLy7DMHT48GEVLFhQLi4u8vb2zuHK7d+6dev0xRdfKF++fJoyZYr8/Pwsz7e9e/dqxowZql27tnr16pVilQ6yx4YNG7Rx40YVKlRIW7du1RtvvKEqVaro7NmzmjRpkkqWLKlBgwZZti1ZsGCBatWqJV9fX8vKaWQcoRRs3u3bt/X++++rTp06atKkiaS7e7H8v/buOyzKM3v4+HfGAQGRZkFFENBAFHtbBTXYsGvW2BIUNYmo2USNxjRbNPZoshqNvWCvMRZiiVGDIAZQERS7gCJNRETaUGbeP7jm+UHM7ru7EYaE87muXLtO854Z53nu+zznPickJISAgAD8/f3p1KnTC8+Tq6Qvl+HkFxcXR3p6Ojk5ObRt2xZTU1MiIyPJy8ujffv2VKlSBZVKxYoVK6hevTpjx46Vk6aR/KsJy6xZs6hevTrTp09XOlap1Wru3r3LlStX8PHxKbXAE/8bw+f/26uZqampfP7554wYMUI5pgE8fvxYmdyU3FIpyl/Jhff9+/c5ffo0np6eNG3alKysLLZt28ajR4/o3LkzvXv3BiApKYlq1apRvXp1VCoVu3btIjQ0lNmzZ1OrVi1jvp0/jd87ZuXl5ZGZmYmFhUWp7eJPnjxh9uzZpKWlMWDAAKVBgCgfV69e5datW9SvXx9PT0/l9kWLFvHo0SOWLVuGmZlZqbnYunXrSE5OVgLyovw9ffoUExMTjhw5QseOHXFxcQGKj3MrVqzAwcGBv//977zyyitGHmnldO7cOdasWcPQoUMZPHhwqRIkYWFhrF+/nmXLlmFjY2Psof5llTwPLVy4kKtXr9KuXTs++ugj5THx8fEsWLAAe3t72rRpQ3p6OhcuXOCrr76SDPf/kVz6FhWeXq/nwYMHZGZmKreZmZnRsWNHmjdvzp07d4AXC8lKQOrlUqlU/Prrr8yfP5/AwEB27tzJokWLOHPmDK1bt8bT0xONRkN2dja7du0iMjISHx8fCUgZScmOVampqSQnJ5OWlgZAz549SUlJYfv27UDxNoyCggL279/P3bt35YroS6JSqYiOjmbJkiWcOXOGgoICAOzs7Gjbti23bt2ioKAAnU6HXq+nVq1amJubS0DKiA4ePKgULgcICQlh/fr13Lp1i5o1awLFxbXfeustHBwcCAkJITAwEChu7mBlZcW9e/fYuHEjP//8M1OnTpWA1H+o5DHLcLx6/PgxZmZm1K5dm+fPn5OVlYWHhwdQXGi+RYsWLFmyhLfeesuYQ690bt++zfr16zl69KgScDcU+p82bRoqlYpjx44BKFnrUBxsV6vVSrMHUb4uXrzIF198wYcffsiPP/5YqsOrq6srkydPJjk5mT179nDv3j0jjvSvz5ATkpaWRlxcHImJiWi1Wry9vRkzZgz79+/nyJEjSoBEr9fTvn17Vq1aJQGpcnL79m0ePHhAs2bNyMzM5Pvvv1e6hzdo0IClS5ei0WgIDw/n9u3bzJw5UwJSf4BsdhQVyu9dJbWwsKBt27bcu3ePxMRE6tWrp9xuaWnJ48ePAWkzXNYMCy1Ddsft27eZNWsWbdq0UR4TFhbGzz//TFJSErNnz5ai5kZSMstjz549REdHk5ycTMOGDXF3d+eNN94gLS2NCxcuMG3aNOrWrcuTJ08oKChg8eLFygRIAop/nCEg8fPPP3P48GFGjBiBh4cHPj4+fP7553Tt2pUmTZoYeZQCihdsycnJSrYaFE88ra2tuXnzJpGRkUpWlI2NDb6+vuzevZtTp05ha2urZItYWlri7OxMv379qFu3rlHey59Nydpd+/fvJyIiAq1WS0FBAUOHDsXb25tq1aqRkZHBuXPnyMvL4+jRo+Tn59OgQYNSGZ+i7NWuXZvu3bsTGBhIaGgo7du3R6PRUFRUhFqtplatWuTl5SmPV6vVJCUlER0dzXvvvVeqWYAoH8nJyezatYtu3bphZWXF8ePHCQ4OxsbGRpnHubq68t5777Fp0yYJfJQhw/zq119/Zd++fWi1WmxtbTE1NWXSpEn06dOHKlWqsGnTJtRqNQMGDFDmY2ZmZkYe/V+fobP4/v37GTduHG3atGHjxo1cunQJlUrFwIEDqVKlCjY2NsycOZOCggL0er1cUPyDZPueqDBKTigzMjIoLCxUrkxHRESwY8cO2rZtS9euXXFwcCA3N5elS5fi7OzM6NGjjTn0SuHs2bMEBwcza9YskpOTmT9/Ps2aNWP8+PFA8XdWvXp1zp07R7NmzWQ/tRFkZGSUmkgePHiQH3/8kQ8//JCaNWty4MABzp8/z8qVK7G2tiY+Pp6QkBD0ej22trYMGjRIipr/QSWDeYbPsbCwkLS0NI4cOcLt27dRq9X079+fkJAQVCoVkyZNwsLCwsgjF/B/56GIiAgaNGhArVq1SElJYfPmzWi1Wnx8fEptVXr69Cnnzp1j0KBBpQIiEiD53xw4cIDjx48zefJkHBwc2Lp1KxERESxfvpx69eoRHh7Od999h42NDZaWlsyZMweNRiNB9DL023/Lhs/6+fPn/Pzzz5w6dYq2bdvy9ttvK4/5+OOPadmy5QsZbDk5OXKsM4K4uDiCgoIoKipi7Nixym0BAQGYmpri4+NT6gJjQUGB0klR/HG/d3y6fv06ixcvZuTIkXh7exMSEsK6desYO3ascvHDUGNq9OjR9O3b1xhDr1QM31NaWhoBAQG0aNGCbt26oVarycnJYdeuXcTGxtK2bVslMCXn+pdHMqVEhVDyKum+ffsIDw/n2bNnWFtbK0Vic3Nz+eGHH4iMjMTGxoacnBy0Wq1SR0ImpWVLq9VSo0YN8vPzmTt3Lq1ateLdd98F4MqVK8THxzNgwAC6d+9u5JFWTjNmzKBjx47069dPWTDcvHmT8ePH07RpUyIjIwkPD8ff3x97e3t0Oh3u7u64u7uXeh2dTicBqf+R4RgUHR3NpUuXSEtLo2nTprRq1Yo6derg7+/PrVu3uHnzJgEBAWRlZUkmTQVhCCCq1Wri4uLYuXMnLi4u+Pr6Ym9vz+jRowkICOD06dMASmDK1taWv//970DpxbtMUv8zJc/beXl53Lp1i3HjxtG8eXPCwsK4du0aY8eOpV69euh0Otq1a8eKFSvIycmhdu3aqNVqCaKXoZJzs1OnTvHo0SNyc3N57bXX8PDwoFevXgAcPXqU+Ph47O3tycvLIzc3l+HDh5d6HZVKJZkERpCbm8vhw4eJiooq1fXQ2dmZUaNGsX37dk6fPk1hYSF/+9vfAKRr2Ev25MkTatasiV6vV35T0dHRdOvWjV69evH06VP2799Pr169lIBUQUEBPj4+aDQa3NzcjPwOKgeVSsWdO3c4deoUmZmZNG3aVDnHWFhY8NZbb7Fr1y6uXLlCfn4+Q4cOlXP9SySfpKgQDJPS77//npMnTzJw4EDef/996tevz6FDhzhy5AidO3dm/Pjx9OrVi1q1atG+fXuWLl2qZHZIQOrl0Ov1Sv2H58+fKyn47u7u/PLLL4wdOxZPT0/GjRunHIwjIiKIjY1VauaI8rV3716eP39O7969ld+BSqUiNTUVOzs7Ll++zNdff42vry/du3ensLCQU6dOcfPmzRdeS06w/5uShUgXL15Meno6VapUYe/evWzfvp3IyEig+Hc0aNAgFi9ejJ+fHx9//LFkDhhZyUBsYmIizs7O9OrViydPnrB7927S0tKoV68eo0ePxsTEhDNnznD27NkXXkd+O/+dkjWknj59qjRbqFevHtHR0axatYo333wTHx8f8vPzOXDgAAkJCVhZWVGnTh3UarUE0ctQye9nx44d7N69m8ePH/P48WPmz5/P3r170ev19OrViwEDBpCWlkZsbCw9evTg22+/VeZmQKnzkigfho0w5ubmDBw4kBYtWhAbG8u5c+eUx7i6uuLn50d6ejohISHKfE++p5cnLCyMWbNmERMTg0qlUs4Tz549o2rVqjx58oTPPvuMVq1aKVls4eHhSmZbt27dpBRGOUpKSiImJoZ79+6RmpoKoBzLDIGp2rVrc+fOHbKzs4082r8WCYWLCkGn05GTk8OlS5cYNmyY0j67efPm7Nixg1OnTuHm5sarr776whUDmZS+HJcvX8bOzg5nZ2dlr/uRI0fIzMzE0dGRDh06MHHiRDZs2ICzszNFRUVkZmZy4sQJLl68yNy5c2Wvu5GkpaXh5OSERqNh06ZNtGrVimbNmlGnTh1OnDjBpUuXGDlyJD4+Psrjr169ip2dnZFH/ud2+fJlatSoodS0SU9PZ9++fYwcOZI+ffoAcPfuXQICAjhz5gwODg7UqlULnU5HrVq16Nu3r0z+jaxkdtPBgwcJCQlhxowZ9O7dG71ez4ULF9izZw8jRoxQAlOrVq0iLi7OuAP/CzB87rt27eLJkyeMHz+edu3a8f3333Pp0iXGjh1Lt27dAMjMzOTOnTvUr1+/1AJNAoFlx/DZpqenk52dzYwZM2jUqBEAJ06cYO/evZiZmTFo0CC8vb0BCA0NJTIykubNmwMS3DCmgoICTE1N0el0uLi48Prrr6PX6zlz5gxqtZouXboA4OLiwsSJE6lWrZrM4V4iw4WqqlWr0rBhQwICAhgzZgyNGzdGp9NRs2ZNrl27RnBwMK1atcLf3x+9Xk9BQQGXLl3CyspK1jdG0KVLF8zMzNi9ezcnTpzA0tISV1fXUoGpd955B61WS/Xq1Y093L8UOZsLoylZzkytVmNubk5OTo4yiTFk3YwcORJra2uOHz/+wvMMzxV/TEZGBps3b+bHH38kJSWFhIQEvvvuO9q0aUP37t2xs7Nj3bp13L59mxEjRrBq1So+/PBDlixZQmhoKDNnzpQrOUbUqVMnwsPDmTt3Lj/99BM1atTAxMQELy8vzp8/T7NmzZRtlTk5OWzZsoW8vDzatm1r5JH/eRl+M4GBgSQkJADFV9MM21yhOODRqFEjRo8eTWRkJDExMcD/HbNkwWZcJQNSW7du5cCBAzx69Ihbt24B0KdPH7y8vEhJSWHv3r08efKEevXqMXXqVKlj+AeUPIdfu3aNy5cv06dPH0xNTalfvz5Xr16lTZs2yqI5JyeHDRs2UFhYSIcOHYw17EopKCiIyZMnc+PGDSwsLJTvrnfv3gwePJj9+/eTkpKCjY0N3bp1o2PHjkRHR7N+/XpA5mfGcuXKFb7++mvmzp3LsmXLePjwIU5OTrzxxhvUqFGDn376ieDgYOXxzs7O0iX0Jbt69SoALVq0oG/fvtjb27N582ZiYmJQq9X07NmT9PR0tFotgwYNAoq3kR84cIDIyEi8vb2lrlcZMxzPHjx4QHR0NMHBwUqXw+HDh5Oens7x48eVi1CGwJS5ubk0AigDkikljKJkHYmQkBBl65G9vT0hISH4+PhgYmJCYWEhGo0GFxcXcnJyAFnIlQUbGxumTp3Khg0bOHbsGNWqVaNHjx4MHjwYKF4UODk5sX37dsaPH89XX31FbGws1tbWODk5ScaNkRgW1S1atMDd3Z2YmBh69uxJgwYNgOIrPs+ePWPHjh189dVX6PV68vLyyMnJYdGiRcr2F1k4/Pd++5vp27cvNWvWJD8/n4yMDABlG2yjRo1wd3fn1q1bvPbaa0YctSipZEDql19+YeHChezatQutVqs8xlDfIzQ0lI0bNzJhwgSlAYf8dv43hnP4L7/8wr1792jSpImSgTNw4EDS0tKIiopi3rx52NnZkZaWhlarlWOWEdjZ2dGkSROuX79OQUEBKpWK/Px8TE1N8fb2JjAwkLi4OOzt7bGysqJ79+5otVqioqKUuqCifF26dInly5crhbFjY2OZMWMG7733Hh06dGDQoEEcO3aMQ4cOoVarSzVuEC9HXFwcixYtonv37vj7+9OkSRP0ej0nT55ky5YtjB49mqZNmzJ9+nTmzp3L119/jVqtxtbWljt37jBjxgyl07goG4Z16MWLF9m+fTuWlpbk5+ezd+9eJk6cSIcOHSgqKuLo0aMcP36cnj170qhRI8lcK0MSlBLlruSE8uHDhxw5cgS9Xo+dnR3Dhw9n2bJlfPPNN3z44YfK4+Lj40sVaBQvn6urK+PGjWPjxo08e/aM1q1bK/dZWFjg5eXF3bt3iYiIoFOnTjg5ORlxtKJkAdpz585haWnJwIEDOXr0KFZWVgwbNgyAAQMGULduXW7fvk1OTg4ODg74+PhIl72XwNXVFX9/f9auXcuPP/7IsGHDGDBgAFu3bsXR0ZHGjRsrj9XpdHJlrQIyTDiXLl1KgwYNUKvVpKSkACiL7969e1NYWEhiYmKpdH0JjPx3ftuMJDw8nPDwcNzd3Ut1+3r77bcJDg4mLi6OnJwcOnTooLRIl2NW2fm9YF+TJk3QaDRkZWWxdOlSFi1ahJWVFYASpDJ8H3q9nurVq9OvXz/69u0rW1uMQKvVEhgYSP/+/Ut1PtywYQPfffcdTk5OODs706dPH06fPq0Eg8XLVbt2bd59910OHDjAxo0beffdd/Hw8ADg5MmTylY+Dw8Pli9fTlBQEE+fPqVu3bqMHj2aOnXqGPkd/PWpVCpu377NunXr8PPzo2vXrqSkpDBp0iTi4+Np0qQJXl5eAOzZswcTExMaNGgg2WtlSKX/7V4oIcrJ9u3bSU1NJSMjg4SEBGxsbOjbty/W1tYEBARgYmKCvb09WVlZ5OTksGzZMpmMloP4+HiWLl2KqakpkydPxtnZWblv9+7dXL58mUWLFkl3FiMquXjYsWMHP//8M0uXLqVmzZqcOHGCgIAABg8erASm/n+vIf6Y2NhY1qxZg6urq7KV8tSpU7z11ltYWlqSkJDA6dOnWbRokVz9rECKioqIjIykXr16ShfEZcuWYWFhwXvvvQcU/07i4+NxcXFRgiry2/nvlQxIBQcHo9Pp6NKlC5s2beLChQuMGDGCzp07/9uaNvK5l53fXiw0nN/r1q2LTqfjzp07BAQEkJ6ezrBhwzA1NSU4OJgnT56wZMkS+V4qiJycHGbOnEnfvn3p0aOHstsAYM6cOdSoUYMPPvgAlUpV6j7x8uXm5hISEsKePXvo0KGD0q36+vXrnDx5kqSkJCVjSo5tZS8pKemFbsdnz54lOjqaSZMmkZSUxPz582nRogX+/v6lHhcaGkrDhg2pXbt2eQ650pGjkTCKc+fOcebMGWbNmkXt2rUpLCxk1apVBAcH07VrV7788ktOnTpFXl4eFhYWvPHGG3KVtJw0aNCATz75hJUrV/Ljjz/St29fJTD1/PlzrKysZDJjZCUL0Gq1WqZPn67Ug+jatSsqlYqtW7eiUqkYOnQo8OKCTiZAL4+hUOy6detQq9V06tSJunXrEhgYiKmpKRYWFsydO1cCUkb2299AlSpVaNWqFWq1Wjmm1a5dm8ePHwPFgZSFCxdiaWnJ5MmTUalUpTIUxX/mtwGPo0ePotPplIKxeXl5BAYGUrVqVTp06KAUZ/7t5yyfe9ko+W963759/Prrr2i1WjQaDYMHD6ZLly64ubkxevRodu3axbp16+jUqRMtW7aka9eusqWyAtBqtVStWhULCwtq1KhBWFgYPXr0QKPRKMe2unXrkpWVpQSHZQ5XtszNzZWtkbt37wZ4IWNqx44d+Pr60qxZM6ONszKIjIxk0aJFTJs2jfbt2yu3P3jwQClpMW/ePFq1asW4ceOA4oBVamoqw4cPp2PHjsYaeqUiRyRhFMnJyTg6OirBDrVazcSJE1m+fDkHDx7EzMyMESNGAJS6Oi0BqfLh5OTEP/7xD1atWsXy5ctp3LgxGo2GX3/9lVmzZkmHlgogKCiIDRs2YG9vT79+/ZTbzczMlE5I27ZtIzc3Fz8/P1kwlDEXFxf8/f1Zv349Op2OESNGKFeqDQtwYTwlF82Graw2NjbKOciwQLOysuLmzZsUFhby1VdfkZKSwqeffiot7f8Aw+duyI42NTXl0aNHBAQEUFRUpJxrDh06hEqlon379lStWtXIo648DP+m9+3bx6lTp5g0aRK1atVi//79rF69mry8PHx8fHBzc2PEiBEcOnSI+/fv4+fnR9WqVZVtrsI4oqKiiImJoV27djRs2JCuXbty5MgRduzYwciRI5VjW2FhIWZmZhQWFlKlShU5lpWBkhmhhvO+oWHDbwNTKpWKQ4cOsX//ftzd3TExMZHvpIy0bNmS7t27s3r1alQqFe3atQPA09OTrVu3MnHiRLy8vPD390en06FSqYiPjycjI4O8vDxZ85QTCUqJcmU4YGs0GgoKCigsLMTU1JTCwkJq1KjBW2+9xZIlSzh58iRFRUV4eXkpB2lZVJcvFxcXJk+ezLJly4iOjqZXr14sXrxYOrRUEHZ2djRu3JgbN24oBbUNV0QNgam8vDyuXLnyQi0XUTZcXV0ZP34869evZ+vWrQwZMkS6UlYAJTNBdu3aRVBQEGZmZjx+/JgBAwbw2muvKWn9lpaWaLValixZQmpqKt988w0ajUaydP+g38uOXr16NT/88ANqtZr333+f1atXs3nzZqpXr07Lli2NPeRK5f79+9y4cYMpU6bQtGlTLl++zJUrV2jVqhWbNm1CpVLRs2dP3N3dGTx4MLt372b+/Pl8/vnn2NraGnv4ldbFixdZvXo1r7/+uhIYbNmyJcnJyVy4cIG7d+/SvHlzEhMTCQsLY8GCBZIhVUYM86yoqCiio6OJi4ujc+fONGrUiB49egDFtYmgODDVpEkT1Go1tWvXlqBuGTJ8L/7+/piYmLBy5UqmTp1Kq1atqFmzppJBaKivlpmZyYkTJwgODuaLL76QgFQ5klW+KFeGhXH79u2JjY3l8OHDAKWu5LRs2RKVSsWZM2coLCw02lhFccbUlClTcHBwoFu3bhKQMhJD0KkkDw8Phg0bhoODAwsXLiQzM1NZPENxxlTfvn354osvlG1Houy5uLjwzjvvkJGRQbVq1Yw9HMH/nXd++OEHpcX9P//5T3r37s2xY8f48ccfSUxMBMDBwYGEhASeP3/O8uXLJSD1kpTMjrawsMDGxoaJEyeiVqvZunUr4eHh/OMf/6B///40bdrU2MP9y/vt+cDa2lrp4nrt2jXWrVvHm2++yZQpU2jWrBkbN27kyJEjqNVqXn31VXx9fdHr9SxbtgydTifnFyO4f/8+mzdv5p133uGNN97A0dERKL6AO3jwYEaOHImZmRmRkZHk5eUxf/585THi5VOpVISFhbFs2TLy8/OVLfwbNmwgMzMTT09PRowYQXh4OKtWrQLg1Vdfle7VZcxwbLpx4wbu7u7odDq+++47IiIisLW15e9//zuOjo58//33TJgwga+++orz588zY8YMuahYzqTQuTCac+fOsW7dOvr164enpyfVqlVjy5YtuLm50b59e6ZNm8aMGTNo3ry5sYda6Ul6vvGU3HYUFhZGeno6er2e5s2b4+DgQGxsLJs3byY7O5s5c+ZgbW39Qs0vyZQqf/KbqVjS0tLYunUrnp6eeHp6EhYWxpo1a2jXrh2hoaF07tyZgQMHYmtry08//UTfvn1Rq9USkPqDDMeeAwcOcOnSJebOnatkR2s0Gq5du8aSJUtwdXVl0KBBStdXqVFUdkp+tsnJyZiZmWFjY6Pcvnr1aqpWrcqYMWPQaDSsX7+e2NhYTExM+OKLL1Cr1ej1eu7du4e1tbVcrDKSCxcucPToURYtWkRhYSEXL17kl19+IT09nZYtWzJq1CgA5eKuZEiVrbS0NJYsWYKPjw89e/ZEq9Uybtw4evXqha+vL1A8L/j55585fvw48+bNw9raWuZm5SA8PJwVK1bw+uuvk5WVxcOHD7l9+zYffPAB7du3JyMjgydPnnD9+nWcnJyoX78+NWvWNPawKx05Qgmj8fb2xszMjE2bNhESEoJer8fa2pr+/fvz7Nkz6tSpg7W1tbGHKUAW10ZUssve+fPncXd3Jzk5mXPnztGrVy+6devGyJEj2bVrF/PmzWPWrFnY2NiUeg2Z9JQ/+c1ULBYWFnh7e+Ph4cGdO3fYsmULw4YNo0+fPlhZWXH69Gm0Wi2jR4+mf//+AFLH8CUomR194MABDh8+zNChQ1/Ijs7OziYwMJBmzZphYmIiAakycOrUKdzc3JQ6art27eLSpUtkZGTQtWtXOnToQKNGjYiPj6dZs2ZoNBry8/PJzMxk6NChLwQMDdtdhHFoNBpyc3PZtm0bd+7cwdLSEmtra1q1asXu3btp3rw5LVq0kGBUOdHpdOh0Ory8vEhOTmbu3Ll4eXkpAambN2/i7OxM165d6dKli2RSlxOtVsuRI0fw8fFhyJAhAMrv5ttvv2Xy5Mm0bdsWGxsbGjZsaOTRVm5ypBJG1aFDB9zc3EhLS6OoqAh3d3fUajU//fQTarVaglJCUNxCPSQkhI8//piGDRty9uxZNmzYoExq3N3dGTVqFKtWrWLbtm1MmjTJyCMWwnh+L8vGwsKCJk2aYG5uTlhYGA0bNqR79+5AcQDR0dERrVaLpaWl8hwJjLw8Tk5OTJgwgXXr1qHVapXs6BMnTpTKjr5x44ZkR5eB1NRUDh06RMuWLRk0aBAJCQkEBQXx9ttv8+DBA65cuUJycjKDBw/G29ub7du3k5ubS1xcHEVFRUqNL+k+aVx5eXkUFhZiaWlJ69atiY+P59atW7i4uODt7Y2rqyvp6emcP3++1LFMvHy/zUDPzs4GICUlheXLl9OiRQulk1tcXBxnz56lT58+SlBYlA+9Xk92dja1a9cGiucHZmZmjBo1isTERDZt2oROpyvVlU8YhwSlhNHZ2dkpe6ofPnzI4cOHuXLlyu9mfAhRGSUnJ9O4cWMaNmxIaGgoW7duZcyYMfztb38jLy+PZ8+e0ahRI6ZOnSp74EWlVnLRfP78eXJycrC0tMTLywsLCwt0Oh2ZmZno9Xq0Wi2mpqbEx8czePBgWrVqBcjWsbIi2dHGU7t2bT755BPWrl3LiRMnUKvVDBs2jPbt29O+fXtcXFw4duwY33//PZ6enowZM4aIiAgcHR0ZN24carVafhdGFhERwalTp0hISMDd3R0PDw+GDh36wnb906dPk5OTIwXoy5AhIHXjxg1SUlLw9vbGxcWFmjVr8umnn9KtWzfGjx+vPD4kJISHDx/KmsYIzMzMqF+/PiEhIXTr1g1TU1OlM2LdunW5e/cumzZtonnz5lLU3MgkKCUqjKKiIgoLC7GysuKLL76QgoyiUvq9ib9Wq6VWrVrcvn2bNWvWMHLkSHx8fNDr9YSGhpKVlUWfPn1wcnL6l68hRGVguHK9a9cuTp8+jbW1NTqdjtDQUD766CPUajVubm5s2bKF5cuXk5mZCaBk50gmSNmS7GjjcXZ2xt/fnw0bNpCcnKxsZQFo06YNer2e48ePExoaSr9+/ejVq5dyv9RWM67Lly/zz3/+k2HDhjFw4EDOnz/Pli1bqFOnjtIYICIigqtXrxISEsKsWbOkgHYZMQSkLl68yIYNG2jdujUNGzbE0dERX19fcnNziYmJISYmhszMTG7dusWZM2eYN2+eBKXKmOG7ef78OTqdTjmfdO3alX379rFt2zalVh4UZ0lPmzaNRo0aSUCqApBC56LC+e1VHyEqi98WoDU1NcXKyop79+4xe/ZsAKZMmULHjh2B4lT+5cuX4+joiJ+fn9HGLYSxGX47er2evLw8Vq9ezbBhw7C1teX69evs3bsXOzs7Zs2aBRQ32nj06BEqlYrhw4dTpUoVCeYawW+zo2VrS9l78OABX331Ffb29vj5+SkXM6A4+LFz505at26t1MKRRhnGY8joXLVqFY0aNeL1118nOzubqVOn0qFDB8aOHas87vDhw8TFxTFkyBDJmC5jd+/eZcGCBYwaNQpvb2/lvKHT6YiLi2Pfvn3ExcVhYWGBra0to0aNkmNbOQkLC+PQoUNkZGTQrl07evTogaOjIydOnCAoKAi9Xk/btm159OgRly9fZvHixdStW9fYwxZIUEoIISqEkhP/nTt3EhERQWZmJvXr18fLywuNRsOmTZuYOHEi7u7u5Obmsn37djIzM1m4cKFcxRaVVslgUlpaGjk5OezcuZPx48djZ2dHYWEhUVFRbNu2jRo1aiiBqZK/OckEKX9FRUU8ePCA8+fP07VrV8mOLkdxcXGsWbMGV1dX+vbtW+qzv3XrFq+88ooEaCsInU7HnDlzGDJkCA4ODsycOZPWrVvj7+8PFHcWq1OnDg4ODmi1WszNzY084r8uwznj9OnThIeHM336dNRqNWq1+oUL6o8ePcLGxgaVSoWFhYURR1153L9/n0WLFtGzZ08sLCw4efIk9erV44033sDNzY3r169z5swZUlNTsbCw4M0335RgYQUiQSkhhDCykovqkJAQAgICGDduHNnZ2SQkJHD8+HG8vb1xcHBg586dSpedatWqMWPGDDQajWR5iEpv165dBAUFYWNjw7Nnz1iwYIGyhcUQmNqxYwcajYalS5caebTCQLKjjSM2Npa1a9fi6upKv379XsiukXOKcT158oTq1atTVFTE8uXLadiwIRcuXMDDw4Px48ejUqnIyMhgx44dNG3aFG9vb2MP+S/rt9mCO3fu5OLFi6xcuRKVSlXq/nv37kkXNyNITk4mPDwcrVarbE1+8OABa9eupXr16gwaNIgmTZoAkJ+fj1qtlvNOBSNnGyGEMDLDxP/69etER0czcOBA2rVrh7e3N4MHD8bPz4/g4GDq1KnD119/zeTJk5k4cSKzZs1Co9FQVFQkiwdR6eh0OuX/X7x4kZCQEHx9ffH09ESj0fDNN99QWFgIFLdPb968OcOGDcPBwaHUc4VxycLAOFxcXJgwYYKy3Sg1NbXU/XJOMQ69Xk9KSgpTp04lPj4ec3NzXnvtNX744QdsbW2ZMGGCEgA5fvw4d+7cURbbomyoVCpu3rxJZGQkAI6OjqjVaqKioigsLESlUqHT6SgsLOTQoUOEhIQYd8CViF6vJysri7lz57Jnzx6ePn2q3Ofk5MT48ePJzMzk2LFjREREAMW1pOS8U/HIGUcIISqAjIwM1q5dS2hoKPn5+crtFhYWeHp64uHhQWRkJPb29jRp0gQXFxelI5JsOxKVkWHRHBwcTHp6OgMHDqRz587079+fyZMn8+zZM+bNm1cqMNWuXTsmT56s/HaEqMxcXFx45513MDMzo2bNmsYejqA4AGJvb0+zZs3YtGkTWVlZdO7cmeHDh3Pr1i02bNjA5s2b+e677zhx4gQffvih0u5evHx6vZ78/Hy+/fZbHjx4ABQ3bDA3N2fPnj1ERUWRn59PXl4eBw8elEypcmTIULO0tOT999+nRo0axMfHc+/ePeUxDRo0YOLEiTx69Ijg4GC0Wq0RRyz+Hdm+J4QQFUR8fDzLli3DwsKCCRMm4OLioty3du1anj59ymeffWbEEQpRseTm5jJlyhQyMjIYMGAAI0eOBIonq/fv32fFihXY2toqWYVCiBcZFneyZc+4Hj9+jJ2dHVWqVOHBgwds3ryZNm3a0K9fP9RqNRcuXCAoKIiioiLq1q2Lj4+PFDUvQyW35f3zn/+kZs2ayjlGq9WycOFCsrKyePbsGQ4ODiQmJvL555+XmruJl++3xyvD/167do01a9bw6quv0r9//1Lfw8OHD6lataoEcCswCUoJIUQFEh8fz6pVq2jQoAH9+/fH2dmZ3NxcFi5cSP369Rk/fryxhyiE0fzeojktLY1vvvmG3NxcPv3001KTzvv37/PFF1/QpUsX3n333fIerhB/GtJlz7hiY2P59NNP6dq1K23btqVt27bs37+fyMhIpk+fjo2NDVBcD8fU1FSaM5SDzMxMrKysgOKahTExMcybN085BxUUFHDjxg3i4+OxtbXFzc1Ngh5lzHCcio6OJiwsjOzsbOrXr0/37t2xtrbm6tWrrF+/Hnd3dwYOHCiFzP9EJCglhBAVTGxsLN9++y1ZWVm4urqi0Wh4/PgxCxYsQKPRyOJBVEolA1JRUVHk5eWhVqtp27YtT548YeHChZiamjJt2jRlK5JerycpKYk6depIBogQosKKiYnhm2++oX79+tSoUQONRoOfnx8ff/wxLVu2VILqhuOgzAPKVkxMDGvXrsXc3Jw6depQpUoV0tPTeeutt3ByckKj0Uj2rZGEhYWxYsUKOnfuTFpaGtnZ2WRmZjJ37lxq1qxJVFQUmzZtwsHBgREjRuDk5GTsIYv/gASlhBCiAnrw4AFLly6lRo0aeHl54ePjA0inKlE5lVyAGbrsWVtbk5CQgKenJyNGjECv17No0SKqVq3KtGnTqFGjRqnXkK1JQoiKpmQ2zt69e/npp5/4+OOP2b9/PwA2NjaEhYXxj3/8g/bt2xtzqH95Jc8ziYmJJCUl8fDhQ1JTU0lKSiImJgYXFxfS0tJwdHSkRo0auLm54ePjI0HCcpKZmcmXX35J586dGThwIFA8X96+fTtJSUksXLgQKysrIiMj2blzJ5999pnShVdUbDI7E0KICsjJyYmPPvqIwsJCYmNjSU5OBqRTlaicDJP9w4cP88svv/DRRx+xZMkSRo0aRVBQEFu2bEGlUvH5559TUFDA7NmzefbsWanXkICUEKIiuX//PqtXr2bfvn0ADB8+nBYtWhAVFcWMGTNo2rQpubm55OXlERoaqjRtEC+fIah0+/ZtTp06RVhYGDY2Nrz++uv4+/szatQoLCwsGDJkCH5+fjRv3pzs7GyaNm0KIAGpMmTInzFsWX369GmpbXn169fH19eXatWqceHCBXQ6HS1btmT+/PkSkPoTkdWNEEJUUM7Ozrz77rts2LCBPXv2MHToUBwcHIw9LCGMIj09nYSEBEaPHk2jRo349ddf2bt3L4MHD+b48eNs2bIFPz8/pk+fzt69e6levbqxhyyEEP9S1apVcXJyIjg4mOvXr+Pr60vjxo25f/8+iYmJDBo0iMTERNzc3GjdurVclCpDKpWKixcvsnbtWlq2bMmTJ0+4ePEizs7O+Pv74+TkRJ06ddDpdHTp0gWQOmzlRaVScf/+fc6ePcvQoUOxt7fn+vXrNG/eHCi+4NSgQQOqVKlCYmKicgHK1NTUmMMW/yW5bCiEEBWYi4sLb7/9NhkZGVhYWBh7OEIYjaWlJe3ataNFixbcu3ePbdu2MXToUIYPH86QIUMIDw9nzZo1VK1alQ8++EDpyiOEEBVJYmIid+/epaCgAF9fX2bOnIlarWbHjh3cvHmTGzduEBwcDEC9evUYOHCgdNkrYwkJCWzbtg1fX1+mTJnC+PHjSUhIwNraGrVajUajwczMjGvXrhl7qJXSzZs3iYmJ4fHjx7i7uxMVFcWvv/6q3K9SqbCzs8PCwgK9Xi8Bwz8hCbkLIUQF16hRIz7//HO56iMqNVNTUyVbIDo6GkdHR7y9vYHiba2dOnXi+fPnpTKkZMueEKIiCQsLIyAgAFNTU1JTU/Hy8sLPz485c+Zw/Phx7t27R2JiIgcPHsTFxYV27doZe8iVwpMnT6hevTo9e/YkNTWVRYsW0aVLF958802guG6Rvb09ubm5Sn1CCXqUHUNQydBtsm/fvly8eJGDBw8ybdo0vvnmG3744Qdu3LiBu7s7N27cIDo6mjfffFO+lz8pCUoJIcSfgASkhEBpgZ6YmEhOTg5Q3CI9MjKSLl264OnpCUhRcyFExXP16lXWrFmDr68v3t7eREdHs3jxYnJzcxk7dix9+vQhPT2d+vXrExgYiKOjo7GHXGmoVCpsbGxITU1lzpw5tGrVSul4eOvWLWJiYmjSpAkuLi5ybikHKpWKyMhIzp8/T5cuXWjRogWTJk1izpw5nD59mkmTJnHgwAGuX7/OlStXsLGxYe7cuVLi4k9Muu8JIYQQ4k/l9u3bzJkzh3r16lFYWIiJiQlLlixRglZCCFGR5OTksGPHDuzs7BgyZAipqal8+eWXuLi4EB0dzauvvoqfnx9169YFIDc3F3NzcyOPuvJITU1l2rRp5Ofn07t3b8aOHavct3nzZh49esT06dMxMzMz4igrD71ez/r16zlz5gzVqlWjd+/eeHt7ExwczP379xk5cqRS4ysrKwtTU1P5bv7kJFNKCCGEEH8qbm5uLFiwgLCwMMzNzenfvz9VqlRRuvMIIURFYmpqSrNmzXBxcSErK4vly5fj4eHBhAkTCAkJYeXKleh0OsaMGUPdunVlgV3OateuzaRJk1i5ciWmpqYkJSVRUFDAL7/8wvnz55k7d658J2WsZB0olUpF9+7dycvLw9HRkfDwcJ49e0ZRURGPHj0iIiKC/v37o1arsbKyMvLIxcsgQSkhhBBC/Om4urri6uqq/FkCUkKIikqj0dCmTRtMTU0JCgrCxMSEoUOHKvc3adKEhIQETExMAKQujhG0adOGd955hy1bthAcHIyZmRkajYbZs2fj5ORk7OH95alUKq5du0ZKSgrdu3fH1dWV6tWrk5KSwuzZszl//rzSmXL79u24u7vzyiuvGHvY4iWRoJQQQggh/vQkICWEqMgMtSFTU1PJzc2latWqAMTHx/O3v/2NHj16oNHI0sxY1Go13t7eNGvWjMePH2NmZoadnZ1k4pQTnU7HnTt32LNnDzdu3KBHjx6MHTuWTz/9lMDAQIYMGUJOTg6mpqaEhYWVamoi/vykppQQQgghhBBClIPY2FhmzpyJq6srpqam3L17l3nz5tGgQQNjD00Io4uPj2fHjh3k5eXRsGFDWrZsyalTpxg0aBDu7u4AZGdnU61aNSOPVLxMEpQSQgghhBBCiHJy+/ZtTp48iYWFBT4+PtJpT4gSMjIyiIqK4tixYyQlJWFtbU2nTp0YMWKEsYcmyogEpYQQQgghhBCiHOl0OlQqldSPEuJfKCwsZOfOnZw4cQJLS0tWrlwpXSn/oiQoJYQQQgghhBBCiAqhZDe+qKgo6tatS61atYw8KlFWJCglhBBCCCGEEEKICqNkYEr8tamNPQAhhBBCCCGEEEIIAwlIVR4SlBJCCCGEEEIIIYQQ5U6CUkIIIYQQQgghhBCi3ElQSgghhBBCCCGEEEKUOwlKCSGEEEIIIYQQQohyJ0EpIYQQQgghhBBCCFHuJCglhBBCCCGEEEIIIcqdBKWEEEIIIYQQQgghRLmToJQQQgghxJ/UsGHD2LRpk7GHIYQQQgjxP9EYewBCCCGEEJXJsGHD/qPHzZkzBw8PjzIejRBCCCGE8UhQSgghhBCiHL3//vul/hwUFERUVNQLtzs4OJTnsIQQQgghyp0EpYQQQgghylGXLl1K/fnOnTtERUW9cLsQQgghxF+dBKWEEEIIISqYvLw89u3bR2hoKM+ePaNWrVp0796dAQMGoFKp/u1zDx48yL59+xgzZgx9+vQB4MqVKxw6dIjY2FhUKhWNGzdm5MiRODo6Ks9bvXo1Fy9eZMWKFWzcuJHo6GhMTU157bXXGDlyJGq1lCIVQgghxMslswshhBBCiApEr9ezdOlSAgMDadGiBX5+ftSrV48dO3YQEBDwb5+7Z88e9u3bx7hx45SAVFBQEIsXL8bMzAxfX1/eeOMNEhISmD17NqmpqaWer9PpWLBgAdWrV2fUqFE0adKEY8eOcfr06TJ7v0IIIYSovCRTSgghhBCiAomIiODatWuMGDGCwYMHA9C7d2++/vprjh8/Tu/evalTp84Lz9u2bRuBgYFMnDgRb29voDjjasuWLXTr1o3x48crj33ttdeYMmUKhw4dKnV7QUEBHTt2ZMiQIQD4+PjwySefcObMGXx8fMrwXQshhBCiMpJMKSGEEEKICuTKlSuo1Wol08mgf//+6PV6IiMjS92u1+vZtGkTx48f54MPPlACUgBRUVFkZ2fj5eVFZmam8p9areaVV17h+vXrL/z9vw0+vfrqq6SkpLy09yeEEEIIYSCZUkIIIYQQFcjjx4+xtbXF3Ny81O3169dX7i8pKCiIvLw83n33XTp16lTqvqSkJADmzZv3u3/Xb/8OExMTrKysSt1WrVo1srOz//s3IoQQQgjx/yFBKSGEEEKIPzF3d3fi4uI4efIknp6eWFpaKvfp9XoA3n//fWxsbF54bpUqVUr9WYqZCyGEEKI8ycxDCCGEEKICqVWrFk+fPiU3N7fU7Y8ePVLuL6lOnTrMnDmT9PR0FixYUOp59vb2AFhbW9O8efMX/vPw8CjjdyOEEEII8a9JUEoIIYQQogJp1aoVOp2OEydOlLo9MDAQlUpFy5YtX3hOgwYN+Oyzz3j06BFLliwhPz8fgBYtWmBubs6hQ4coLCx84XmZmZll8h6EEEIIIf4Tsn1PCCGEEKICadOmDR4eHuzZs4fHjx/ToEEDrl69SkREBH379v3dznsAbm5ufPzxxyxatIjly5czffp0LCwsGDduHN9++y2ffPIJXl5eWFlZkZaWxuXLl3F3d+edd94p53cohBBCCFFMglJCCCGEEBWIWq3mk08+Ye/evVy4cIGzZ89Su3ZtRo4cyYABA/7tc5s2bcqHH37I8uXLWbVqFZMmTaJTp07Y2tryww8/cOTIEQoKCrCzs6Nx48Z07dq1nN6VEEIIIcSLVHpDBUwhhBBCCCGEEEIIIcqJ1JQSQgghhBBCCCGEEOVOglJCCCGEEEIIIYQQotxJUEoIIYQQQgghhBBClDsJSgkhhBBCCCGEEEKIcidBKSGEEEIIIYQQQghR7iQoJYQQQgghhBBCCCHKnQSlhBBCCCGEEEIIIUS5k6CUEEIIIYQQQgghhCh3EpQSQgghhBBCCCGEEOVOglJCCCGEEEIIIYQQotxJUEoIIYQQQgghhBBClDsJSgkhhBBCCCGEEEKIcidBKSGEEEIIIYQQQghR7v4fiaaPmX/g8GYAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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YrHo0qVL2YpFkg4fPqzp06dryJAh6tevn55++mlNmDDBvuxOWSwWbd68WdKNSyFvVrduXdWtW1fx8fHauXNnuuW2S5rSqlatmiRlec6hjRs3SroxmbHtcsS0evbsma5tx44diomJUdOmTVW6dOl0y/39/dWwYUNdvnzZ/jrVr19fHh4emjt3rj755JMc5SMrbOPJ6PX09fXVE0884dAvrevXr6tDhw76+uuv9fTTTys6Olq+vr4Ofe7kPdqwYUPddddd6dozylnDhg0lSYMGDdL69euVkpKS4fPlFg8PD02ZMkXHjx/X9OnT1aVLF1WoUEHSjfnHBgwYoC5dujjMs+Ru+29mbJeaplWsWDGVKFFCUsb7lW1y7LQ5upPxVqxYUdWrV0/Xnt39N6uy897Lybh+++03SbLvXzc/x60urztz5oxmz56tYcOG2Y+zAwYMkI+Pj1OOswAA52NOKQBArrH9cLFNdHwrKSkpunz5siTZ59tJy/YjNqtsk85mdLevtGJiYtK1lStXLsO+RYsWlaR0k5PfimEYevnllzVt2jSHSYfTunbtWpa3l5lLly7JbDarZMmSKlKkSIZ9KlWqpF27dunMmTPplmU05uyO1zbxd8WKFTN9/pvZ8rRmzZrbzjUUExOj6tWrq1q1anr77bf1r3/9S88995xeeOEF1a5dWw899JCefvrpTOecyS7beDKbRN/WntHr+f777yslJUXt27fXZ599luHYbGO/7777bhnHnb5HX3nlFf36669av369WrZsqcDAQDVp0kSPPPKInn76aRUrVuyWzy/dmN/p22+/Tddum3w/K8LCwjR06FANHTpU0o1JzN9991199tln+vbbb7VgwQJ7AdBd9t/bySy+wMBAXbp0KcPltrmH0sZhG+8//vGPDIugNnk93uw+X07e47aiVvny5TPsW6FCBe3YsSNd+9SpU/Xaa6/JYrHc8rkAAO6FohQAINfUq1dPv/32m7Zv367evXvfsu+ePXtkNptVrFgxhYeHp1vu5+eXree2nXWRdiLajNx7773p2m6eZP1OLFq0SFOnTlX58uU1bdo0NWnSRKVKlZK3t7fMZrN8fX0zLVY5262KPs4cc3bY8hQREXHbH65pz84YPny4unXrpm+//VZr1qzRxo0bNW3aNL3//vuaNm2aXnzxxVyNW7r169muXTv98ssvWr16tb755huHSaVtbGPv2rVrpoVEKeNJqLOTr6CgIK1bt06//fabli1bpvXr12vdunVas2aN3nzzTW3cuFFVq1a95TZ27typefPmpWvPTlHqZjVq1NCcOXN0+fJlLV26VD/++KO9IOMu++/t3O65shqLbbxt27bN8IxBm4wK9q7ed7PzfHfyHs+OzZs3a/jw4SpWrJimT5+uFi1aqEyZMvazE8PCwnL9LpMAgJyhKAUAyDXt27fXrFmzFB0drXfeeeeWd+D7z3/+I+nG5S7O+JFVrlw5/f3333rvvfcyvNTEVZYuXSpJ+vDDD/XII484LDt69KjTnueuu+6Sj4+PYmJidP369Qx/AGb17JOcst3B7cSJExkuz6jddtZFZGRktgsc5cuX15AhQzRkyBClpKRo4cKF6tu3r0aMGKGnnnoq3WVV2RUWFqaDBw/qxIkTqlWrVrrlt3o97777br3++utq06aNevbsKQ8Pj3SXMJUrV04HDx7Ua6+9Zr/ELreYTCY1a9bMfvewCxcuaNiwYVqwYIFef/11LV68+Jbrjx07VmPHjs2V2B588EEtXbrU4WwZd9l/XcW2H/Tv319dunTJ42icJyfv8dDQUB0/flynTp3K8LLEjO68ajvOTpo0KV0hMzExUefPn89B9AAAV2BOKQBArmnXrp0iIyN15swZvfXWW5n2O3jwoGbOnCmTyaSXXnrJKc/dunVrSf/7sZJbbHPXZDZPj+2SxIwuecmsEHC7bWbE29tbjRs3liQtXLgw3fI9e/Zo165dCgwMzPLt7rPLNj/MN9984zA/kE1GcTVq1EjFihXThg0bFBsbm+Pn9vLyUu/evdWoUSOZzeYszR9zu9fZNp4FCxakW2Y2m/X111879LvZvffeq5UrV8rf3189evTQd99957DcVe/RjISEhNiLTHv27MnV57rdmYBHjhyR5Fjcc5f911Vc+V7w9vZ22XhzMi7bGZPffPNNumVHjhzRn3/+ma79VsfZr7/+OsP3oLvkHgAKO4pSAIBc4+HhoS+++EI+Pj4aM2aMJk+enO4HwKZNm9S6dWslJiZq2LBh9sLKnRo+fLj8/f318ssva8mSJemWJycnKzo6WqdPn76j5wkLC5N0o7CWEdvkvx9//LHDD6ONGzfqnXfeydE2MzNkyBBJN85qSXsW1rVr1zR48GAZhqHnn38+25dCZlWLFi0UGRmpv//+WxMnTnRY9tFHH+n3339Pt46vr69GjBiha9euqXPnzhmePXbmzBl9+eWX9sc///yz1q5dm67wdezYMe3fv18mkynTeW/Sut3r/Mwzz8jf318LFy7Ujz/+aG+3Wq0aOXKkzpw5o4YNG97yssMmTZpo5cqV8vX1Vbdu3bRs2TL7sueff14hISF6++239fHHH6cbT0pKilatWnXHRaPZs2fr2LFj6dqXL18uKfO5e5yladOmmjt3rq5fv55u2Q8//KDZs2dLksMlju6y/7pKly5dVLNmTX311VeaMGFCunmgDMPQb7/9Zp8E/E6EhYXp//7v/xQXF3fH27qdnLzHn3/+eUk35ojat2+fvT0xMVFDhw7NsOBtO87OmTPHYU6pffv26dVXX80wNnfJPQAUdly+BwDIVY0aNdKPP/6obt266fXXX9e0adPUtGlT+fv768CBA9q1a5ekGwWVd99912nPGxERoQULFqhXr17q0qWLIiIiVKNGDRUpUkRnzpzRjh07dP36df35559ZKmBkplKlSqpbt662b9+uqKgo1apVS56ennrsscf02GOPaejQofr88881a9YsrV+/XnXr1tWZM2f066+/avjw4RmOuXHjxgoJCVF0dLRatGihypUry8PDQ/369VPTpk0zjaVr16567rnn9PHHH6t27dp68MEHFRAQoPXr1+vixYtq3Lixxo8fn+Ox3o6Hh4c+//xzPfTQQxozZoyio6NVu3ZtHTlyRNu3b9fAgQM1a9asdOu99tprOnDggL788kvVqFFDDRo0UHh4uMxmsw4ePKh9+/apbt26evLJJyVJu3bt0j//+U+VKlXKfiewixcvasOGDUpOTtaQIUPsPzhv5Xa5q1Chgj766CM9/fTTevTRR3XfffepfPny2rFjhw4ePKjSpUtr/vz5t32epk2basWKFWrbtq26du2qJUuW6JFHHlFwcLC+++47Pfroo3r++ec1ceJE1a5dW8WLF9f58+e1Y8cOxcXFaenSpapdu3b2E/Jfs2fP1oABA1SzZk3VqFFDXl5e9n3Pz89Po0ePzvG2s2L//v3q16+fBg4cqLvvvlsVKlRQYmKiDh48qAMHDkiSXnjhBYfLW91l/3UVLy8vffvtt3r44Yc1evRozZw5U3Xr1lVISIhiYmK0c+dOXbhwQdOmTbvt3Gu389hjj2nGjBm6++671bRpU/n5+al69ep65ZVXnDSa/8nJe/z+++/XsGHD9P777+vuu+9Wy5YtFRQUpI0bN8rHx0ePPvqoli1b5nCHxb59++q9997TsmXLVL16dTVq1EixsbHasGGDOnXqpK1bt6a7fNhdcg8AhZ4BAIALxMTEGKNHjzYaNGhgBAUFGb6+vkaFChWMJ5980ti0aVOm6zVv3tyQZBw7dizTPpKMihUrZrjsyJEjxsCBA42qVasafn5+RtGiRY3q1asbPXr0MBYvXmwkJyfb+86dO9eQZIwZMyZbsRw+fNjo1KmTcddddxkeHh7ptrF//37j0UcfNUJCQoyAgACjQYMGxscff3zL2Ldt22a0bt3aKFasmGEymQxJxty5cw3DMIxjx44ZkozmzZtnGOcXX3xhNG3a1AgMDDT8/PyMWrVqGZMmTTISEhLS9e3Tp48hyfj5558z3NatXtvM7N6923j00UeNYsWKGUWKFDGaNGli/PDDD8bPP/9sSDL69OmT4Xrfffed8cgjjxghISGGt7e3ERISYjRs2NAYMWKE8ccff9j7HT582HjjjTeM++67zwgNDTV8fHyMsmXLGg899JDxzTffGFarNcux3i53hmEYv/32m/Hoo48ad911l+Ht7W1UqFDBGDBggHH69Ol027vVe2jDhg1GkSJFDF9fX2PFihX29nPnzhkjRowwatWqZQQEBBgBAQFGlSpVjI4dOxqff/65ce3aNXvf272GGeXz+++/N/r162fUqlXLCA4ONgICAoxq1aoZ/fv3Nw4cOJDl1yqndu3aZUyZMsVo06aNUaVKFSMgIMC+/3ft2tVYvnx5puu6w/6b0T5wuzxUrFjRyOxr9pgxYxz257Ti4uKMiRMnGnfffbd9/61UqZLx8MMPGx988IFx8eJFe9/bHQcye574+Hhj8ODBRvny5Q0vL69bbiM7Y77VsSQ773HDMAyr1WrMnj3bqFOnjuHr62uEhIQYffr0Mc6dO2e0atXKkJTuvXvq1CmjV69eRtmyZQ0/Pz+jRo0axltvvWWkpKRkmo+c5N4wbp1fAED2mAzDRbf8AQAAAIAcio+PV3h4uJKSkhQXFydPT8+8DgkAcIeYUwoAAACA29i/f78SEhIc2q5evarnnntOMTEx6tGjBwUpACggOFMKAAAAgNt44YUXNH/+fDVs2FChoaGKiYnRn3/+qdjYWFWuXFmbN29WqVKl8jpMAIAT5OuJzvft26fvv/9ex44d0+XLl/Xyyy8rKirKvtwwDC1evFg//fSTrl+/rsjISPXv31+hoaH2PvHx8frss8/0xx9/yGQy6d5771Xfvn1z7c5EAAAAADLXuXNnnT9/Xn/88Ye2bt0qSQoPD1f//v01YsQI3XXXXXkcIQDAWfL1mVJ/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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["def analyser_tokens(df, tokens_to_analyze=None):\n"," \"\"\"Analyse les tokens spécifiques et leur relation avec le sentiment\"\"\"\n","\n"," # Liste par défaut de tokens intéressants à analyser si non spécifiée\n"," if tokens_to_analyze is None:\n"," tokens_to_analyze = [\n"," # Pronoms\n"," 'I', 'you', 'we', 'they', 'me', 'my', 'your', 'our', 'their',\n","\n"," # Mots de sentiment positif\n"," 'love', 'good', 'great', 'best', 'happy', 'awesome', 'amazing', 'excellent', 'wonderful', 'perfect',\n","\n"," # Mots de sentiment négatif\n"," 'hate', 'bad', 'worst', 'sad', 'terrible', 'awful', 'horrible', 'sorry', 'disappointed', 'annoyed',\n","\n"," # Expressions internet\n"," 'lol', 'wtf', 'omg', 'bro', 'lmao', 'rofl', 'yolo', 'idk', 'smh', 'btw', 'imo', 'tbh',\n","\n"," # Intensificateurs\n"," 'very', 'really', 'so', 'too', 'absolutely', 'completely', 'totally',\n","\n"," # Négations\n"," 'not', 'no', 'never', \"don't\", \"can't\", \"won't\", \"didn't\",\n","\n"," # Émoticônes\n"," ':)', ':(', ':D', ';)', ':-)', ':-(', ':-D', ';-)'\n"," ]\n","\n"," print(f\"Analyse de {len(tokens_to_analyze)} tokens spécifiques...\")\n","\n"," # Créer un échantillon équilibré pour l'analyse\n"," sample_size = min(100000, df[df['sentiment'] == 'Négatif'].shape[0], df[df['sentiment'] == 'Positif'].shape[0])\n"," neg_samples = df[df['sentiment'] == 'Négatif'].sample(sample_size, random_state=42)\n"," pos_samples = df[df['sentiment'] == 'Positif'].sample(sample_size, random_state=42)\n","\n"," balanced_df = pd.concat([neg_samples, pos_samples])\n","\n"," # Analyser les tokens spécifiques\n"," results = {}\n"," for token in tokens_to_analyze:\n"," # Vérifier présence du token dans l'échantillon équilibré\n"," token_lower = token.lower() if isinstance(token, str) else \"\"\n","\n"," if token in [\"don't\", \"can't\", \"won't\", \"didn't\"]:\n"," # Cas spécial pour les contractions\n"," has_token = balanced_df['text'].astype(str).apply(\n"," lambda x: token.lower() in x.lower() or token.lower().replace(\"'\", \"\") in x.lower()\n"," )\n"," elif token in [':)', ':(', ':D', ';)', ':-)', ':-(', ':-D', ';-)']:\n"," # Cas exact pour les émoticônes\n"," has_token = balanced_df['text'].astype(str).apply(\n"," lambda x: token in x\n"," )\n"," else:\n"," # Recherche standard\n"," has_token = balanced_df['text'].astype(str).apply(\n"," lambda x: token_lower in x.lower() and\n"," (not token_lower.isalnum() or\n"," re.search(r'\\b' + re.escape(token_lower) + r'\\b', x.lower()) is not None)\n"," )\n","\n"," balanced_df[f'has_{token}'] = has_token\n","\n"," # Calculer les pourcentages par sentiment\n"," pos_with = balanced_df[(balanced_df['sentiment'] == 'Positif') & (balanced_df[f'has_{token}'] == True)].shape[0]\n"," pos_total = balanced_df[balanced_df['sentiment'] == 'Positif'].shape[0]\n"," pos_pct = pos_with / pos_total * 100 if pos_total > 0 else 0\n","\n"," neg_with = balanced_df[(balanced_df['sentiment'] == 'Négatif') & (balanced_df[f'has_{token}'] == True)].shape[0]\n"," neg_total = balanced_df[balanced_df['sentiment'] == 'Négatif'].shape[0]\n"," neg_pct = neg_with / neg_total * 100 if neg_total > 0 else 0\n","\n"," # Calculer le ratio de présence pos/neg (valeur positive = plus présent dans les tweets positifs)\n"," ratio = (pos_pct - neg_pct) / ((pos_pct + neg_pct) / 2) * 100 if (pos_pct + neg_pct) > 0 else 0\n","\n"," # Calculer la significativité (fréquence moyenne)\n"," avg_frequency = (pos_pct + neg_pct) / 2\n","\n"," results[token] = {\n"," 'pos_pct': pos_pct,\n"," 'neg_pct': neg_pct,\n"," 'diff': abs(pos_pct - neg_pct),\n"," 'ratio': ratio,\n"," 'avg_freq': avg_frequency,\n"," 'orientation': 'Positif' if ratio > 0 else 'Négatif'\n"," }\n","\n"," # Créer un dataframe pour faciliter la visualisation\n"," tokens_df = pd.DataFrame(results).T\n"," tokens_df['token'] = tokens_df.index\n","\n"," # Trier par différence absolue et afficher les plus discriminants\n"," most_discriminative = tokens_df.sort_values('diff', ascending=False)\n","\n"," print(\"\\nTokens les plus discriminants entre tweets positifs et négatifs:\")\n"," for token, row in most_discriminative.head(15).iterrows():\n"," print(f\"Token '{token}':\")\n"," print(f\" - Dans tweets positifs: {row['pos_pct']:.2f}%\")\n"," print(f\" - Dans tweets négatifs: {row['neg_pct']:.2f}%\")\n"," print(f\" - Différence absolue: {row['diff']:.2f}%\")\n"," print(f\" - Orientation: {row['orientation']} ({row['ratio']:.1f}%)\")\n"," print()\n","\n"," # Classification des tokens\n"," categories = {\n"," 'Pronoms': ['I', 'you', 'we', 'they', 'me', 'my', 'your', 'our', 'their'],\n"," 'Sentiment positif': ['love', 'good', 'great', 'best', 'happy', 'awesome', 'amazing', 'excellent', 'wonderful', 'perfect'],\n"," 'Sentiment négatif': ['hate', 'bad', 'worst', 'sad', 'terrible', 'awful', 'horrible', 'sorry', 'disappointed', 'annoyed'],\n"," 'Expressions internet': ['lol', 'wtf', 'omg', 'bro', 'lmao', 'rofl', 'yolo', 'idk', 'smh', 'btw', 'imo', 'tbh'],\n"," 'Intensificateurs': ['very', 'really', 'so', 'too', 'absolutely', 'completely', 'totally'],\n"," 'Négations': ['not', 'no', 'never', \"don't\", \"can't\", \"won't\", \"didn't\"],\n"," 'Émoticônes': [':)', ':(', ':D', ';)', ':-)', ':-(', ':-D', ';-)']\n"," }\n","\n"," # Créer des visuels par catégorie\n"," for category, cat_tokens in categories.items():\n"," # Filtrer les tokens de cette catégorie qui sont présents dans l'analyse\n"," valid_tokens = [t for t in cat_tokens if t in tokens_df.index]\n"," if not valid_tokens:\n"," continue\n","\n"," cat_df = tokens_df.loc[valid_tokens]\n","\n"," if len(cat_df) > 0:\n"," plt.figure(figsize=(12, 6))\n","\n"," # Trier par ratio\n"," cat_df_sorted = cat_df.sort_values('ratio')\n","\n"," # Créer des couleurs basées sur l'orientation (positif/négatif)\n"," colors = ['firebrick' if r < -20 else 'indianred' if r < 0 else 'royalblue' if r < 20 else 'darkblue'\n"," for r in cat_df_sorted['ratio']]\n","\n"," # Calculer les tailles mais utiliser un alpha fixe\n"," sizes = np.clip(cat_df_sorted['avg_freq'] / cat_df_sorted['avg_freq'].max() * 0.8 + 0.2, 0.3, 1.0)\n","\n"," # Créer le graphique\n"," bars = plt.bar(cat_df_sorted.index, cat_df_sorted['ratio'], color=colors,\n"," alpha=0.8, linewidth=1)\n","\n"," # Ajouter les valeurs d'occurrence moyenne\n"," for bar, freq in zip(bars, cat_df_sorted['avg_freq']):\n"," plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + (5 if bar.get_height() > 0 else -15),\n"," f\"{freq:.1f}%\", ha='center', va='bottom', fontsize=9, color='black')\n","\n"," plt.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n"," plt.title(f'Orientation des tokens - {category}', fontsize=15)\n"," plt.xlabel('Token')\n"," plt.ylabel('Orientation (% relatif)')\n"," plt.ylim(min(cat_df_sorted['ratio'].min() * 1.2, -100), max(cat_df_sorted['ratio'].max() * 1.2, 100))\n"," plt.xticks(rotation=45)\n"," plt.grid(axis='y', linestyle='--', alpha=0.3)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Visualisation globale des tokens les plus discriminants et les plus fréquents\n"," plt.figure(figsize=(15, 8))\n","\n"," # Sélectionner les top tokens par discrimination et fréquence\n"," top_tokens = tokens_df.sort_values('diff', ascending=False).head(20)\n","\n"," # Trier par ratio\n"," top_tokens = top_tokens.sort_values('ratio')\n","\n"," # Créer des couleurs basées sur l'orientation (positif/négatif)\n"," colors = ['firebrick' if r < -20 else 'indianred' if r < 0 else 'royalblue' if r < 20 else 'darkblue'\n"," for r in top_tokens['ratio']]\n","\n"," # Calculer les tailles mais utiliser un alpha fixe\n"," sizes = np.clip(top_tokens['avg_freq'] / top_tokens['avg_freq'].max() * 0.8 + 0.2, 0.3, 1.0)\n","\n"," # Créer le graphique\n"," bars = plt.bar(top_tokens.index, top_tokens['ratio'], color=colors,\n"," alpha=0.8, linewidth=1)\n","\n"," # Ajouter les valeurs d'occurrence moyenne\n"," for bar, freq in zip(bars, top_tokens['avg_freq']):\n"," plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + (5 if bar.get_height() > 0 else -15),\n"," f\"{freq:.1f}%\", ha='center', va='bottom', fontsize=9, color='black')\n","\n"," plt.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n"," plt.title('Top 20 tokens les plus discriminants (orientation relative)', fontsize=15)\n"," plt.xlabel('Token')\n"," plt.ylabel('Orientation relative (% positif vs négatif)')\n"," plt.ylim(min(top_tokens['ratio'].min() * 1.2, -100), max(top_tokens['ratio'].max() * 1.2, 100))\n"," plt.xticks(rotation=45)\n"," plt.grid(axis='y', linestyle='--', alpha=0.3)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," return tokens_df\n","\n","# Pour utiliser la fonction\n","tokens_df = analyser_tokens(df)"]},{"cell_type":"markdown","source":["#### 9. Stratégie de prétraitement pour la détection de sentiment dans les tweets\n","\n","Les résultats présents ici proposent une stratégie relativement exhaustive (bien qu'elle ne prétende pas l'être complètement) de traitement des tweets et de l'importance que l'on peut donner ou non à certains éléments textuels.\n","Toutes les méthodes proposées n'ont pas forcément été implémentées dans la fonction de prétraitement `preprocess_tweet` utilisée dans les notebooks d'entraînement de modèles prédictifs (pour des raisons de timings à respecter dans la réalisation de ce projet).\n","\n","Les pictogrammes suivants permettent d'identifier les fonctionnalités implémentées ou non :\n","✅ Implémenté dans `preprocess_tweet`\n","⚠️ Non implémenté (proposé mais pas utilisé)\n","\n","\n","##### Différences entre tweets positifs et négatifs\n","\n","Les résultats montrent des différences significatives dans la structure des tweets selon leur sentiment :\n","\n","- Les tweets positifs contiennent **93% plus d'URLs** que les négatifs\n","- Les tweets positifs contiennent **48% plus de mentions (@)**\n","- Les tweets positifs utilisent **39% plus de hashtags (#)**\n","- Les tweets positifs utilisent **39% plus de points d'exclamation (!)**\n","- Les tweets négatifs contiennent **24% plus d'ellipses (...)**\n","- Les tweets négatifs sont légèrement plus longs et contiennent plus de mots\n","\n","##### Stratégie de prétraitement recommandée\n","\n","Voici une stratégie de prétraitement complète basée sur ces observations :\n","\n","1. **Nettoyage de base** :\n"," - ⚠️ Correction des fautes d'orthographe courantes → *Non implémentée*\n"," - ✅ Suppression des caractères spéciaux non significatifs\n"," - ✅ Conversion en minuscules\n"," - ✅ Conversion des caractères HTML (`html.unescape`)\n"," - ⚠️ Conservation d'une feature sur le **ratio de majuscules** → *Non implémentée* (la casse est supprimée)\n","\n","2. **Traitement des éléments spéciaux** :\n"," - ✅ **URLs** : Remplacées par un token spécial \"``\"\n"," - ✅ **Mentions (@)** : Remplacées par un token \"``\"\n"," - ✅ **Hashtags (#)** : Hashtag séparé du mot et conservé sous forme \"# happy\"\n","\n","3. **Feature engineering** :\n"," - ⚠️ Création de features numériques pour :\n"," - Nombre d'URLs\n"," - Mentions\n"," - Hashtags\n"," - Points d'exclamation, de question, ellipses\n"," → *Non implémenté*\n"," - ⚠️ Calcul du ratio de majuscules → *Non implémenté*\n"," - ⚠️ Détection et classification des émoticônes → *Non implémenté*\n","\n","4. **Tokenisation et normalisation** :\n"," - ✅ Tokenisation (`nltk.word_tokenize`)\n"," - ✅ Lemmatisation (`WordNetLemmatizer`)\n"," - ✅ Suppression des stopwords, en conservant les négations importantes (`no`, `not`, etc.)\n","\n","##### Influence des éléments spécifiques sur le sentiment\n","\n","- **Présence de 'http'** : Très significatif (93% de différence). \n"," ✅ Remplacé par `` \n"," ⚠️ *Pas de feature numérique pour le comptage*\n","\n","- **Présence de '#'** : Significatif (39% de différence). \n"," ✅ Hashtag conservé via token `#` et mot séparé \n"," ⚠️ *Pas de feature de comptage du nombre de hashtags*\n","\n","- **Présence de '@'** : Très significatif (48% de différence). \n"," ✅ Remplacé par `` \n"," ⚠️ *Pas de feature de comptage du nombre de mentions*\n","\n","##### Traitement recommandé pour les éléments spéciaux\n","\n","- **URLs** : \n"," ✅ Remplacement par `` \n"," ⚠️ *Pas de feature numérique associée*\n","\n","- **Mentions (@)** : \n"," ✅ Remplacement par `` \n"," ⚠️ *Pas de feature numérique associée*\n","\n","- **Hashtags (#)** : \n"," ✅ Séparation du `#` et du mot qui suit \n"," ⚠️ *Pas de feature de comptage du nombre de `#`*\n","\n","---\n","\n","#### ⚙️ Bilan\n","\n","La fonction de prétraitement actuelle implémente **les opérations textuelles de base** et le **traitement structurel des éléments Twitter (URLs, mentions, hashtags)**.\n","\n","En revanche, les stratégies suivantes **n'ont pas encore été mises en œuvre** :\n","- Création de **features numériques structurées** (longueur, nombre de ponctuations, etc.)\n","- Conservation et exploitation de la **casse** (ratio de majuscules)\n","- Traitement spécifique des **émoticônes**\n","- **Correction orthographique**\n","\n","Ces éléments pourraient enrichir les modèles classiques (avec `TF-IDF`) ou être injectés comme **features auxiliaires** dans des modèles plus avancés (LSTM, BERT).\n"],"metadata":{"id":"akVkIiD4AplZ"}},{"cell_type":"markdown","metadata":{"id":"fUfJHL0rWjJC"},"source":["##### Exemple de prétraitement"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"zMCpAoskWjJC","executionInfo":{"status":"ok","timestamp":1751537534024,"user_tz":-120,"elapsed":62,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["df = raw_data.copy()"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"f0NIpj9hWjJC","executionInfo":{"status":"ok","timestamp":1751537534039,"user_tz":-120,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"3beee82f-3685-480b-80e6-801fb7d2e06a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" target ids date flag user text\n","0 0 1467810369 Mon Apr 06 22:19:45 PDT 2009 NO_QUERY _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t...\n","1 0 1467810672 Mon Apr 06 22:19:49 PDT 2009 NO_QUERY scotthamilton is upset that he can't update his Facebook by ...\n","2 0 1467810917 Mon Apr 06 22:19:53 PDT 2009 NO_QUERY mattycus @Kenichan I dived many times for the ball. Man...\n","3 0 1467811184 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY ElleCTF my whole body feels itchy and like its on fire \n","4 0 1467811193 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY Karoli @nationwideclass no, it's not behaving at all...."],"text/html":["\n","
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targetidsdateflagusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df"}},"metadata":{},"execution_count":31}],"source":["df.head()"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"OkZs7W-nWjJC","executionInfo":{"status":"ok","timestamp":1751537534086,"user_tz":-120,"elapsed":40,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def example_preprocess_tweet(tweet):\n"," # Features numériques\n"," features = {\n"," 'url_count': tweet.lower().count('http'),\n"," 'mention_count': tweet.count('@'),\n"," 'hashtag_count': tweet.count('#'),\n"," 'exclamation_count': tweet.count('!'),\n"," 'question_count': tweet.count('?'),\n"," 'ellipsis_count': tweet.count('...'),\n"," 'capitals_ratio': sum(1 for c in tweet if c.isupper()) / len(tweet) if len(tweet) > 0 else 0\n"," }\n","\n"," # Prétraitement du texte\n"," processed_text = tweet\n","\n"," # Remplacer les URLs\n"," processed_text = re.sub(r'https?://\\S+', '', processed_text)\n","\n"," # Traiter les mentions\n"," processed_text = re.sub(r'@\\w+', '', processed_text)\n","\n"," # Traiter les hashtags (séparer # du mot)\n"," processed_text = re.sub(r'#(\\w+)', r'# \\1', processed_text)\n","\n"," # Traiter le langage html\n"," processed_text = html.unescape(processed_text)\n","\n"," # Tokenisation simple (split par espaces)\n"," tokens = processed_text.lower().split()\n","\n"," # Autres étapes de nettoyage...\n","\n"," return processed_text, features"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"PMJiRgr3WjJC","executionInfo":{"status":"ok","timestamp":1751537534120,"user_tz":-120,"elapsed":71,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"864b28f5-cc48-43e2-ee70-4e355ec66cdc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tweet prétraité: I'm SO frustrated w/ flight # delayed AGAIN!!\n"," 😡 Their customer service doesn't care :( Check this horrible experience: \n"," Can't wait to fly with \"someone\" else next time!\n","\n","Caractéristiques extraites:\n","- url_count: 1\n","- mention_count: 1\n","- hashtag_count: 1\n","- exclamation_count: 3\n","- question_count: 0\n","- ellipsis_count: 1\n","- capitals_ratio: 0.06422018348623854\n"]}],"source":["# Exemple de tweet\n","tweet_example = \"\"\"I'm SO frustrated w/ @AirParadis flight #delayed AGAIN!!\n"," 😡 Their customer service doesn't care :( Check this horrible experience: http://bit.ly/2xYz...\n"," Can't wait to fly with "someone" else next time!\"\"\"\n","\n","# Prétraitement\n","processed_tweet, tweet_features = example_preprocess_tweet(tweet_example)\n","print(\"Tweet prétraité:\", processed_tweet)\n","print(\"\\nCaractéristiques extraites:\")\n","for feature, value in tweet_features.items():\n"," print(f\"- {feature}: {value}\")"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"1tP38oGLWjJC","executionInfo":{"status":"ok","timestamp":1751537534157,"user_tz":-120,"elapsed":36,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import re\n","import string\n","from tqdm import tqdm\n","\n","def pretraiter_tweets_dataframe(df, colonne_texte='text', afficher_progression=True, optimiser=True):\n"," \"\"\"\n"," Fonction complète pour prétraiter tous les tweets d'un DataFrame.\n","\n"," Args:\n"," df (pd.DataFrame): DataFrame contenant les tweets\n"," colonne_texte (str): Nom de la colonne contenant les tweets à prétraiter\n"," afficher_progression (bool): Afficher une barre de progression\n"," optimiser (bool): Si True, ne conserve que les colonnes utiles à l'entraînement\n","\n"," Returns:\n"," tuple: (DataFrame prétraité, liste des noms de features)\n"," - DataFrame avec les tweets prétraités et les features extraites\n"," - Liste des noms de features extraites pour faciliter l'analyse\n"," \"\"\"\n"," # Dictionnaire pour les contractions\n"," contractions = {\n"," \"ain't\": \"am not\", \"aren't\": \"are not\", \"can't\": \"cannot\",\n"," \"couldn't\": \"could not\", \"didn't\": \"did not\", \"doesn't\": \"does not\",\n"," \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\",\n"," \"haven't\": \"have not\", \"he'd\": \"he would\", \"he'll\": \"he will\",\n"," \"he's\": \"he is\", \"i'd\": \"i would\", \"i'll\": \"i will\", \"i'm\": \"i am\",\n"," \"i've\": \"i have\", \"isn't\": \"is not\", \"it's\": \"it is\",\n"," \"let's\": \"let us\", \"shouldn't\": \"should not\", \"that's\": \"that is\",\n"," \"there's\": \"there is\", \"they'd\": \"they would\", \"they'll\": \"they will\",\n"," \"they're\": \"they are\", \"they've\": \"they have\", \"wasn't\": \"was not\",\n"," \"we'd\": \"we would\", \"we're\": \"we are\", \"we've\": \"we have\",\n"," \"weren't\": \"were not\", \"what's\": \"what is\", \"where's\": \"where is\",\n"," \"who's\": \"who is\", \"won't\": \"will not\", \"wouldn't\": \"would not\",\n"," \"you'd\": \"you would\", \"you'll\": \"you will\", \"you're\": \"you are\",\n"," \"you've\": \"you have\", \"gonna\": \"going to\", \"wanna\": \"want to\"\n"," }\n","\n"," # Dictionnaire pour les émoticônes\n"," emoticons = {\n"," # Positifs\n"," ':)': '', ':-)': '',\n"," ':D': '', '=)': '',\n"," ':-D': '', ':p': '',\n"," ':-p': '', ';)': '',\n"," ';-)': '', ':3': '',\n"," '<3': '',\n","\n"," # Négatifs\n"," ':(': '', ':-(': '',\n"," '=(': '', ':-(': '',\n"," ':\\'(': '', ':/': '',\n"," ':\\\\': '', ':|': '',\n"," ':-|': '', '>:(': '',\n"," 'D:': ''\n"," }\n","\n"," # Liste simplifiée de stopwords en anglais\n"," english_stopwords = {\n"," 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by',\n"," 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\n"," 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again',\n"," 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both',\n"," 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so',\n"," 'than', 'too', 'very', 'can', 'will', 'just', 'should', 'now'\n"," }\n"," # Conserver les négations\n"," negations = {'no', 'not', 'none', 'nor', 'never', 'neither'}\n"," english_stopwords = english_stopwords - negations\n","\n"," # Définir la liste des noms de features\n"," feature_names = ['text_length', 'word_count', 'url_count', 'mention_count',\n"," 'hashtag_count', 'exclamation_count', 'question_count',\n"," 'ellipsis_count', 'capitals_ratio', 'positive_emoticon',\n"," 'negative_emoticon']\n","\n"," # Fonction pour prétraiter un tweet individuel\n"," def preprocess_tweet(tweet):\n"," if pd.isna(tweet) or not isinstance(tweet, str):\n"," # Gérer les valeurs NaN/None ou non-string\n"," return \"\", {\n"," 'text_length': 0, 'word_count': 0, 'url_count': 0, 'mention_count': 0,\n"," 'hashtag_count': 0, 'exclamation_count': 0, 'question_count': 0,\n"," 'ellipsis_count': 0, 'capitals_ratio': 0, 'positive_emoticon': 0,\n"," 'negative_emoticon': 0\n"," }\n","\n"," # Extraction des features numériques\n"," features = {\n"," 'text_length': len(tweet),\n"," 'word_count': len(tweet.split()),\n"," 'url_count': tweet.lower().count('http'),\n"," 'mention_count': tweet.count('@'),\n"," 'hashtag_count': tweet.count('#'),\n"," 'exclamation_count': tweet.count('!'),\n"," 'question_count': tweet.count('?'),\n"," 'ellipsis_count': tweet.count('...'),\n"," 'capitals_ratio': sum(1 for c in tweet if c.isupper()) / len(tweet) if len(tweet) > 0 else 0,\n"," 'positive_emoticon': sum(tweet.count(emot) for emot in [':)', ':D', '=)', ':-)', ':-D']),\n"," 'negative_emoticon': sum(tweet.count(emot) for emot in [':(', '=(', ':-(', ':\\'(', ':-(']),\n"," }\n","\n"," # Prétraitement initial du texte\n"," processed_text = tweet\n","\n"," # Remplacer les émoticônes par des tokens spéciaux\n"," for emoticon, replacement in emoticons.items():\n"," processed_text = processed_text.replace(emoticon, ' ' + replacement + ' ')\n","\n"," # Remplacer les URLs\n"," processed_text = re.sub(r'https?://\\S+', ' ', processed_text)\n","\n"," # Traiter les mentions\n"," processed_text = re.sub(r'@\\w+', ' ', processed_text)\n","\n"," # Traiter les hashtags (séparer # du mot)\n"," processed_text = re.sub(r'#(\\w+)', r' # \\1 ', processed_text)\n","\n"," # Traiter le langage html\n"," processed_text = html.unescape(processed_text)\n","\n"," # Convertir les contractions\n"," words = processed_text.split()\n"," processed_words = []\n"," for word in words:\n"," word_lower = word.lower()\n"," # Vérifier les contractions\n"," if word_lower in contractions:\n"," processed_words.append(contractions[word_lower])\n"," else:\n"," processed_words.append(word)\n"," processed_text = ' '.join(processed_words)\n","\n"," # Tokenisation simple (split par espaces)\n"," tokens = processed_text.lower().split()\n","\n"," # Conserver les tokens spéciaux\n"," special_tokens = ['', '', '', '', '#']\n","\n"," # Suppression des stopwords\n"," tokens = [token for token in tokens if token.lower() not in english_stopwords or token.lower() in special_tokens]\n","\n"," # Nettoyer la ponctuation (sauf pour les tokens spéciaux)\n"," cleaned_tokens = []\n"," for token in tokens:\n"," if token.lower() in special_tokens:\n"," cleaned_tokens.append(token)\n"," else:\n"," # Supprimer la ponctuation\n"," cleaned_token = ''.join(c for c in token if c not in string.punctuation)\n"," if cleaned_token: # Vérifier que le token n'est pas vide\n"," cleaned_tokens.append(cleaned_token)\n","\n"," # Reconstruire le texte traité\n"," processed_text = ' '.join(cleaned_tokens)\n","\n"," # Nettoyer les espaces multiples\n"," processed_text = re.sub(r'\\s+', ' ', processed_text).strip()\n","\n"," return processed_text, features\n","\n"," # Créer une copie du DataFrame pour éviter de modifier l'original\n"," df_processed = df.copy()\n","\n"," # Initialiser les colonnes pour les features et le texte prétraité\n"," for feature in feature_names:\n"," df_processed[feature] = np.nan\n","\n"," df_processed['processed_text'] = ''\n","\n"," # Afficher les informations sur le DataFrame avant prétraitement\n"," print(f\"DataFrame original : {df.shape[0]} lignes × {df.shape[1]} colonnes\")\n","\n"," # Appliquer le prétraitement à chaque tweet\n"," if afficher_progression:\n"," print(\"Prétraitement des tweets en cours...\")\n"," tqdm.pandas(desc=\"Progression\")\n"," processed_data = df_processed[colonne_texte].progress_apply(preprocess_tweet)\n"," else:\n"," processed_data = df_processed[colonne_texte].apply(preprocess_tweet)\n","\n"," # Extraire le texte prétraité et les features\n"," for i, (processed_text, features) in enumerate(processed_data):\n"," df_processed.at[i, 'processed_text'] = processed_text\n"," for feature, value in features.items():\n"," df_processed.at[i, feature] = value\n","\n"," print(f\"Prétraitement terminé. {len(df_processed)} tweets ont été traités.\")\n","\n"," # Si optimisation demandée, ne garder que les colonnes utiles\n"," if optimiser:\n"," # Identifier les colonnes à conserver : target (si elle existe) + processed_text + features\n"," colonnes_utiles = ['processed_text'] + feature_names\n","\n"," # Ajouter la colonne target si elle existe (différents noms possibles)\n"," target_cols = ['target', 'sentiment', 'label']\n"," for target_col in target_cols:\n"," if target_col in df_processed.columns:\n"," colonnes_utiles.append(target_col)\n","\n"," # Créer un DataFrame optimisé\n"," colonnes_avant = df_processed.shape[1]\n"," df_optimise = df_processed[colonnes_utiles].copy()\n"," colonnes_apres = df_optimise.shape[1]\n","\n"," print(f\"DataFrame optimisé : {colonnes_avant - colonnes_apres} colonnes supprimées\")\n"," print(f\"Nouvelles dimensions : {df_optimise.shape[0]} lignes × {df_optimise.shape[1]} colonnes\")\n","\n"," return df_optimise, feature_names\n","\n"," return df_processed, feature_names"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"KFTu3YlGWjJD","executionInfo":{"status":"ok","timestamp":1751537536475,"user_tz":-120,"elapsed":2317,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9db8d046-4061-4cf9-b016-965ad0eaa1a6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Distribution des sentiments dans l'échantillon : target\n","0 5000\n","4 5000\n","Name: count, dtype: int64\n","DataFrame original : 10000 lignes × 6 colonnes\n","Prétraitement des tweets en cours...\n"]},{"output_type":"stream","name":"stderr","text":["Progression: 100%|██████████| 10000/10000 [00:00<00:00, 21211.54it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Prétraitement terminé. 10000 tweets ont été traités.\n","DataFrame optimisé : 5 colonnes supprimées\n","Nouvelles dimensions : 10000 lignes × 13 colonnes\n"]}],"source":["# Après avoir chargé le DataFrame complet\n","# Assurez-vous d'avoir des exemples des deux classes\n","df_complet = raw_data.copy()\n","\n","# Échantillonnage stratifié pour avoir des tweets des deux sentiments\n","df_negatifs = df_complet[df_complet['target'] == 0].sample(5000, random_state=42)\n","df_positifs = df_complet[df_complet['target'] == 4].sample(5000, random_state=42)\n","df_echantillon = pd.concat([df_negatifs, df_positifs]).reset_index(drop=True)\n","\n","print(f\"Distribution des sentiments dans l'échantillon : {df_echantillon['target'].value_counts()}\")\n","\n","# Prétraiter l'échantillon équilibré\n","df_optimise, feature_names = pretraiter_tweets_dataframe(df_echantillon, colonne_texte='text')"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":765},"id":"4WOZMce9WjJI","executionInfo":{"status":"ok","timestamp":1751537536525,"user_tz":-120,"elapsed":47,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"51cab5f2-f204-4cd2-839c-4a12474030fb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" processed_text text_length word_count url_count mention_count hashtag_count exclamation_count question_count ellipsis_count capitals_ratio positive_emoticon negative_emoticon target\n","0 oh no did u order from that is horrible 59.0 10.0 0.0 1.0 0.0 1.0 1.0 0.0 0.000000 0.0 0.0 0\n","1 great hard training weekend is over couple day... 121.0 25.0 0.0 0.0 0.0 1.0 0.0 0.0 0.016529 0.0 0.0 0\n","2 right work 5 hours go i am free xd 56.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 0.071429 0.0 0.0 0\n","3 i am craving japanese food 31.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.032258 0.0 0.0 0\n","4 jean michel jarre concert tomorrow gotta work ... 65.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.046154 0.0 0.0 0\n","5 dammiitt i wish i had mtv 45.0 7.0 0.0 1.0 0.0 2.0 0.0 0.0 0.133333 0.0 0.0 0\n","6 you knowits really sad that u know t... 112.0 18.0 0.0 1.0 0.0 0.0 0.0 0.0 0.035714 0.0 0.0 0\n","7 i know 23.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.086957 0.0 0.0 0\n","8 i wish i had that much say 44.0 9.0 0.0 1.0 0.0 0.0 0.0 0.0 0.045455 0.0 0.0 0\n","9 ow i cannot move my neck my back hurts much i ... 76.0 18.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013158 0.0 0.0 0"],"text/html":["\n","
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processed_texttext_lengthword_counturl_countmention_counthashtag_countexclamation_countquestion_countellipsis_countcapitals_ratiopositive_emoticonnegative_emoticontarget
0<mention> oh no did u order from that is horrible59.010.00.01.00.01.01.00.00.0000000.00.00
1great hard training weekend is over couple day...121.025.00.00.00.01.00.00.00.0165290.00.00
2right work 5 hours go i am free xd56.013.00.00.00.00.00.00.00.0714290.00.00
3i am craving japanese food31.06.00.00.00.00.00.00.00.0322580.00.00
4jean michel jarre concert tomorrow gotta work ...65.010.00.00.00.00.00.00.00.0461540.00.00
5<mention> dammiitt i wish i had mtv45.07.00.01.00.02.00.00.00.1333330.00.00
6<mention> you knowits really sad that u know t...112.018.00.01.00.00.00.00.00.0357140.00.00
7<mention> i know23.03.00.01.00.00.00.00.00.0869570.00.00
8<mention> i wish i had that much say44.09.00.01.00.00.00.00.00.0454550.00.00
9ow i cannot move my neck my back hurts much i ...76.018.00.00.00.00.00.00.00.0131580.00.00
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_optimise","summary":"{\n \"name\": \"df_optimise\",\n \"rows\": 10000,\n \"fields\": [\n {\n \"column\": \"processed_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9892,\n \"samples\": [\n \"we are going see flamenco dance fire sand tatiana garrido 900 pm dar alassad opera house damascus i cannot take my camera\",\n \" looks like you are having fun that pic you are amazing i cannot wait see you wrestling soon take care \\u00e2\\u0099\\u00a5\",\n \"belated rip great advocate friend jim stephenson nc coastal federation http bitlylpfm2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text_length\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 36.46088940159834,\n \"min\": 8.0,\n \"max\": 282.0,\n \"num_unique_values\": 151,\n \"samples\": [\n 34.0,\n 98.0,\n 51.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.967032850839397,\n \"min\": 1.0,\n \"max\": 34.0,\n \"num_unique_values\": 34,\n \"samples\": [\n 19.0,\n 5.0,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"url_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.21370570187224106,\n \"min\": 0.0,\n \"max\": 2.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.0,\n 1.0,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"mention_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5909474781164555,\n \"min\": 0.0,\n \"max\": 8.0,\n \"num_unique_values\": 9,\n \"samples\": [\n 7.0,\n 0.0,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hashtag_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.19936831436432761,\n \"min\": 0.0,\n \"max\": 8.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.0,\n 8.0,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"exclamation_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.2836116257968069,\n \"min\": 0.0,\n \"max\": 35.0,\n \"num_unique_values\": 20,\n \"samples\": [\n 1.0,\n 25.0,\n 14.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"question_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7557943056832718,\n \"min\": 0.0,\n \"max\": 45.0,\n \"num_unique_values\": 14,\n \"samples\": [\n 6.0,\n 16.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ellipsis_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5081963954590616,\n \"min\": 0.0,\n \"max\": 7.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 7.0,\n 4.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"capitals_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07180467551206295,\n \"min\": 0.0,\n \"max\": 0.875,\n \"num_unique_values\": 992,\n \"samples\": [\n 0.07476635514018691,\n 0.07092198581560284,\n 0.30612244897959184\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"positive_emoticon\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02448877232522874,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"negative_emoticon\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03462195644366273,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 2,\n \"samples\": [\n 4,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":36}],"source":["df_optimise.head(10)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"RRKMq2yvWjJI","executionInfo":{"status":"ok","timestamp":1751537536543,"user_tz":-120,"elapsed":17,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b6e781e1-3e70-479e-ece9-6adb4c3d9bfe"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tweet 1 : oh no did u order from that is horrible\n","Tweet 2 : great hard training weekend is over couple days rest lets do it again lots computer time put\n","Tweet 3 : right work 5 hours go i am free xd\n","Tweet 4 : i am craving japanese food\n","Tweet 5 : jean michel jarre concert tomorrow gotta work 73 though\n","Tweet 6 : dammiitt i wish i had mtv\n","Tweet 7 : you knowits really sad that u know that ur classmate do not care you sad memories tt\n","Tweet 8 : i know\n","Tweet 9 : i wish i had that much say\n","Tweet 10 : ow i cannot move my neck my back hurts much i do not know \n"]}],"source":["for row in enumerate(df_optimise['processed_text'].head(10)):\n"," print(f\"Tweet {row[0] + 1} : {row[1]}\")"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"FjgFb1bJWjJI","executionInfo":{"status":"ok","timestamp":1751537536549,"user_tz":-120,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def analyser_differences_sentiments(df, feature_names):\n"," \"\"\"\n"," Analyse les différences de features entre tweets positifs et négatifs.\n"," Affiche les résultats avec les features en lignes et les sentiments en colonnes.\n","\n"," Args:\n"," df (pd.DataFrame): DataFrame prétraité avec colonne 'target'\n"," feature_names (list): Liste des noms de features à analyser\n"," \"\"\"\n"," # Convertir les valeurs numériques de target en catégories lisibles\n"," sentiment_mapping = {0: 'Négatif', 4: 'Positif'}\n"," if 'target' in df.columns:\n"," df['sentiment'] = df['target'].map(sentiment_mapping)\n","\n"," # Compter le nombre de tweets par sentiment\n"," sentiment_counts = df['sentiment'].value_counts()\n"," print(f\"\\nDistribution des sentiments : {sentiment_counts.to_dict()}\")\n","\n"," # Vérifier si les deux sentiments sont présents\n"," if 'Négatif' in sentiment_counts and 'Positif' in sentiment_counts:\n"," # Calculer les moyennes par sentiment\n"," neg_means = df[df['sentiment'] == 'Négatif'][feature_names].mean()\n"," pos_means = df[df['sentiment'] == 'Positif'][feature_names].mean()\n","\n"," # Calculer la différence relative (en pourcentage)\n"," epsilon = 0.0001 # Pour éviter division par zéro\n"," diff_pct = ((pos_means - neg_means) / neg_means.replace(0, epsilon) * 100)\n","\n"," # Créer un DataFrame complet avec toutes les statistiques\n"," stats_df = pd.DataFrame({\n"," 'Négatif': neg_means,\n"," 'Positif': pos_means,\n"," 'Différence (%)': diff_pct.round(1)\n"," }).round(4)\n","\n"," # Ajouter les tendances avant de renommer les index\n"," stats_df['Tendance'] = stats_df['Différence (%)'].apply(\n"," lambda x: '↑' if x > 0 else ('↓' if x < 0 else '−')\n"," )\n","\n"," # Trier par valeur absolue de la différence\n"," # Nous utilisons sort_values() directement sur le DataFrame au lieu de iloc\n"," stats_df = stats_df.reindex(stats_df['Différence (%)'].abs().sort_values(ascending=False).index)\n","\n"," # Renommer les index pour plus de clarté (après le tri)\n"," stats_df.index = [f.replace('_', ' ').title() for f in stats_df.index]\n","\n"," # Réorganiser les colonnes\n"," stats_df = stats_df[['Négatif', 'Positif', 'Différence (%)', 'Tendance']]\n","\n"," # Afficher le tableau des statistiques\n"," print(\"\\nMoyennes des features par sentiment (triées par impact) :\")\n"," print(stats_df)\n","\n"," # Afficher un résumé des features les plus discriminantes\n"," print(\"\\nFeatures les plus discriminantes entre sentiments positifs et négatifs :\")\n","\n"," for i, (feature, row) in enumerate(stats_df.iterrows()):\n"," # if i >= 5: # Limiter à 5 features\n"," # break\n","\n"," diff = row['Différence (%)']\n"," if abs(diff) > 1000: # Pour éviter les valeurs extrêmes\n"," print(f\"{feature}: {row['Tendance']} très significatif\")\n"," else:\n"," print(f\"{feature}: {row['Tendance']} {abs(diff):.1f}%\")\n"," else:\n"," print(\"\\nAttention : Les deux sentiments (positif et négatif) ne sont pas représentés dans l'échantillon.\")\n"," print(\"L'analyse comparative ne peut pas être effectuée.\")\n","\n"," if 'Négatif' in sentiment_counts:\n"," print(\"\\nStatistiques pour les tweets négatifs uniquement :\")\n"," neg_stats = df[df['sentiment'] == 'Négatif'][feature_names].mean().round(4)\n"," # Renommer les index pour plus de clarté\n"," neg_stats.index = [f.replace('_', ' ').title() for f in neg_stats.index]\n"," print(neg_stats)\n"," elif 'Positif' in sentiment_counts:\n"," print(\"\\nStatistiques pour les tweets positifs uniquement :\")\n"," pos_stats = df[df['sentiment'] == 'Positif'][feature_names].mean().round(4)\n"," # Renommer les index pour plus de clarté\n"," pos_stats.index = [f.replace('_', ' ').title() for f in pos_stats.index]\n"," print(pos_stats)"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"v5xMFoR2WjJI","executionInfo":{"status":"ok","timestamp":1751537536600,"user_tz":-120,"elapsed":47,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c40bbf14-0ff4-4fd7-fd4b-107fb22a747a"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Distribution des sentiments : {'Négatif': 5000, 'Positif': 5000}\n","\n","Moyennes des features par sentiment (triées par impact) :\n"," Négatif Positif Différence (%) Tendance\n","Url Count 0.0280 0.0644 130.0 ↑\n","Positive Emoticon 0.0008 0.0004 -50.0 ↓\n","Mention Count 0.4012 0.5908 47.3 ↑\n","Exclamation Count 0.4792 0.6580 37.3 ↑\n","Negative Emoticon 0.0014 0.0010 -28.6 ↓\n","Ellipsis Count 0.2074 0.1496 -27.9 ↓\n","Hashtag Count 0.0250 0.0300 20.0 ↑\n","Capitals Ratio 0.0428 0.0491 14.8 ↑\n","Word Count 13.5776 12.8846 -5.1 ↓\n","Question Count 0.1524 0.1570 3.0 ↑\n","Text Length 74.2904 74.5670 0.4 ↑\n","\n","Features les plus discriminantes entre sentiments positifs et négatifs :\n","Url Count: ↑ 130.0%\n","Positive Emoticon: ↓ 50.0%\n","Mention Count: ↑ 47.3%\n","Exclamation Count: ↑ 37.3%\n","Negative Emoticon: ↓ 28.6%\n","Ellipsis Count: ↓ 27.9%\n","Hashtag Count: ↑ 20.0%\n","Capitals Ratio: ↑ 14.8%\n","Word Count: ↓ 5.1%\n","Question Count: ↑ 3.0%\n","Text Length: ↑ 0.4%\n"]}],"source":["analyser_differences_sentiments(df_optimise, feature_names)"]},{"cell_type":"markdown","metadata":{"id":"2cOCpJN4WjJI"},"source":["#### 10. Recommandations de modélisation\n","\n","- Approches recommandées pour la vectorisation \n","- Modèles à privilégier \n","- Stratégies d’évaluation adaptées "]},{"cell_type":"markdown","source":["##### Recommandations générales (aspects théoriques)\n","\n","1. Approches classiques de Machine Learning:\n"," - Modèles basés sur les sacs de mots (BoW) ou TF-IDF avec classifieurs comme Régression Logistique, SVM, Random Forest ou Naive Bayes\n"," - Avantages: rapides à entraîner, interprétables\n"," - Inconvénients: ne capturent pas la sémantique et l'ordre des mots\n","\n","2. Word Embeddings + Classifieurs:\n"," - Utiliser des embeddings pré-entraînés (Word2Vec, GloVe, FastText) avec des classifieurs ML\n"," - Avantages: capture la sémantique des mots, gère les mots hors vocabulaire (FastText)\n"," - Inconvénients: perd l'information de séquence\n","\n","3. Réseaux de neurones récurrents (RNN, **LSTM**, GRU):\n"," - Avantages: capture l'information séquentielle, efficace pour le texte\n"," - Inconvénients: temps d'entraînement plus long, risque de surapprentissage\n","\n","4. Modèles transformers (BERT, Sentence Transformers, RoBERTa, DistilBERT):\n"," - Fine-tuning de modèles pré-entraînés spécifiques à Twitter comme BERTweet\n"," - Avantages: état de l'art en NLP, capture le contexte bidirectionnel\n"," - Inconvénients: coûteux en ressources, complexe à déployer\n","\n","5. Approches d'ensemble:\n"," - Combiner plusieurs modèles pour obtenir de meilleures performances\n"," - Avantages: souvent meilleures performances, plus robuste\n"," - Inconvénients: complexité accrue, temps d'inférence plus long\n","\n","Considérations importantes:\n","\n","1. Déséquilibre des classes: utiliser des techniques comme SMOTE, sous-échantillonnage, ou pondération des classes\n","2. Validation croisée: essentielle pour évaluer correctement les performances\n","3. Métriques d'évaluation: ne pas se limiter à l'accuracy, utiliser F1-score, précision, rappel, et AUC-ROC\n","4. Interprétabilité: pour certaines applications, privilégier des modèles interprétables ou utiliser SHAP/LIME\n","5. Dépendance temporelle: considérer l'évolution du langage sur Twitter au fil du temps"],"metadata":{"id":"I2LPvavPLiie"}},{"cell_type":"markdown","source":["##### Ce que nous ferons:\n","\n","- **Analyse classique** : \n"," - Solutions retenues : vectorisation par **BoW** et **BoW+TF-IDF**, avec classification par **Naive Bayes**, **Random Forest**, **SVM** et **Régression Logistique**. \n"," - Raisons : \n"," - Ces modèles sont des classiques éprouvés en classification supervisée de texte, offrant un bon équilibre entre simplicité, rapidité d’entraînement, et robustesse. \n"," - **Naive Bayes** est simple et efficace sur des données textuelles où l’indépendance conditionnelle approximative est acceptable. \n"," - **SVM linéaire** est souvent performant pour la classification de texte, capable de gérer des espaces de grande dimension avec des marges maximales. \n"," - **Régression logistique** est facilement interprétable et probabiliste, ce qui est utile pour estimer la confiance des prédictions. \n"," - **Random Forest** apporte une approche non-linéaire, robuste au bruit, et peut capter des interactions complexes. \n"," - Ce choix restreint permet une comparaison claire des performances tout en évitant la surcomplexité. \n"," - Cette étape sert de base pour évaluer les performances des modèles classiques avant de passer à des méthodes plus sophistiquées.\n","\n","- **Classification par word embeddings** : \n"," - Solutions retenues : utilisation d’embeddings pré-entraînés **Word2Vec** et **GloVe 300d**, combinés à un modèle Deep Learning intégrant un **layer LSTM**. \n"," - Raisons : \n"," - Les embeddings capturent la sémantique des mots de manière dense et continue, bien plus informative que BoW/TF-IDF. \n"," - Le choix du **LSTM** (Long Short-Term Memory) s’explique par sa capacité à gérer efficacement la dépendance à long terme dans les séquences, un problème où les réseaux RNN classiques échouent souvent à cause du gradient qui disparaît ou explose. \n"," - Comparé au GRU (Gated Recurrent Unit), le LSTM est plus éprouvé et offre souvent une meilleure gestion des dépendances complexes, même si GRU est plus léger. \n"," - Cette architecture permet ainsi de tirer parti à la fois des représentations vectorielles riches des mots et de la dynamique temporelle des textes (l’ordre des mots). \n"," - Ce compromis performance/complexité est bien adapté à la ressource machine disponible et à l’objectif du projet.\n","\n","- **Exploration du modèle BERT** : \n"," - Solutions retenues : expérimentation avec un modèle **DistillBERT** pour classification binaire. \n"," - Raisons : modèle Transformer plus récent et efficace, permettant de mesurer un éventuel gain de performance. \n"," - Cette étape vise à évaluer le rapport coût/performance en termes de précision et de ressources nécessaires.\n","\n","\n"],"metadata":{"id":"FJr9Hww7IGI_"}},{"cell_type":"markdown","source":["# Enregistrement de dépendances"],"metadata":{"id":"-KIwrBGntpka"}},{"cell_type":"code","source":["!pip freeze > requirements_analyse_exploratoire.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"R50V0pO8IKUE","executionInfo":{"status":"ok","timestamp":1751540223044,"user_tz":-120,"elapsed":5745,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0a1bc19a-87ec-4b66-ef94-acdbf3817699"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO: Not scanning for jupyter notebooks.\n","INFO: Successfully saved requirements file in ./requirements.txt\n"]}]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.10.12"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/notebooks/p07_02-notebook-modele-simple.ipynb b/notebooks/p07_02-notebook-modele-simple.ipynb deleted file mode 100644 index b0c168f..0000000 --- a/notebooks/p07_02-notebook-modele-simple.ipynb +++ /dev/null @@ -1,5026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "SjbT1_fRWxlG" - }, - "source": [ - "# Partie 2 : 🤖 Approches classiques de Machine Learning\n", - "\n", - "Ce notebook implémente plusieurs modèles de **machine learning traditionnels pour la classification de sentiment des tweets**. Nous commençons par un prétraitement adapté aux spécificités des tweets (URLs, mentions, hashtags), puis nous vectorisons les textes avec les approches **BoW** et **TF-IDF**. Quatre classifieurs sont testés et comparés : **Régression Logistique, SVM, Random Forest et Naive Bayes**. Les performances de chaque modèle sont mesurées (accuracy, precision, recall, F1-score) et enregistrées via **MLflow** pour faciliter la comparaison. Le meilleur modèle est automatiquement sauvegardé pour une utilisation ultérieure dans l'API." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "99IN9CIrWxlH" - }, - "source": [ - "## Importation des bibliothèques" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 16353, - "status": "ok", - "timestamp": 1751704230169, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "nT5hy4pKcc81", - "outputId": "92cbb5ad-11d9-4339-e117-f68959e1c2a4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n" - ] - }, - { - "data": { - "text/plain": [ - "['Copie de notebook-connexion-mlflow (1).ipynb',\n", - " 'Copie de 03-notebook-modele-avance.ipynb',\n", - " 'Copie de 02-notebook-modele-simple.ipynb',\n", - " 'Copie de 01-notebook-analyse-exploratoire.ipynb',\n", - " 'Copie de 04-notebook-modele-bert.ipynb',\n", - " 'content',\n", - " 'Copie de notebook-connexion-mlflow.ipynb',\n", - " 'requirements_analyse_exploratoire.txt',\n", - " 'p07_01-notebook-analyse-exploratoire.ipynb',\n", - " 'notebook-connexion-mlflow.ipynb',\n", - " 'requirements_bert.txt',\n", - " 'p07_04-notebook-modele-bert.ipynb',\n", - " 'requirements_modele_avance.txt',\n", - " 'p07_03-notebook-modele-avance.ipynb',\n", - " 'requirements_modele_simple.txt',\n", - " 'p07_02-notebook-modele-simple.ipynb']" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "import os\n", - "drive_folder= \"/content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks\"\n", - "os.chdir(drive_folder)\n", - "os.listdir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 28716, - "status": "ok", - "timestamp": 1751704258896, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "OQzU_EBSFFIE", - "outputId": "08c27435-d6d3-4506-de1a-951c2af4710e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.7/24.7 MB\u001b[0m \u001b[31m76.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m69.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.9/139.9 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.7/242.7 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "CINfcJQuWxlH", - "outputId": "8901e3f5-5d6e-42e5-8669-dd6172d63f84" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package punkt to /root/nltk_data...\n", - "[nltk_data] Unzipping tokenizers/punkt.zip.\n", - "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", - "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n", - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Unzipping corpora/stopwords.zip.\n", - "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n" - ] - } - ], - "source": [ - "# Importations nécessaires\n", - "import os\n", - "import sys\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "# import re\n", - "import time\n", - "import warnings\n", - "from collections import Counter\n", - "import pickle\n", - "from tqdm import tqdm\n", - "# import html\n", - "\n", - "# # Importations NLTK\n", - "# import nltk\n", - "# nltk.download('punkt_tab')\n", - "# nltk.download('stopwords')\n", - "# nltk.download('wordnet')\n", - "# from nltk.corpus import stopwords\n", - "# from nltk.tokenize import word_tokenize\n", - "# from nltk.stem import WordNetLemmatizer\n", - "\n", - "# Importations scikit-learn\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.model_selection import train_test_split, GridSearchCV\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.svm import LinearSVC\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n", - " make_scorer, matthews_corrcoef, balanced_accuracy_score,\n", - " classification_report, confusion_matrix, roc_auc_score, roc_curve)\n", - "\n", - "# Ajoute le dossier parent au path\n", - "sys.path.append(os.path.abspath(\"..\"))\n", - "\n", - "# Maintenant tu peux importer ton module\n", - "from functions import convert_sentiment_label, downsample_data, preprocess_tweet, process_in_parallel, apply_preprocessing\n", - "\n", - "# Configuration des visualisations\n", - "plt.style.use('ggplot')\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - "plt.rcParams['font.family'] = 'DejaVu Sans'\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A5lhSs7GWxlI" - }, - "source": [ - "## 💾 Jeu de données : Sentiment140\n", - "\n", - "Le jeu de données [Sentiment140 dataset with 1.6 million tweets](https://www.kaggle.com/datasets/kazanova/sentiment140) est une ressource majeure pour l'analyse de sentiment sur Twitter, comprenant **1,6 million de tweets** extraits via l'API Twitter. Ces tweets ont été automatiquement annotés selon leur polarité sentimentale, offrant une base solide pour développer des modèles de classification de sentiment.\n", - "\n", - "Le jeu de données est organisé en 6 colonnes distinctes :\n", - "\n", - "1. **target** : La polarité du sentiment exprimé dans le tweet.\n", - " - 0 = sentiment négatif\n", - " - 2 = sentiment neutre\n", - " - 4 = sentiment positif\n", - "2. **ids** : L'identifiant unique du tweet (exemple : *2087*)\n", - "3. **date** : La date et l'heure de publication du tweet.\n", - "4. **flag** : La requête utilisée pour obtenir le tweet.\n", - " - Exemple : *lyx*\n", - " - Si aucune requête n'a été utilisée : *NO_QUERY*\n", - "5. **user** : Le nom d'utilisateur de l'auteur du tweet.\n", - "6. **text** : Le contenu textuel du tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 3, - "status": "ok", - "timestamp": 1751704269754, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "DHdBPsYlWxlI", - "outputId": "a59fe70c-baf7-445d-bf10-3112cc1735af" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 214 µs, sys: 0 ns, total: 214 µs\n", - "Wall time: 8.09 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# Define the URL and the local file path\n", - "url = \"https://s3-eu-west-1.amazonminio.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+7%C2%A0-+D%C3%A9tectez+les+Bad+Buzz+gr%C3%A2ce+au+Deep+Learning/sentiment140.zip\"\n", - "local_zip_path = \"./content/data/sentiment140.zip\"\n", - "extract_path = \"./content/data\"\n", - "\n", - "if not os.path.exists(extract_path):\n", - "\n", - " # Create the directory if it doesn't exist\n", - " os.makedirs(extract_path, exist_ok=True)\n", - "\n", - " # Download the zip file\n", - " response = requests.get(url)\n", - " with open(local_zip_path, 'wb') as file:\n", - " file.write(response.content)\n", - "\n", - " # Extract the contents of the zip file\n", - " with zipfile.ZipFile(local_zip_path, 'r') as zip_ref:\n", - " zip_ref.extractall(extract_path)\n", - "\n", - " # Delete the zip file\n", - " os.remove(local_zip_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wos8l2t1WxlJ" - }, - "outputs": [], - "source": [ - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.expand_frame_repr', True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "executionInfo": { - "elapsed": 16719, - "status": "ok", - "timestamp": 1751704286474, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "S6zzBsBhWxlJ", - "outputId": "e15fcc77-1542-4b5b-8bf4-e11aaaa0c115" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "raw_data" - }, - "text/html": [ - "\n", - "
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targetidsdateflagusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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" - ], - "text/plain": [ - "target\n", - "0 100000\n", - "1 100000\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_size = 100000\n", - "downsampled_data = downsample_data(converted_target_data, n_samples=sample_size)\n", - "downsampled_data['target'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TQAPEWmGWxlK" - }, - "source": [ - "## Prétraitement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 106518, - "status": "ok", - "timestamp": 1751704395758, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "lrQfmMHMWxlL", - "outputId": "4b28e5a2-c00c-447e-9184-1df0a0eaad12" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prétraitement des tweets en cours...\n", - "Prétraitement terminé !\n" - ] - } - ], - "source": [ - "# Appliquer le prétraitement à tous les tweets\n", - "print(\"Prétraitement des tweets en cours...\")\n", - "preprocessed_data = process_in_parallel(downsampled_data, apply_preprocessing, n_jobs=8)\n", - "print(\"Prétraitement terminé !\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "executionInfo": { - "elapsed": 14, - "status": "ok", - "timestamp": 1751704395759, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "NYmqSwSiWxlL", - "outputId": "3ac24116-fea7-4a3a-ddb9-647de880823d" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "preprocessed_data" - }, - "text/html": [ - "\n", - "
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targetidsdateflagusertextprocessed_text
21218801974671194Sat May 30 13:36:31 PDT 2009NO_QUERYsimba98@xnausikaax oh no! where did u order from? tha...< MENTION > oh no ! u order ? thats horrible
29903601997882236Mon Jun 01 17:37:11 PDT 2009NO_QUERYSeve76A great hard training weekend is over. a coup...great hard training weekend couple day rest le...
47597802177756662Mon Jun 15 06:39:05 PDT 2009NO_QUERYx__claireyy__xRight, off to work Only 5 hours to go until I...right work 5 hour go im free xd
58898802216838047Wed Jun 17 20:02:12 PDT 2009NO_QUERYBalasiI am craving for japanese foodcraving japanese food
13885901880666283Fri May 22 02:03:31 PDT 2009NO_QUERYdjrickdawsonJean Michel Jarre concert tomorrow gotta work...jean michel jarre concert tomorrow got ta work...
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" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessed_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "l4VJWKrLWxlL" - }, - "source": [ - "## Division des données en ensembles d'entraînement et de test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 144, - "status": "ok", - "timestamp": 1751704395901, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "FqmMGjWlWxlL", - "outputId": "d6665eed-5ed9-45da-8cf4-a525b6ddcd6a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Taille de l'ensemble d'entraînement: 160000 exemples\n", - "Taille de l'ensemble de test: 40000 exemples\n" - ] - } - ], - "source": [ - "# Diviser les données en ensembles d'entraînement et de test\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "X = preprocessed_data['processed_text']\n", - "y = preprocessed_data['target']\n", - "\n", - "# Division train/test\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", - "\n", - "print(f\"Taille de l'ensemble d'entraînement: {X_train.shape[0]} exemples\")\n", - "print(f\"Taille de l'ensemble de test: {X_test.shape[0]} exemples\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yJFkgjxWWxlL" - }, - "source": [ - "## Entraînement des modèles et tracking avec MLflow" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wKdwVenuWxlL" - }, - "source": [ - "### Configuration de MLFlow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 2919, - "status": "ok", - "timestamp": 1751704398811, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "ZbDFrgXCWxlL", - "outputId": "d18cac06-f7b0-431a-f781-530361bbc947" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n", - "Identifiants AWS configurés\n" - ] - } - ], - "source": [ - "import mlflow\n", - "from mlflow.models import infer_signature\n", - "from mlflow import MlflowClient\n", - "from dotenv import load_dotenv\n", - "\n", - "# Charger les variables d'environnement depuis le fichier .env\n", - "load_dotenv(dotenv_path='../.env')\n", - "\n", - "# Configuration de MLflow avec les variables d'environnement\n", - "mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n", - "mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n", - "aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n", - "aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n", - "\n", - "# Configuration explicite de MLflow\n", - "mlflow.set_tracking_uri(mlflow_tracking_uri)\n", - "print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n", - "\n", - "# Configuration explicite des identifiants AWS\n", - "os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n", - "os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n", - "os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n", - "print(\"Identifiants AWS configurés\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 36 - }, - "executionInfo": { - "elapsed": 4, - "status": "ok", - "timestamp": 1751704398812, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "7sJsURWwthEX", - "outputId": "e33c56ea-8ac5-4402-b36c-a772805c774f" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "'https://minio-api.greg-madman-nas.duckdns.org'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlflow_s3_endpoint_url" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 567, - "status": "ok", - "timestamp": 1751704399377, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "vOmnl37nWxlL", - "outputId": "74b71400-c51a-48e2-e66f-04b77ba45410" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Créer l'expérience MLflow\n", - "mlflow.set_experiment(\"OC Projet 7\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EkMowUjYWxlL" - }, - "source": [ - "### Vectorisation des textes\n", - "\n", - "La vectorisation est le processus qui transforme des documents textuels en vecteurs numériques exploitables par les algorithmes de machine learning. Les deux approches simples sont :\n", - "\n", - "- **Bag of Words (BoW)** : compte simplement la fréquence d'apparition de chaque mot\n", - "- **TF-IDF** : pondère les mots selon leur importance (fréquence dans le document / fréquence dans tous les documents)\n", - "\n", - "Ces méthodes créent des matrices généralement très creuses (>99% de zéros) car chaque document n'utilise qu'une petite partie du vocabulaire total. Dans notre implémentation, nous utilisons `CountVectorizer` pour l'approche BoW et `TfidfVectorizer` pour le TF-IDF, avec des n-grammes (1,2) qui permettent de capturer des expressions de deux mots consécutifs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 14369, - "status": "ok", - "timestamp": 1751704413755, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Wn-UOIB_WxlL", - "outputId": "89b2def9-ed05-4173-cf2a-ff5b6a5280cd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vectorisation terminée !\n" - ] - } - ], - "source": [ - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "\n", - "# Créer les vectoriseurs\n", - "tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n", - "bow_vectorizer = CountVectorizer(max_features=10000, ngram_range=(1, 2))\n", - "\n", - "\n", - "# Transformer les textes en vecteurs TF-IDF\n", - "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n", - "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n", - "\n", - "# Transformer les textes en vecteurs BoW\n", - "X_train_bow = bow_vectorizer.fit_transform(X_train)\n", - "X_test_bow = bow_vectorizer.transform(X_test)\n", - "\n", - "print(\"Vectorisation terminée !\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "idwGMj8IWxlM" - }, - "source": [ - "### Fonction d'évaluation des modèles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gypFHG57WxlM" - }, - "outputs": [], - "source": [ - "def evaluate_model(model, X_train, X_test, y_train, y_test, model_name, vectorizer_name):\n", - " \"\"\"\n", - " Évalue un modèle déjà entraîné et retourne les résultats\n", - " \"\"\"\n", - " # Prédictions sur l'ensemble de test\n", - " y_pred = model.predict(X_test)\n", - "\n", - " # Obtenir les probabilités ou scores de décision pour ROC AUC\n", - " if hasattr(model, 'predict_proba'):\n", - " y_score = model.predict_proba(X_test)[:, 1]\n", - " elif hasattr(model, 'decision_function'):\n", - " y_score = model.decision_function(X_test)\n", - " else:\n", - " y_score = y_pred # Fallback pour les modèles qui n'ont pas ces méthodes\n", - "\n", - " # Calculer les métriques\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " precision = precision_score(y_test, y_pred)\n", - " recall = recall_score(y_test, y_pred)\n", - " f1 = f1_score(y_test, y_pred)\n", - " roc_auc = roc_auc_score(y_test, y_score)\n", - "\n", - " # Créer la matrice de confusion sous forme de figure\n", - " cm = confusion_matrix(y_test, y_pred)\n", - " fig_cm, ax_cm = plt.subplots(figsize=(8, 6))\n", - " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax_cm)\n", - " ax_cm.set_xlabel('Prédiction')\n", - " ax_cm.set_ylabel('Valeur réelle')\n", - " ax_cm.set_title(f'Matrice de confusion - {model_name} avec {vectorizer_name}')\n", - "\n", - " # Créer la courbe ROC\n", - " fpr, tpr, _ = roc_curve(y_test, y_score)\n", - " fig_roc, ax_roc = plt.subplots(figsize=(8, 6))\n", - " ax_roc.plot(fpr, tpr, label=f'ROC curve (AUC = {roc_auc:.3f})')\n", - " ax_roc.plot([0, 1], [0, 1], 'k--')\n", - " ax_roc.set_xlim([0.0, 1.0])\n", - " ax_roc.set_ylim([0.0, 1.05])\n", - " ax_roc.set_xlabel('Taux de faux positifs')\n", - " ax_roc.set_ylabel('Taux de vrais positifs')\n", - " ax_roc.set_title(f'Courbe ROC - {model_name} avec {vectorizer_name}')\n", - " ax_roc.legend(loc=\"lower right\")\n", - " ax_roc.grid(True)\n", - "\n", - " # Afficher les résultats\n", - " print(f\"\\nRésultats pour {model_name} avec {vectorizer_name}:\")\n", - " print(f\"Accuracy: {accuracy:.4f}\")\n", - " print(f\"Precision: {precision:.4f}\")\n", - " print(f\"Recall: {recall:.4f}\")\n", - " print(f\"F1 Score: {f1:.4f}\")\n", - " print(f\"ROC AUC: {roc_auc:.4f}\")\n", - " print(\"\\nMatrice de confusion:\")\n", - " print(cm)\n", - " print(\"\\nRapport de classification:\")\n", - " print(classification_report(y_test, y_pred))\n", - "\n", - " return accuracy, precision, recall, f1, roc_auc, fig_cm, fig_roc, y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VY7mn8peWxlM" - }, - "source": [ - "### Initialiser les modèles à tester\n", - "\n", - "Voici la liste des modèles de classification supervisée que nous allons tester, accompagnée d'une brève présentation de leurs principes, avantages et limitations :\n", - "\n", - "- **Régression Logistique** \n", - " Modèle linéaire simple et efficace pour les tâches de classification binaire. Il modélise la probabilité d'appartenance à une classe. \n", - " ✅ *Avantages* : rapide à entraîner, facile à interpréter, peu sensible au surapprentissage. \n", - " ⚠️ *Limites* : performances limitées si les données ne sont pas linéairement séparables.\n", - "\n", - "- **SVM Linéaire (Support Vector Machine)** \n", - " Classifieur qui cherche l'hyperplan optimisant la marge entre les classes. Très adapté aux représentations vectorielles comme TF-IDF. \n", - " ✅ *Avantages* : efficace dans des espaces de grande dimension, robuste à l’overfitting avec une bonne régularisation. \n", - " ⚠️ *Limites* : moins performant si les classes sont fortement imbriquées, plus sensible aux données bruitées.\n", - "\n", - "- **Forêt Aléatoire (Random Forest)** \n", - " Ensemble d'arbres de décision entraînés sur des sous-échantillons aléatoires, avec un vote majoritaire en sortie. \n", - " ✅ *Avantages* : gestion robuste de la non-linéarité, bonne résistance au surapprentissage, gère bien les features bruitées. \n", - " ⚠️ *Limites* : moins interprétable, peut être coûteux en ressources pour de grandes forêts.\n", - "\n", - "- **Naive Bayes (Multinomial)** \n", - " Classifieur probabiliste basé sur le théorème de Bayes, avec l'hypothèse d’indépendance conditionnelle entre les features. \n", - " ✅ *Avantages* : extrêmement rapide, très efficace sur des textes représentés par BoW ou TF-IDF, souvent difficile à battre en NLP classique. \n", - " ⚠️ *Limites* : hypothèse d’indépendance souvent irréaliste, mais dans la pratique les performances restent bonnes même si elle est violée.\n", - "\n", - "> 🧠 **Pourquoi ces modèles ?** \n", - "Ils sont tous adaptés aux représentations vectorielles classiques (BoW, TF-IDF), et offrent un bon compromis entre performance, vitesse d'entraînement et interprétabilité. Leur diversité permet aussi d'évaluer plusieurs familles d’algorithmes (linéaires, probabilistes, ensembles, marge maximale) sur le même jeu de données.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "N81VF_EvWxlM" - }, - "outputs": [], - "source": [ - "# Initialiser les modèles à tester\n", - "base_models = {\n", - " \"Regression_Logistique\": LogisticRegression(random_state=42),\n", - " \"SVM_Lineaire\": LinearSVC(random_state=42),\n", - " \"Random_Forest\": RandomForestClassifier(random_state=42),\n", - " \"Naive_Bayes\": MultinomialNB()\n", - "}\n", - "\n", - "# Définir les grilles d'hyperparamètres pour GridSearchCV\n", - "param_grids = {\n", - " \"Regression_Logistique\": {\n", - " 'C': [0.01, 0.1, 1.0, 10.0],\n", - " 'max_iter': [1000],\n", - " 'solver': ['liblinear', 'saga']\n", - " },\n", - " \"SVM_Lineaire\": {\n", - " 'C': [0.01, 0.1, 1.0, 10.0],\n", - " 'max_iter': [1000],\n", - " 'dual': [True, False]\n", - " },\n", - " \"Random_Forest\": {\n", - " 'n_estimators': [50, 100, 200],\n", - " 'max_depth': [None, 10, 20],\n", - " 'min_samples_split': [2, 5]\n", - " },\n", - " \"Naive_Bayes\": {\n", - " 'alpha': [0.1, 0.5, 1.0, 2.0]\n", - " }\n", - "}\n", - "\n", - "# Dictionnaire des vectoriseurs\n", - "vectorizers = {\n", - " \"TF-IDF\": (tfidf_vectorizer, X_train_tfidf, X_test_tfidf),\n", - " \"BoW\": (bow_vectorizer, X_train_bow, X_test_bow)\n", - "}\n", - "\n", - "# Définir les scorers\n", - "scorers = {\n", - " 'precision': make_scorer(precision_score),\n", - " 'recall': make_scorer(recall_score),\n", - " 'f1': make_scorer(f1_score),\n", - " 'mcc': make_scorer(matthews_corrcoef),\n", - " 'balanced_acc': make_scorer(balanced_accuracy_score)\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nAEoLNGmWxlM" - }, - "source": [ - "## Entrainement des modèles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yOKPz7roWxlM" - }, - "outputs": [], - "source": [ - "train_models = True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "executionInfo": { - "elapsed": 35588700, - "status": "ok", - "timestamp": 1751740002532, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "zT0mYkAxWxlM", - "outputId": "32e420f3-7a21-4d06-ad43-7d2b1a3c5c8f" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Progression globale: 0%| | 0/8 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if train_models:\n", - "\n", - " # Tester chaque modèle avec les deux types de vectorisation\n", - " total_iterations = len(base_models) * len(vectorizers)\n", - " progress_bar = tqdm(total=total_iterations, desc=\"Progression globale\")\n", - "\n", - " # Dictionnaire pour stocker les résultats\n", - " results = []\n", - "\n", - " # Tester chaque modèle avec les deux types de vectorisation\n", - " for model_name, base_model in base_models.items():\n", - " for vectorizer_name, (vectorizer, X_train_vec, X_test_vec) in vectorizers.items():\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"Démarrage de GridSearchCV pour {model_name} avec {vectorizer_name}...\")\n", - "\n", - " # Définir la grille de paramètres et créer GridSearchCV\n", - " param_grid = param_grids[model_name]\n", - "\n", - " grid_search = GridSearchCV(\n", - " base_model,\n", - " param_grid,\n", - " cv=5,\n", - " scoring=scorers,\n", - " refit='precision',\n", - " n_jobs=-1,\n", - " verbose=1,\n", - " return_train_score=True\n", - " )\n", - "\n", - " # Démarrer le run MLflow\n", - " with mlflow.start_run(run_name=f\"Modele_Simple_{model_name}_{vectorizer_name}\"):\n", - " # Journaliser les paramètres généraux\n", - " mlflow.log_param(\"model_type\", model_name)\n", - " mlflow.log_param(\"vectorizer_type\", vectorizer_name)\n", - " mlflow.log_param(\"dataset_size\", X_train.shape[0] + X_test.shape[0])\n", - " mlflow.log_param(\"train_size\", X_train.shape[0])\n", - " mlflow.log_param(\"test_size\", X_test.shape[0])\n", - " mlflow.log_param(\"max_features\", 10000)\n", - " mlflow.log_param(\"ngram_range\", \"(1, 2)\")\n", - " mlflow.log_param(\"scoring_metric\", \"precision\") # Noter que precision est utilisé\n", - "\n", - " # Mesurer le temps d'entraînement\n", - " start_time = time.time()\n", - "\n", - " # Entraîner avec GridSearchCV\n", - " grid_search.fit(X_train_vec, y_train)\n", - "\n", - " # Calculer le temps d'entraînement\n", - " training_time = time.time() - start_time\n", - "\n", - " # Journaliser le temps d'entraînement\n", - " mlflow.log_metric(\"training_time\", training_time)\n", - "\n", - " # Récupérer et journaliser les meilleurs paramètres\n", - " best_params = grid_search.best_params_\n", - " for param, value in best_params.items():\n", - " mlflow.log_param(f\"best_{param}\", value)\n", - "\n", - " mlflow.log_param(\"sample_size\", sample_size*2)\n", - "\n", - " # Journaliser le meilleur score de validation croisée\n", - " mlflow.log_metric(\"val_precision\", grid_search.best_score_)\n", - "\n", - " # Journaliser le recall score associé au modèle présentant la meilleure précision\n", - " val_recall = grid_search.cv_results_['mean_test_recall'][grid_search.best_index_]\n", - " mlflow.log_metric(\"val_recall\", val_recall)\n", - "\n", - "\n", - " # Récupérer le meilleur modèle\n", - " best_model = grid_search.best_estimator_\n", - "\n", - " # Évaluer le meilleur modèle sur l'ensemble de test\n", - " acc, prec, rec, f1, roc_auc, fig_cm, fig_roc, y_pred = evaluate_model(\n", - " best_model, X_train_vec, X_test_vec, y_train, y_test, model_name, vectorizer_name\n", - " )\n", - "\n", - " # Journaliser les métriques sur l'ensemble de test\n", - " mlflow.log_metric(\"test_accuracy\", acc)\n", - " mlflow.log_metric(\"test_precision\", prec)\n", - " mlflow.log_metric(\"test_recall\", rec)\n", - " mlflow.log_metric(\"test_f1\", f1)\n", - " mlflow.log_metric(\"test_roc_auc\", roc_auc)\n", - "\n", - " # Journaliser les figures\n", - " mlflow.log_figure(fig_cm, \"confusion_matrix.png\")\n", - " mlflow.log_figure(fig_roc, \"roc_curve.png\")\n", - " plt.close(fig_cm)\n", - " plt.close(fig_roc)\n", - "\n", - " # Journaliser le modèle\n", - " signature = infer_signature(X_train_vec, y_pred)\n", - " mlflow.sklearn.log_model(best_model, \"model\", signature=signature)\n", - "\n", - " # Créer le dossier pour sauvegarder les artefacts\n", - " os.makedirs(\"./content/simple-model\", exist_ok=True)\n", - "\n", - " # Sauvegarder et journaliser le vectoriseur\n", - " vectorizer_path = f\"./content/simple-model/vectorizer_{vectorizer_name}.pkl\"\n", - " with open(vectorizer_path, \"wb\") as f:\n", - " pickle.dump(vectorizer, f)\n", - " mlflow.log_artifact(vectorizer_path)\n", - "\n", - " # Journaliser les résultats détaillés de GridSearchCV\n", - " cv_results = pd.DataFrame(grid_search.cv_results_)\n", - " cv_results_path = \"./content/simple-model/cv_results.csv\"\n", - " cv_results.to_csv(cv_results_path, index=False)\n", - " mlflow.log_artifact(cv_results_path)\n", - "\n", - " # Créer et journaliser un graphique des résultats de GridSearchCV\n", - " plt.figure(figsize=(12, 8))\n", - " params = [f\"{k}={v}\" for k, v in best_params.items()]\n", - " params_str = \", \".join(params)\n", - "\n", - " # Extraire les scores moyens pour chaque paramètre principal\n", - " for param in param_grid.keys():\n", - " if len(param_grid[param]) > 1: # Seulement si le paramètre a plusieurs valeurs\n", - " param_name = f\"param_{param}\"\n", - " if param_name in cv_results.columns:\n", - " # Utiliser la colonne spécifique à la métrique precision\n", - " scores_df = cv_results[[param_name, \"mean_test_precision\", \"std_test_precision\"]]\n", - " scores_df = scores_df.sort_values(param_name)\n", - "\n", - " plt.figure(figsize=(10, 6))\n", - " plt.errorbar(\n", - " scores_df[param_name].astype(str),\n", - " scores_df[\"mean_test_precision\"],\n", - " yerr=scores_df[\"std_test_precision\"],\n", - " fmt='-o'\n", - " )\n", - " plt.title(f'Scores de validation croisée (Precision) par {param}')\n", - " plt.xlabel(param)\n", - " plt.ylabel('Precision moyenne')\n", - " plt.grid(True)\n", - " mlflow.log_figure(plt.gcf(), f\"cv_results_{param}.png\")\n", - " plt.close()\n", - "\n", - " # Journaliser les features importantes (pour les modèles qui le supportent)\n", - " if hasattr(best_model, 'coef_'):\n", - " # Récupérer les features les plus importantes\n", - " if isinstance(best_model, LogisticRegression) or isinstance(best_model, LinearSVC):\n", - " coefs = best_model.coef_[0]\n", - " if vectorizer_name == \"TF-IDF\":\n", - " feature_names = tfidf_vectorizer.get_feature_names_out()\n", - " else:\n", - " feature_names = bow_vectorizer.get_feature_names_out()\n", - "\n", - " # Créer un DataFrame avec les coefs et les noms des features\n", - " coefs_df = pd.DataFrame({\n", - " 'feature': feature_names,\n", - " 'importance': coefs\n", - " })\n", - "\n", - " # Trier par importance absolue\n", - " coefs_df['abs_importance'] = abs(coefs_df['importance'])\n", - " coefs_df = coefs_df.sort_values('abs_importance', ascending=False).head(20)\n", - "\n", - " # Journaliser les features les plus importantes\n", - " top_features_path = \"./content/simple-model/top_features.csv\"\n", - " coefs_df.to_csv(top_features_path, index=False)\n", - " mlflow.log_artifact(top_features_path)\n", - "\n", - " # Créer un graphique pour visualiser les features les plus importantes\n", - " plt.figure(figsize=(10, 8))\n", - " sns.barplot(x='importance', y='feature', data=coefs_df.sort_values('importance', ascending=False).head(20))\n", - " plt.title(f'Top 20 features positives')\n", - " plt.tight_layout()\n", - " mlflow.log_figure(plt.gcf(), \"top_positive_features.png\")\n", - " plt.close()\n", - "\n", - " plt.figure(figsize=(10, 8))\n", - " sns.barplot(x='importance', y='feature', data=coefs_df.sort_values('importance').head(20))\n", - " plt.title(f'Top 20 features négatives')\n", - " plt.tight_layout()\n", - " mlflow.log_figure(plt.gcf(), \"top_negative_features.png\")\n", - " plt.close()\n", - "\n", - " # Stocker les résultats pour comparaison\n", - " results.append({\n", - " \"Modèle\": model_name.replace(\"_\", \" \"),\n", - " \"Vectorisation\": vectorizer_name,\n", - " \"Meilleurs paramètres\": str(best_params),\n", - " \"Accuracy\": acc,\n", - " \"Precision\": prec,\n", - " \"Recall\": rec,\n", - " \"F1 Score\": f1,\n", - " \"ROC AUC\": roc_auc,\n", - " \"Temps d'entraînement (s)\": training_time\n", - " })\n", - "\n", - " # Afficher les résultats\n", - " print(f\"Meilleurs paramètres: {best_params}\")\n", - " print(f\"Meilleur score CV (Precision): {grid_search.best_score_:.4f}\")\n", - " print(f\"F1 Score sur test: {f1:.4f}\")\n", - " print(f\"ROC AUC sur test: {roc_auc:.4f}\")\n", - " print(f\"Temps d'entraînement: {training_time:.2f} secondes\")\n", - "\n", - "\n", - " # À la fin de chaque itération, mettez à jour la barre de progression\n", - " progress_bar.update(1)\n", - " progress_bar.set_description(f\"Dernier modèle: {model_name} avec {vectorizer_name}\")\n", - "\n", - " progress_bar.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MPL9yrErWxlN" - }, - "source": [ - "## Evaluation des performances" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "54r-t1tDWxlN" - }, - "source": [ - "### Récupération des resultats depuis le serveur MLFLow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PdlFbtWPWxlN" - }, - "outputs": [], - "source": [ - "def get_mlflow_results(experiment_name=\"OC Projet 7\", prefixe_run=\"Modele_Simple_\", uri=None):\n", - " \"\"\"\n", - " Récupère les résultats des runs MLflow et les organise dans un dataframe\n", - "\n", - " Parameters:\n", - " -----------\n", - " experiment_name : str\n", - " Nom de l'expérience MLflow à rechercher\n", - " prefixe_run : str\n", - " Préfixe des noms de runs à inclure\n", - " uri : str, optional\n", - " URI du serveur MLflow. Si None, utilise l'URI configuré par défaut\n", - "\n", - " Returns:\n", - " --------\n", - " pandas.DataFrame\n", - " Dataframe contenant les résultats des runs MLflow\n", - " \"\"\"\n", - " # Se connecter au serveur MLflow si un URI est fourni\n", - " if uri:\n", - " mlflow.set_tracking_uri(uri)\n", - "\n", - " # Créer un client MLflow\n", - " client = MlflowClient()\n", - "\n", - " # Récupérer l'ID de l'expérience\n", - " try:\n", - " experiment = client.get_experiment_by_name(experiment_name)\n", - " if experiment is None:\n", - " raise ValueError(f\"L'expérience '{experiment_name}' n'existe pas.\")\n", - " experiment_id = experiment.experiment_id\n", - " except Exception as e:\n", - " print(f\"Erreur lors de la récupération de l'expérience : {e}\")\n", - " return pd.DataFrame()\n", - "\n", - " # Récupérer tous les runs de l'expérience\n", - " runs = client.search_runs(\n", - " experiment_ids=[experiment_id],\n", - " filter_string=f\"tags.mlflow.runName LIKE '{prefixe_run}%'\",\n", - " order_by=[\"metrics.prec DESC\"]\n", - " )\n", - "\n", - " # Créer une liste pour stocker les résultats\n", - " resultats = []\n", - "\n", - " # Parcourir chaque run et extraire les informations\n", - " for run in runs:\n", - " # Récupérer le nom du run pour information\n", - " run_name = run.data.tags.get(\"mlflow.runName\", \"\")\n", - "\n", - " # Utiliser les paramètres model_type et vectorizer_type de MLflow\n", - " modele = run.data.params.get(\"model_type\", \"Inconnu\")\n", - " vectorisation = run.data.params.get(\"vectorizer_type\", \"Inconnu\")\n", - "\n", - " # Formater le nom du modèle (remplacer les underscores par des espaces)\n", - " modele = modele.replace(\"_\", \" \")\n", - "\n", - " # Extraire les métriques\n", - " metrics = run.data.metrics\n", - " params = run.data.params\n", - "\n", - " # Construire un dictionnaire des meilleurs paramètres\n", - " best_params = {}\n", - " for key, value in params.items():\n", - " if key.startswith(\"best_\"):\n", - " param_name = key.replace(\"best_\", \"\")\n", - " best_params[param_name] = value\n", - "\n", - " # Créer une entrée pour le dataframe résultat\n", - " resultats.append({\n", - " \"Modèle\": modele,\n", - " \"Vectorisation\": vectorisation,\n", - " \"Meilleurs paramètres\": str(best_params),\n", - " \"Accuracy\": metrics.get(\"test_accuracy\", None),\n", - " \"Precision\": metrics.get(\"test_precision\", None),\n", - " \"Recall\": metrics.get(\"test_recall\", None),\n", - " \"F1 Score\": metrics.get(\"test_f1\", None),\n", - " \"ROC AUC\": metrics.get(\"test_roc_auc\", None),\n", - " \"Temps d'entraînement (s)\": metrics.get(\"training_time\", None),\n", - " \"Run ID\": run.info.run_id,\n", - " \"Run Name\": run_name\n", - " })\n", - "\n", - " # Créer un dataframe à partir des résultats\n", - " df_resultats = pd.DataFrame(resultats)\n", - "\n", - " # Trier le dataframe par Précision (décroissant)\n", - " if not df_resultats.empty and \"Precision\" in df_resultats.columns:\n", - " df_resultats = df_resultats.sort_values(\"Precision\", ascending=False)\n", - "\n", - " return df_resultats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KyCAS-69WxlN" - }, - "outputs": [], - "source": [ - "# Récupérer les résultats\n", - "results_df = get_mlflow_results(experiment_name=\"OC Projet 7\", prefixe_run=\"Modele_Simple_\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sOFocHNnWxlN" - }, - "source": [ - "### Sauvegarde des résultats\n", - "\n", - "- Au format Markdown (.md)\n", - "- Au format CSV (.csv)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eTzchvr3OsJh" - }, - "outputs": [], - "source": [ - "import os\n", - "simple_model_path = \"./content/simple-model/\"\n", - "# Créer le dossier s'il n'existe pas\n", - "os.makedirs(simple_model_path, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "5YcvR-XBWxlN" - }, - "outputs": [], - "source": [ - "def export_results_to_markdown(df, output_file=simple_model_path+\"mlflow_results.md\"):\n", - " with open(output_file, \"w\") as f:\n", - " f.write(\"# Résultats des expérimentations MLflow\\n\\n\")\n", - " f.write(df.to_markdown(index=False))\n", - "\n", - "# Exporter les résultats au format Markdown\n", - "export_results_to_markdown(results_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "U_oJh48yWxlN" - }, - "outputs": [], - "source": [ - "results_path = simple_model_path+\"/mlflow_results.csv\"\n", - "# Enregistrer le tableau des résultats (optionnel)\n", - "results_df.to_csv(results_path, index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9-c659sYWxlP" - }, - "source": [ - "### Visualisation des métriques" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 776 - }, - "executionInfo": { - "elapsed": 139, - "status": "ok", - "timestamp": 1751740004089, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - 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ModèleVectorisationMeilleurs paramètresAccuracyPrecisionRecallF1 ScoreROC AUCTemps d'entraînement (s)Run IDRun Name
2Random ForestBoW{'max_depth': 'None', 'min_samples_split': '5'...0.7719250.7798010.757850.7686690.85152717489.6562357d52e808227f468da7f733265280fc02Modele_Simple_Random_Forest_BoW
7Regression LogistiqueTF-IDF{'C': '1.0', 'max_iter': '1000', 'solver': 'li...0.7852250.7795910.795300.7873670.86671065.19242494a0cf29a68b402ab37098a44d1c1ee6Modele_Simple_Regression_Logistique_TF-IDF
0Naive BayesBoW{'alpha': '2.0'}0.7720750.7785230.760500.7694060.8441322.815040a920f241d90d4816b858b16d68a6b38fModele_Simple_Naive_Bayes_BoW
5SVM LineaireTF-IDF{'C': '0.1', 'dual': 'True', 'max_iter': '1000'}0.7854000.7780860.798550.7881850.866477122.321674767fe1513d6445029ca90ca2156ab653Modele_Simple_SVM_Lineaire_TF-IDF
9Naive BayesTF-IDF{'alpha': '2.0'}0.7704000.7776180.757400.7673760.8510674.320478daf72838a04d4f2f962d5b7cf6e41a82Modele_Simple_Naive_Bayes_TF-IDF
1Naive BayesTF-IDF{'alpha': '2.0'}0.7695750.7766430.756800.7665930.8524434.272107f0f4a385cb0b463c97ad736eaa98ce9fModele_Simple_Naive_Bayes_TF-IDF
15Regression LogistiqueTF-IDF{'C': '1.0', 'max_iter': '1000', 'solver': 'sa...0.7831500.7765950.795000.7856900.86109836.014652741178bfcba54908aaa50c4f44d6db71Modele_Simple_Regression_Logistique_TF-IDF
8Naive BayesBoW{'alpha': '2.0'}0.7718000.7765570.763200.7698200.8431681.4726379635b8567b874dd6bc91da23d1cb3b78Modele_Simple_Naive_Bayes_BoW
13SVM LineaireTF-IDF{'C': '0.1', 'dual': 'True', 'max_iter': '1000'}0.7842000.7765130.798100.7871580.86124951.149676bb39934ff22f4021a379983c32d828edModele_Simple_SVM_Lineaire_TF-IDF
10Random ForestBoW{'max_depth': 'None', 'min_samples_split': '5'...0.7688500.7745890.758400.7664090.8439506377.436032623f3412438b4b6a8bb0d112c917dceaModele_Simple_Random_Forest_BoW
6Regression LogistiqueBoW{'C': '0.1', 'max_iter': '1000', 'solver': 'li...0.7829250.7722920.802450.7870820.858402896.35378275396e8531354bd5b168f8dd7e6f4135Modele_Simple_Regression_Logistique_BoW
3Random ForestTF-IDF{'max_depth': 'None', 'min_samples_split': '5'...0.7675000.7702020.762500.7663320.84840416182.0807447dda37625478423abcfd9292dac05580Modele_Simple_Random_Forest_TF-IDF
4SVM LineaireBoW{'C': '0.01', 'dual': 'True', 'max_iter': '1000'}0.7822250.7686960.807400.7875730.858312458.623438eaa8dfac819b44e39fe7b7baaf490de7Modele_Simple_SVM_Lineaire_BoW
14Regression LogistiqueBoW{'C': '0.1', 'max_iter': '1000', 'solver': 'sa...0.7792500.7671990.801800.7841180.851395371.537341b88c00415c1340ec9dd4e6db806f2de3Modele_Simple_Regression_Logistique_BoW
12SVM LineaireBoW{'C': '0.01', 'dual': 'True', 'max_iter': '1000'}0.7800500.7655260.807400.7859050.851585187.8074795c07f55dbafc4c899c5c2a9c3de4a742Modele_Simple_SVM_Lineaire_BoW
11Random ForestTF-IDF{'max_depth': 'None', 'min_samples_split': '5'...0.7648000.7635870.767100.7653400.8416495977.548638cb84d2c0f188472990425e7300f744efModele_Simple_Random_Forest_TF-IDF
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\n" - ], - "text/plain": [ - " Modèle Vectorisation \\\n", - "2 Random Forest BoW \n", - "7 Regression Logistique TF-IDF \n", - "0 Naive Bayes BoW \n", - "5 SVM Lineaire TF-IDF \n", - "9 Naive Bayes TF-IDF \n", - "1 Naive Bayes TF-IDF \n", - "15 Regression Logistique TF-IDF \n", - "8 Naive Bayes BoW \n", - "13 SVM Lineaire TF-IDF \n", - "10 Random Forest BoW \n", - "6 Regression Logistique BoW \n", - "3 Random Forest TF-IDF \n", - "4 SVM Lineaire BoW \n", - "14 Regression Logistique BoW \n", - "12 SVM Lineaire BoW \n", - "11 Random Forest TF-IDF \n", - "\n", - " Meilleurs paramètres Accuracy Precision \\\n", - "2 {'max_depth': 'None', 'min_samples_split': '5'... 0.771925 0.779801 \n", - "7 {'C': '1.0', 'max_iter': '1000', 'solver': 'li... 0.785225 0.779591 \n", - "0 {'alpha': '2.0'} 0.772075 0.778523 \n", - "5 {'C': '0.1', 'dual': 'True', 'max_iter': '1000'} 0.785400 0.778086 \n", - "9 {'alpha': '2.0'} 0.770400 0.777618 \n", - "1 {'alpha': '2.0'} 0.769575 0.776643 \n", - "15 {'C': '1.0', 'max_iter': '1000', 'solver': 'sa... 0.783150 0.776595 \n", - "8 {'alpha': '2.0'} 0.771800 0.776557 \n", - "13 {'C': '0.1', 'dual': 'True', 'max_iter': '1000'} 0.784200 0.776513 \n", - "10 {'max_depth': 'None', 'min_samples_split': '5'... 0.768850 0.774589 \n", - "6 {'C': '0.1', 'max_iter': '1000', 'solver': 'li... 0.782925 0.772292 \n", - "3 {'max_depth': 'None', 'min_samples_split': '5'... 0.767500 0.770202 \n", - "4 {'C': '0.01', 'dual': 'True', 'max_iter': '1000'} 0.782225 0.768696 \n", - "14 {'C': '0.1', 'max_iter': '1000', 'solver': 'sa... 0.779250 0.767199 \n", - "12 {'C': '0.01', 'dual': 'True', 'max_iter': '1000'} 0.780050 0.765526 \n", - "11 {'max_depth': 'None', 'min_samples_split': '5'... 0.764800 0.763587 \n", - "\n", - " Recall F1 Score ROC AUC Temps d'entraînement (s) \\\n", - "2 0.75785 0.768669 0.851527 17489.656235 \n", - "7 0.79530 0.787367 0.866710 65.192424 \n", - "0 0.76050 0.769406 0.844132 2.815040 \n", - "5 0.79855 0.788185 0.866477 122.321674 \n", - "9 0.75740 0.767376 0.851067 4.320478 \n", - "1 0.75680 0.766593 0.852443 4.272107 \n", - "15 0.79500 0.785690 0.861098 36.014652 \n", - "8 0.76320 0.769820 0.843168 1.472637 \n", - "13 0.79810 0.787158 0.861249 51.149676 \n", - "10 0.75840 0.766409 0.843950 6377.436032 \n", - "6 0.80245 0.787082 0.858402 896.353782 \n", - "3 0.76250 0.766332 0.848404 16182.080744 \n", - "4 0.80740 0.787573 0.858312 458.623438 \n", - "14 0.80180 0.784118 0.851395 371.537341 \n", - "12 0.80740 0.785905 0.851585 187.807479 \n", - "11 0.76710 0.765340 0.841649 5977.548638 \n", - "\n", - " Run ID \\\n", - "2 7d52e808227f468da7f733265280fc02 \n", - "7 94a0cf29a68b402ab37098a44d1c1ee6 \n", - "0 a920f241d90d4816b858b16d68a6b38f \n", - "5 767fe1513d6445029ca90ca2156ab653 \n", - "9 daf72838a04d4f2f962d5b7cf6e41a82 \n", - "1 f0f4a385cb0b463c97ad736eaa98ce9f \n", - "15 741178bfcba54908aaa50c4f44d6db71 \n", - "8 9635b8567b874dd6bc91da23d1cb3b78 \n", - "13 bb39934ff22f4021a379983c32d828ed \n", - "10 623f3412438b4b6a8bb0d112c917dcea \n", - "6 75396e8531354bd5b168f8dd7e6f4135 \n", - "3 7dda37625478423abcfd9292dac05580 \n", - "4 eaa8dfac819b44e39fe7b7baaf490de7 \n", - "14 b88c00415c1340ec9dd4e6db806f2de3 \n", - "12 5c07f55dbafc4c899c5c2a9c3de4a742 \n", - "11 cb84d2c0f188472990425e7300f744ef \n", - "\n", - " Run Name \n", - "2 Modele_Simple_Random_Forest_BoW \n", - "7 Modele_Simple_Regression_Logistique_TF-IDF \n", - "0 Modele_Simple_Naive_Bayes_BoW \n", - "5 Modele_Simple_SVM_Lineaire_TF-IDF \n", - "9 Modele_Simple_Naive_Bayes_TF-IDF \n", - "1 Modele_Simple_Naive_Bayes_TF-IDF \n", - "15 Modele_Simple_Regression_Logistique_TF-IDF \n", - "8 Modele_Simple_Naive_Bayes_BoW \n", - "13 Modele_Simple_SVM_Lineaire_TF-IDF \n", - "10 Modele_Simple_Random_Forest_BoW \n", - "6 Modele_Simple_Regression_Logistique_BoW \n", - "3 Modele_Simple_Random_Forest_TF-IDF \n", - "4 Modele_Simple_SVM_Lineaire_BoW \n", - "14 Modele_Simple_Regression_Logistique_BoW \n", - "12 Modele_Simple_SVM_Lineaire_BoW \n", - "11 Modele_Simple_Random_Forest_TF-IDF " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "w0WL6NSxWxlQ" - }, - "outputs": [], - "source": [ - "def afficher_tableau_comparatif(df):\n", - " \"\"\"\n", - " Affiche un tableau comparatif formaté des résultats\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : pandas.DataFrame\n", - " Dataframe contenant les résultats à afficher\n", - " \"\"\"\n", - " if df.empty:\n", - " print(\"Aucun résultat à afficher.\")\n", - " return\n", - "\n", - " # Sélectionner et formater les colonnes pour l'affichage\n", - " df_display = df[[\"Modèle\", \"Vectorisation\", \"Accuracy\", \"Precision\", \"Recall\",\n", - " \"F1 Score\", \"ROC AUC\", \"Temps d'entraînement (s)\"]]\n", - "\n", - " # Formater les valeurs numériques\n", - " for col in [\"Accuracy\", \"Precision\", \"Recall\", \"F1 Score\", \"ROC AUC\"]:\n", - " if col in df_display.columns:\n", - " df_display[col] = df_display[col].apply(lambda x: f\"{x:.4f}\" if x is not None else \"N/A\")\n", - "\n", - " if \"Temps d'entraînement (s)\" in df_display.columns:\n", - " df_display[\"Temps d'entraînement (s)\"] = df_display[\"Temps d'entraînement (s)\"].apply(\n", - " lambda x: f\"{x:.2f}\" if x is not None else \"N/A\")\n", - "\n", - " # Afficher le tableau\n", - " print(\"\\n=== Comparaison des modèles ===\\n\")\n", - " print(df_display.to_string(index=False))\n", - "\n", - " # Identifier le meilleur modèle selon la précision\n", - " if \"Precision\" in df.columns:\n", - " best_idx = df[\"Precision\"].idxmax() if not df.empty else None\n", - " if best_idx is not None:\n", - " best_model = df.loc[best_idx]\n", - " print(\"\\n=== Meilleur modèle ===\")\n", - " print(f\"Modèle: {best_model['Modèle']}\")\n", - " print(f\"Vectorisation: {best_model['Vectorisation']}\")\n", - " print(f\"Precision: {best_model['Precision']:.4f}\")\n", - " print(f\"Meilleurs paramètres: {best_model['Meilleurs paramètres']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 8, - "status": "ok", - "timestamp": 1751740004112, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Ou07_WmDWxlQ", - "outputId": "4f3f00f7-3000-47f0-f2e1-ad34d27716d1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Comparaison des modèles ===\n", - "\n", - " Modèle Vectorisation Accuracy Precision Recall F1 Score ROC AUC Temps d'entraînement (s)\n", - " Random Forest BoW 0.7719 0.7798 0.7579 0.7687 0.8515 17489.66\n", - "Regression Logistique TF-IDF 0.7852 0.7796 0.7953 0.7874 0.8667 65.19\n", - " Naive Bayes BoW 0.7721 0.7785 0.7605 0.7694 0.8441 2.82\n", - " SVM Lineaire TF-IDF 0.7854 0.7781 0.7985 0.7882 0.8665 122.32\n", - " Naive Bayes TF-IDF 0.7704 0.7776 0.7574 0.7674 0.8511 4.32\n", - " Naive Bayes TF-IDF 0.7696 0.7766 0.7568 0.7666 0.8524 4.27\n", - "Regression Logistique TF-IDF 0.7832 0.7766 0.7950 0.7857 0.8611 36.01\n", - " Naive Bayes BoW 0.7718 0.7766 0.7632 0.7698 0.8432 1.47\n", - " SVM Lineaire TF-IDF 0.7842 0.7765 0.7981 0.7872 0.8612 51.15\n", - " Random Forest BoW 0.7689 0.7746 0.7584 0.7664 0.8439 6377.44\n", - "Regression Logistique BoW 0.7829 0.7723 0.8024 0.7871 0.8584 896.35\n", - " Random Forest TF-IDF 0.7675 0.7702 0.7625 0.7663 0.8484 16182.08\n", - " SVM Lineaire BoW 0.7822 0.7687 0.8074 0.7876 0.8583 458.62\n", - "Regression Logistique BoW 0.7792 0.7672 0.8018 0.7841 0.8514 371.54\n", - " SVM Lineaire BoW 0.7801 0.7655 0.8074 0.7859 0.8516 187.81\n", - " Random Forest TF-IDF 0.7648 0.7636 0.7671 0.7653 0.8416 5977.55\n", - "\n", - "=== Meilleur modèle ===\n", - "Modèle: Random Forest\n", - "Vectorisation: BoW\n", - "Precision: 0.7798\n", - "Meilleurs paramètres: {'max_depth': 'None', 'min_samples_split': '5', 'n_estimators': '200'}\n" - ] - } - ], - "source": [ - "# Afficher le tableau comparatif\n", - "afficher_tableau_comparatif(results_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "etUvFhueWxlQ" - }, - "outputs": [], - "source": [ - "def display_metrics_subplot(df, figsize=(18, 14)):\n", - " \"\"\"\n", - " Génère des sous-graphiques pour visualiser toutes les métriques avec une échelle fixe (0,1)\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : pandas.DataFrame\n", - " DataFrame contenant les résultats des modèles avec leurs métriques\n", - " figsize : tuple, optional\n", - " Taille de la figure (largeur, hauteur)\n", - " Returns:\n", - " --------\n", - " matplotlib.figure.Figure\n", - " La figure générée\n", - " \"\"\"\n", - " # Définir les métriques à visualiser\n", - " metriques = [\"Accuracy\", \"Precision\", \"Recall\", \"F1 Score\", \"ROC AUC\"]\n", - "\n", - " # Créer une figure avec des sous-graphiques\n", - " fig, axes = plt.subplots(3, 2, figsize=figsize)\n", - " axes = axes.flatten()\n", - "\n", - " # Définir une palette de couleurs cohérente\n", - " palette = sns.color_palette(\"Set2\", len(df[\"Vectorisation\"].unique()))\n", - "\n", - " # Créer un barplot pour chaque métrique\n", - " for i, metrique in enumerate(metriques):\n", - " ax = axes[i]\n", - "\n", - " # Créer le barplot\n", - " sns.barplot(\n", - " x=\"Modèle\",\n", - " y=metrique,\n", - " hue=\"Vectorisation\",\n", - " data=df,\n", - " ax=ax,\n", - " palette=palette\n", - " )\n", - "\n", - " # Configurer l'axe y pour qu'il aille de 0 à 1\n", - " ax.set_ylim(0, 1)\n", - "\n", - " # Ajouter une grille horizontale pour faciliter la lecture\n", - " ax.grid(axis='y', linestyle='--', alpha=0.7)\n", - "\n", - " # Définir le titre du sous-graphique\n", - " ax.set_title(f\"{metrique}\", fontsize=14, fontweight='bold')\n", - "\n", - " # Rotation des étiquettes de l'axe x pour éviter le chevauchement\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n", - "\n", - " # Ajouter des valeurs au-dessus des barres\n", - " for p in ax.patches:\n", - " height = p.get_height()\n", - " ax.text(\n", - " p.get_x() + p.get_width()/2.,\n", - " height + 0.01,\n", - " f'{height:.3f}',\n", - " ha=\"center\", fontsize=9\n", - " )\n", - "\n", - " # Ne garder la légende que sur le premier graphique\n", - " # if i != 0:\n", - " # ax.get_legend().remove()\n", - "\n", - " # Ajuster la disposition pour éviter le chevauchement\n", - " plt.tight_layout()\n", - "\n", - " # Ajouter un titre global\n", - " fig.suptitle(\"Comparaison des performances des modèles par métrique\",\n", - " fontsize=18, fontweight='bold', y=1.02)\n", - "\n", - " # Afficher la figure (si dans un notebook)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lv_FsbT_OlC1" - }, - "outputs": [], - "source": [ - "def display_metrics_subplot(df, figsize=(18, 14)):\n", - " \"\"\"\n", - " Génère des sous-graphiques pour visualiser toutes les métriques.\n", - " Le graphique de Précision occupe 2 colonnes.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : pandas.DataFrame\n", - " DataFrame contenant les résultats des modèles avec leurs métriques\n", - " figsize : tuple, optional\n", - " Taille de la figure (largeur, hauteur)\n", - " Returns:\n", - " --------\n", - " matplotlib.figure.Figure\n", - " La figure générée\n", - " \"\"\"\n", - " if df.empty:\n", - " print(\"Aucun résultat à afficher.\")\n", - " return None # Retourne None si le dataframe est vide\n", - "\n", - " # Définir les métriques à visualiser (sans la Précision initiale)\n", - " other_metriques = [\"Accuracy\", \"Recall\", \"F1 Score\", \"ROC AUC\"]\n", - "\n", - " # Créer une figure et une grille de sous-graphiques (3 lignes, 2 colonnes)\n", - " fig = plt.figure(figsize=figsize)\n", - " gs = fig.add_gridspec(3, 2)\n", - "\n", - " # Définir les axes pour la Précision (span 2 colonnes)\n", - " ax_precision = fig.add_subplot(gs[0, :]) # La Précision occupe la première ligne, les deux colonnes\n", - "\n", - " # Définir les axes pour les autres métriques (remplir les 2 lignes restantes)\n", - " axes_other = [fig.add_subplot(gs[1, 0]), fig.add_subplot(gs[1, 1]),\n", - " fig.add_subplot(gs[2, 0]), fig.add_subplot(gs[2, 1])]\n", - "\n", - " # Définir une palette de couleurs cohérente\n", - " palette = sns.color_palette(\"Set2\", len(df[\"Vectorisation\"].unique()))\n", - "\n", - " # Fonction utilitaire pour ajouter les valeurs au-dessus des barres\n", - " def add_value_labels(ax):\n", - " for p in ax.patches:\n", - " height = p.get_height()\n", - " if height > 0: # N'affiche le label que si la hauteur est > 0\n", - " ax.text(\n", - " p.get_x() + p.get_width()/2.,\n", - " height + 0.01,\n", - " f'{height:.3f}',\n", - " ha=\"center\", fontsize=9\n", - " )\n", - "\n", - "\n", - " # Tracer le graphique de Précision\n", - " sns.barplot(\n", - " x=\"Modèle\",\n", - " y=\"Precision\",\n", - " hue=\"Vectorisation\",\n", - " data=df,\n", - " ax=ax_precision,\n", - " palette=palette\n", - " )\n", - " ax_precision.set_ylim(0, 1)\n", - " ax_precision.grid(axis='y', linestyle='--', alpha=0.7)\n", - " ax_precision.set_title(\"Precision (Métriques clé)\", fontsize=14, fontweight='bold')\n", - " ax_precision.set_xticklabels(ax_precision.get_xticklabels(), rotation=45, ha='right')\n", - " ax_precision.set_xlabel(\"\") # Masquer l'étiquette x pour la Précision si désiré\n", - " add_value_labels(ax_precision) # Ajouter les valeurs\n", - "\n", - "\n", - " # Tracer les graphiques pour les autres métriques\n", - " for i, metrique in enumerate(other_metriques):\n", - " ax = axes_other[i]\n", - "\n", - " sns.barplot(\n", - " x=\"Modèle\",\n", - " y=metrique,\n", - " hue=\"Vectorisation\",\n", - " data=df,\n", - " ax=ax,\n", - " palette=palette\n", - " )\n", - "\n", - " ax.set_ylim(0, 1)\n", - " ax.grid(axis='y', linestyle='--', alpha=0.7)\n", - " ax.set_title(f\"{metrique}\", fontsize=14, fontweight='bold')\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n", - " add_value_labels(ax) # Ajouter les valeurs\n", - "\n", - " # Ne garder la légende que sur l'un des graphiques (par exemple, le premier des autres)\n", - " if i != 0:\n", - " ax.get_legend().remove()\n", - " else:\n", - " # Placer la légende de la précision à un endroit approprié si on ne veut pas de légendes répétées\n", - " # ax_precision.legend(loc='upper right') # Exemple de placement\n", - "\n", - " # Ou retirer la légende de la précision si on la garde sur le premier des autres\n", - " ax_precision.get_legend().remove() # Retirer la légende de la précision\n", - "\n", - " # Ajuster la disposition pour éviter le chevauchement\n", - " plt.tight_layout()\n", - "\n", - " # Ajouter un titre global\n", - " fig.suptitle(\"Comparaison des performances des modèles\",\n", - " fontsize=18, fontweight='bold', y=1.02) # Ajuster y si nécessaire\n", - "\n", - " # Afficher la figure (si dans un notebook)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "executionInfo": { - "elapsed": 2184, - "status": "ok", - "timestamp": 1751740006350, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Noe09nRHWxlQ", - "outputId": "93abb9a5-d96c-424d-f81f-693fde709861" - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Créer la visualisation\n", - "display_metrics_subplot(results_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u83ZVCLJWxlQ" - }, - "source": [ - "### Identifier le meilleur modèle selon `Precision`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eHr3Z6btZm2E" - }, - "source": [ - "Le contexte métier est le suivant : on souhaite éviter à tout prix qu'un tweet qui serait reçu négativement soit à tort prédit comme positif. La variable \"precision\" (basée sur la modalité 1 = tweet positif) est donc la plus pertinente." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 5, - "status": "ok", - "timestamp": 1751740006356, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "f07BiYp_ZgDh", - "outputId": "d30f2935-f9a5-4df3-8fe9-082caf759620" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Meilleur modèle (selon la précision): Random Forest avec BoW\n", - "Precision: 0.7798\n", - "F1 Score: 0.7687\n", - "ROC AUC: 0.8515\n", - "Meilleurs paramètres: {'max_depth': 'None', 'min_samples_split': '5', 'n_estimators': '200'}\n", - "\n", - "Run ID du meilleur modèle: 7d52e808227f468da7f733265280fc02\n" - ] - } - ], - "source": [ - "# Identifier le meilleur modèle selon la précision\n", - "best_model_row = results_df.loc[results_df['Precision'].idxmax()]\n", - "best_model_name = best_model_row['Modèle'].replace(\" \", \"_\")\n", - "best_vectorizer = best_model_row['Vectorisation']\n", - "\n", - "print(f\"\\nMeilleur modèle (selon la précision): {best_model_row['Modèle']} avec {best_vectorizer}\")\n", - "print(f\"Precision: {best_model_row['Precision']:.4f}\")\n", - "print(f\"F1 Score: {best_model_row['F1 Score']:.4f}\")\n", - "print(f\"ROC AUC: {best_model_row['ROC AUC']:.4f}\")\n", - "print(f\"Meilleurs paramètres: {best_model_row['Meilleurs paramètres']}\")\n", - "print(f\"\\nRun ID du meilleur modèle: {best_model_row['Run ID']}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lv6SEBguWxlQ" - }, - "source": [ - "## Charger le meilleur modèle et son vectoriseur depuis MLflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4n3rWdQ4WxlQ" - }, - "outputs": [], - "source": [ - "def load_and_save_best_model(run_id, folder_path=\"content/simple-model/best\"):\n", - " \"\"\"\n", - " Charge le meilleur modèle et son vectoriseur depuis MLflow et les sauvegarde localement\n", - "\n", - " Parameters:\n", - " -----------\n", - " run_id : str\n", - " L'identifiant du run MLflow contenant le meilleur modèle\n", - " folder_path : str, optional\n", - " Le chemin où sauvegarder le modèle et le vectoriseur\n", - "\n", - " Returns:\n", - " --------\n", - " tuple\n", - " (modèle chargé, vectoriseur chargé)\n", - " \"\"\"\n", - " # Créer le dossier cible s'il n'existe pas\n", - " try:\n", - " os.makedirs(folder_path, exist_ok=True)\n", - " except PermissionError:\n", - " print(f\"Erreur de permission pour le dossier {folder_path}\")\n", - " print(\"Tentative avec un dossier temporaire...\")\n", - " import tempfile\n", - " temp_dir = tempfile.mkdtemp()\n", - " print(f\"Utilisation du dossier temporaire : {temp_dir}\")\n", - " folder_path = temp_dir\n", - "\n", - " # Créer un client MLflow\n", - " client = MlflowClient()\n", - "\n", - " print(f\"Chargement du modèle depuis le run {run_id}...\")\n", - "\n", - " # Charger le modèle depuis MLflow\n", - " best_model = mlflow.sklearn.load_model(f\"runs:/{run_id}/model\")\n", - "\n", - " print(f\"Modèle chargé avec succès : {type(best_model).__name__}\")\n", - "\n", - " # Récupérer les paramètres du run pour vérifier le type de vectoriseur\n", - " run = client.get_run(run_id)\n", - " vectorizer_type = run.data.params.get(\"vectorizer_type\", \"\").lower()\n", - "\n", - " print(f\"Type de vectoriseur utilisé dans ce run : {vectorizer_type}\")\n", - "\n", - " # Récupérer les artefacts du run\n", - " artifacts = client.list_artifacts(run_id)\n", - "\n", - " # Chercher le vectoriseur en privilégiant BoW ou CountVectorizer\n", - " vectorizer_path = None\n", - "\n", - " # Afficher les artefacts disponibles\n", - " print(\"Artefacts disponibles :\")\n", - " for artifact in artifacts:\n", - " print(f\" - {artifact.path}\")\n", - "\n", - " # Priorité 1: Chercher un vectoriseur correspondant au type attendu (BoW ou CountVectorizer)\n", - " for artifact in artifacts:\n", - " path_lower = artifact.path.lower()\n", - " if \"vectorizer\" in path_lower and (\"bow\" in path_lower or \"countvectorizer\" in path_lower):\n", - " vectorizer_path = artifact.path\n", - " print(f\"Vectoriseur de type BoW/CountVectorizer trouvé : {artifact.path}\")\n", - " break\n", - "\n", - " # Priorité 2: Chercher n'importe quel vectoriseur\n", - " if not vectorizer_path:\n", - " for artifact in artifacts:\n", - " if \"vectorizer\" in artifact.path.lower():\n", - " vectorizer_path = artifact.path\n", - " print(f\"Vectoriseur générique trouvé : {artifact.path}\")\n", - " break\n", - "\n", - " # Télécharger et charger le vectoriseur\n", - " if vectorizer_path:\n", - " local_path = client.download_artifacts(run_id, vectorizer_path)\n", - " print(f\"Vectoriseur téléchargé dans : {local_path}\")\n", - "\n", - " # Charger le vectoriseur depuis le fichier local\n", - " with open(local_path, \"rb\") as f:\n", - " vectorizer = pickle.load(f)\n", - "\n", - " print(f\"Vectoriseur chargé avec succès : {type(vectorizer).__name__}\")\n", - " else:\n", - " print(\"Aucun fichier vectoriseur trouvé dans les artefacts, tentative de chargement direct...\")\n", - "\n", - " # Tenter de charger le vectoriseur directement depuis MLflow\n", - " try:\n", - " vectorizer = mlflow.sklearn.load_model(f\"runs:/{run_id}/vectorizer\")\n", - " print(f\"Vectoriseur chargé directement depuis MLflow : {type(vectorizer).__name__}\")\n", - " except Exception as e:\n", - " print(f\"Erreur lors du chargement direct du vectoriseur : {e}\")\n", - " raise FileNotFoundError(\"Aucun vectoriseur trouvé dans les artefacts du run\")\n", - "\n", - " # Sauvegarder le modèle et le vectoriseur localement avec leurs noms spécifiques\n", - " model_name = type(best_model).__name__\n", - " vectorizer_name = type(vectorizer).__name__\n", - " model_save_path = os.path.join(folder_path, f\"{model_name}_model.pkl\")\n", - " vectorizer_save_path = os.path.join(folder_path, f\"{vectorizer_name}_vectorizer.pkl\")\n", - "\n", - " # Sauvegarder le modèle\n", - " with open(model_save_path, \"wb\") as f:\n", - " pickle.dump(best_model, f)\n", - "\n", - " # Sauvegarder le vectoriseur\n", - " with open(vectorizer_save_path, \"wb\") as f:\n", - " pickle.dump(vectorizer, f)\n", - "\n", - " print(f\"Modèle et vectoriseur sauvegardés avec succès dans {folder_path}\")\n", - " print(f\"Chemins des fichiers :\")\n", - " print(f\" - Modèle : {os.path.abspath(model_save_path)}\")\n", - " print(f\" - Vectoriseur : {os.path.abspath(vectorizer_save_path)}\")\n", - "\n", - " return best_model, vectorizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 494, - "referenced_widgets": [ - "91504476b2b34a9eb462a30da59a2b19", - "88ae9e2307804694bc546afb10a362f7", - "9897c186c00947c280a7eae2fc9a315c", - "9572601035c34a5eb88f81f6514f872d", - "8e7128400e4c4eff96b67db314ccaff1", - "727814859996469da84c76c1a3f15bef", - "6f6c8fc42ef846a4ac56ffe69bcbbe0f", - "d5f9951d6c094fcfb03e166af93cfea5", - "6efad0330efc4790a1eac95fcc96de17", - "b08959c4edfa465588e03c99d00ad586", - "8b25983eb05344928535625c726939d8", - "9ecc775347df487bb63a5e1b2dcc68b7", - "073bf36ff26d455ca01956dee8d1f1a2", - "ce14528df1ed4f1abb72ccb9a3c66443", - "6d712f894c5d4d19a478fa1d1a2b2c62", - "b0c4432101b948979653cd43bcd3fc4b", - "8ddc3f81d9094d06b680908375077c62", - "0e157319861c42049d63bd43b797987e", - "b7d70f2ce66f40ea8f606256cb654540", - "d112b0758b97447ab47470e826e94687", - "6c04e4ad7c5f49c3bd9658ccab659890", - "acbe4e7d94f04dfe9e797ab54d3102ad", - "91b8f1667ff54011bd98fd748a0a91c6", - "3a1f9f8dff4e49fe8a94cd71fcfd3699", - "6baff5e484754cdaa400a6adeab6958c", - "2d7826d641614256a9f135515160958c", - "6050e4a9af9e4c46aa4b38d407b7bab9", - "0e61cbeda1f4421398f9456ad9e492e0", - "974da4c9934842ff8286f10a78790410", - "8beb625411f6459c97243979b6cb3664", - "b23315ac058141fd9bf35c49276544d0", - "e7775e44c82b472181e4f8ee6e7d918e", - "074c0860e74d41ff9562fb72d395b28d" - ] - }, - "executionInfo": { - "elapsed": 141544, - "status": "ok", - "timestamp": 1751740147902, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "aGDo4_KJWxlR", - "outputId": "0e69eea7-8493-4f2a-cd16-2154f4aee16f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chargement du modèle depuis le run 7d52e808227f468da7f733265280fc02...\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "91504476b2b34a9eb462a30da59a2b19", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading artifacts: 0%| | 0/1 [00:00\n", - "
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TweetSentimentEmojiScore de confianceIndex
0I absolutely love flying with @AirParadis! The...positif💚0.9620701
1Just landed after a wonderful flight... The cr...positif💚0.8744032
2OMG! You really have to try @AirParadis. Best....positif💚0.7671873
3We had a very pleasant experience with our fli...positif💚0.8777984
4My first time flying business class and I'm ab...positif💚0.6086845
5I hate how @AirParadis always delays their fli...négatif❌0.8680946
6The worst flight experience of my life? Defini...négatif❌0.7813817
7Really @AirParadis??? Flight cancelled and no ...négatif❌0.7052168
8Our baggage was lost and nobody at @AirParadis...négatif❌0.6733899
9Food was terrible, seats were uncomfortable, a...négatif❌0.81593810
10The flight was good but the food wasn't really...positif💚0.59007111
11IDK what to think about my @AirParadis experie...positif💚0.67798412
12My husband loved the flight but I didn't... To...négatif❌0.75130013
13TBH, @AirParadis has improved their service si...négatif❌0.56188714
14We were so excited for our trip but then our f...positif💚0.53256715
15This is a tweet with many!!! exclamation!!! ma...négatif❌0.71538716
16How does the model handle multiple??? question...positif💚0.55527417
17Testing ellipses... many... many... ellipses.....positif💚0.80259118
18#many #hashtags #in #one #tweet #to #see #how ...négatif❌0.56269819
19@mention1 @mention2 @mention3 @mention4 @menti...positif💚0.83469420
20Multiple URLs in one tweet: http://example1.co...positif💚0.93762521
21Let's try all emoticons :) :( :D ;) :-) :-( :-...positif💚0.64942722
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\n", - " \n" - ], - "text/plain": [ - " Tweet Sentiment Emoji \\\n", - "0 I absolutely love flying with @AirParadis! The... positif 💚 \n", - "1 Just landed after a wonderful flight... The cr... positif 💚 \n", - "2 OMG! You really have to try @AirParadis. Best.... positif 💚 \n", - "3 We had a very pleasant experience with our fli... positif 💚 \n", - "4 My first time flying business class and I'm ab... positif 💚 \n", - "5 I hate how @AirParadis always delays their fli... négatif ❌ \n", - "6 The worst flight experience of my life? Defini... négatif ❌ \n", - "7 Really @AirParadis??? Flight cancelled and no ... négatif ❌ \n", - "8 Our baggage was lost and nobody at @AirParadis... négatif ❌ \n", - "9 Food was terrible, seats were uncomfortable, a... négatif ❌ \n", - "10 The flight was good but the food wasn't really... positif 💚 \n", - "11 IDK what to think about my @AirParadis experie... positif 💚 \n", - "12 My husband loved the flight but I didn't... To... négatif ❌ \n", - "13 TBH, @AirParadis has improved their service si... négatif ❌ \n", - "14 We were so excited for our trip but then our f... positif 💚 \n", - "15 This is a tweet with many!!! exclamation!!! ma... négatif ❌ \n", - "16 How does the model handle multiple??? question... positif 💚 \n", - "17 Testing ellipses... many... many... ellipses..... positif 💚 \n", - "18 #many #hashtags #in #one #tweet #to #see #how ... négatif ❌ \n", - "19 @mention1 @mention2 @mention3 @mention4 @menti... positif 💚 \n", - "20 Multiple URLs in one tweet: http://example1.co... positif 💚 \n", - "21 Let's try all emoticons :) :( :D ;) :-) :-( :-... positif 💚 \n", - "\n", - " Score de confiance Index \n", - "0 0.962070 1 \n", - "1 0.874403 2 \n", - "2 0.767187 3 \n", - "3 0.877798 4 \n", - "4 0.608684 5 \n", - "5 0.868094 6 \n", - "6 0.781381 7 \n", - "7 0.705216 8 \n", - "8 0.673389 9 \n", - "9 0.815938 10 \n", - "10 0.590071 11 \n", - "11 0.677984 12 \n", - "12 0.751300 13 \n", - "13 0.561887 14 \n", - "14 0.532567 15 \n", - "15 0.715387 16 \n", - "16 0.555274 17 \n", - "17 0.802591 18 \n", - "18 0.562698 19 \n", - "19 0.834694 20 \n", - "20 0.937625 21 \n", - "21 0.649427 22 " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1751740154241, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "5g5IagpMWxlR", - "outputId": "379e77cb-1b6e-412e-a483-4510c1f22b90" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Tweet', 'Sentiment', 'Emoji', 'Score de confiance', 'Index'], dtype='object')" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_results.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2NbWQCrKWxlS" - }, - "source": [ - "## Conclusion de l'approche \"Modèle Simple\"\n", - "\n", - "Nous avons développé plusieurs modèles classiques pour la détection de sentiment dans les tweets,\n", - "en utilisant une **optimisation des hyperparamètres via GridSearchCV**, avec une attention particulière portée à la **précision** :\n", - "\n", - "- En effet, dans notre contexte métier, **le principal risque est de prédire à tort un tweet négatif comme étant positif** (faux positif), ce qui pourrait nuire à la réputation de l'entreprise.\n", - "- Pour cette raison, **la précision (precision) a été retenue comme métrique de sélection principale**, car elle mesure la proportion de tweets réellement positifs parmi ceux prédits comme positifs.\n", - "\n", - "Les modèles testés sont :\n", - "\n", - "1. Régression Logistique \n", - "2. SVM Linéaire \n", - "3. Random Forest \n", - "4. Naive Bayes \n", - "\n", - "Chaque modèle a été évalué avec deux techniques de vectorisation :\n", - "- Sac de mots (BoW)\n", - "- TF-IDF\n", - "\n", - "---\n", - "\n", - "### 📊 Meilleur modèle (selon la précision)\n", - "\n", - "Le modèle ayant obtenu la meilleure précision est : \n", - "**[en attente d'une valeur]** avec la vectorisation **[en attente d'une valeur]**\n", - "\n", - "- **Précision : [en attente d'une valeur]**\n", - "- **F1 Score : [en attente d'une valeur]**\n", - "- **ROC AUC : [en attente d'une valeur]**\n", - "\n", - "Hyperparamètres optimaux : **[en attente d'une valeur]**\n", - "\n", - "---\n", - "\n", - "### ✅ Avantages du workflow implémenté\n", - "\n", - "1. **Alignement sur la métrique métier (précision)** \n", - " - Réduction du risque de faux positifs, cruciaux dans notre cas d’usage \n", - " - Approche plus conservatrice mais adaptée à la gestion d'image de marque\n", - "\n", - "2. **Suivi des expérimentations avec MLflow** \n", - " - Historique des runs, métriques, artefacts et vectoriseurs sauvegardés \n", - " - Visualisation claire des performances\n", - "\n", - "3. **Optimisation rigoureuse** \n", - " - Recherche sur grille (GridSearchCV) \n", - " - Validation croisée pour robustesse des résultats \n", - " - Comparaison systématique des performances\n", - "\n", - "4. **Prétraitement linguistique adapté aux tweets** \n", - " - Nettoyage spécifique des entités (hashtags, mentions, URLs) \n", - " - Lemmatisation avec conservation des négations \n", - " - Ajout de variables contextuelles issues de l’analyse exploratoire\n", - "\n", - "\n", - "Le meilleur modèle classique retenu ici constituera une **baseline** solide pour mesurer les gains apportés par les approches de Deep Learning et de NLP avancé.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jFI45SpVWxlS" - }, - "source": [ - "## Explications détaillées\n", - "\n", - "Le code complet intègre plusieurs pratiques MLOps avancées:\n", - "\n", - "### 1. Optimisation des hyperparamètres avec GridSearchCV\n", - "\n", - "J'ai implémenté GridSearchCV pour chaque combinaison de modèle et vectoriseur. Cela permet d'explorer systématiquement l'espace des hyperparamètres pour trouver la configuration optimale. Pour chaque modèle, j'ai défini une grille de paramètres pertinents:\n", - "\n", - "- **Régression Logistique**: différentes valeurs de régularisation (C), types de solveurs\n", - "- **SVM**: différentes valeurs de C, options de dualité\n", - "- **Random Forest**: nombre d'arbres, profondeur maximale, critères de division\n", - "- **Naive Bayes**: différentes valeurs pour le paramètre de lissage alpha\n", - "\n", - "L'optimisation est effectuée avec validation croisée à 5 plis (5-fold) pour garantir la robustesse des résultats.\n", - "\n", - "### 2. Tracking complet avec MLflow\n", - "\n", - "Pour chaque expérience, j'enregistre dans MLflow:\n", - "\n", - "- **Paramètres**: tous les hyperparamètres utilisés, y compris les meilleurs trouvés par GridSearchCV\n", - "- **Métriques**: accuracy, precision, recall, F1 score, ROC AUC, temps d'entraînement\n", - "- **Artifacts**:\n", - " - Le modèle optimisé avec sa signature d'entrée/sortie\n", - " - Le vectoriseur utilisé (nouveauté demandée)\n", - " - La matrice de confusion et la courbe ROC\n", - " - Les résultats détaillés de la validation croisée\n", - " - Les features les plus importantes (pour les modèles qui le permettent)\n", - " - Des graphiques d'analyse de l'influence des hyperparamètres\n", - "\n", - "### 3. Sauvegarde et utilisation du meilleur modèle\n", - "\n", - "J'ai implémenté une méthode pour:\n", - "\n", - "1. Identifier le meilleur modèle dans l'historique MLflow\n", - "2. Le récupérer avec son vectoriseur\n", - "3. Sauvegarder les deux dans un dossier local spécifié (\"content/simple-model/best\")\n", - "4. Créer une fonction de prédiction prête pour l'API\n", - "\n", - "### 4. Visualisations avancées\n", - "\n", - "Pour faciliter l'analyse:\n", - "\n", - "- Matrices de confusion pour chaque modèle\n", - "- Courbes ROC avec AUC calculé\n", - "- Graphiques des features les plus importantes\n", - "- Graphiques d'évolution des scores en fonction des hyperparamètres" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hJSt92gut0Vu" - }, - "source": [ - "# Enregistrement de dépendances" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LeieF6LStjLc" - }, - "outputs": [], - "source": [ - "!pip freeze > requirements_modele_simple.txt" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/p07_03-notebook-modele-avance.ipynb b/notebooks/p07_03-notebook-modele-avance.ipynb deleted file mode 100644 index a781056..0000000 --- a/notebooks/p07_03-notebook-modele-avance.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"ojWck8JFNwMp"},"source":["## Partie 3 : 🧠 Approches avancée (embeddings pré-entraînés et deep learning)\n","\n","Dans ce notebook, nous développons des **modèles avancés d'analyse de sentiment** pour détecter les bad buzz potentiels sur Twitter. Nous implémentons deux architectures de deep learning basées sur des réseaux **LSTM bidirectionnels**, utilisant respectivement les embeddings **Word2Vec** et **GloVe 300d** comme représentations vectorielles des mots. Ces modèles exploitent les techniques modernes d'apprentissage profond pour prédire le sentiment de tweets.\n","\n","Concernant l'entrainement, le choix a été fait d'entrainer par optimisation de la variable de perte de validation `val_loss` avec en particulier un critère d'`EarlyStopping` au bout d'un certain nombre d'époques lancées (5, en l'ocurrence) afin de garantir une bonne généralisation ainsi qu'une limitation de l'overfitting.\n","\n","Cependant, le critère qui sera retenu comme le plus important en raison de notre problématique métier est la `precision` car nous souhaitons plus que tout éviter les faux positifs (tweet prédit comme positif, alors qu'il sera mal reçu)."]},{"cell_type":"markdown","metadata":{"id":"hQ2lFBinNwMq"},"source":["## Importation des bibliothèques"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","drive_folder= \"/content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks\"\n","os.chdir(drive_folder)\n","os.listdir()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"khhtDEa-Phih","executionInfo":{"status":"ok","timestamp":1751829649428,"user_tz":-120,"elapsed":962,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ce7f19f8-a0ca-4fcf-fa46-ce54b3c96da7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]},{"output_type":"execute_result","data":{"text/plain":["['content',\n"," 'requirements_analyse_exploratoire.txt',\n"," 'p07_01-notebook-analyse-exploratoire.ipynb',\n"," 'requirements_bert.txt',\n"," 'requirements_modele_avance.txt',\n"," 'p07_03-notebook-modele-avance.ipynb',\n"," 'requirements_modele_simple.txt',\n"," 'p07_02-notebook-modele-simple.ipynb',\n"," 'p07_04-notebook-modele-bert.ipynb',\n"," 'notebook-connexion-mlflow.ipynb']"]},"metadata":{},"execution_count":1}]},{"cell_type":"code","source":["!pip install mlflow boto3 dotenv gensim --quiet"],"metadata":{"id":"vN2rVNmf86bI"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ae66AtL4NwMq","executionInfo":{"status":"ok","timestamp":1751829672248,"user_tz":-120,"elapsed":11453,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b8b32a26-9249-40fa-f306-7bcaf14d3061"},"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data] Unzipping tokenizers/punkt.zip.\n","[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n","[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Unzipping corpora/stopwords.zip.\n","[nltk_data] Downloading package wordnet to /root/nltk_data...\n","[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"]}],"source":["# Importations nécessaires\n","import os\n","import sys\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import time\n","import warnings\n","from collections import Counter\n","import pickle\n","from tqdm import tqdm\n","\n","# Importations scikit-learn\n","from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import LinearSVC\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n"," make_scorer, matthews_corrcoef, balanced_accuracy_score,\n"," classification_report, confusion_matrix, roc_auc_score, roc_curve)\n","\n","\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","\n","# Ajoute le dossier parent au path\n","sys.path.append(os.path.abspath(\"..\"))\n","\n","# Maintenant tu peux importer ton module\n","from functions import convert_sentiment_label, downsample_data, preprocess_tweet, process_in_parallel, apply_preprocessing\n","\n","\n","# Configuration des visualisations\n","plt.style.use('ggplot')\n","plt.rcParams['figure.figsize'] = (12, 8)\n","plt.rcParams['font.family'] = 'DejaVu Sans'\n","warnings.filterwarnings('ignore')"]},{"cell_type":"markdown","metadata":{"id":"E1TqvSBHNwMr"},"source":["## 💾 Jeu de données : Sentiment140\n","\n","Le jeu de données [Sentiment140 dataset with 1.6 million tweets](https://www.kaggle.com/datasets/kazanova/sentiment140) est une ressource majeure pour l'analyse de sentiment sur Twitter, comprenant **1,6 million de tweets** extraits via l'API Twitter. Ces tweets ont été automatiquement annotés selon leur polarité sentimentale, offrant une base solide pour développer des modèles de classification de sentiment.\n","\n","Le jeu de données est organisé en 6 colonnes distinctes :\n","\n","1. **target** : La polarité du sentiment exprimé dans le tweet.\n"," - 0 = sentiment négatif\n"," - 2 = sentiment neutre\n"," - 4 = sentiment positif\n","2. **ids** : L'identifiant unique du tweet (exemple : *2087*)\n","3. **date** : La date et l'heure de publication du tweet.\n","4. **flag** : La requête utilisée pour obtenir le tweet.\n"," - Exemple : *lyx*\n"," - Si aucune requête n'a été utilisée : *NO_QUERY*\n","5. **user** : Le nom d'utilisateur de l'auteur du tweet.\n","6. **text** : Le contenu textuel du tweet."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jDsgh8mGNwMr","outputId":"e0836993-b471-4040-e453-ce648a7ebefd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829672256,"user_tz":-120,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 58 µs, sys: 0 ns, total: 58 µs\n","Wall time: 460 µs\n"]}],"source":["%%time\n","\n","# Define the URL and the local file path\n","url = \"https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+7%C2%A0-+D%C3%A9tectez+les+Bad+Buzz+gr%C3%A2ce+au+Deep+Learning/sentiment140.zip\"\n","local_zip_path = \"./content/data/sentiment140.zip\"\n","extract_path = \"./content/data\"\n","\n","if not os.path.exists(extract_path):\n","\n"," # Create the directory if it doesn't exist\n"," os.makedirs(extract_path, exist_ok=True)\n","\n"," # Download the zip file\n"," response = requests.get(url)\n"," with open(local_zip_path, 'wb') as file:\n"," file.write(response.content)\n","\n"," # Extract the contents of the zip file\n"," with zipfile.ZipFile(local_zip_path, 'r') as zip_ref:\n"," zip_ref.extractall(extract_path)\n","\n"," # Delete the zip file\n"," os.remove(local_zip_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c-6C8QR_NwMr"},"outputs":[],"source":["pd.set_option('display.max_columns', None)\n","pd.set_option('display.expand_frame_repr', True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MtMJv7S-NwMr","outputId":"4b66b3a9-3182-4541-8683-22eaed885d24","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1751829684281,"user_tz":-120,"elapsed":12010,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" target ids date flag \\\n","0 0 1467810369 Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n","1 0 1467810672 Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n","2 0 1467810917 Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n","3 0 1467811184 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","4 0 1467811193 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","\n"," user text \n","0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n","1 scotthamilton is upset that he can't update his Facebook by ... \n","2 mattycus @Kenichan I dived many times for the ball. Man... \n","3 ElleCTF my whole body feels itchy and like its on fire \n","4 Karoli @nationwideclass no, it's not behaving at all.... "],"text/html":["\n","
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targetidsdateflagusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"raw_data"}},"metadata":{},"execution_count":6}],"source":["# Define the path to the CSV file\n","csv_file_path = os.path.join(extract_path, 'training.1600000.processed.noemoticon.csv')\n","\n","# Define the column names\n","column_names = ['target', 'ids', 'date', 'flag', 'user', 'text']\n","\n","# Load the dataset into a pandas DataFrame\n","raw_data = pd.read_csv(csv_file_path, encoding='latin-1', names=column_names)\n","\n","# Display the first few rows of the DataFrame\n","raw_data.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bIeawbN6NwMs","outputId":"a847e733-a6cc-4635-cd01-0bc887dc7bfd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829684282,"user_tz":-120,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Ce dataframe contient 1600000 lignes et 6 colonnes.\n"]}],"source":["print(f\"Ce dataframe contient {raw_data.shape[0]} lignes et {raw_data.shape[1]} colonnes.\")"]},{"cell_type":"markdown","source":["## ⏩🧠 Approches avancées de Deep Learning\n","Notre démarche pour la classification de sentiment avec des approches avancées comprend:\n","\n","1. **Prétraitement des tweets**\n"," - Nettoyage\n"," - Tokenisation et lemmatisation\n"," - Remplacement des URLs et mentions par des tokens spéciaux\n","\n","3. **Modèles testés**\n"," - **Modèle LSTM bidirectionnel avec Word2Vec:** Ce modèle utilisera une architecture de réseau de neurones récurrents (LSTM) bidirectionnel et les embeddings pré-entraînés **Word2Vec** pour représenter les mots.\n"," - **Modèle LSTM bidirectionnel avec GloVe 300d:** Ce modèle similaire utilisera également une architecture LSTM bidirectionnel, mais avec les embeddings pré-entraînés **GloVe de dimension 300**.\n","\n","\n","3. **Évaluation et suivi**\n"," - Métriques: précision, accuracy, recall, F1-score, ROC-AUC\n"," - Tracking avec MLflow pour la reproductibilité et la comparaison\n","\n","\n","Il est important de noter que l'entraînement est fait sur un sous-échantillon et non sur l'intégralité du dataset."],"metadata":{"id":"Lq2h1qmW2PqP"}},{"cell_type":"markdown","metadata":{"id":"UXHYqYWiNwMs"},"source":["## Prétraitement\n","\n","Notre démarche pour la classification de sentiment avec des approches classiques comprend:\n","\n","1. **Prétraitement des tweets**\n"," - Nettoyage\n"," - Tokenisation et lemmatisation\n"," - Remplacement des URLs et mentions par des tokens spéciaux\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XC0M0gPXNwMs"},"outputs":[],"source":["converted_target_data = convert_sentiment_label(raw_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gMSXKh2RNwMs","outputId":"d9635826-13f5-464f-f230-0bd4a0d6c743","colab":{"base_uri":"https://localhost:8080/","height":178},"executionInfo":{"status":"ok","timestamp":1751829686127,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["target\n","0 800000\n","1 800000\n","Name: count, dtype: int64"],"text/html":["
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"]},"metadata":{},"execution_count":10}],"source":["sample_size = 100000\n","downsampled_data = downsample_data(converted_target_data, n_samples=sample_size)\n","downsampled_data['target'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f2CkXR7XNwMt","outputId":"62b2f9e7-3dcb-49fe-e366-aff4eb32c5c1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829778185,"user_tz":-120,"elapsed":90859,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Prétraitement des tweets en cours...\n","Prétraitement terminé !\n"]}],"source":["# Appliquer le prétraitement à tous les tweets\n","print(\"Prétraitement des tweets en cours...\")\n","preprocessed_data = process_in_parallel(downsampled_data, apply_preprocessing, n_jobs=8)\n","print(\"Prétraitement terminé !\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LN5ul0JBNwMt","outputId":"cb4d7277-bbce-4bc8-b424-9b406f9ab93a","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1751829778186,"user_tz":-120,"elapsed":4,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" target ids date flag \\\n","212188 0 1974671194 Sat May 30 13:36:31 PDT 2009 NO_QUERY \n","299036 0 1997882236 Mon Jun 01 17:37:11 PDT 2009 NO_QUERY \n","475978 0 2177756662 Mon Jun 15 06:39:05 PDT 2009 NO_QUERY \n","588988 0 2216838047 Wed Jun 17 20:02:12 PDT 2009 NO_QUERY \n","138859 0 1880666283 Fri May 22 02:03:31 PDT 2009 NO_QUERY \n","\n"," user text \\\n","212188 simba98 @xnausikaax oh no! where did u order from? tha... \n","299036 Seve76 A great hard training weekend is over. a coup... \n","475978 x__claireyy__x Right, off to work Only 5 hours to go until I... \n","588988 Balasi I am craving for japanese food \n","138859 djrickdawson Jean Michel Jarre concert tomorrow gotta work... \n","\n"," processed_text \n","212188 < MENTION > oh no ! u order ? thats horrible \n","299036 great hard training weekend couple day rest le... \n","475978 right work 5 hour go im free xd \n","588988 craving japanese food \n","138859 jean michel jarre concert tomorrow got ta work... "],"text/html":["\n","
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targetidsdateflagusertextprocessed_text
21218801974671194Sat May 30 13:36:31 PDT 2009NO_QUERYsimba98@xnausikaax oh no! where did u order from? tha...< MENTION > oh no ! u order ? thats horrible
29903601997882236Mon Jun 01 17:37:11 PDT 2009NO_QUERYSeve76A great hard training weekend is over. a coup...great hard training weekend couple day rest le...
47597802177756662Mon Jun 15 06:39:05 PDT 2009NO_QUERYx__claireyy__xRight, off to work Only 5 hours to go until I...right work 5 hour go im free xd
58898802216838047Wed Jun 17 20:02:12 PDT 2009NO_QUERYBalasiI am craving for japanese foodcraving japanese food
13885901880666283Fri May 22 02:03:31 PDT 2009NO_QUERYdjrickdawsonJean Michel Jarre concert tomorrow gotta work...jean michel jarre concert tomorrow got ta work...
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"preprocessed_data"}},"metadata":{},"execution_count":12}],"source":["preprocessed_data.head()"]},{"cell_type":"markdown","metadata":{"id":"zyb9Fj38NwMt"},"source":["## Division des données en ensembles d'entraînement et de test"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z_y6Ja0INwMt","outputId":"900bbf3f-2716-478d-b03b-8f04f23a819f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829778219,"user_tz":-120,"elapsed":35,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Taille de l'ensemble d'entraînement: 160000 exemples\n","Taille de l'ensemble de test: 40000 exemples\n"]}],"source":["# Diviser les données en ensembles d'entraînement et de test\n","from sklearn.model_selection import train_test_split\n","\n","X = preprocessed_data['processed_text']\n","y = preprocessed_data['target']\n","\n","# Division train/test\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","\n","print(f\"Taille de l'ensemble d'entraînement: {X_train.shape[0]} exemples\")\n","print(f\"Taille de l'ensemble de test: {X_test.shape[0]} exemples\")"]},{"cell_type":"markdown","metadata":{"id":"wPE9r-G4NwMt"},"source":["## Entraînement des modèles et tracking avec MLflow"]},{"cell_type":"markdown","metadata":{"id":"zpsZDWuANwMt"},"source":["### Configuration de MLFlow"]},{"cell_type":"code","source":["import mlflow\n","import mlflow.keras\n","from mlflow.models import infer_signature\n","from mlflow import MlflowClient\n","from dotenv import load_dotenv\n","\n","# Charger les variables d'environnement depuis le fichier .env\n","load_dotenv(dotenv_path='../.env')\n","\n","# Configuration de MLflow avec les variables d'environnement\n","mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n","mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n","aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n","aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n","\n","# Configuration explicite de MLflow\n","mlflow.set_tracking_uri(mlflow_tracking_uri)\n","print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n","\n","# Configuration explicite des identifiants AWS\n","os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n","os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n","os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n","print(\"Identifiants AWS configurés\")"],"metadata":{"id":"qoQ4njujN758","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829780115,"user_tz":-120,"elapsed":1895,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c3bd3672-bdfe-41cf-e3ea-4f05c5dc85e9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","Identifiants AWS configurés\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CZ7unEtvNwMt","outputId":"9d070ad6-ba13-4f7d-d0bc-59415062d3b7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829782348,"user_tz":-120,"elapsed":2232,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":15}],"source":["# Créer l'expérience MLflow\n","mlflow.set_experiment(\"OC Projet 7\")"]},{"cell_type":"markdown","metadata":{"id":"33JkcVxcNwMt"},"source":["## Préparation des données pour les modèles de deep learning\n","\n","D'abord, nous devons tokeniser nos textes et les préparer pour l'entraînement des modèles de deep learning."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WKfNMY_JNwMt","outputId":"db116bc5-51d4-4ca0-d4ec-3bcbf92e763f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829788323,"user_tz":-120,"elapsed":5944,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Taille du vocabulaire: 83731\n","Forme des données d'entraînement: (160000, 50)\n","Forme des données de test: (40000, 50)\n"]}],"source":["# Définir la longueur maximale des séquences (nombre de mots par tweet)\n","MAX_SEQUENCE_LENGTH = 50\n","\n","# Définir la taille du vocabulaire (nombre de mots uniques à considérer)\n","MAX_VOCAB_SIZE = 20000\n","\n","# Tokenisation des textes\n","tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE, oov_token='')\n","tokenizer.fit_on_texts(X_train)\n","\n","# Convertir les textes en séquences d'indices\n","X_train_seq = tokenizer.texts_to_sequences(X_train)\n","X_test_seq = tokenizer.texts_to_sequences(X_test)\n","\n","# Padding des séquences pour qu'elles aient toutes la même longueur\n","X_train_pad = pad_sequences(X_train_seq, maxlen=MAX_SEQUENCE_LENGTH, padding='post', truncating='post')\n","X_test_pad = pad_sequences(X_test_seq, maxlen=MAX_SEQUENCE_LENGTH, padding='post', truncating='post')\n","\n","# Créer le dossier pour sauvegarder les modèles\n","os.makedirs(\"content/advanced-model\", exist_ok=True)\n","# os.makedirs(\"content/advanced-model/best\", exist_ok=True)\n","\n","# Sauvegarder le tokenizer pour une utilisation ultérieure\n","import pickle\n","with open('content/advanced-model/tokenizer.pickle', 'wb') as handle:\n"," pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n","\n","print(f\"Taille du vocabulaire: {len(tokenizer.word_index) + 1}\")\n","print(f\"Forme des données d'entraînement: {X_train_pad.shape}\")\n","print(f\"Forme des données de test: {X_test_pad.shape}\")"]},{"cell_type":"markdown","metadata":{"id":"876rS4__NwMt"},"source":["## Fonctions d'évaluation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OrUjzG-6NwMt"},"outputs":[],"source":["def evaluate_model(model, X_test, y_test, class_names=['Négatif', 'Positif']):\n"," \"\"\"\n"," Évalue le modèle sur l'ensemble de test et retourne les métriques de performance.\n"," \"\"\"\n"," # Prédictions du modèle\n"," y_pred_proba = model.predict(X_test)\n"," y_pred = (y_pred_proba > 0.5).astype(int)\n","\n"," # Convertir les prédictions de format numpy à format list pour éviter les erreurs de sérialisation\n"," if isinstance(y_pred, np.ndarray):\n"," y_pred = y_pred.flatten().tolist()\n"," if isinstance(y_pred_proba, np.ndarray):\n"," y_pred_proba = y_pred_proba.flatten().tolist()\n","\n"," # Calculer les métriques\n"," accuracy = accuracy_score(y_test, y_pred)\n"," precision = precision_score(y_test, y_pred)\n"," recall = recall_score(y_test, y_pred)\n"," f1 = f1_score(y_test, y_pred)\n"," roc_auc = roc_auc_score(y_test, y_pred_proba)\n","\n"," # Matrice de confusion\n"," cm = confusion_matrix(y_test, y_pred)\n","\n"," # Rapport de classification\n"," report = classification_report(y_test, y_pred, target_names=class_names)\n","\n"," metrics = {\n"," 'accuracy': accuracy,\n"," 'precision': precision,\n"," 'recall': recall,\n"," 'f1': f1,\n"," 'roc_auc': roc_auc\n"," }\n","\n"," return metrics, cm, report, y_pred, y_pred_proba\n","\n","def plot_confusion_matrix(cm, class_names, title='Matrice de confusion'):\n"," \"\"\"\n"," Crée une figure de matrice de confusion.\n"," \"\"\"\n"," fig, ax = plt.subplots(figsize=(8, 6))\n"," im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n"," ax.figure.colorbar(im, ax=ax)\n"," ax.set(xticks=np.arange(cm.shape[1]),\n"," yticks=np.arange(cm.shape[0]),\n"," xticklabels=class_names,\n"," yticklabels=class_names,\n"," title=title,\n"," ylabel='Vraie étiquette',\n"," xlabel='Étiquette prédite')\n","\n"," # Pivoter les labels de l'axe des x\n"," plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n","\n"," # Annoter la matrice de confusion\n"," thresh = cm.max() / 2.\n"," for i in range(cm.shape[0]):\n"," for j in range(cm.shape[1]):\n"," ax.text(j, i, format(cm[i, j], 'd'),\n"," ha=\"center\", va=\"center\",\n"," color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n"," plt.tight_layout()\n"," return fig\n"]},{"cell_type":"markdown","metadata":{"id":"3_YfW-7yNwMt"},"source":["## Fonction de logging pour MLflow"]},{"cell_type":"code","source":["def log_model_to_mlflow(model, model_name, vectorizer_name, metrics, cm, report, X_test, y_test, history=None, params=None):\n"," \"\"\"\n"," Enregistre le modèle et ses performances dans MLflow.\n"," \"\"\"\n"," class_names = ['Négatif', 'Positif']\n","\n"," with mlflow.start_run(run_name=f\"Modele_Avance_{model_name}_{vectorizer_name}\"):\n"," # Enregistrer les paramètres du modèle\n"," if params:\n"," for key, value in params.items():\n"," mlflow.log_param(key, value)\n","\n"," # Enregistrer les métriques de validation issues de l'entraînement\n"," if history is not None:\n"," hist = history.history\n"," if 'val_precision' in hist:\n"," best_epoch = int(np.argmax(hist['val_precision']))\n"," mlflow.log_metric(\"val_precision\", float(hist['val_precision'][best_epoch]))\n"," if 'val_loss' in hist:\n"," mlflow.log_metric(\"val_loss\", float(hist['val_loss'][best_epoch]))\n"," if 'val_recall' in hist:\n"," mlflow.log_metric(\"val_recall\", float(hist['val_recall'][best_epoch]))\n","\n"," # Enregistrer les métriques de performance\n"," for key, value in metrics.items():\n"," mlflow.log_metric('test_' + key, value)\n","\n"," # Tracer et enregistrer la matrice de confusion\n"," fig = plot_confusion_matrix(cm, class_names)\n"," mlflow.log_figure(fig, f\"confusion_matrix_{model_name}_{vectorizer_name}.png\")\n"," plt.close(fig)\n","\n"," # Enregistrer le rapport de classification\n"," report_path = f\"content/advanced-model/{model_name}_{vectorizer_name}/classification_report.txt\"\n"," os.makedirs(os.path.dirname(report_path), exist_ok=True)\n"," with open(report_path, \"w\") as f:\n"," f.write(report)\n"," mlflow.log_artifact(report_path)\n","\n"," # Enregistrer le modèle dans MLflow\n"," signature = infer_signature(X_test, y_test)\n"," mlflow.keras.log_model(\n"," model,\n"," f\"model_{model_name}_{vectorizer_name}\",\n"," signature=signature\n"," )\n","\n"," # Log du dossier local qui contient déjà tous les artefacts\n"," artifact_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}\"\n"," if os.path.exists(artifact_dir):\n"," mlflow.log_artifacts(artifact_dir, \"local_artifacts\")\n","\n"," return mlflow.active_run().info.run_id\n","\n","\n","def plot_training_history(history, model_name, vectorizer_name, run_id=None):\n"," \"\"\"\n"," Trace l'historique d'entraînement du modèle et l'enregistre dans la même exécution MLflow.\n","\n"," Args:\n"," history: Historique d'entraînement du modèle\n"," model_name: Nom du modèle\n"," vectorizer_name: Nom du vectoriseur\n"," run_id: ID de l'exécution MLflow existante\n","\n"," Returns:\n"," fig: Figure matplotlib créée\n"," \"\"\"\n"," fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n","\n"," # Tracer precision (train & val)\n"," ax1.plot(history.history['precision'], label='train')\n"," ax1.plot(history.history['val_precision'], label='validation')\n"," ax1.set_title(f'Précision - {model_name} avec {vectorizer_name}')\n"," ax1.set_xlabel('Epoch')\n"," ax1.set_ylabel('Précision')\n"," ax1.legend()\n","\n"," # Tracer loss (train & val)\n"," ax2.plot(history.history['loss'], label='train')\n"," ax2.plot(history.history['val_loss'], label='validation')\n"," ax2.set_title(f'Loss - {model_name} avec {vectorizer_name}')\n"," ax2.set_xlabel('Epoch')\n"," ax2.set_ylabel('Loss')\n"," ax2.legend()\n","\n","\n"," plt.tight_layout()\n","\n"," # Sauvegarder la figure localement\n"," filename = f'content/advanced-model/training_history_{model_name}_{vectorizer_name}.png'\n"," plt.savefig(filename)\n","\n"," # Enregistrer dans MLflow en utilisant l'exécution existante\n"," if run_id:\n"," with mlflow.start_run(run_id=run_id):\n"," mlflow.log_figure(fig, f\"training_history_{model_name}_{vectorizer_name}.png\")\n"," else:\n"," # Fallback si aucun run_id n'est fourni\n"," with mlflow.start_run(run_name=f\"ModeleAvance_{model_name}_{vectorizer_name}\"):\n"," mlflow.log_figure(fig, f\"training_history_{model_name}_{vectorizer_name}.png\")"],"metadata":{"id":"etdkfzD9x8t3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1UorrWtSNwMu"},"source":["## Chargement et préparation des embeddings pré-entraînés"]},{"cell_type":"markdown","metadata":{"id":"yzl1y9HSNwMu"},"source":["### Word2vec"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K0hDRRZmNwMu","outputId":"f27eb320-f434-478d-defd-62e9b14d7e05","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829788865,"user_tz":-120,"elapsed":511,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Word2Vec model already exists at content/pretrained-embeddings/word2vec/GoogleNews-vectors-negative300.bin\n","Word2Vec model is available at: content/pretrained-embeddings/word2vec/GoogleNews-vectors-negative300.bin\n"]}],"source":["def download_word2vec():\n"," \"\"\"\n"," Downloads the pre-trained Word2Vec embeddings.\n"," Creates the necessary directories, downloads the file, extracts it, and cleans up unneeded files.\n","\n"," Returns:\n"," str: Path to the extracted Word2Vec model file\n"," \"\"\"\n"," import os\n"," import gzip\n"," import shutil\n"," from urllib.request import urlretrieve\n"," import time\n"," import zipfile\n","\n"," # Create necessary directories\n"," content_dir = 'content/pretrained-embeddings'\n"," word2vec_dir = os.path.join(content_dir, 'word2vec')\n","\n"," os.makedirs(word2vec_dir, exist_ok=True)\n","\n"," # Define file paths\n"," model_file = os.path.join(word2vec_dir, 'GoogleNews-vectors-negative300.bin')\n"," zip_file = os.path.join(word2vec_dir, 'GoogleNews-vectors-negative300.bin.zip')\n","\n"," # Skip download if file already exists\n"," if os.path.exists(model_file):\n"," print(f\"Word2Vec model already exists at {model_file}\")\n"," return model_file\n","\n"," # URL for the pre-trained Word2Vec embeddings\n"," # Using huggingface repository as a reliable source\n"," url = 'https://huggingface.co/fse/word2vec-google-news-300/resolve/main/GoogleNews-vectors-negative300.bin.gz'\n","\n"," try:\n"," # Download the file with progress reporting\n"," print(f\"Downloading Word2Vec embeddings from {url}...\")\n"," print(\"This may take a while as the file is ~1.5GB\")\n","\n"," start_time = time.time()\n","\n"," def progress_callback(count, block_size, total_size):\n"," elapsed_time = time.time() - start_time\n"," downloaded = count * block_size / (1024 * 1024) # MB\n"," total = total_size / (1024 * 1024) if total_size > 0 else 0 # MB\n"," percent = min(100, count * block_size * 100 / total_size) if total_size > 0 else 0\n"," speed = downloaded / elapsed_time if elapsed_time > 0 else 0\n","\n"," print(f\"\\rDownloaded: {downloaded:.1f}MB / {total:.1f}MB ({percent:.1f}%) - {speed:.1f}MB/s\",\n"," end='', flush=True)\n","\n"," gz_file = os.path.join(word2vec_dir, 'GoogleNews-vectors-negative300.bin.gz')\n"," urlretrieve(url, gz_file, progress_callback)\n"," print(\"\\nDownload complete!\")\n","\n"," # Extract the gzip file\n"," print(f\"Extracting {gz_file}...\")\n"," with gzip.open(gz_file, 'rb') as f_in:\n"," with open(model_file, 'wb') as f_out:\n"," shutil.copyfileobj(f_in, f_out)\n"," print(\"Extraction complete!\")\n","\n"," # Delete the gzip file to save space\n"," os.remove(gz_file)\n"," print(f\"Deleted temporary file: {gz_file}\")\n","\n"," return model_file\n","\n"," except Exception as e:\n"," print(f\"Error downloading or extracting Word2Vec embeddings: {str(e)}\")\n"," print(\"\\nTrying alternative download source...\")\n","\n"," # Alternative URL (Google Drive mirror)\n"," try:\n"," # Install gdown if not available\n"," import subprocess\n"," import sys\n","\n"," def install_gdown():\n"," subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"gdown\"])\n","\n"," try:\n"," import gdown\n"," except ImportError:\n"," print(\"Installing gdown package for Google Drive downloads...\")\n"," install_gdown()\n"," import gdown\n","\n"," # Google Drive file ID for Word2Vec\n"," file_id = \"0B7XkCwpI5KDYNlNUTTlSS21pQmM\"\n","\n"," print(f\"Downloading Word2Vec embeddings from Google Drive (ID: {file_id})...\")\n"," output = gz_file\n"," gdown.download(id=file_id, output=output, quiet=False)\n","\n"," # Extract the gzip file\n"," print(f\"Extracting {gz_file}...\")\n"," with gzip.open(gz_file, 'rb') as f_in:\n"," with open(model_file, 'wb') as f_out:\n"," shutil.copyfileobj(f_in, f_out)\n"," print(\"Extraction complete!\")\n","\n"," # Delete the gzip file to save space\n"," os.remove(gz_file)\n"," print(f\"Deleted temporary file: {gz_file}\")\n","\n"," return model_file\n","\n"," except Exception as alt_error:\n"," print(f\"Error with alternative download: {str(alt_error)}\")\n","\n"," # Final fallback: direct user to manual download\n"," print(\"\\nAutomatic download failed. Please follow these manual steps:\")\n"," print(\"1. Download the Word2Vec embeddings from: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/view?usp=sharing\")\n"," print(\"2. Extract the downloaded file\")\n"," print(f\"3. Place the 'GoogleNews-vectors-negative300.bin' file in the '{word2vec_dir}' directory\")\n","\n"," # Clean up any partial downloads\n"," if os.path.exists(gz_file):\n"," os.remove(gz_file)\n"," if os.path.exists(model_file) and os.path.getsize(model_file) < 1000000: # If file exists but is too small\n"," os.remove(model_file)\n","\n"," raise Exception(\"Failed to download Word2Vec embeddings automatically. Please download manually.\")\n","\n","# Example usage:\n","model_path = download_word2vec()\n","print(f\"Word2Vec model is available at: {model_path}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WMvPJS5tNwMu"},"outputs":[],"source":["from gensim.models import KeyedVectors\n","\n","def load_word2vec_embeddings():\n"," \"\"\"\n"," Charge les embeddings Word2Vec pré-entraînés.\n"," \"\"\"\n"," print(\"Chargement des embeddings Word2Vec...\")\n","\n"," # Charger le modèle Word2Vec\n"," word2vec_model = KeyedVectors.load_word2vec_format('content/pretrained-embeddings/word2vec/GoogleNews-vectors-negative300.bin', binary=True, limit=500000)\n","\n"," # Convertir en dictionnaire pour une utilisation plus facile\n"," embeddings_index = {}\n"," for word in word2vec_model.key_to_index:\n"," embeddings_index[word] = word2vec_model[word]\n","\n"," print(f\"Trouvé {len(embeddings_index)} vecteurs d'embeddings Word2Vec.\")\n"," return embeddings_index"]},{"cell_type":"markdown","metadata":{"id":"Xor_kNOVNwMu"},"source":["## Création des matrices d'embedding à partir des embeddings pré-entraînés"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H1nz57olNwMu"},"outputs":[],"source":["def create_embedding_matrix(word_index, embeddings_index, embedding_dim):\n"," \"\"\"\n"," Crée une matrice d'embedding à partir des embeddings pré-entraînés.\n"," Affiche une barre de progression pendant le traitement.\n","\n"," Args:\n"," word_index (dict): Dictionnaire de mots et leurs indices\n"," embeddings_index (dict): Dictionnaire des embeddings pré-entraînés\n"," embedding_dim (int): Dimension des vecteurs d'embedding\n","\n"," Returns:\n"," numpy.ndarray: Matrice d'embedding\n"," \"\"\"\n"," vocab_size = len(word_index) + 1\n"," embedding_matrix = np.zeros((vocab_size, embedding_dim))\n","\n"," found_words = 0\n","\n"," # Utiliser tqdm pour afficher une barre de progression\n"," print(f\"Création de la matrice d'embedding pour {len(word_index)} mots...\")\n"," for word, i in tqdm(word_index.items(), total=len(word_index), desc=\"Création matrice d'embedding\"):\n"," embedding_vector = embeddings_index.get(word)\n"," if embedding_vector is not None:\n"," embedding_matrix[i] = embedding_vector\n"," found_words += 1\n","\n"," print(f\"Trouvé {found_words} mots ({found_words/len(word_index)*100:.2f}%) dans les embeddings pré-entraînés.\")\n"," return embedding_matrix"]},{"cell_type":"markdown","metadata":{"id":"XsGjsmVYNwMu"},"source":["## Optimisations matérielle (utilisation du GPU)\n","\n","1. **Désactivation du recurrent_dropout** - Permet l'utilisation de CuDNNLSTM optimisé pour GPU\n","2. **Activation de XLA** (Accelerated Linear Algebra) - Optimise les graphes d'opérations pour GPU\n","3. **Précision mixte** - Utilise float16 pour les calculs et float32 pour les variables\n","4. **Augmentation de la taille du batch** - Passée à 256 pour mieux exploiter le parallélisme GPU\n","5. **Optimisation du pipeline de données** - Utilisation de tf.data.Dataset avec prefetch\n","6. **Scheduler de taux d'apprentissage** - Décroissance exponentielle du taux pour améliorer la convergence\n","\n","Ces modifications permettent d'utiliser efficacement les capacités de notre **GTX 1060 3GB** tout en respectant les limitations de la mémoire."]},{"cell_type":"markdown","metadata":{"id":"lF2h27fxNwMu"},"source":["### Utilisation du GPU"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TcLWpxKgNwMu","outputId":"77043b88-2bfc-4082-a05d-578cb6d16aa0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829790359,"user_tz":-120,"elapsed":1095,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:From /tmp/ipython-input-22-656146420.py:12: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.config.list_physical_devices('GPU')` instead.\n"]},{"output_type":"stream","name":"stdout","text":["GPU disponible : [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n","TensorFlow utilise GPU: True\n","Devices disponibles: [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n","Compute dtype: float16\n","Variable dtype: float32\n"]}],"source":["import tensorflow as tf\n","\n","# Configuration de TensorFlow pour utiliser la GPU si disponible\n","physical_devices = tf.config.list_physical_devices('GPU')\n","if physical_devices:\n"," tf.config.experimental.set_memory_growth(physical_devices[0], True)\n"," print(\"GPU disponible :\", physical_devices)\n","else:\n"," print(\"Pas de GPU disponible, utilisation du CPU\")\n","\n","# Vérifier que TensorFlow utilise le GPU\n","print(f\"TensorFlow utilise GPU: {tf.test.is_gpu_available()}\")\n","print(f\"Devices disponibles: {tf.config.list_physical_devices()}\")\n","\n","# Optimiser les performances avec mixed precision\n","# Cette option accélère l'entraînement sur les GPU récents\n","policy = tf.keras.mixed_precision.Policy('mixed_float16')\n","tf.keras.mixed_precision.set_global_policy(policy)\n","print(f'Compute dtype: {policy.compute_dtype}')\n","print(f'Variable dtype: {policy.variable_dtype}')"]},{"cell_type":"markdown","metadata":{"id":"K6WAGmVVNwMu"},"source":["### Optimisations de la mémoire et configuration TensorFlow"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EHdEo-FBNwMu","outputId":"e75df72d-816f-4d2a-95af-60b6b73515f4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829790365,"user_tz":-120,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mémoire GPU dynamique activée\n","Politique de calcul: mixed_float16\n"]}],"source":["# Activer la mémoire GPU dynamique\n","gpus = tf.config.experimental.list_physical_devices('GPU')\n","if gpus:\n"," try:\n"," for gpu in gpus:\n"," tf.config.experimental.set_memory_growth(gpu, True)\n"," print(\"Mémoire GPU dynamique activée\")\n"," except RuntimeError as e:\n"," print(f\"Erreur: {e}\")\n","\n","# Activer XLA (Accelerated Linear Algebra) pour optimiser les graphes\n","tf.config.optimizer.set_jit(True) # Active la compilation Just-In-Time\n","\n","# Précision mixte pour accélérer les calculs\n","policy = tf.keras.mixed_precision.Policy('mixed_float16')\n","tf.keras.mixed_precision.set_global_policy(policy)\n","print(f'Politique de calcul: {policy.name}')"]},{"cell_type":"markdown","metadata":{"id":"5tWaCIapNwMu"},"source":["### Optimisation du chargement des données"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_OYSmRuiNwMw"},"outputs":[],"source":["# 2. Optimiser le chargement des données\n","AUTOTUNE = tf.data.AUTOTUNE\n","BATCH_SIZE = 256 # Tester avec différentes valeurs: 128, 256, 512\n","\n","# Créer un dataset TensorFlow optimisé\n","def create_optimized_dataset(x, y, is_training=True):\n"," dataset = tf.data.Dataset.from_tensor_slices((x, y))\n"," if is_training:\n"," dataset = dataset.shuffle(10000)\n","\n"," # Utiliser un batch plus grand pour mieux utiliser le GPU\n"," dataset = dataset.batch(BATCH_SIZE)\n","\n"," # Précharger les données pour éviter les goulots d'étranglement\n"," dataset = dataset.prefetch(AUTOTUNE)\n"," return dataset\n","\n","# Séparer les données de validation et créer les datasets optimisés\n","val_size = int(0.1 * len(X_train_pad))\n","x_val = X_train_pad[-val_size:]\n","y_val = y_train[-val_size:]\n","x_train_final = X_train_pad[:-val_size]\n","y_train_final = y_train[:-val_size]\n","\n","train_dataset = create_optimized_dataset(x_train_final, y_train_final)\n","val_dataset = create_optimized_dataset(x_val, y_val, is_training=False)"]},{"cell_type":"markdown","metadata":{"id":"5angsL01NwMx"},"source":["Nos optimisations fonctionnent correctement :\n","\n","1. **XLA activé** : `Loaded cuDNN version 90300` et `Compiled cluster using XLA!` confirment que l'accélération XLA est bien activée.\n","2. **CuDNN chargé** : Le message indique que cuDNN (version 9.03) est correctement chargé, ce qui signifie que vos couches LSTM utiliseront l'implémentation optimisée pour GPU.\n","3. **GPU reconnu** : Le système a bien reconnu votre NVIDIA GeForce GTX 1060 3GB avec Compute Capability 6.1.\n","\n","Nous observervons :\n","- Une utilisation GPU bien supérieure à 20% (idéalement 70-90%)\n","- Un temps d'entraînement par epoch considérablement réduit\n","\n","Pour vérifier l'utilisation réelle du GPU pendant l'entraînement, nous pouvons exécuter :\n","\n","```shell\n","nvidia-smi -l 1\n","```"]},{"cell_type":"markdown","metadata":{"id":"PicWq6E0NwMx"},"source":["### Bilan de l'optimisation\n","\n","Nous avons réussi à optimiser significativement l'utilisation de notre GPU. Les modifications que nous avons apportées ont considérablement amélioré les performances :\n","\n","- Nous avons augmenté l'**utilisation de votre GPU de 20% à 73%**\n","- Nous utilisons maintenant intelligemment la mémoire disponible\n","- Nous avons activé les technologies d'**accélération comme XLA et CuDNN**\n","- Nous avons désactivé le **recurrent_dropout** pour permettre l'utilisation des implémentations GPU optimisées\n","- Nous avons implémenté la **précision mixte** pour accélérer les calculs\n","- Nous avons optimisé la **taille du batch** et le pipeline de données\n","\n","Nous avons atteint un excellent équilibre entre l'utilisation maximale des ressources et la stabilité du système."]},{"cell_type":"markdown","metadata":{"id":"NlECzxylNwMx"},"source":["## Création d'un modèles LSTM avec embeddings pré-entraînés\n","\n","Voici une description de la fonction `create_lstm_model_with_pretrained` qui crée un **modèle LSTM** avec des **embeddings pré-entraînés** :\n","\n","- Crée un modèle de réseau de neurones pour la **classification de texte** utilisant des **embeddings pré-entraînés**\n","- Initialise une couche d'embedding avec les poids fournis dans `embedding_matrix`\n","- Utilise une architecture profonde avec deux couches LSTM bidirectionnelles (128 puis 64 unités)\n","- Implémente plusieurs techniques de régularisation (dropout spatial et classique) pour éviter le surapprentissage\n","- Inclut une couche dense intermédiaire avec activation ReLU\n","- Se termine par une **couche de sortie avec activation sigmoïde**, adaptée aux problèmes de **classification binaire**\n","- Compile le modèle avec l'**optimiseur Adam**, une fonction de perte d'entropie croisée binaire et suit la métrique de précision\n","- Permet de contrôler si les embeddings sont entraînables ou figés via le paramètre `trainable`"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1KZ4x6cGNwMx"},"outputs":[],"source":["def create_optimized_lstm_model(embedding_matrix, max_seq_length=MAX_SEQUENCE_LENGTH, trainable=False):\n"," vocab_size, embedding_dim = embedding_matrix.shape\n","\n"," # Entrée du modèle\n"," input_layer = tf.keras.layers.Input(shape=(max_seq_length,))\n","\n"," # Couche d'embedding avec des poids pré-entraînés\n"," embedding_layer = tf.keras.layers.Embedding(\n"," input_dim=vocab_size,\n"," output_dim=embedding_dim,\n"," weights=[embedding_matrix],\n"," input_length=max_seq_length,\n"," trainable=trainable\n"," )(input_layer)\n","\n"," # Dropout spatial\n"," dropout_1 = tf.keras.layers.SpatialDropout1D(0.3)(embedding_layer)\n","\n"," # Couche CuDNNLSTM optimisée pour GPU (via le backend GPU automatique de TF)\n"," # Note: TF2.x utilise automatiquement l'implémentation CuDNNLSTM quand recurrent_dropout=0\n"," lstm_layer = tf.keras.layers.Bidirectional(\n"," tf.keras.layers.LSTM(\n"," units=128,\n"," dropout=0.2,\n"," recurrent_dropout=0.0, # Mettre à 0 pour utiliser l'implémentation CuDNN optimisée\n"," return_sequences=True\n"," )\n"," )(dropout_1)\n","\n"," # Deuxième couche LSTM\n"," lstm_layer_2 = tf.keras.layers.Bidirectional(\n"," tf.keras.layers.LSTM(\n"," units=64,\n"," dropout=0.2,\n"," recurrent_dropout=0.0 # Mettre à 0 pour utiliser l'implémentation CuDNN optimisée\n"," )\n"," )(lstm_layer)\n","\n"," # Couche dense avec activation ReLU\n"," dense_1 = tf.keras.layers.Dense(64, activation='relu')(lstm_layer_2)\n"," dropout_2 = tf.keras.layers.Dropout(0.4)(dense_1)\n","\n"," # Couche de sortie\n"," output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(dropout_2)\n","\n"," # Créer et compiler le modèle avec un optimiseur efficace sur GPU\n"," model = tf.keras.Model(inputs=input_layer, outputs=output_layer)\n","\n"," # Utiliser un taux d'apprentissage avec décroissance\n"," # lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n"," # initial_learning_rate=0.001,\n"," # decay_steps=1000,\n"," # decay_rate=0.9,\n"," # staircase=True\n"," # )\n","\n"," model.compile(\n"," optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # taux fixe\n"," loss='binary_crossentropy',\n"," # on ajoute aussi le rappel car on veut tout de même limiter le nombre de faux négatifs\n"," metrics=[keras.metrics.Precision(name='precision'), keras.metrics.Recall(name='recall')]\n"," )\n","\n"," return model"]},{"cell_type":"markdown","metadata":{"id":"S_tupAxrNwMx"},"source":["## Fonction de sauvegarde locale des artefacts du modèle\n","\n","Sauvegarde tous les artefacts du modèle (modèle, tokenizer, fonction de prétraitement, historique) dans un sous-dossier dédié."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mxy90vFuNwMx"},"outputs":[],"source":["import os\n","import dill # Plus puissant que pickle pour sauvegarder des fonctions\n","import datetime\n","import json\n","\n","def save_model_artifacts(model, model_name, vectorizer_name, history, tokenizer):\n"," \"\"\"\n"," Sauvegarde tous les artefacts du modèle (modèle, tokenizer, fonction de prétraitement, historique)\n"," dans un sous-dossier dédié.\n"," \"\"\"\n","\n"," # Créer un sous-dossier dédié pour ce modèle\n"," model_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}\"\n"," os.makedirs(model_dir, exist_ok=True)\n","\n"," # Sous-dossiers pour les différents types d'artefacts\n"," checkpoints_dir = f\"{model_dir}/checkpoints\"\n"," os.makedirs(checkpoints_dir, exist_ok=True)\n","\n"," # 1. Sauvegarder le modèle final\n"," model_path = f\"{model_dir}/final_model_{model_name}_{vectorizer_name}.keras\"\n"," model.save(model_path)\n"," print(f\"Modèle sauvegardé dans {model_path}\")\n","\n"," # 2. Sauvegarder le tokenizer\n"," tokenizer_path = f\"{model_dir}/tokenizer.pickle\"\n"," with open(tokenizer_path, 'wb') as handle:\n"," pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"," print(f\"Tokenizer sauvegardé dans {tokenizer_path}\")\n","\n"," # 3. Sauvegarder la fonction de prétraitement\n"," preprocess_path = f\"{model_dir}/preprocess_function.dill\"\n"," with open(preprocess_path, 'wb') as handle:\n"," dill.dump(preprocess_tweet, handle)\n"," print(f\"Fonction de prétraitement sauvegardée dans {preprocess_path}\")\n","\n"," # 4. Sauvegarder l'historique d'entraînement\n"," if history is not None:\n"," history_path = f\"{model_dir}/training_history.pickle\"\n"," with open(history_path, 'wb') as handle:\n"," pickle.dump(history.history, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"," print(f\"Historique d'entraînement sauvegardé dans {history_path}\")\n","\n"," # 5. Sauvegarder les paramètres utilisés - Version ultra-sécurisée\n"," params_path = f\"{model_dir}/parameters.json\"\n","\n"," # Déterminer la dimension d'embedding de façon sûre\n"," embedding_dim = None\n","\n"," # Méthode 1: Accéder directement à l'attribut output_dim des couches Embedding\n"," for layer in model.layers:\n"," if isinstance(layer, tf.keras.layers.Embedding):\n"," embedding_dim = layer.output_dim\n"," break\n","\n"," # Méthode 2: Si ce n'est pas trouvé, utiliser une valeur basée sur le nom du vectorizer\n"," if embedding_dim is None:\n"," if 'word2vec' in vectorizer_name.lower():\n"," embedding_dim = 300 # Dimension standard pour Word2Vec\n"," elif 'glove' in vectorizer_name.lower() and '300d' in vectorizer_name.lower():\n"," embedding_dim = 300\n"," else:\n"," embedding_dim = 100 # Valeur par défaut\n","\n"," params = {\n"," \"model_name\": model_name,\n"," \"vectorizer_name\": vectorizer_name,\n"," \"max_sequence_length\": MAX_SEQUENCE_LENGTH,\n"," \"vocab_size\": MAX_VOCAB_SIZE,\n"," \"embedding_dim\": embedding_dim,\n"," \"date_created\": datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n"," }\n"," with open(params_path, 'w') as f:\n"," json.dump(params, f, indent=4)\n"," print(f\"Paramètres sauvegardés dans {params_path}\")\n","\n"," return model_dir"]},{"cell_type":"markdown","metadata":{"id":"BZLUgq5gNwMx"},"source":["## Entraînement : LSTM avec Word2Vec\n","\n","Nous créons un modèle d'analyse de sentiment des tweets basé sur des **réseaux de neurones récurrents (LSTM)** avec des **embeddings Word2Vec**.\n","\n","Nous chargeons d'abord les embeddings Word2Vec pré-entraînés et les intègrons dans la matrice d'embedding, puis nous définissons une **fonction d'entraînement complète** qui:\n","\n","- Crée un **modèle LSTM bidirectionnel** optimisé pour GPU\n","- Configure des **callbacks** comme early stopping, réduction du taux d'apprentissage et sauvegarde des meilleurs modèles\n","- Entraîne le modèle sur des **données de tweets** prétraitées en utilisant des optimisations GPU\n","- Évalue les **performances du modèle** avec diverses métriques (précision, rappel, F1, etc.)\n","- **Sauvegarde les artefacts du modèle** (modèle final, tokenizer, fonction de prétraitement)\n","- **Enregistre tous les paramètres et résultats dans MLflow** pour faciliter le suivi des expériences\n","- Génère des **visualisations des courbes d'apprentissage**\n","- Retourne le modèle entraîné, son historique et ses métriques de performance\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qOq86PFNNwMx","outputId":"8ff016a9-09d8-4a8b-fc9e-d0cbef7197f2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751829803818,"user_tz":-120,"elapsed":13373,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Chargement des embeddings Word2Vec...\n","Trouvé 500000 vecteurs d'embeddings Word2Vec.\n","Création de la matrice d'embedding pour 83730 mots...\n"]},{"output_type":"stream","name":"stderr","text":["Création matrice d'embedding: 100%|██████████| 83730/83730 [00:00<00:00, 355633.78it/s]"]},{"output_type":"stream","name":"stdout","text":["Trouvé 22721 mots (27.14%) dans les embeddings pré-entraînés.\n","Forme de la matrice d'embedding: (83731, 300)\n","Échantillon de valeurs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n","Type de données: float64\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["word2vec_embeddings = load_word2vec_embeddings()\n","word2vec_matrix = create_embedding_matrix(tokenizer.word_index, word2vec_embeddings, 300)\n","\n","# Vérifiez la forme et les valeurs de la matrice d'embedding\n","print(\"Forme de la matrice d'embedding:\", word2vec_matrix.shape)\n","print(\"Échantillon de valeurs:\", word2vec_matrix[0, :10]) # Affiche les 10 premières valeurs de la première ligne\n","print(\"Type de données:\", word2vec_matrix.dtype)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KXMLd7jqNwMx"},"outputs":[],"source":["# Créer une fonction pour configurer le callback ModelCheckpoint\n","def create_model_checkpoint(model_name, vectorizer_name):\n"," \"\"\"\n"," Crée un callback ModelCheckpoint qui sauvegarde uniquement le meilleur modèle.\n"," \"\"\"\n"," # Créer le dossier pour les checkpoints\n"," checkpoint_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}/checkpoints\"\n"," os.makedirs(checkpoint_dir, exist_ok=True)\n","\n"," # Créer un seul checkpoint pour le meilleur modèle\n"," best_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n"," filepath=f\"{checkpoint_dir}/best_model.keras\",\n"," # on décide de monitorer val_precision car c'est ce que l'on souhaite maximiser\n"," # monitor='val_loss',\n"," monitor='val_precision',\n"," save_best_only=True,\n"," # mode='min',\n"," mode='max',\n"," verbose=1\n"," )\n","\n"," return best_checkpoint"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JGLT8JhWNwMx"},"outputs":[],"source":["def train_word2vec_lstm_model(epochs=20):\n"," \"\"\"\n"," Entraîne un modèle LSTM avec embeddings Word2Vec en utilisant les optimisations GPU\n"," et le chargement de données optimisé.\n"," \"\"\"\n"," # Définir le nom du modèle et de la méthode d'embedding\n"," model_name = \"LSTM\"\n"," vectorizer_name = \"Word2Vec-Fige\"\n","\n"," # Créer les callbacks pour ce modèle\n"," checkpoint_callback = create_model_checkpoint(model_name, vectorizer_name)\n","\n"," # Autres callbacks\n"," early_stopping = tf.keras.callbacks.EarlyStopping(\n"," monitor='val_loss',\n"," patience=5,\n"," restore_best_weights=True\n"," )\n","\n"," reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n"," monitor='val_loss',\n"," factor=0.2,\n"," patience=2,\n"," min_lr=0.00001\n"," )\n","\n"," # TensorBoard pour visualiser l'entraînement\n"," tensorboard_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}/logs\"\n"," os.makedirs(tensorboard_dir, exist_ok=True)\n"," tensorboard_callback = tf.keras.callbacks.TensorBoard(\n"," log_dir=tensorboard_dir,\n"," histogram_freq=1,\n"," update_freq='epoch'\n"," )\n","\n"," # Créer le modèle optimisé pour GPU\n"," model = create_optimized_lstm_model(word2vec_matrix, trainable=False)\n","\n"," print(f\"Entraînement du modèle {model_name} avec embeddings {vectorizer_name}\")\n"," print(f\"Taille du batch: {BATCH_SIZE}\")\n"," print(f\"Utilisation de la précision mixte: {tf.keras.mixed_precision.global_policy().name}\")\n","\n"," # Entraîner le modèle avec les datasets optimisés\n"," history = model.fit(\n"," train_dataset,\n"," epochs=epochs,\n"," validation_data=val_dataset,\n"," callbacks=[early_stopping, reduce_lr, tensorboard_callback, checkpoint_callback],\n"," verbose=1\n"," )\n","\n"," # Déterminer le nombre réel d'époques complétées (en cas d'early stopping)\n"," real_epochs = len(history.history['loss'])\n"," print(f\"Nombre réel d'époques effectuées: {real_epochs}/{epochs}\")\n","\n"," # Sauvegarder tous les artefacts\n"," model_dir = save_model_artifacts(model, model_name, vectorizer_name, history, tokenizer)\n","\n"," # Utiliser evaluate_model pour obtenir les métriques\n"," metrics, cm, report, y_pred, y_pred_proba = evaluate_model(\n"," model,\n"," X_test_pad,\n"," y_test\n"," )\n","\n"," # Afficher les métriques\n"," print(\"\\nMétriques pour le modèle LSTM avec embedding Word2Vec:\")\n"," for key, value in metrics.items():\n"," print(f\"{key}: {value:.4f}\")\n","\n"," print(\"\\nRapport de classification:\")\n"," print(report)\n","\n","\n"," # Enregistrer avec MLflow\n"," params = {\n"," 'model_type': model_name,\n"," 'embedding_type': vectorizer_name,\n"," 'embedding_dim': word2vec_matrix.shape[1],\n"," 'vocab_size': len(tokenizer.word_index) + 1,\n"," 'max_sequence_length': MAX_SEQUENCE_LENGTH,\n"," 'trainable_embedding': False,\n"," 'max_epochs': epochs,\n"," 'real_epochs': real_epochs, # Ajouter le nombre réel d'époques\n"," 'batch_size': BATCH_SIZE,\n"," 'precision_policy': tf.keras.mixed_precision.global_policy().name,\n"," 'gpu_optimization': 'XLA,dynamic_memory,prefetch',\n"," 'sample_size': sample_size*2\n","\n"," }\n","\n"," # Enregistrer dans MLflow\n"," # run_id = log_model_to_mlflow(model, model_name, vectorizer_name, metrics, cm, report, X_test_pad, y_test, params)\n"," run_id = log_model_to_mlflow(model, model_name, vectorizer_name, metrics, cm, report, X_test_pad, y_test, history, params)\n","\n"," # Visualiser la courbe d'apprentissage\n"," plot_training_history(history, \"LSTM\", \"Word2Vec-Fige\", run_id=run_id)\n","\n"," # Récupérer le lien vers le run MLflow\n"," print(f\"\\nRun MLflow ID: {run_id}\")\n"," print(f\"Accédez à l'interface MLflow pour voir les détails complets.\")\n","\n"," return model, history, metrics, run_id"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O8S9kFSFNwMx","outputId":"734480a6-2aa0-45e8-e3df-36ba91d7328f","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1751830269652,"user_tz":-120,"elapsed":465796,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Entraînement du modèle LSTM avec embeddings Word2Vec-Fige\n","Taille du batch: 256\n","Utilisation de la précision mixte: mixed_float16\n","Epoch 1/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.5872 - precision: 0.6836 - recall: 0.6817\n","Epoch 1: val_precision improved from -inf to 0.74873, saving model to content/advanced-model/LSTM_Word2Vec-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 58ms/step - loss: 0.5871 - precision: 0.6837 - recall: 0.6818 - val_loss: 0.5082 - val_precision: 0.7487 - val_recall: 0.7563 - learning_rate: 0.0010\n","Epoch 2/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.5207 - precision: 0.7410 - recall: 0.7456\n","Epoch 2: val_precision improved from 0.74873 to 0.77025, saving model to content/advanced-model/LSTM_Word2Vec-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 45ms/step - loss: 0.5207 - precision: 0.7410 - recall: 0.7456 - val_loss: 0.4784 - val_precision: 0.7703 - val_recall: 0.7720 - learning_rate: 0.0010\n","Epoch 3/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4919 - precision: 0.7598 - recall: 0.7715\n","Epoch 3: val_precision did not improve from 0.77025\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4919 - precision: 0.7598 - recall: 0.7715 - val_loss: 0.4653 - val_precision: 0.7639 - val_recall: 0.7960 - learning_rate: 0.0010\n","Epoch 4/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4779 - precision: 0.7699 - recall: 0.7760\n","Epoch 4: val_precision did not improve from 0.77025\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 29ms/step - loss: 0.4779 - precision: 0.7699 - recall: 0.7760 - val_loss: 0.4610 - val_precision: 0.7637 - val_recall: 0.8117 - learning_rate: 0.0010\n","Epoch 5/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4692 - precision: 0.7706 - recall: 0.7891\n","Epoch 5: val_precision did not improve from 0.77025\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 29ms/step - loss: 0.4691 - precision: 0.7706 - recall: 0.7891 - val_loss: 0.4646 - val_precision: 0.7382 - val_recall: 0.8591 - learning_rate: 0.0010\n","Epoch 6/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4627 - precision: 0.7774 - recall: 0.7892\n","Epoch 6: val_precision improved from 0.77025 to 0.77874, saving model to content/advanced-model/LSTM_Word2Vec-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4627 - precision: 0.7774 - recall: 0.7892 - val_loss: 0.4524 - val_precision: 0.7787 - val_recall: 0.7977 - learning_rate: 0.0010\n","Epoch 7/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4556 - precision: 0.7805 - recall: 0.7927\n","Epoch 7: val_precision did not improve from 0.77874\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 32ms/step - loss: 0.4556 - precision: 0.7805 - recall: 0.7927 - val_loss: 0.4540 - val_precision: 0.7754 - val_recall: 0.8076 - learning_rate: 0.0010\n","Epoch 8/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4521 - precision: 0.7847 - recall: 0.7925\n","Epoch 8: val_precision did not improve from 0.77874\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 29ms/step - loss: 0.4521 - precision: 0.7847 - recall: 0.7925 - val_loss: 0.4514 - val_precision: 0.7682 - val_recall: 0.8142 - learning_rate: 0.0010\n","Epoch 9/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4450 - precision: 0.7874 - recall: 0.7984\n","Epoch 9: val_precision did not improve from 0.77874\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - loss: 0.4450 - precision: 0.7874 - recall: 0.7984 - val_loss: 0.4492 - val_precision: 0.7741 - val_recall: 0.8102 - learning_rate: 0.0010\n","Epoch 10/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4412 - precision: 0.7906 - recall: 0.7971\n","Epoch 10: val_precision improved from 0.77874 to 0.79515, saving model to content/advanced-model/LSTM_Word2Vec-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4412 - precision: 0.7906 - recall: 0.7971 - val_loss: 0.4496 - val_precision: 0.7952 - val_recall: 0.7727 - learning_rate: 0.0010\n","Epoch 11/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4344 - precision: 0.7928 - recall: 0.8052\n","Epoch 11: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 31ms/step - loss: 0.4344 - precision: 0.7928 - recall: 0.8052 - val_loss: 0.4515 - val_precision: 0.7818 - val_recall: 0.8014 - learning_rate: 0.0010\n","Epoch 12/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4256 - precision: 0.7980 - recall: 0.8075\n","Epoch 12: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 29ms/step - loss: 0.4256 - precision: 0.7980 - recall: 0.8075 - val_loss: 0.4455 - val_precision: 0.7865 - val_recall: 0.7943 - learning_rate: 2.0000e-04\n","Epoch 13/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4206 - precision: 0.8006 - recall: 0.8104\n","Epoch 13: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 29ms/step - loss: 0.4206 - precision: 0.8006 - recall: 0.8104 - val_loss: 0.4451 - val_precision: 0.7867 - val_recall: 0.8019 - learning_rate: 2.0000e-04\n","Epoch 14/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4170 - precision: 0.8040 - recall: 0.8117\n","Epoch 14: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 29ms/step - loss: 0.4170 - precision: 0.8040 - recall: 0.8117 - val_loss: 0.4419 - val_precision: 0.7950 - val_recall: 0.7912 - learning_rate: 2.0000e-04\n","Epoch 15/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.4170 - precision: 0.8052 - recall: 0.8142\n","Epoch 15: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 31ms/step - loss: 0.4170 - precision: 0.8053 - recall: 0.8142 - val_loss: 0.4468 - val_precision: 0.7831 - val_recall: 0.8031 - learning_rate: 2.0000e-04\n","Epoch 16/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4142 - precision: 0.8058 - recall: 0.8151\n","Epoch 16: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 29ms/step - loss: 0.4142 - precision: 0.8058 - recall: 0.8151 - val_loss: 0.4477 - val_precision: 0.7841 - val_recall: 0.7965 - learning_rate: 2.0000e-04\n","Epoch 17/50\n","\u001b[1m560/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4102 - precision: 0.8085 - recall: 0.8159\n","Epoch 17: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 28ms/step - loss: 0.4102 - precision: 0.8085 - recall: 0.8159 - val_loss: 0.4485 - val_precision: 0.7838 - val_recall: 0.8012 - learning_rate: 4.0000e-05\n","Epoch 18/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4108 - precision: 0.8076 - recall: 0.8137\n","Epoch 18: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 29ms/step - loss: 0.4108 - precision: 0.8076 - recall: 0.8138 - val_loss: 0.4487 - val_precision: 0.7842 - val_recall: 0.7999 - learning_rate: 4.0000e-05\n","Epoch 19/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4088 - precision: 0.8077 - recall: 0.8180\n","Epoch 19: val_precision did not improve from 0.79515\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 29ms/step - loss: 0.4088 - precision: 0.8077 - recall: 0.8180 - val_loss: 0.4472 - val_precision: 0.7844 - val_recall: 0.8043 - learning_rate: 1.0000e-05\n","Nombre réel d'époques effectuées: 19/50\n","Modèle sauvegardé dans content/advanced-model/LSTM_Word2Vec-Fige/final_model_LSTM_Word2Vec-Fige.keras\n","Tokenizer sauvegardé dans content/advanced-model/LSTM_Word2Vec-Fige/tokenizer.pickle\n","Fonction de prétraitement sauvegardée dans content/advanced-model/LSTM_Word2Vec-Fige/preprocess_function.dill\n","Historique d'entraînement sauvegardé dans content/advanced-model/LSTM_Word2Vec-Fige/training_history.pickle\n","Paramètres sauvegardés dans content/advanced-model/LSTM_Word2Vec-Fige/parameters.json\n","\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step\n","\n","Métriques pour le modèle LSTM avec embedding Word2Vec:\n","accuracy: 0.7898\n","precision: 0.7970\n","recall: 0.7777\n","f1: 0.7872\n","roc_auc: 0.8748\n","\n","Rapport de classification:\n"," precision recall f1-score support\n","\n"," Négatif 0.78 0.80 0.79 20000\n"," Positif 0.80 0.78 0.79 20000\n","\n"," accuracy 0.79 40000\n"," macro avg 0.79 0.79 0.79 40000\n","weighted avg 0.79 0.79 0.79 40000\n","\n"]},{"output_type":"stream","name":"stderr","text":["2025/07/06 19:30:24 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"]},{"output_type":"stream","name":"stdout","text":["🏃 View run Modele_Avance_LSTM_Word2Vec-Fige at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1/runs/1de54538ee73443687db131fcc86d922\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1\n","🏃 View run Modele_Avance_LSTM_Word2Vec-Fige at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1/runs/1de54538ee73443687db131fcc86d922\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1\n","\n","Run MLflow ID: 1de54538ee73443687db131fcc86d922\n","Accédez à l'interface MLflow pour voir les détails complets.\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Entraîner le modèle LSTM avec Word2Vec\n","model_lstm_word2vec, history_lstm_word2vec, metrics_lstm_word2vec, run_id_lstm_word2vec = train_word2vec_lstm_model(epochs=50)"]},{"cell_type":"markdown","metadata":{"id":"CD8guTB_NwMx"},"source":["## Chargement du modèle entraîné\n","\n","Dans cette fonction, nous créons un mécanisme pour charger un **modèle complet** avec tous ses composants associés. Nous récupérons l'ensemble des artefacts nécessaires pour faire fonctionner notre modèle d'analyse de sentiment à partir d'un emplacement spécifique.\n","\n","- Nous chargeons d'abord le **modèle Keras entraîné** à partir du fichier .keras sauvegardé\n","- Nous récupérons le **tokenizer** qui convertit le texte brut en séquences numériques\n","- Nous importons la **fonction de prétraitement** qui nettoie les tweets avant l'analyse\n","- Nous lisons les **paramètres du modèle** à partir d'un fichier JSON\n","- Nous retournons tous ces éléments dans un dictionnaire cohérent, prêt à être utilisé pour prédire le sentiment de nouveaux tweets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9nXo7O_XNwMx"},"outputs":[],"source":["# Fonction pour charger un modèle complet avec tous ses artefacts\n","def load_complete_model(model_name, vectorizer_name):\n"," \"\"\"\n"," Charge un modèle complet avec son tokenizer et sa fonction de prétraitement.\n"," \"\"\"\n"," model_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}\"\n","\n"," # Charger le modèle\n"," model_path = f\"{model_dir}/final_model_{model_name}_{vectorizer_name}.keras\"\n"," model = tf.keras.models.load_model(model_path)\n","\n"," # Charger le tokenizer\n"," tokenizer_path = f\"{model_dir}/tokenizer.pickle\"\n"," with open(tokenizer_path, 'rb') as handle:\n"," tokenizer = pickle.load(handle)\n","\n"," # Charger la fonction de prétraitement\n"," preprocess_path = f\"{model_dir}/preprocess_function.dill\"\n"," with open(preprocess_path, 'rb') as handle:\n"," preprocess_function = dill.load(handle)\n","\n"," # Charger les paramètres\n"," params_path = f\"{model_dir}/parameters.json\"\n"," with open(params_path, 'r') as f:\n"," params = json.load(f)\n","\n"," return {\n"," \"model\": model,\n"," \"tokenizer\": tokenizer,\n"," \"preprocess\": preprocess_function,\n"," \"params\": params\n"," }"]},{"cell_type":"markdown","metadata":{"id":"P7Ge212GNwMx"},"source":["## Prédire avec le modèle entraîné\n","\n","Dans cette fonction, nous mettons en œuvre le cœur de notre système d'analyse de sentiment, permettant de prédire si un tweet exprime un sentiment positif ou négatif. Nous utilisons l'ensemble des composants précédemment chargés pour traiter le texte et générer une prédiction fiable.\n","\n","- **Prétraitement du texte** : Application des transformations de nettoyage sur le tweet brut\n","- **Tokenisation** : Conversion du texte prétraité en séquences numériques\n","- **Padding** : Ajustement des séquences à une longueur uniforme\n","- **Prédiction** : Calcul de la probabilité de sentiment par le modèle LSTM\n","- **Interprétation** : Classification du tweet comme positif ou négatif selon le seuil de 0.5\n","- **Évaluation de confiance** : Détermination du niveau de certitude de la prédiction\n","- **Formatage des résultats** : Présentation des résultats sous forme de dictionnaire structuré"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LpuAgS8_NwMx"},"outputs":[],"source":["# Fonction pour prédire le sentiment avec un modèle chargé\n","def predict_sentiment(text, model_pack):\n"," \"\"\"\n"," Prédit le sentiment d'un texte en utilisant un modèle chargé.\n"," \"\"\"\n"," model = model_pack[\"model\"]\n"," tokenizer = model_pack[\"tokenizer\"]\n"," preprocess = model_pack[\"preprocess\"]\n"," params = model_pack[\"params\"]\n","\n"," # Prétraiter le texte\n"," preprocessed_text = preprocess(text)\n","\n"," # Tokeniser et encoder le texte\n"," tokens = tokenizer.texts_to_sequences([preprocessed_text])\n"," max_length = params.get(\"max_sequence_length\", MAX_SEQUENCE_LENGTH)\n"," padded_tokens = pad_sequences(tokens, maxlen=max_length, padding='post', truncating='post')\n","\n"," # Prédire le sentiment\n"," prediction = model.predict(padded_tokens)[0][0]\n","\n"," # Interpréter la prédiction\n"," sentiment = \"Positif\" if prediction >= 0.5 else \"Négatif\"\n"," confidence = prediction if prediction >= 0.5 else 1 - prediction\n","\n"," return {\n"," 'sentiment': sentiment,\n"," 'confidence': float(confidence),\n"," 'raw_score': float(prediction)\n"," }"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eWAj3vceNwMx"},"outputs":[],"source":["# Liste améliorée de tweets pour tester le modèle\n","test_tweets_improved = [\n"," # Tweets positifs avec différentes caractéristiques\n"," \"I absolutely love flying with @AirParadis! Their service is amazing and their staff is so helpful :) #bestairline #travel\",\n"," \"Just landed after a wonderful flight... The crew was totally awesome and the food was perfect!!! Can't wait to fly with them again :D\",\n"," \"OMG! You really have to try @AirParadis. Best. Flight. Ever. Their new seats are extremely comfortable. http://airparadis.com/newseats #travel #comfort\",\n"," \"We had a very pleasant experience with our flight today. The staff completely exceeded our expectations ;) #happy #AirParadis\",\n"," \"My first time flying business class and I'm absolutely amazed!!! The service is 100% worth it... @AirParadis never disappoints :)\",\n","\n"," # Tweets négatifs avec différentes caractéristiques\n"," \"I hate how @AirParadis always delays their flights... This is the third time this month!!! :( #disappointed #travel\",\n"," \"The worst flight experience of my life? Definitely today with @AirParadis. Their customer service won't even respond to my complaints... #terrible\",\n"," \"Really @AirParadis??? Flight cancelled and no compensation??? That's how you treat your customers? SMH :(\",\n"," \"Our baggage was lost and nobody at @AirParadis seems to care! Don't they understand that we can't enjoy our vacation without our stuff??? http://badservice.com/complaint :-(\",\n"," \"Food was terrible, seats were uncomfortable, and the staff was not helpful at all... Never flying with @AirParadis again! #worstairline\",\n","\n"," # Tweets mixtes et nuancés\n"," \"The flight was good but the food wasn't really what I expected... @AirParadis can do better! #mixedfeelings\",\n"," \"IDK what to think about my @AirParadis experience? The crew was nice but the flight was delayed for 2 hours... #confused\",\n"," \"My husband loved the flight but I didn't... Too bad they can't make everyone happy :-) #AirParadis\",\n"," \"TBH, @AirParadis has improved their service since last year! Not perfect yet, but they're trying... #progress\",\n"," \"We were so excited for our trip but then our flight got delayed... at least the staff was very apologetic and gave us free drinks! @AirParadis\",\n","\n"," # Tweets spécifiquement pour tester les caractéristiques spéciales\n"," \"This is a tweet with many!!! exclamation!!! marks!!! to test how they affect sentiment analysis :)\",\n"," \"How does the model handle multiple??? question??? marks??? in a single tweet? @AirParadis\",\n"," \"Testing ellipses... many... many... ellipses... in... one... tweet... @AirParadis #test\",\n"," \"#many #hashtags #in #one #tweet #to #see #how #they #affect #the #analysis #AirParadis\",\n"," \"@mention1 @mention2 @mention3 @mention4 @mention5 multiple mentions in one tweet\",\n"," \"Multiple URLs in one tweet: http://example1.com http://example2.com http://example3.com @AirParadis\",\n"," \"Let's try all emoticons :) :( :D ;) :-) :-( :-D ;-) in a single tweet to see the impact\"\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hRAWxNusNwMy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751830278905,"user_tz":-120,"elapsed":9239,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"da9971f0-92e6-49fd-a56f-9da5946ddc8f"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n","\n","Test du modèle sur les exemples:\n","==================================================\n","Résumé: 22 tweets analysés\n","Tweets positifs: 10 (45.5%)\n","Tweets négatifs: 12 (54.5%)\n"]}],"source":["def test_model_on_examples(model_pack, test_tweets):\n"," \"\"\"\n"," Teste le modèle sur une liste de tweets et retourne un DataFrame avec les résultats des prédictions.\n"," \"\"\"\n"," results = {\n"," \"Positif\": 0,\n"," \"Négatif\": 0\n"," }\n","\n"," # Créer un DataFrame pour stocker les résultats\n"," predictions_data = []\n","\n"," for i, tweet in enumerate(test_tweets):\n"," # Utiliser la fonction predict_sentiment avec model_pack\n"," result = predict_sentiment(tweet, model_pack)\n","\n"," # Ajouter l'emoji approprié pour le sentiment\n"," emoji = \"💚\" if result['sentiment'] == \"Positif\" else \"❌\"\n","\n"," # Ajouter les données au DataFrame\n"," predictions_data.append({\n"," \"Tweet\": tweet,\n"," \"Sentiment\": result['sentiment'],\n"," \"Emoji\": emoji,\n"," \"Score de confiance\": result['confidence'],\n"," \"Score brut\": result['raw_score'],\n"," \"Index\": i+1\n"," })\n","\n"," # Comptage des résultats\n"," results[result['sentiment']] += 1\n","\n"," # Créer le DataFrame\n"," df_predictions = pd.DataFrame(predictions_data)\n","\n"," # Afficher un résumé\n"," print(\"\\nTest du modèle sur les exemples:\")\n"," print(\"=\"*50)\n"," print(f\"Résumé: {len(test_tweets)} tweets analysés\")\n"," print(f\"Tweets positifs: {results['Positif']} ({(results['Positif']/len(test_tweets)*100):.1f}%)\")\n"," print(f\"Tweets négatifs: {results['Négatif']} ({(results['Négatif']/len(test_tweets)*100):.1f}%)\")\n","\n"," # Retourner le DataFrame\n"," return df_predictions\n","\n","# Exemple d'utilisation:\n","# Charger le model_pack (qui contient model, tokenizer, preprocess, params)\n","lstm_word2vec_model_pack = load_complete_model(\"LSTM\", \"Word2Vec-Fige\")\n","\n","# Obtenir le DataFrame des prédictions\n","df_results_word2vec = test_model_on_examples(lstm_word2vec_model_pack, test_tweets_improved)\n","\n","# Optionnel: sauvegarder les résultats dans un fichier CSV\n","df_results_word2vec.to_csv(\"content/advanced-model/LSTM_Word2Vec-Fige/predictions_results.csv\", index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aCXO6s63NwMy","colab":{"base_uri":"https://localhost:8080/","height":739},"executionInfo":{"status":"ok","timestamp":1751830278990,"user_tz":-120,"elapsed":84,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9f52a557-cf1d-4738-be5d-c44e17e7b163"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Tweet Sentiment Emoji \\\n","0 I absolutely love flying with @AirParadis! The... Positif 💚 \n","1 Just landed after a wonderful flight... The cr... Positif 💚 \n","2 OMG! You really have to try @AirParadis. Best.... Positif 💚 \n","3 We had a very pleasant experience with our fli... Positif 💚 \n","4 My first time flying business class and I'm ab... Négatif ❌ \n","5 I hate how @AirParadis always delays their fli... Négatif ❌ \n","6 The worst flight experience of my life? Defini... Négatif ❌ \n","7 Really @AirParadis??? Flight cancelled and no ... Négatif ❌ \n","8 Our baggage was lost and nobody at @AirParadis... Négatif ❌ \n","9 Food was terrible, seats were uncomfortable, a... Négatif ❌ \n","10 The flight was good but the food wasn't really... Positif 💚 \n","11 IDK what to think about my @AirParadis experie... Négatif ❌ \n","12 My husband loved the flight but I didn't... To... Négatif ❌ \n","13 TBH, @AirParadis has improved their service si... Positif 💚 \n","14 We were so excited for our trip but then our f... Négatif ❌ \n","15 This is a tweet with many!!! exclamation!!! ma... Négatif ❌ \n","16 How does the model handle multiple??? question... Négatif ❌ \n","17 Testing ellipses... many... many... ellipses..... Positif 💚 \n","18 #many #hashtags #in #one #tweet #to #see #how ... Négatif ❌ \n","19 @mention1 @mention2 @mention3 @mention4 @menti... Positif 💚 \n","20 Multiple URLs in one tweet: http://example1.co... Positif 💚 \n","21 Let's try all emoticons :) :( :D ;) :-) :-( :-... Positif 💚 \n","\n"," Score de confiance Score brut Index \n","0 0.995117 0.995117 1 \n","1 0.995117 0.995117 2 \n","2 0.979980 0.979980 3 \n","3 0.986328 0.986328 4 \n","4 0.541992 0.458008 5 \n","5 0.992825 0.007175 6 \n","6 0.971252 0.028748 7 \n","7 0.727051 0.272949 8 \n","8 0.942810 0.057190 9 \n","9 0.909424 0.090576 10 \n","10 0.918457 0.918457 11 \n","11 0.915649 0.084351 12 \n","12 0.536621 0.463379 13 \n","13 0.588379 0.588379 14 \n","14 0.541748 0.458252 15 \n","15 0.836792 0.163208 16 \n","16 0.510498 0.489502 17 \n","17 0.675293 0.675293 18 \n","18 0.591064 0.408936 19 \n","19 0.909180 0.909180 20 \n","20 0.994141 0.994141 21 \n","21 0.951660 0.951660 22 "],"text/html":["\n","
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TweetSentimentEmojiScore de confianceScore brutIndex
0I absolutely love flying with @AirParadis! The...Positif💚0.9951170.9951171
1Just landed after a wonderful flight... The cr...Positif💚0.9951170.9951172
2OMG! You really have to try @AirParadis. Best....Positif💚0.9799800.9799803
3We had a very pleasant experience with our fli...Positif💚0.9863280.9863284
4My first time flying business class and I'm ab...Négatif❌0.5419920.4580085
5I hate how @AirParadis always delays their fli...Négatif❌0.9928250.0071756
6The worst flight experience of my life? Defini...Négatif❌0.9712520.0287487
7Really @AirParadis??? Flight cancelled and no ...Négatif❌0.7270510.2729498
8Our baggage was lost and nobody at @AirParadis...Négatif❌0.9428100.0571909
9Food was terrible, seats were uncomfortable, a...Négatif❌0.9094240.09057610
10The flight was good but the food wasn't really...Positif💚0.9184570.91845711
11IDK what to think about my @AirParadis experie...Négatif❌0.9156490.08435112
12My husband loved the flight but I didn't... To...Négatif❌0.5366210.46337913
13TBH, @AirParadis has improved their service si...Positif💚0.5883790.58837914
14We were so excited for our trip but then our f...Négatif❌0.5417480.45825215
15This is a tweet with many!!! exclamation!!! ma...Négatif❌0.8367920.16320816
16How does the model handle multiple??? question...Négatif❌0.5104980.48950217
17Testing ellipses... many... many... ellipses.....Positif💚0.6752930.67529318
18#many #hashtags #in #one #tweet #to #see #how ...Négatif❌0.5910640.40893619
19@mention1 @mention2 @mention3 @mention4 @menti...Positif💚0.9091800.90918020
20Multiple URLs in one tweet: http://example1.co...Positif💚0.9941410.99414121
21Let's try all emoticons :) :( :D ;) :-) :-( :-...Positif💚0.9516600.95166022
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_results_word2vec","summary":"{\n \"name\": \"df_results_word2vec\",\n \"rows\": 22,\n \"fields\": [\n {\n \"column\": \"Tweet\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"I absolutely love flying with @AirParadis! Their service is amazing and their staff is so helpful :) #bestairline #travel\",\n \"TBH, @AirParadis has improved their service since last year! Not perfect yet, but they're trying... #progress\",\n \"Our baggage was lost and nobody at @AirParadis seems to care! Don't they understand that we can't enjoy our vacation without our stuff??? http://badservice.com/complaint :-(\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentiment\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"N\\u00e9gatif\",\n \"Positif\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Emoji\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"\\u274c\",\n \"\\ud83d\\udc9a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score de confiance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1866448337305405,\n \"min\": 0.510498046875,\n \"max\": 0.9951171875,\n \"num_unique_values\": 21,\n \"samples\": [\n 0.9951171875,\n 0.591064453125\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score brut\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3730689855539598,\n \"min\": 0.007175445556640625,\n \"max\": 0.9951171875,\n \"num_unique_values\": 21,\n \"samples\": [\n 0.9951171875,\n 0.408935546875\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 1,\n \"max\": 22,\n \"num_unique_values\": 22,\n \"samples\": [\n 1,\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":35}],"source":["df_results_word2vec"]},{"cell_type":"markdown","metadata":{"id":"Yk_yH3-INwMy"},"source":["## Entrainement : LSTM avec GloVe 300d"]},{"cell_type":"markdown","metadata":{"id":"E-yiOEKYNwMy"},"source":["### Chargement et préparation des embeddings pré-entraînés GloVe"]},{"cell_type":"code","source":["import os\n","import requests\n","from tqdm import tqdm\n","from zipfile import ZipFile\n","\n","# Télécharger et préparer les embeddings GloVe\n","def download_glove_embeddings(url=\"https://nlp.stanford.edu/data/glove.6B.zip\",\n"," output_dir=\"content/pretrained-embeddings/glove.6B\"):\n"," \"\"\"\n"," Télécharge et extrait les embeddings GloVe si nécessaire.\n"," Inclut une barre de progression et vérifie si les fichiers existent déjà.\n"," \"\"\"\n"," # Check if directory already exists\n"," if os.path.exists(output_dir) and os.listdir(output_dir):\n"," print(f\"Le dossier {output_dir} existe déjà. Téléchargement ignoré.\")\n"," return\n","\n"," # Create directory if it doesn't exist\n"," os.makedirs(output_dir, exist_ok=True)\n","\n"," # Temporary zip file path\n"," zip_path = \"glove.6B.zip\"\n","\n"," # Download the file with progress bar\n"," print(\"Téléchargement du fichier...\")\n"," # Add verify=False to ignore SSL certificate issues\n"," response = requests.get(url, stream=True, verify=False)\n"," total_size = int(response.headers.get('content-length', 0))\n","\n"," with open(zip_path, 'wb') as f, tqdm(\n"," desc=\"Téléchargement\",\n"," total=total_size,\n"," unit='B',\n"," unit_scale=True,\n"," unit_divisor=1024,\n"," ) as bar:\n"," for data in response.iter_content(chunk_size=1024):\n"," size = f.write(data)\n"," bar.update(size)\n","\n"," # Extract with progress bar\n"," print(\"Extraction des fichiers...\")\n"," with ZipFile(zip_path, 'r') as zip_ref:\n"," for file in tqdm(zip_ref.namelist(), desc=\"Extraction\"):\n"," zip_ref.extract(file, output_dir)\n","\n"," # Remove the zip file\n"," os.remove(zip_path)\n"," print(\"Terminé!\")\n","\n","# Exécuter la fonction\n","download_glove_embeddings()"],"metadata":{"id":"9y9NubYo_DLG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751830278993,"user_tz":-120,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"12613b2b-a219-4860-ba02-f8a00932f053"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Le dossier content/pretrained-embeddings/glove.6B existe déjà. Téléchargement ignoré.\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vk_8gucuNwMy"},"outputs":[],"source":["def load_glove_embeddings(embedding_dim=100):\n"," \"\"\"\n"," Charge les embeddings GloVe pré-entraînés.\n"," \"\"\"\n"," print(f\"Chargement des embeddings GloVe {embedding_dim}d...\")\n"," embeddings_index = {}\n","\n"," with open(f'content/pretrained-embeddings/glove.6B/glove.6B.{embedding_dim}d.txt', encoding='utf-8') as f:\n"," for line in f:\n"," values = line.rstrip().split(' ')\n"," word = values[0]\n"," coefs = np.asarray(values[1:], dtype='float32')\n"," embeddings_index[word] = coefs\n","\n"," print(f\"Trouvé {len(embeddings_index)} vecteurs d'embeddings GloVe.\")\n"," return embeddings_index"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AegR2uCfNwMy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751830310660,"user_tz":-120,"elapsed":31665,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"6150eeae-cf38-4ec3-a548-eeb1f3bbd2fd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Chargement des embeddings GloVe 300d...\n","Trouvé 400000 vecteurs d'embeddings GloVe.\n","Création de la matrice d'embedding pour 83730 mots...\n"]},{"output_type":"stream","name":"stderr","text":["Création matrice d'embedding: 100%|██████████| 83730/83730 [00:00<00:00, 353325.63it/s]"]},{"output_type":"stream","name":"stdout","text":["Trouvé 37172 mots (44.40%) dans les embeddings pré-entraînés.\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["glove_embeddings_300d = load_glove_embeddings(embedding_dim=300)\n","glove_matrix_300d = create_embedding_matrix(tokenizer.word_index, glove_embeddings_300d, 300)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gPwavY_xNwMy"},"outputs":[],"source":["def train_glove_lstm_model(epochs=20):\n"," \"\"\"\n"," Entraîne un modèle LSTM avec embeddings GloVe 300d en utilisant les optimisations GPU\n"," et le chargement de données optimisé.\n"," \"\"\"\n"," # Définir le nom du modèle et de la méthode d'embedding\n"," model_name = \"LSTM\"\n"," vectorizer_name = \"GloVe-300d-Fige\"\n","\n"," # Créer les callbacks pour ce modèle\n"," checkpoint_callback = create_model_checkpoint(model_name, vectorizer_name)\n","\n"," # Autres callbacks\n"," early_stopping = tf.keras.callbacks.EarlyStopping(\n"," monitor='val_loss',\n"," patience=5,\n"," restore_best_weights=True\n"," )\n","\n"," reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n"," monitor='val_loss',\n"," factor=0.2,\n"," patience=2,\n"," min_lr=0.00001\n"," )\n","\n"," # TensorBoard pour visualiser l'entraînement\n"," tensorboard_dir = f\"content/advanced-model/{model_name}_{vectorizer_name}/logs\"\n"," os.makedirs(tensorboard_dir, exist_ok=True)\n"," tensorboard_callback = tf.keras.callbacks.TensorBoard(\n"," log_dir=tensorboard_dir,\n"," histogram_freq=1,\n"," update_freq='epoch'\n"," )\n","\n"," # Créer le modèle optimisé pour GPU\n"," model = create_optimized_lstm_model(glove_matrix_300d, trainable=False)\n","\n"," print(f\"Entraînement du modèle {model_name} avec embeddings {vectorizer_name}\")\n"," print(f\"Taille du batch: {BATCH_SIZE}\")\n"," print(f\"Utilisation de la précision mixte: {tf.keras.mixed_precision.global_policy().name}\")\n","\n"," # Entraîner le modèle avec les datasets optimisés\n"," history = model.fit(\n"," train_dataset,\n"," epochs=epochs,\n"," validation_data=val_dataset,\n"," callbacks=[early_stopping, reduce_lr, tensorboard_callback, checkpoint_callback],\n"," verbose=1\n"," )\n","\n"," # Déterminer le nombre réel d'époques complétées (en cas d'early stopping)\n"," real_epochs = len(history.history['loss'])\n"," print(f\"Nombre réel d'époques effectuées: {real_epochs}/{epochs}\")\n","\n"," # Sauvegarder tous les artefacts\n"," model_dir = save_model_artifacts(model, model_name, vectorizer_name, history, tokenizer)\n","\n"," # Utiliser evaluate_model pour obtenir les métriques\n"," metrics, cm, report, y_pred, y_pred_proba = evaluate_model(\n"," model,\n"," X_test_pad,\n"," y_test\n"," )\n","\n"," # Afficher les métriques\n"," print(\"\\nMétriques pour le modèle LSTM avec embedding GloVe 300d:\")\n"," for key, value in metrics.items():\n"," print(f\"{key}: {value:.4f}\")\n","\n"," print(\"\\nRapport de classification:\")\n"," print(report)\n","\n"," # Enregistrer avec MLflow\n"," params = {\n"," 'model_type': model_name,\n"," 'embedding_type': vectorizer_name,\n"," 'embedding_dim': 300,\n"," 'vocab_size': len(tokenizer.word_index) + 1,\n"," 'max_sequence_length': MAX_SEQUENCE_LENGTH,\n"," 'trainable_embedding': False,\n"," 'max_epochs': epochs,\n"," 'real_epochs': real_epochs,\n"," 'batch_size': BATCH_SIZE,\n"," 'precision_policy': tf.keras.mixed_precision.global_policy().name,\n"," 'gpu_optimization': 'XLA,dynamic_memory,prefetch',\n"," 'sample_size': sample_size*2\n","\n"," }\n","\n"," # Enregistrer dans MLflow\n"," run_id = log_model_to_mlflow(model, model_name, vectorizer_name, metrics, cm, report, X_test_pad, y_test, history, params)\n","\n"," # Visualiser la courbe d'apprentissage\n"," plot_training_history(history, \"LSTM\", \"GloVe-300d-Fige\", run_id=run_id)\n","\n"," # Récupérer le lien vers le run MLflow\n"," print(f\"\\nRun MLflow ID: {run_id}\")\n"," print(f\"Accédez à l'interface MLflow pour voir les détails complets.\")\n","\n"," return model, history, metrics, run_id"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HwGyTzEwNwMy","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1751830700356,"user_tz":-120,"elapsed":389684,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"cba5a433-e6c6-4898-b9fc-9f133a85cca8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Entraînement du modèle LSTM avec embeddings GloVe-300d-Fige\n","Taille du batch: 256\n","Utilisation de la précision mixte: mixed_float16\n","Epoch 1/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.5846 - precision: 0.6888 - recall: 0.6777\n","Epoch 1: val_precision improved from -inf to 0.74994, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 51ms/step - loss: 0.5845 - precision: 0.6889 - recall: 0.6778 - val_loss: 0.4928 - val_precision: 0.7499 - val_recall: 0.7811 - learning_rate: 0.0010\n","Epoch 2/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.5067 - precision: 0.7539 - recall: 0.7542\n","Epoch 2: val_precision improved from 0.74994 to 0.76657, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 45ms/step - loss: 0.5067 - precision: 0.7539 - recall: 0.7542 - val_loss: 0.4712 - val_precision: 0.7666 - val_recall: 0.7825 - learning_rate: 0.0010\n","Epoch 3/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4808 - precision: 0.7700 - recall: 0.7696\n","Epoch 3: val_precision improved from 0.76657 to 0.77947, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 32ms/step - loss: 0.4808 - precision: 0.7700 - recall: 0.7696 - val_loss: 0.4607 - val_precision: 0.7795 - val_recall: 0.7838 - learning_rate: 0.0010\n","Epoch 4/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4680 - precision: 0.7794 - recall: 0.7787\n","Epoch 4: val_precision did not improve from 0.77947\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - loss: 0.4680 - precision: 0.7794 - recall: 0.7787 - val_loss: 0.4558 - val_precision: 0.7790 - val_recall: 0.7973 - learning_rate: 0.0010\n","Epoch 5/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4549 - precision: 0.7842 - recall: 0.7902\n","Epoch 5: val_precision improved from 0.77947 to 0.78158, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 32ms/step - loss: 0.4549 - precision: 0.7842 - recall: 0.7902 - val_loss: 0.4493 - val_precision: 0.7816 - val_recall: 0.7962 - learning_rate: 0.0010\n","Epoch 6/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4480 - precision: 0.7879 - recall: 0.7926\n","Epoch 6: val_precision improved from 0.78158 to 0.79068, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 31ms/step - loss: 0.4480 - precision: 0.7879 - recall: 0.7926 - val_loss: 0.4510 - val_precision: 0.7907 - val_recall: 0.7846 - learning_rate: 0.0010\n","Epoch 7/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4383 - precision: 0.7925 - recall: 0.7982\n","Epoch 7: val_precision improved from 0.79068 to 0.79648, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 32ms/step - loss: 0.4383 - precision: 0.7925 - recall: 0.7982 - val_loss: 0.4497 - val_precision: 0.7965 - val_recall: 0.7820 - learning_rate: 0.0010\n","Epoch 8/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4264 - precision: 0.8006 - recall: 0.8040\n","Epoch 8: val_precision did not improve from 0.79648\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 30ms/step - loss: 0.4264 - precision: 0.8006 - recall: 0.8040 - val_loss: 0.4476 - val_precision: 0.7895 - val_recall: 0.7979 - learning_rate: 2.0000e-04\n","Epoch 9/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4218 - precision: 0.8034 - recall: 0.8065\n","Epoch 9: val_precision did not improve from 0.79648\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 29ms/step - loss: 0.4217 - precision: 0.8034 - recall: 0.8065 - val_loss: 0.4470 - val_precision: 0.7929 - val_recall: 0.7899 - learning_rate: 2.0000e-04\n","Epoch 10/50\n","\u001b[1m561/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4177 - precision: 0.8069 - recall: 0.8114\n","Epoch 10: val_precision did not improve from 0.79648\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4176 - precision: 0.8069 - recall: 0.8114 - val_loss: 0.4476 - val_precision: 0.7918 - val_recall: 0.7935 - learning_rate: 2.0000e-04\n","Epoch 11/50\n","\u001b[1m560/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4160 - precision: 0.8084 - recall: 0.8073\n","Epoch 11: val_precision did not improve from 0.79648\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4160 - precision: 0.8084 - recall: 0.8073 - val_loss: 0.4439 - val_precision: 0.7879 - val_recall: 0.7983 - learning_rate: 2.0000e-04\n","Epoch 12/50\n","\u001b[1m560/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.4114 - precision: 0.8107 - recall: 0.8128\n","Epoch 12: val_precision did not improve from 0.79648\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 29ms/step - loss: 0.4114 - precision: 0.8107 - recall: 0.8128 - val_loss: 0.4477 - val_precision: 0.7933 - val_recall: 0.7859 - learning_rate: 2.0000e-04\n","Epoch 13/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4082 - precision: 0.8122 - recall: 0.8132\n","Epoch 13: val_precision improved from 0.79648 to 0.79806, saving model to content/advanced-model/LSTM_GloVe-300d-Fige/checkpoints/best_model.keras\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 33ms/step - loss: 0.4082 - precision: 0.8122 - recall: 0.8132 - val_loss: 0.4512 - val_precision: 0.7981 - val_recall: 0.7848 - learning_rate: 2.0000e-04\n","Epoch 14/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.4061 - precision: 0.8131 - recall: 0.8138\n","Epoch 14: val_precision did not improve from 0.79806\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - loss: 0.4061 - precision: 0.8131 - recall: 0.8138 - val_loss: 0.4503 - val_precision: 0.7909 - val_recall: 0.7964 - learning_rate: 4.0000e-05\n","Epoch 15/50\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4043 - precision: 0.8120 - recall: 0.8154\n","Epoch 15: val_precision did not improve from 0.79806\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 29ms/step - loss: 0.4043 - precision: 0.8120 - recall: 0.8154 - val_loss: 0.4489 - val_precision: 0.7887 - val_recall: 0.8076 - learning_rate: 4.0000e-05\n","Epoch 16/50\n","\u001b[1m562/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.4059 - precision: 0.8114 - recall: 0.8184\n","Epoch 16: val_precision did not improve from 0.79806\n","\u001b[1m563/563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 30ms/step - loss: 0.4058 - precision: 0.8114 - recall: 0.8184 - val_loss: 0.4540 - val_precision: 0.7889 - val_recall: 0.7955 - learning_rate: 1.0000e-05\n","Nombre réel d'époques effectuées: 16/50\n","Modèle sauvegardé dans content/advanced-model/LSTM_GloVe-300d-Fige/final_model_LSTM_GloVe-300d-Fige.keras\n","Tokenizer sauvegardé dans content/advanced-model/LSTM_GloVe-300d-Fige/tokenizer.pickle\n","Fonction de prétraitement sauvegardée dans content/advanced-model/LSTM_GloVe-300d-Fige/preprocess_function.dill\n","Historique d'entraînement sauvegardé dans content/advanced-model/LSTM_GloVe-300d-Fige/training_history.pickle\n","Paramètres sauvegardés dans content/advanced-model/LSTM_GloVe-300d-Fige/parameters.json\n","\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step\n","\n","Métriques pour le modèle LSTM avec embedding GloVe 300d:\n","accuracy: 0.7916\n","precision: 0.7915\n","recall: 0.7918\n","f1: 0.7917\n","roc_auc: 0.8761\n","\n","Rapport de classification:\n"," precision recall f1-score support\n","\n"," Négatif 0.79 0.79 0.79 20000\n"," Positif 0.79 0.79 0.79 20000\n","\n"," accuracy 0.79 40000\n"," macro avg 0.79 0.79 0.79 40000\n","weighted avg 0.79 0.79 0.79 40000\n","\n"]},{"output_type":"stream","name":"stderr","text":["2025/07/06 19:37:36 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"]},{"output_type":"stream","name":"stdout","text":["🏃 View run Modele_Avance_LSTM_GloVe-300d-Fige at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1/runs/5b50de7aafde4b5793aaf188e07caa49\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1\n","🏃 View run Modele_Avance_LSTM_GloVe-300d-Fige at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1/runs/5b50de7aafde4b5793aaf188e07caa49\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/1\n","\n","Run MLflow ID: 5b50de7aafde4b5793aaf188e07caa49\n","Accédez à l'interface MLflow pour voir les détails complets.\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Entraîner le modèle LSTM avec GloVe 300d\n","model_lstm_glove, history_lstm_glove, metrics_lstm_glove, run_id_lstm_glove = train_glove_lstm_model(epochs=50)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qq5sL0HcNwMy"},"outputs":[],"source":["# Charger le modèle pour la prédiction\n","lstm_glove_model_pack = load_complete_model(\"LSTM\", \"GloVe-300d-Fige\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Kc95Dyz8NwMy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751830703470,"user_tz":-120,"elapsed":2246,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9fac4c06-5630-4f50-bba5-f41005c5d833"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Test de prédiction avec le modèle GloVe:\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n","{'sentiment': 'Positif', 'confidence': 0.9921875, 'raw_score': 0.9921875}\n"]}],"source":["# Tester le modèle sur quelques exemples\n","print(\"\\nTest de prédiction avec le modèle GloVe:\")\n","result = predict_sentiment(\"I love flying with this airline, the service is amazing!\", lstm_glove_model_pack)\n","print(result)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gAhcxPUoNwMy","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1751830705513,"user_tz":-120,"elapsed":2042,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b49cfcf6-a4e7-4069-e497-0b789f6f7865"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n","\n","Test du modèle sur les exemples:\n","==================================================\n","Résumé: 22 tweets analysés\n","Tweets positifs: 13 (59.1%)\n","Tweets négatifs: 9 (40.9%)\n","\n","Aperçu des prédictions GloVe :\n"]},{"output_type":"execute_result","data":{"text/plain":[" Tweet Sentiment Emoji \\\n","0 I absolutely love flying with @AirParadis! The... Positif 💚 \n","1 Just landed after a wonderful flight... The cr... Positif 💚 \n","2 OMG! You really have to try @AirParadis. Best.... Positif 💚 \n","3 We had a very pleasant experience with our fli... Positif 💚 \n","4 My first time flying business class and I'm ab... Positif 💚 \n","5 I hate how @AirParadis always delays their fli... Négatif ❌ \n","6 The worst flight experience of my life? Defini... Négatif ❌ \n","7 Really @AirParadis??? Flight cancelled and no ... Négatif ❌ \n","8 Our baggage was lost and nobody at @AirParadis... Négatif ❌ \n","9 Food was terrible, seats were uncomfortable, a... Négatif ❌ \n","10 The flight was good but the food wasn't really... Positif 💚 \n","11 IDK what to think about my @AirParadis experie... Négatif ❌ \n","12 My husband loved the flight but I didn't... To... Positif 💚 \n","13 TBH, @AirParadis has improved their service si... Négatif ❌ \n","14 We were so excited for our trip but then our f... Positif 💚 \n","15 This is a tweet with many!!! exclamation!!! ma... Négatif ❌ \n","16 How does the model handle multiple??? question... Négatif ❌ \n","17 Testing ellipses... many... many... ellipses..... Positif 💚 \n","18 #many #hashtags #in #one #tweet #to #see #how ... Positif 💚 \n","19 @mention1 @mention2 @mention3 @mention4 @menti... Positif 💚 \n","20 Multiple URLs in one tweet: http://example1.co... Positif 💚 \n","21 Let's try all emoticons :) :( :D ;) :-) :-( :-... Positif 💚 \n","\n"," Score de confiance Score brut Index \n","0 0.995117 0.995117 1 \n","1 0.990234 0.990234 2 \n","2 0.939453 0.939453 3 \n","3 0.963379 0.963379 4 \n","4 0.649902 0.649902 5 \n","5 0.980865 0.019135 6 \n","6 0.975449 0.024551 7 \n","7 0.978058 0.021942 8 \n","8 0.933167 0.066833 9 \n","9 0.896606 0.103394 10 \n","10 0.651855 0.651855 11 \n","11 0.875000 0.125000 12 \n","12 0.576172 0.576172 13 \n","13 0.681396 0.318604 14 \n","14 0.731445 0.731445 15 \n","15 0.841064 0.158936 16 \n","16 0.666748 0.333252 17 \n","17 0.689941 0.689941 18 \n","18 0.819336 0.819336 19 \n","19 0.907227 0.907227 20 \n","20 0.984375 0.984375 21 \n","21 0.929199 0.929199 22 "],"text/html":["\n","
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TweetSentimentEmojiScore de confianceScore brutIndex
0I absolutely love flying with @AirParadis! The...Positif💚0.9951170.9951171
1Just landed after a wonderful flight... The cr...Positif💚0.9902340.9902342
2OMG! You really have to try @AirParadis. Best....Positif💚0.9394530.9394533
3We had a very pleasant experience with our fli...Positif💚0.9633790.9633794
4My first time flying business class and I'm ab...Positif💚0.6499020.6499025
5I hate how @AirParadis always delays their fli...Négatif❌0.9808650.0191356
6The worst flight experience of my life? Defini...Négatif❌0.9754490.0245517
7Really @AirParadis??? Flight cancelled and no ...Négatif❌0.9780580.0219428
8Our baggage was lost and nobody at @AirParadis...Négatif❌0.9331670.0668339
9Food was terrible, seats were uncomfortable, a...Négatif❌0.8966060.10339410
10The flight was good but the food wasn't really...Positif💚0.6518550.65185511
11IDK what to think about my @AirParadis experie...Négatif❌0.8750000.12500012
12My husband loved the flight but I didn't... To...Positif💚0.5761720.57617213
13TBH, @AirParadis has improved their service si...Négatif❌0.6813960.31860414
14We were so excited for our trip but then our f...Positif💚0.7314450.73144515
15This is a tweet with many!!! exclamation!!! ma...Négatif❌0.8410640.15893616
16How does the model handle multiple??? question...Négatif❌0.6667480.33325217
17Testing ellipses... many... many... ellipses.....Positif💚0.6899410.68994118
18#many #hashtags #in #one #tweet #to #see #how ...Positif💚0.8193360.81933619
19@mention1 @mention2 @mention3 @mention4 @menti...Positif💚0.9072270.90722720
20Multiple URLs in one tweet: http://example1.co...Positif💚0.9843750.98437521
21Let's try all emoticons :) :( :D ;) :-) :-( :-...Positif💚0.9291990.92919922
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_results_glove","summary":"{\n \"name\": \"df_results_glove\",\n \"rows\": 22,\n \"fields\": [\n {\n \"column\": \"Tweet\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"I absolutely love flying with @AirParadis! Their service is amazing and their staff is so helpful :) #bestairline #travel\",\n \"TBH, @AirParadis has improved their service since last year! Not perfect yet, but they're trying... #progress\",\n \"Our baggage was lost and nobody at @AirParadis seems to care! Don't they understand that we can't enjoy our vacation without our stuff??? http://badservice.com/complaint :-(\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentiment\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"N\\u00e9gatif\",\n \"Positif\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Emoji\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"\\u274c\",\n \"\\ud83d\\udc9a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score de confiance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.13888291998594515,\n \"min\": 0.576171875,\n \"max\": 0.9951171875,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.9951171875,\n 0.681396484375\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score brut\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3794701732781929,\n \"min\": 0.019134521484375,\n \"max\": 0.9951171875,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.9951171875,\n 0.318603515625\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 1,\n \"max\": 22,\n \"num_unique_values\": 22,\n \"samples\": [\n 1,\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":43}],"source":["# Obtenir le DataFrame des prédictions pour le modèle GloVe\n","df_results_glove = test_model_on_examples(lstm_glove_model_pack, test_tweets_improved)\n","\n","# Sauvegarder les résultats dans un fichier CSV\n","df_results_glove.to_csv(\"content/advanced-model/LSTM_GloVe-300d-Fige/predictions_results.csv\", index=False)\n","\n","# Afficher un aperçu des prédictions\n","print(\"\\nAperçu des prédictions GloVe :\")\n","df_results_glove"]},{"cell_type":"markdown","source":["# Enregistrement du meilleur modèle\n","Dans le Model Registry de MLFlow, qui sera utilisé et mis à jour comme source pour l'API."],"metadata":{"id":"8D9ukAB3Xee4"}},{"cell_type":"code","source":["# Comparer les métriques de précision sur l'ensemble de test\n","precision_word2vec = metrics_lstm_word2vec.get('precision', 0)\n","precision_glove = metrics_lstm_glove.get('precision', 0)\n","\n","print(f\"Précision (Test) Word2Vec + LSTM: {precision_word2vec:.4f}\")\n","print(f\"Précision (Test) GloVe 300d + LSTM: {precision_glove:.4f}\")\n","\n","best_model_run_id = None\n","best_model_name = None\n","best_vectorizer_name = None\n","best_precision = 0\n","\n","# Déterminer le modèle avec la meilleure précision\n","if precision_word2vec >= precision_glove:\n"," best_model_run_id = run_id_lstm_word2vec\n"," best_model_name = \"LSTM\"\n"," best_vectorizer_name = \"Word2Vec-Fige\"\n"," best_precision = precision_word2vec\n","else:\n"," best_model_run_id = run_id_lstm_glove\n"," best_model_name = \"LSTM\"\n"," best_vectorizer_name = \"GloVe-300d-Fige\"\n"," best_precision = precision_glove\n","\n","\n","print(f\"\\nLe modèle {best_model_name} + {best_vectorizer_name} a la meilleure précision ({best_precision:.4f}).\")\n","\n","# Construire l'URI du modèle dans le run MLflow\n","logged_model_uri = f\"runs:/{best_model_run_id}/model_{best_model_name}_{best_vectorizer_name}\"\n","print(f\"\\nChargement du meilleur modèle depuis MLflow URI: {logged_model_uri}\")\n","\n","model_registry_name = \"SentimentAnalysisLSTM\"\n","client = mlflow.tracking.MlflowClient()\n","\n","try:\n"," # Charger le modèle Keras depuis MLflow (pas strictement nécessaire pour l'enregistrement, mais utile pour validation)\n"," # best_keras_model = mlflow.keras.load_model(logged_model_uri)\n"," # print(\"Modèle Keras chargé avec succès.\")\n","\n"," print(f\"Enregistrement du meilleur modèle dans le Model Registry sous le nom '{model_registry_name}'...\")\n","\n"," # Créer une nouvelle version dans le Model Registry\n"," model_version = mlflow.register_model(\n"," model_uri=logged_model_uri,\n"," name=model_registry_name\n"," )\n"," print(f\"Modèle enregistré dans le Model Registry sous la version {model_version.version}.\")\n","\n"," # Ajouter des tags au modèle enregistré\n"," client.set_model_version_tag(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," key=\"embedding_type\",\n"," value=best_vectorizer_name\n"," )\n"," client.set_model_version_tag(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," key=\"test_precision\",\n"," value=str(best_precision)\n"," )\n"," client.set_model_version_tag(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," key=\"source_run_id\",\n"," value=best_model_run_id\n"," )\n","\n"," print(f\"Tags ajoutés à la version {model_version.version}.\")\n","\n"," # --- Logique de comparaison et de transition des stages ---\n","\n"," # Récupérer la version du modèle actuellement en production\n"," production_version = None\n"," try:\n"," for mv in client.search_model_versions(f\"name='{model_registry_name}'\"):\n"," if mv.current_stage == \"Production\":\n"," production_version = mv\n"," break\n"," except Exception as e:\n"," print(f\"Erreur lors de la recherche de la version en production : {e}\")\n","\n","\n"," if production_version:\n"," print(f\"\\nVersion actuelle en production: Version {production_version.version}\")\n"," # Récupérer la précision de la version en production (stockée dans les tags)\n"," production_precision_str = production_version.tags.get(\"test_precision\", \"0.0\")\n"," try:\n"," production_precision = float(production_precision_str)\n"," print(f\"Précision (Test) de la version en production: {production_precision:.4f}\")\n","\n"," # Comparer les précisions\n"," if best_precision > production_precision:\n"," print(f\"\\nLa nouvelle version ({model_version.version}) est meilleure que la version en production ({production_version.version}).\")\n"," print(\"Transition de la nouvelle version vers 'Production' et de l'ancienne version vers 'Staging'...\")\n","\n"," # Mettre la nouvelle version en production\n"," client.transition_model_version_stage(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," stage=\"Production\",\n"," archive_existing_versions=True # Cela archive automatiquement l'ancienne version en production\n"," )\n"," print(f\"Version {model_version.version} mise en production.\")\n"," print(f\"Ancienne version {production_version.version} archivée.\")\n","\n"," else:\n"," print(f\"\\nLa nouvelle version ({model_version.version}) n'est pas meilleure que la version en production ({production_version.version}).\")\n"," print(\"Transition de la nouvelle version vers 'Staging'...\")\n"," # Mettre la nouvelle version en staging\n"," client.transition_model_version_stage(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," stage=\"Staging\",\n"," archive_existing_versions=False # Ne pas archiver l'ancienne version en production\n"," )\n"," print(f\"Version {model_version.version} mise en staging.\")\n"," print(f\"La version {production_version.version} reste en production.\")\n","\n"," except ValueError:\n"," print(f\"Erreur: Impossible de convertir le tag 'test_precision' ({production_precision_str}) de la version en production en nombre flottant.\")\n"," print(f\"La nouvelle version {model_version.version} sera mise en 'Staging' par défaut.\")\n"," # Mettre la nouvelle version en staging si la précision de l'ancienne ne peut pas être lue\n"," client.transition_model_version_stage(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," stage=\"Staging\",\n"," archive_existing_versions=False\n"," )\n","\n"," else:\n"," print(\"\\nAucune version en production trouvée. La nouvelle version sera mise en production.\")\n"," # Si aucune version n'est en production, mettre directement la nouvelle version en production\n"," client.transition_model_version_stage(\n"," name=model_registry_name,\n"," version=model_version.version,\n"," stage=\"Production\",\n"," archive_existing_versions=False\n"," )\n"," print(f\"Version {model_version.version} mise en production.\")\n","\n"," print(f\"\\nGestion des stages du modèle '{model_registry_name}' terminée.\")\n","\n","except Exception as e:\n"," print(f\"Erreur générale lors du traitement MLflow : {e}\")\n"," print(f\"Vérifiez que le run_id '{best_model_run_id}' est valide et que l'artefact existe bien.\")"],"metadata":{"id":"7Pf-LJdNOCHe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1751830706459,"user_tz":-120,"elapsed":945,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"f9bdde12-3102-454d-c44c-60a35a0d60a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Précision (Test) Word2Vec + LSTM: 0.7970\n","Précision (Test) GloVe 300d + LSTM: 0.7915\n","\n","Le modèle LSTM + Word2Vec-Fige a la meilleure précision (0.7970).\n","\n","Chargement du meilleur modèle depuis MLflow URI: runs:/1de54538ee73443687db131fcc86d922/model_LSTM_Word2Vec-Fige\n","Enregistrement du meilleur modèle dans le Model Registry sous le nom 'SentimentAnalysisLSTM'...\n"]},{"output_type":"stream","name":"stderr","text":["Registered model 'SentimentAnalysisLSTM' already exists. Creating a new version of this model...\n","2025/07/06 19:38:25 WARNING mlflow.tracking._model_registry.fluent: Run with id 1de54538ee73443687db131fcc86d922 has no artifacts at artifact path 'model_LSTM_Word2Vec-Fige', registering model based on models:/m-b4c7c871087e4a2587f21367961f6d04 instead\n","2025/07/06 19:38:25 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: SentimentAnalysisLSTM, version 12\n","Created version '12' of model 'SentimentAnalysisLSTM'.\n"]},{"output_type":"stream","name":"stdout","text":["Modèle enregistré dans le Model Registry sous la version 12.\n","Tags ajoutés à la version 12.\n","\n","Version actuelle en production: Version 11\n","Précision (Test) de la version en production: 0.7830\n","\n","La nouvelle version (12) est meilleure que la version en production (11).\n","Transition de la nouvelle version vers 'Production' et de l'ancienne version vers 'Staging'...\n","Version 12 mise en production.\n","Ancienne version 11 archivée.\n","\n","Gestion des stages du modèle 'SentimentAnalysisLSTM' terminée.\n"]}]},{"cell_type":"markdown","source":["# Enregistrement de dépendances"],"metadata":{"id":"5iEc-YxAt3BU"}},{"cell_type":"code","source":["!pip freeze > requirements_modele_avance.txt"],"metadata":{"id":"jIw_YhFztVF2"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.10.12"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/notebooks/p07_04-notebook-modele-bert.ipynb b/notebooks/p07_04-notebook-modele-bert.ipynb deleted file mode 100644 index 98973d1..0000000 --- a/notebooks/p07_04-notebook-modele-bert.ipynb +++ /dev/null @@ -1,3364 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "y3x3_GfST76Z" - }, - "source": [ - "# Partie 4 : Analyse de Sentiment de Tweets avec BERT\n", - "\n", - "Dans ce notebook, nous explorons l'analyse de sentiment de tweets en utilisant **BERT (Bidirectional Encoder Representations from Transformers)**, un modèle de deep learning à la pointe de la technologie pour le traitement du langage naturel. Le dataset **Sentiment140** contient 1,6 million de tweets étiquetés (positifs ou négatifs), ce qui en fait une excellente ressource pour entraîner notre modèle. Contrairement aux approches traditionnelles qui nécessitent une ingénierie de caractéristiques complexe, **BERT comprend le contexte bidirectionnel des mots**, ce qui est particulièrement utile pour capturer les subtilités linguistiques des tweets comme l'ironie, les abréviations et les expressions idiomatiques propres aux réseaux sociaux.\n", - "\n", - "Concernant l'entrainement, le choix a été fait d'entrainer par optimisation de la variable de perte de validation `val_loss` avec en particulier un critère d'`EarlyStopping` au bout d'un certain nombre d'époques lancées (7, en l'ocurrence) afin de garantir une bonne généralisation ainsi qu'une limitation de l'overfitting.\n", - "\n", - "Cependant, le critère qui sera retenu comme le plus important en raison de notre problématique métier est la `precision` car nous souhaitons plus que tout éviter les faux positifs (tweet prédit comme positif, alors qu'il sera mal reçu)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kW-8vumuT76b" - }, - "source": [ - "## Importation des bibliothèques" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 18367, - "status": "ok", - "timestamp": 1752351535443, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Xplx_nQRg0rF", - "outputId": "aa1a2afc-5393-4e50-de54-a2cd7e0acc05" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n" - ] - }, - { - "data": { - "text/plain": [ - "['content',\n", - " 'requirements_analyse_exploratoire.txt',\n", - " 'p07_01-notebook-analyse-exploratoire.ipynb',\n", - " 'requirements_bert.txt',\n", - " 'requirements_modele_simple.txt',\n", - " 'p07_02-notebook-modele-simple.ipynb',\n", - " 'requirements_modele_avance.txt',\n", - " 'p07_03-notebook-modele-avance.ipynb',\n", - " 'notebook-connexion-mlflow.ipynb',\n", - " 'p07_04-notebook-modele-bert.ipynb']" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "import os\n", - "drive_folder= \"/content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks\"\n", - "os.chdir(drive_folder)\n", - "os.listdir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 32608, - "status": "ok", - "timestamp": 1752351568062, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "tXTBgiq7U8gK", - "outputId": "97fd036f-0f54-494b-8e5d-3a261d029091" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.7/24.7 MB\u001b[0m \u001b[31m47.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m61.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"\u001b[?25h" - ] - } - ], - "source": [ - "!pip install mlflow boto3 dotenv --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "executionInfo": { - "elapsed": 6287, - "status": "ok", - "timestamp": 1752351574355, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "W50jr7rUT76d", - "outputId": "437c63ff-9e8f-4251-a053-0216012499ba" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PyTorch version: 2.6.0+cu124\n", - "CUDA disponible: True\n", - "Appareil CUDA: Tesla T4\n" - ] - } - ], - "source": [ - "import torch\n", - "print(f\"PyTorch version: {torch.__version__}\")\n", - "print(f\"CUDA disponible: {torch.cuda.is_available()}\")\n", - "if torch.cuda.is_available():\n", - " print(f\"Appareil CUDA: {torch.cuda.get_device_name(0)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MMB0gwbBT76d" - }, - "outputs": [], - "source": [ - "import os\n", - "import pickle\n", - "import subprocess\n", - "import re\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from torch.amp import GradScaler\n", - "from torch.optim import AdamW\n", - "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, get_scheduler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", - "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from tqdm import tqdm\n", - "import html\n", - "import pickle\n", - "import zipfile\n", - "import requests\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 2633, - "status": "ok", - "timestamp": 1752351604138, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "OhgBsQDCUq4A", - "outputId": "f63ccc04-79c1-40fc-8c92-5ed975f67336" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n", - "Identifiants AWS configurés\n" - ] - } - ], - "source": [ - "import mlflow\n", - "from mlflow.models.signature import infer_signature\n", - "from mlflow import MlflowClient\n", - "from mlflow.models.signature import ModelSignature\n", - "from mlflow.types import Schema, ColSpec, TensorSpec\n", - "from dotenv import load_dotenv\n", - "from google.colab import userdata\n", - "import mlflow.pytorch\n", - "\n", - "\n", - "# Charger les variables d'environnement depuis le fichier .env\n", - "load_dotenv(dotenv_path='../.env')\n", - "\n", - "# Configuration de MLflow avec les variables d'environnement\n", - "mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n", - "mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n", - "aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n", - "aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n", - "\n", - "# Configuration explicite de MLflow\n", - "mlflow.set_tracking_uri(mlflow_tracking_uri)\n", - "print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n", - "\n", - "# Configuration explicite des identifiants AWS\n", - "os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n", - "os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n", - "os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n", - "print(\"Identifiants AWS configurés\")\n", - "\n", - "# Créer l'expérience MLflow\n", - "mlflow.set_experiment(\"OC Projet 7\")\n", - "\n", - "#Création du dossier de sauvegarde du modèle\n", - "os.makedirs('content/bert-model/model', exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SuNqSc3UT76d" - }, - "source": [ - "## 💾 Jeu de données : Sentiment140\n", - "\n", - "Le jeu de données [Sentiment140 dataset with 1.6 million tweets](https://www.kaggle.com/datasets/kazanova/sentiment140) est une ressource majeure pour l'analyse de sentiment sur Twitter, comprenant **1,6 million de tweets** extraits via l'API Twitter. Ces tweets ont été automatiquement annotés selon leur polarité sentimentale, offrant une base solide pour développer des modèles de classification de sentiment.\n", - "\n", - "Le jeu de données est organisé en 6 colonnes distinctes :\n", - "\n", - "1. **target** : La polarité du sentiment exprimé dans le tweet.\n", - " - 0 = sentiment négatif\n", - " - 2 = sentiment neutre\n", - " - 4 = sentiment positif\n", - "2. **ids** : L'identifiant unique du tweet (exemple : *2087*)\n", - "3. **date** : La date et l'heure de publication du tweet.\n", - "4. **flag** : La requête utilisée pour obtenir le tweet.\n", - " - Exemple : *lyx*\n", - " - Si aucune requête n'a été utilisée : *NO_QUERY*\n", - "5. **user** : Le nom d'utilisateur de l'auteur du tweet.\n", - "6. **text** : Le contenu textuel du tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 26, - "status": "ok", - "timestamp": 1752351604172, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Q5ACc3OUT76d", - "outputId": "940ac231-d2b1-4c8d-940c-3cc323079d56" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 60 µs, sys: 9 µs, total: 69 µs\n", - "Wall time: 259 µs\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# Define the URL and the local file path\n", - "url = \"https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+7%C2%A0-+D%C3%A9tectez+les+Bad+Buzz+gr%C3%A2ce+au+Deep+Learning/sentiment140.zip\"\n", - "local_zip_path = \"./content/data/sentiment140.zip\"\n", - "extract_path = \"./content/data\"\n", - "\n", - "if not os.path.exists(extract_path):\n", - "\n", - " # Create the directory if it doesn't exist\n", - " os.makedirs(extract_path, exist_ok=True)\n", - "\n", - " # Download the zip file\n", - " response = requests.get(url)\n", - " with open(local_zip_path, 'wb') as file:\n", - " file.write(response.content)\n", - "\n", - " # Extract the contents of the zip file\n", - " with zipfile.ZipFile(local_zip_path, 'r') as zip_ref:\n", - " zip_ref.extractall(extract_path)\n", - "\n", - " # Delete the zip file\n", - " os.remove(local_zip_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_D2V2EgHT76e" - }, - "outputs": [], - "source": [ - "# Définir le chemin vers le fichier CSV\n", - "csv_file_path = './content/data/training.1600000.processed.noemoticon.csv'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Sv9nlaIq4BZo" - }, - "source": [ - "## 🧠📚 Entraînement spécifique de BERT\n", - "Notre démarche pour la classification de sentiment avec des approches avancées comprend:\n", - "\n", - "1. **Prétraitement des tweets**\n", - " - Nettoyage\n", - " - Tokenisation et lemmatisation\n", - " - Remplacement des URLs et mentions par des tokens spéciaux\n", - "\n", - "3. **Modèles testés**\n", - " - BERT\n", - "\n", - "\n", - "3. **Évaluation et suivi**\n", - " - Métriques: précision, accuracy, recall, F1-score, ROC-AUC\n", - " - Tracking avec MLflow pour la reproductibilité et la comparaison" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AFdxLghUT76e" - }, - "source": [ - "## Prétraitement des tweets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NmOIhsWET76e" - }, - "outputs": [], - "source": [ - "# PRÉTRAITEMENT DES TWEETS\n", - "def preprocess_tweet_for_bert(tweet):\n", - " \"\"\"\n", - " Prétraite un tweet pour l'entraînement BERT en conservant la structure naturelle\n", - " du langage mais en normalisant certains éléments spécifiques aux réseaux sociaux.\n", - " \"\"\"\n", - " # Vérifier si le tweet est une chaîne de caractères\n", - " if not isinstance(tweet, str):\n", - " return \"\"\n", - "\n", - " # Remplacer les URLs par un token spécial\n", - " tweet = re.sub(r'https?://\\S+|www\\.\\S+', '[URL]', tweet)\n", - "\n", - " # Remplacer les mentions par un token spécial\n", - " tweet = re.sub(r'@\\w+', '[USER]', tweet)\n", - "\n", - " # Normaliser les hashtags (conserver le hashtag comme entité)\n", - " tweet = re.sub(r'#(\\w+)', r'#\\1', tweet)\n", - "\n", - " # Traiter le langage html\n", - " tweet = html.unescape(tweet)\n", - "\n", - " # Normaliser les espaces multiples\n", - " tweet = re.sub(r'\\s+', ' ', tweet)\n", - "\n", - " # Supprimer les caractères non imprimables et certains caractères spéciaux inutiles\n", - " tweet = re.sub(r'[^\\x20-\\x7E]', '', tweet)\n", - "\n", - " # Nettoyer les espaces en début et fin\n", - " tweet = tweet.strip()\n", - "\n", - " return tweet" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M6ViLmc7T76e" - }, - "source": [ - "## Préparations des données" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ap7LohJzT76e" - }, - "outputs": [], - "source": [ - "# PRÉPARATION DES DONNÉES\n", - "def prepare_data(df, sample_size=50000, random_state=42):\n", - " \"\"\"\n", - " Préparation des données pour l'entraînement BERT\n", - " \"\"\"\n", - " # Remappage des labels (0=négatif, 4=positif) vers (0=négatif, 1=positif)\n", - " df['target'] = df['target'].replace({0: 0, 4: 1})\n", - "\n", - " # Prétraitement des tweets\n", - " print(\"Prétraitement des tweets...\")\n", - " df['processed_text'] = df['text'].apply(preprocess_tweet_for_bert)\n", - "\n", - " # Sélection d'un échantillon pour l'entraînement.\n", - " # La distribution uniforme (50%/50%) des modalités ne posera pas problème pour le sous-échantillonnage.\n", - " sample_data = df.sample(n=sample_size, random_state=random_state)\n", - "\n", - " # Division train/val/test\n", - " train_val_texts, test_texts, train_val_labels, test_labels = train_test_split(\n", - " sample_data['processed_text'].values,\n", - " sample_data['target'].values,\n", - " test_size=0.2,\n", - " random_state=random_state\n", - " )\n", - "\n", - " train_texts, val_texts, train_labels, val_labels = train_test_split(\n", - " train_val_texts,\n", - " train_val_labels,\n", - " test_size=0.2,\n", - " random_state=random_state\n", - " )\n", - "\n", - " return {\n", - " 'train': {'texts': train_texts, 'labels': train_labels},\n", - " 'val': {'texts': val_texts, 'labels': val_labels},\n", - " 'test': {'texts': test_texts, 'labels': test_labels}\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lgWXn2SKT76e" - }, - "outputs": [], - "source": [ - "# CRÉATION D'UN DATASET PYTORCH\n", - "class TweetDataset(Dataset):\n", - " def __init__(self, texts, labels, tokenizer, max_length=128):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - "\n", - " def __len__(self):\n", - " return len(self.texts)\n", - "\n", - " def __getitem__(self, idx):\n", - " text = str(self.texts[idx])\n", - " label = self.labels[idx]\n", - "\n", - " # Tokenisation du texte\n", - " encoding = self.tokenizer(\n", - " text,\n", - " add_special_tokens=True,\n", - " max_length=self.max_length,\n", - " truncation=True,\n", - " padding='max_length',\n", - " return_tensors='pt'\n", - " )\n", - "\n", - " return {\n", - " 'input_ids': encoding['input_ids'].flatten(),\n", - " 'attention_mask': encoding['attention_mask'].flatten(),\n", - " 'label': torch.tensor(label, dtype=torch.long)\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P_S6MpCjT76f" - }, - "source": [ - "## Fonctions d'entraînement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EsK_IQRlbu3C" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.cuda.amp import GradScaler\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", - "from tqdm import tqdm\n", - "\n", - "def train_model(\n", - " model,\n", - " train_loader,\n", - " val_loader,\n", - " test_loader,\n", - " device,\n", - " epochs=20,\n", - " gradient_accumulation_steps=4,\n", - " patience=7,\n", - " lr=2e-5,\n", - " lr_factor=0.2,\n", - " min_lr=1e-6,\n", - "):\n", - " \"\"\"\n", - " Train a BERT model with gradient accumulation,\n", - " EarlyStopping on val_loss, and best model selection on val_precision.\n", - "\n", - " Args:\n", - " model: The BERT model to train.\n", - " train_loader: DataLoader for training data.\n", - " val_loader: DataLoader for validation data.\n", - " test_loader: DataLoader for test data.\n", - " device: 'cuda' or 'cpu' for training.\n", - " epochs: Maximum number of epochs to train.\n", - " gradient_accumulation_steps: Number of steps for gradient accumulation.\n", - " patience: Patience for EarlyStopping based on val_loss.\n", - " lr: Initial learning rate.\n", - " lr_factor: Factor to reduce learning rate in scheduler.\n", - " min_lr: Minimum learning rate allowed.\n", - "\n", - " Returns:\n", - " history: Training and validation history.\n", - " test_metrics: Metrics on the test set.\n", - " \"\"\"\n", - "\n", - " best_val_precision = 0.0\n", - " best_val_loss = float('inf')\n", - " no_val_loss_improvement_count = 0\n", - "\n", - " optimizer = torch.optim.AdamW(model.parameters(), lr=lr, eps=1e-8)\n", - " scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n", - " optimizer, mode=\"min\", factor=lr_factor, patience=2, min_lr=min_lr, verbose=True\n", - " )\n", - " scaler = GradScaler()\n", - "\n", - " history = {\n", - " \"train_loss\": [],\n", - " \"val_loss\": [],\n", - " \"train_accuracy\": [],\n", - " \"val_accuracy\": [],\n", - " \"train_precision\": [],\n", - " \"val_precision\": [],\n", - " \"val_recall\": []\n", - " }\n", - "\n", - " for epoch in range(epochs):\n", - " print(f\"\\n=== Epoch {epoch + 1}/{epochs} ===\")\n", - "\n", - " # Training phase\n", - " model.train()\n", - " train_loss, train_true, train_preds = 0.0, [], []\n", - "\n", - " for batch_idx, batch in enumerate(tqdm(train_loader, desc=\"Training\")):\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"label\"].to(device)\n", - "\n", - " optimizer.zero_grad()\n", - "\n", - " # with autocast(enabled=torch.cuda.is_available()):\n", - " with torch.amp.autocast(device_type='cuda' if torch.cuda.is_available() else 'cpu'):\n", - "\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", - " loss = outputs.loss / gradient_accumulation_steps\n", - "\n", - " scaler.scale(loss).backward()\n", - "\n", - " if (batch_idx + 1) % gradient_accumulation_steps == 0 or (batch_idx + 1) == len(train_loader):\n", - " scaler.unscale_(optimizer)\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", - " scaler.step(optimizer)\n", - " scaler.update()\n", - "\n", - " train_loss += loss.item() * gradient_accumulation_steps\n", - " train_preds.extend(torch.argmax(outputs.logits, dim=1).cpu().numpy())\n", - " train_true.extend(labels.cpu().numpy())\n", - "\n", - " avg_train_loss = train_loss / len(train_loader)\n", - " train_accuracy = accuracy_score(train_true, train_preds)\n", - " train_precision = precision_score(train_true, train_preds, zero_division=0)\n", - " history[\"train_loss\"].append(avg_train_loss)\n", - " history[\"train_accuracy\"].append(train_accuracy)\n", - " history[\"train_precision\"].append(train_precision)\n", - "\n", - " print(f\"Train Loss: {avg_train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Train Precision: {train_precision:.4f}\")\n", - "\n", - " # Validation phase\n", - " model.eval()\n", - " val_loss, val_true, val_preds = 0.0, [], []\n", - "\n", - " with torch.no_grad():\n", - " for batch in tqdm(val_loader, desc=\"Validation\"):\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"label\"].to(device)\n", - "\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", - " loss = outputs.loss\n", - "\n", - " val_loss += loss.item()\n", - " val_preds.extend(torch.argmax(outputs.logits, dim=1).cpu().numpy())\n", - " val_true.extend(labels.cpu().numpy())\n", - "\n", - " avg_val_loss = val_loss / len(val_loader)\n", - " val_accuracy = accuracy_score(val_true, val_preds)\n", - " val_precision = precision_score(val_true, val_preds, zero_division=0)\n", - " val_recall = recall_score(val_true, val_preds, zero_division=0) # calcul recall ajouté\n", - "\n", - " history[\"val_loss\"].append(avg_val_loss)\n", - " history[\"val_accuracy\"].append(val_accuracy)\n", - " history[\"val_precision\"].append(val_precision)\n", - " history[\"val_recall\"].append(val_recall) # ajout dans l'historique\n", - "\n", - " print(f\"Val Loss: {avg_val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}, Val Precision: {val_precision:.4f}, Val Recall: {val_recall:.4f}\")\n", - "\n", - " # Scheduler step on val_loss\n", - " scheduler.step(avg_val_loss)\n", - "\n", - " # Save best model based on val_precision\n", - " if val_precision > best_val_precision:\n", - " best_val_precision = val_precision\n", - " torch.save(model.state_dict(), \"./content/bert-model/model/best_model.pth\")\n", - " print(\"New best model saved based on val_precision.\")\n", - "\n", - " # EarlyStopping on val_loss\n", - " if avg_val_loss < best_val_loss:\n", - " best_val_loss = avg_val_loss\n", - " no_val_loss_improvement_count = 0\n", - " else:\n", - " no_val_loss_improvement_count += 1\n", - " print(f\"No val_loss improvement. Patience: {no_val_loss_improvement_count}/{patience}\")\n", - "\n", - " if no_val_loss_improvement_count >= patience:\n", - " print(\"EarlyStopping activated.\")\n", - " break\n", - "\n", - " # Load best model\n", - " model.load_state_dict(torch.load(\"./content/bert-model/model/best_model.pth\"))\n", - " print(\"Best model loaded for evaluation.\")\n", - "\n", - " # Evaluate on test set\n", - " test_metrics = evaluate_model(model, test_loader, device)\n", - "\n", - " return history, test_metrics" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3gDy-63XT76f" - }, - "source": [ - "## Fonction d'évaluation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "g2DZWDoNT76f" - }, - "outputs": [], - "source": [ - "# FONCTION D'ÉVALUATION\n", - "def evaluate_model(model, test_loader, device):\n", - " \"\"\"\n", - " Évalue le modèle sur le jeu de test et retourne les métriques et prédictions\n", - " \"\"\"\n", - " model.eval()\n", - " test_preds, test_true = [], []\n", - " test_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for batch in tqdm(test_loader, desc=\"Évaluation sur le jeu de test\"):\n", - " input_ids = batch['input_ids'].to(device)\n", - " attention_mask = batch['attention_mask'].to(device)\n", - " labels = batch['label'].to(device)\n", - "\n", - " outputs = model(\n", - " input_ids=input_ids,\n", - " attention_mask=attention_mask\n", - " )\n", - "\n", - " # Récupération des prédictions et probabilités\n", - " logits = outputs.logits\n", - " probs = torch.nn.functional.softmax(logits, dim=1)\n", - " preds = torch.argmax(logits, dim=1).cpu().numpy()\n", - " true = labels.cpu().numpy()\n", - "\n", - " test_preds.extend(preds)\n", - " test_true.extend(true)\n", - " test_probs.extend(probs[:, 1].cpu().numpy()) # Probabilité de la classe positive\n", - "\n", - " # Calcul des métriques\n", - " accuracy = accuracy_score(test_true, test_preds)\n", - " precision = precision_score(test_true, test_preds)\n", - " recall = recall_score(test_true, test_preds)\n", - " f1 = f1_score(test_true, test_preds)\n", - "\n", - " # Matrice de confusion\n", - " cm = confusion_matrix(test_true, test_preds)\n", - "\n", - " # Classification report\n", - " report = classification_report(test_true, test_preds, target_names=['Négatif', 'Positif'])\n", - "\n", - " # Courbe ROC et AUC\n", - " fpr, tpr, _ = roc_curve(test_true, test_probs)\n", - " roc_auc = auc(fpr, tpr)\n", - "\n", - " # Regrouper toutes les métriques\n", - " metrics = {\n", - " 'accuracy': accuracy,\n", - " 'precision': precision,\n", - " 'recall': recall,\n", - " 'f1': f1,\n", - " 'roc_auc': roc_auc,\n", - " 'fpr': fpr,\n", - " 'tpr': tpr,\n", - " 'confusion_matrix': cm,\n", - " 'classification_report': report,\n", - " 'predictions': test_preds,\n", - " 'true_labels': test_true,\n", - " 'probabilities': test_probs\n", - " }\n", - "\n", - " return metrics" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "o0S7DzDJdvbV" - }, - "source": [ - "## Afficher la matrice de confusion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nsUgEh2pT76f" - }, - "outputs": [], - "source": [ - "# FONCTION POUR TRACER LA MATRICE DE CONFUSION\n", - "def plot_confusion_matrix(cm, class_names):\n", - " \"\"\"\n", - " Tracer la matrice de confusion\n", - " \"\"\"\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n", - " plt.xlabel('Prédictions')\n", - " plt.ylabel('Valeurs réelles')\n", - " plt.title('Matrice de confusion')\n", - "\n", - " # Save confusion matrix\n", - " filename = f'content/bert-model/confusion_matrix.png'\n", - " plt.savefig(filename)\n", - "\n", - " return plt.gcf()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ORnBybYLT76g" - }, - "source": [ - "## Fonction pour tracer la courbe ROC AUC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Pf-tDY3OT76g" - }, - "outputs": [], - "source": [ - "# FONCTION POUR TRACER LA COURBE ROC\n", - "def plot_roc_curve(fpr, tpr, roc_auc, model_name, run_id=None):\n", - " \"\"\"\n", - " Trace la courbe ROC et l'enregistre dans MLflow\n", - " \"\"\"\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.3f})')\n", - " plt.plot([0, 1], [0, 1], 'k--')\n", - " plt.xlim([0.0, 1.0])\n", - " plt.ylim([0.0, 1.05])\n", - " plt.xlabel('Taux de faux positifs')\n", - " plt.ylabel('Taux de vrais positifs')\n", - " plt.title(f'Courbe ROC - {model_name}')\n", - " plt.legend(loc=\"lower right\")\n", - "\n", - " # Sauvegarder la figure localement\n", - " filename = f'content/bert-model/roc_curve_{model_name}.png'\n", - " plt.savefig(filename)\n", - "\n", - " # Enregistrer dans MLflow en utilisant l'exécution existante\n", - " # if run_id:\n", - " # with mlflow.start_run(run_id=run_id):\n", - " # mlflow.log_figure(plt.gcf(), f\"roc_curve_{model_name}.png\")\n", - "\n", - " return plt.gcf()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WD_nm9eDT76f" - }, - "source": [ - "## Fonction pour tracer l'historique d'entraînement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UKzSUOlqT76g" - }, - "outputs": [], - "source": [ - "# FONCTION POUR TRACER L'HISTORIQUE D'ENTRAÎNEMENT\n", - "def plot_training_history(history, model_name, run_id=None):\n", - " \"\"\"\n", - " Trace l'historique d'entraînement du modèle et l'enregistre dans MLflow\n", - " \"\"\"\n", - " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - " # Tracer l'accuracy\n", - " ax1.plot(history['train_precision'], label='train')\n", - " ax1.plot(history['val_precision'], label='validation')\n", - " ax1.set_title(f'Precision - {model_name}')\n", - " ax1.set_xlabel('Epoch')\n", - " ax1.set_ylabel('Precision')\n", - " ax1.legend()\n", - "\n", - " # Tracer la loss\n", - " ax2.plot(history['train_loss'], label='train')\n", - " ax2.plot(history['val_loss'], label='validation')\n", - " ax2.set_title(f'Loss - {model_name}')\n", - " ax2.set_xlabel('Epoch')\n", - " ax2.set_ylabel('Loss')\n", - " ax2.legend()\n", - "\n", - " plt.tight_layout()\n", - "\n", - " # Sauvegarder la figure localement\n", - " os.makedirs('content/bert-model', exist_ok=True)\n", - " filename = f'content/bert-model/training_history_{model_name}.png'\n", - " plt.savefig(filename)\n", - "\n", - " # Enregistrer dans MLflow en utilisant l'exécution existante\n", - " if run_id:\n", - " with mlflow.start_run(run_id=run_id):\n", - " mlflow.log_figure(fig, f\"training_history_{model_name}.png\")\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aXZRXEMnT76g" - }, - "source": [ - "## Enregistrement du modèle dans MLFlow\n", - "\n", - "1. Définition de la signature du modèle pour MLflow\n", - "2. Démarrage d'une exécution MLflow avec un nom spécifique\n", - "3. Enregistrement des paramètres du modèle\n", - "4. Enregistrement des métriques de performance (accuracy, precision, recall, f1, roc_auc)\n", - "5. Création et enregistrement de visualisations (matrice de confusion, courbe ROC)\n", - "6. Enregistrement du rapport de classification\n", - "7. Sauvegarde locale du modèle BERT\n", - "8. Enregistrement du modèle PyTorch dans MLflow avec sa signature\n", - "9. Sauvegarde locale du tokenizer et enregistrement dans MLflow\n", - "10. Sauvegarde locale de la fonction de prétraitement (preprocessor) via pickle et enregistrement dans MLflow\n", - "11. Génération et enregistrement du fichier requirements.txt\n", - "10. Retourne l'**identifiant unique (Run ID)** de l'exécution MLflow qui vient d'être complétée, permettant de la référencer ultérieurement.\n", - "\n", - "## Tableau des chemins des artefacts\n", - "\n", - "| Artefact | Chemin local | Chemin MLflow |\n", - "|----------|--------------|---------------|\n", - "| Matrice de confusion | `content/bert-model/confusion_matrix.png` | `confusion_matrix_{model_name}.png` |\n", - "| Courbe ROC | `content/bert-model/roc_curve_{model_name}.png` | `roc_curve_{model_name}.png` |\n", - "| Historique d'entraînement | `content/bert-model/training_history_{model_name}.png` | `training_history_{model_name}.png` (si run_id fourni) |\n", - "| Rapport de classification | `content/bert-model/classification_report.txt` | Racine des artefacts |\n", - "| Modèle BERT | `content/bert-model/model/` | `model/` |\n", - "| Tokenizer | `content/bert-model/tokenizer/` | `tokenizer/` |\n", - "| Preprocessor | `content/bert-model/preprocessor/preprocessor.pkl` | `preprocessor/preprocessor.pkl` |\n", - "| Requirements | `content/bert-model/requirements.txt` | (Utilisé par MLflow comme dépendances du modèle) |\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "3OppER9A_iPX" - }, - "outputs": [], - "source": [ - "# FONCTION POUR ENREGISTRER LE MODÈLE DANS MLFLOW\n", - "def log_model_to_mlflow(model, tokenizer, model_name, metrics, params=None):\n", - " \"\"\"\n", - " Enregistre le modèle et ses performances dans MLflow, y compris la signature.\n", - " Gère correctement les devices et la sérialisation JSON des métriques NumPy.\n", - " Supprime input_example pour éviter l'avertissement de validation avec le flavor pytorch.\n", - "\n", - " Args:\n", - " model (torch.nn.Module): Le modèle PyTorch (Hugging Face) entraîné.\n", - " tokenizer: Le tokenizer Hugging Face associé au modèle.\n", - " model_name (str): Un nom pour identifier ce modèle/cette exécution.\n", - " metrics (dict): Un dictionnaire contenant les métriques de performance.\n", - " params (dict, optional): Un dictionnaire des hyperparamètres utilisés. Defaults to None.\n", - "\n", - " Returns:\n", - " str: L'ID de l'exécution MLflow.\n", - " \"\"\"\n", - " class_names = ['Négatif', 'Positif']\n", - "\n", - " # --- Définir la signature du modèle ---\n", - " sample_input_text = [\"Ceci est un exemple de texte pour inférer la signature.\"]\n", - " inputs = tokenizer(\n", - " sample_input_text, return_tensors=\"pt\", padding=\"max_length\",\n", - " truncation=True, max_length=128\n", - " )\n", - " try:\n", - " model_device = next(model.parameters()).device\n", - " except StopIteration:\n", - " model_device = torch.device(\"cpu\")\n", - " except Exception:\n", - " model_device = torch.device(\"cpu\")\n", - "\n", - " inputs_on_device = None\n", - " try:\n", - " inputs_on_device = {k: v.to(model_device) for k, v in inputs.items()}\n", - " except Exception as e:\n", - " print(f\"Error moving input tensors to device {model_device}: {e}. Skipping signature inference.\")\n", - "\n", - " input_data_for_signature = {k: v.cpu().numpy() for k, v in inputs.items()} # For potential future use, still on CPU\n", - "\n", - " predictions = None\n", - " signature = None\n", - " if inputs_on_device:\n", - " model.eval()\n", - " with torch.no_grad():\n", - " try:\n", - " outputs = model(**inputs_on_device)\n", - " if hasattr(outputs, 'logits'):\n", - " predictions = outputs.logits.cpu().numpy()\n", - " elif isinstance(outputs, torch.Tensor):\n", - " predictions = outputs.cpu().numpy()\n", - " else:\n", - " # Fallback attempt\n", - " for val in getattr(outputs, 'values', lambda: [])(): # Handle dicts or sequences\n", - " if isinstance(val, torch.Tensor):\n", - " predictions = val.cpu().numpy()\n", - " break\n", - "\n", - " if predictions is not None:\n", - " # Infer signature using the CPU numpy inputs and CPU numpy predictions\n", - " signature = infer_signature(input_data_for_signature, predictions)\n", - " print(\"Model signature inferred successfully.\")\n", - " else:\n", - " print(\"Warning: Could not extract prediction tensor for signature.\")\n", - "\n", - " except Exception as e:\n", - " print(f\"Error during model prediction or signature inference: {e}\")\n", - " else:\n", - " print(\"Skipping signature inference because input tensors could not be moved to model device.\")\n", - " # --- Fin de la définition de la signature ---\n", - "\n", - " # Démarrer une exécution MLflow\n", - " with mlflow.start_run(run_name=f\"Model_Bert-{model_name}\") as run:\n", - "\n", - " # Enregistrer les paramètres du modèle\n", - " if params:\n", - " for key, value in params.items():\n", - " mlflow.log_param(key, value)\n", - "\n", - " # Enregistrer les métriques de performance\n", - " mlflow.log_metric(\"accuracy\", metrics['accuracy'])\n", - " mlflow.log_metric(\"precision\", metrics['precision'])\n", - " mlflow.log_metric(\"recall\", metrics['recall'])\n", - " mlflow.log_metric(\"f1\", metrics['f1'])\n", - " mlflow.log_metric(\"roc_auc\", metrics['roc_auc'])\n", - "\n", - " mlflow.log_param(\"model_base\", model_name)\n", - " mlflow.set_tag(\"model_base\", model_name)\n", - "\n", - " # Tracer et enregistrer la matrice de confusion\n", - " fig_cm = plot_confusion_matrix(metrics['confusion_matrix'], class_names)\n", - " mlflow.log_figure(fig_cm, f\"confusion_matrix_{model_name}.png\")\n", - "\n", - " # Tracer et enregistrer la courbe ROC\n", - " fig_roc = plot_roc_curve(metrics['fpr'], metrics['tpr'], metrics['roc_auc'], model_name)\n", - " mlflow.log_figure(fig_roc, f\"roc_curve_{model_name}.png\")\n", - "\n", - " # Enregistrer le rapport de classification\n", - " report_path = f\"content/bert-model/classification_report.txt\"\n", - " os.makedirs(os.path.dirname(report_path), exist_ok=True)\n", - " with open(report_path, \"w\") as f:\n", - " f.write(metrics['classification_report'])\n", - " mlflow.log_artifact(report_path)\n", - "\n", - "\n", - " # Sauvegarder le modèle localement\n", - " model_path = f\"content/bert-model/model\"\n", - " os.makedirs(model_path, exist_ok=True)\n", - " try:\n", - " model.save_pretrained(model_path)\n", - " print(\"Model logged to MLflow artifact path 'model'.\")\n", - " except Exception as e:\n", - " print(f\"Warning: Failed to save or log model: {e}\")\n", - "\n", - "\n", - " # Enregistrer le tokenizer localement et sur MLFlow\n", - " tokenizer_path = f\"content/bert-model/tokenizer\"\n", - " os.makedirs(tokenizer_path, exist_ok=True)\n", - " try:\n", - " tokenizer.save_pretrained(tokenizer_path)\n", - " mlflow.log_artifact(tokenizer_path)\n", - " print(\"Tokenizer logged to MLflow artifact path 'tokenizer'.\")\n", - " except Exception as e:\n", - " print(f\"Warning: Failed to save or log tokenizer: {e}\")\n", - "\n", - "\n", - " # --- Sauvegarder la fonction preprocessor via pickle ---\n", - " local_save_directory = \"content/bert-model/preprocessor\" # Attention au double \"content\" si c'est bien voulu\n", - " local_pickle_filename = \"preprocessor.pkl\"\n", - " local_pickle_filepath = os.path.join(local_save_directory, local_pickle_filename)\n", - " # Sous-dossier cible dans les artefacts MLflow\n", - " mlflow_artifact_subdir = \"preprocessor\"\n", - " try:\n", - " os.makedirs(local_save_directory, exist_ok=True)\n", - " with open(local_pickle_filepath, \"wb\") as f_out:\n", - " pickle.dump(preprocess_tweet_for_bert, f_out)\n", - " mlflow.log_artifact(local_pickle_filepath, artifact_path=mlflow_artifact_subdir)\n", - " except NameError:\n", - " print(f\"\\nWarning: La fonction 'preprocess_tweet_for_bert' n'est pas définie. Impossible de la sauvegarder.\\n\")\n", - " except Exception as e:\n", - " print(f\"\\nWarning: Impossible de sauvegarder (pickle) ou logger la fonction de prétraitement : {e}\\n\")\n", - "\n", - "\n", - " # Enregistrer le fichier requirements.txt\n", - " requirements_path = \"content/bert-model/requirements.txt\"\n", - " try:\n", - " with open(requirements_path, 'w') as f:\n", - " import sys\n", - " subprocess.check_call([sys.executable, '-m', 'pip', 'freeze'], stdout=f)\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Warning: Failed to generate requirements.txt: {e}. Logging without pip_requirements.\")\n", - " requirements_path = None\n", - " except Exception as e:\n", - " print(f\"An unexpected error occurred during requirements generation: {e}\")\n", - " requirements_path = None\n", - "\n", - " # --- Enregistrer le modèle PyTorch sans input_example ---\n", - " try:\n", - " mlflow.pytorch.log_model(\n", - " pytorch_model=model,\n", - " artifact_path=\"model\", # Simplement \"model\" pour être cohérent\n", - " signature=signature,\n", - " pip_requirements=requirements_path if requirements_path else None\n", - " )\n", - " print(f\"Model '{model_name}' logged to MLflow artifact path 'model_{model_name}'.\")\n", - " except Exception as e:\n", - " print(f\"FATAL: Error logging PyTorch model to MLflow: {e}\")\n", - " return None\n", - "\n", - " print(f\"MLflow logging attempt finished for run '{run.info.run_id}'.\")\n", - "\n", - " return run.info.run_id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "1PHu82H-YNtN" - }, - "outputs": [], - "source": [ - "# FONCTION POUR ENREGISTRER LE MODÈLE DANS MLFLOW\n", - "def log_model_to_mlflow(model, tokenizer, model_name, metrics, history=None, params=None):\n", - " \"\"\"\n", - " Enregistre le modèle et ses performances dans MLflow, y compris la signature.\n", - " Gère correctement les devices et la sérialisation JSON des métriques NumPy.\n", - " Supprime input_example pour éviter l'avertissement de validation avec le flavor pytorch.\n", - "\n", - " Args:\n", - " model (torch.nn.Module): Le modèle PyTorch (Hugging Face) entraîné.\n", - " tokenizer: Le tokenizer Hugging Face associé au modèle.\n", - " model_name (str): Un nom pour identifier ce modèle/cette exécution.\n", - " metrics (dict): Un dictionnaire contenant les métriques de performance.\n", - " params (dict, optional): Un dictionnaire des hyperparamètres utilisés. Defaults to None.\n", - "\n", - " Returns:\n", - " str: L'ID de l'exécution MLflow.\n", - " \"\"\"\n", - " class_names = ['Négatif', 'Positif']\n", - "\n", - " # --- Définir la signature du modèle ---\n", - " sample_input_text = [\"Ceci est un exemple de texte pour inférer la signature.\"]\n", - " inputs = tokenizer(\n", - " sample_input_text, return_tensors=\"pt\", padding=\"max_length\",\n", - " truncation=True, max_length=128\n", - " )\n", - " try:\n", - " model_device = next(model.parameters()).device\n", - " except StopIteration:\n", - " model_device = torch.device(\"cpu\")\n", - " except Exception:\n", - " model_device = torch.device(\"cpu\")\n", - "\n", - " inputs_on_device = None\n", - " try:\n", - " inputs_on_device = {k: v.to(model_device) for k, v in inputs.items()}\n", - " except Exception as e:\n", - " print(f\"Error moving input tensors to device {model_device}: {e}. Skipping signature inference.\")\n", - "\n", - " input_data_for_signature = {k: v.cpu().numpy() for k, v in inputs.items()} # For potential future use, still on CPU\n", - "\n", - " predictions = None\n", - " signature = None\n", - " if inputs_on_device:\n", - " model.eval()\n", - " with torch.no_grad():\n", - " try:\n", - " outputs = model(**inputs_on_device)\n", - " if hasattr(outputs, 'logits'):\n", - " predictions = outputs.logits.cpu().numpy()\n", - " elif isinstance(outputs, torch.Tensor):\n", - " predictions = outputs.cpu().numpy()\n", - " else:\n", - " # Fallback attempt\n", - " for val in getattr(outputs, 'values', lambda: [])(): # Handle dicts or sequences\n", - " if isinstance(val, torch.Tensor):\n", - " predictions = val.cpu().numpy()\n", - " break\n", - "\n", - " if predictions is not None:\n", - " # Infer signature using the CPU numpy inputs and CPU numpy predictions\n", - " signature = infer_signature(input_data_for_signature, predictions)\n", - " print(\"Model signature inferred successfully.\")\n", - " else:\n", - " print(\"Warning: Could not extract prediction tensor for signature.\")\n", - "\n", - " except Exception as e:\n", - " print(f\"Error during model prediction or signature inference: {e}\")\n", - " else:\n", - " print(\"Skipping signature inference because input tensors could not be moved to model device.\")\n", - " # --- Fin de la définition de la signature ---\n", - "\n", - " # Démarrer une exécution MLflow\n", - " with mlflow.start_run(run_name=f\"Model_Bert-{model_name}\") as run:\n", - "\n", - " # Enregistrer les paramètres du modèle\n", - " if params:\n", - " for key, value in params.items():\n", - " mlflow.log_param(key, value)\n", - "\n", - " if history is not None:\n", - " val_precisions = history.get(\"val_precision\", [])\n", - " if val_precisions:\n", - " best_epoch_idx = val_precisions.index(max(val_precisions))\n", - "\n", - " val_precision_at_best = val_precisions[best_epoch_idx]\n", - " val_loss_at_best = history.get(\"val_loss\", [None])[best_epoch_idx]\n", - " val_recall_at_best = history.get(\"val_recall\", [None])[best_epoch_idx]\n", - "\n", - " mlflow.log_metric(\"val_precision\", val_precision_at_best)\n", - " mlflow.log_metric(\"val_loss\", val_loss_at_best)\n", - " mlflow.log_metric(\"val_recall\", val_recall_at_best)\n", - "\n", - "\n", - " # Enregistrer les métriques de performance\n", - " mlflow.log_metric(\"test_precision\", metrics['precision'])\n", - " mlflow.log_metric(\"test_accuracy\", metrics['accuracy'])\n", - " mlflow.log_metric(\"test_recall\", metrics['recall'])\n", - " mlflow.log_metric(\"test_f1\", metrics['f1'])\n", - " mlflow.log_metric(\"test_roc_auc\", metrics['roc_auc'])\n", - "\n", - " mlflow.log_param(\"model_base\", model_name)\n", - " mlflow.set_tag(\"model_base\", model_name)\n", - "\n", - " # Tracer et enregistrer la matrice de confusion\n", - " fig_cm = plot_confusion_matrix(metrics['confusion_matrix'], class_names)\n", - " mlflow.log_figure(fig_cm, f\"confusion_matrix_{model_name}.png\")\n", - "\n", - " # Tracer et enregistrer la courbe ROC\n", - " fig_roc = plot_roc_curve(metrics['fpr'], metrics['tpr'], metrics['roc_auc'], model_name)\n", - " mlflow.log_figure(fig_roc, f\"roc_curve_{model_name}.png\")\n", - "\n", - " # Enregistrer le rapport de classification\n", - " report_path = f\"content/bert-model/classification_report.txt\"\n", - " os.makedirs(os.path.dirname(report_path), exist_ok=True)\n", - " with open(report_path, \"w\") as f:\n", - " f.write(metrics['classification_report'])\n", - " mlflow.log_artifact(report_path)\n", - "\n", - "\n", - " # Sauvegarder le modèle localement\n", - " model_path = f\"content/bert-model/model\"\n", - " os.makedirs(model_path, exist_ok=True)\n", - " try:\n", - " model.save_pretrained(model_path)\n", - " print(\"Model logged to MLflow artifact path 'model'.\")\n", - " except Exception as e:\n", - " print(f\"Warning: Failed to save or log model: {e}\")\n", - "\n", - "\n", - " # Enregistrer le tokenizer localement et sur MLFlow\n", - " tokenizer_path = f\"content/bert-model/tokenizer\"\n", - " os.makedirs(tokenizer_path, exist_ok=True)\n", - " try:\n", - " tokenizer.save_pretrained(tokenizer_path)\n", - " mlflow.log_artifact(tokenizer_path)\n", - " print(\"Tokenizer logged to MLflow artifact path 'tokenizer'.\")\n", - " except Exception as e:\n", - " print(f\"Warning: Failed to save or log tokenizer: {e}\")\n", - "\n", - "\n", - " # --- Sauvegarder la fonction preprocessor via pickle ---\n", - " local_save_directory = \"content/bert-model/preprocessor\" # Attention au double \"content\" si c'est bien voulu\n", - " local_pickle_filename = \"preprocessor.pkl\"\n", - " local_pickle_filepath = os.path.join(local_save_directory, local_pickle_filename)\n", - " # Sous-dossier cible dans les artefacts MLflow\n", - " mlflow_artifact_subdir = \"preprocessor\"\n", - " try:\n", - " os.makedirs(local_save_directory, exist_ok=True)\n", - " with open(local_pickle_filepath, \"wb\") as f_out:\n", - " pickle.dump(preprocess_tweet_for_bert, f_out)\n", - " mlflow.log_artifact(local_pickle_filepath, artifact_path=mlflow_artifact_subdir)\n", - " except NameError:\n", - " print(f\"\\nWarning: La fonction 'preprocess_tweet_for_bert' n'est pas définie. Impossible de la sauvegarder.\\n\")\n", - " except Exception as e:\n", - " print(f\"\\nWarning: Impossible de sauvegarder (pickle) ou logger la fonction de prétraitement : {e}\\n\")\n", - "\n", - "\n", - " # Enregistrer le fichier requirements.txt\n", - " requirements_path = \"content/bert-model/requirements.txt\"\n", - " try:\n", - " with open(requirements_path, 'w') as f:\n", - " import sys\n", - " subprocess.check_call([sys.executable, '-m', 'pip', 'freeze'], stdout=f)\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Warning: Failed to generate requirements.txt: {e}. Logging without pip_requirements.\")\n", - " requirements_path = None\n", - " except Exception as e:\n", - " print(f\"An unexpected error occurred during requirements generation: {e}\")\n", - " requirements_path = None\n", - "\n", - " # --- Enregistrer le modèle PyTorch sans input_example ---\n", - " try:\n", - " mlflow.pytorch.log_model(\n", - " pytorch_model=model,\n", - " artifact_path=\"model\", # Simplement \"model\" pour être cohérent\n", - " signature=signature,\n", - " pip_requirements=requirements_path if requirements_path else None\n", - " )\n", - " print(f\"Model '{model_name}' logged to MLflow artifact path 'model_{model_name}'.\")\n", - " except Exception as e:\n", - " print(f\"FATAL: Error logging PyTorch model to MLflow: {e}\")\n", - " return None\n", - "\n", - " print(f\"MLflow logging attempt finished for run '{run.info.run_id}'.\")\n", - "\n", - " return run.info.run_id" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "o1uxWQimT76g" - }, - "source": [ - "## Entrainement du modèle : `DistilBertForSequenceClassification`\n", - "\n", - "[DistilBertForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertForSequenceClassification) est un modèle basé sur **DistilBERT** avec **une couche de classification ou de régression ajoutée à la tête (head)**. Il est conçu pour les tâches de **classification de séquence**.\n", - "\n", - "**DistilBertForSequenceClassification** est une version allégée de BERT, idéale pour des tâches de classification de texte. Il est facile à utiliser grâce aux classes `AutoTokenizer` et `AutoModel`, et il prend en charge plusieurs types de classification (mono-étiquette, multi-étiquettes). Il s’intègre naturellement avec **PyTorch** et peut être fine-tuné sur des tâches spécifiques.\n", - "\n", - "#### 📌 **Caractéristiques principales :** \n", - "- **Hérite de `PreTrainedModel`**, ce qui lui permet d’utiliser les fonctionnalités standards des modèles pré-entraînés (téléchargement, sauvegarde, ajustement des embeddings, etc.). \n", - "- **Sous-classe de `torch.nn.Module`**, donc il fonctionne comme un module PyTorch classique. \n", - "- Utilisé pour **la classification de texte** (binaire, multi-classes ou multi-étiquettes). \n", - "\n", - "### ⚙️ **Méthode `forward` (passage avant du modèle)** \n", - "\n", - "**Entrées possibles :** \n", - "- `input_ids` : Identifiants des tokens dans le vocabulaire (obtenus avec un tokenizer). \n", - "- `attention_mask` : Masque pour ignorer les tokens de padding (1 = pris en compte, 0 = ignoré). \n", - "- `head_mask` : Masque pour désactiver certaines têtes d’attention. \n", - "- `inputs_embeds` : Embeddings d’entrée à utiliser à la place de `input_ids`. \n", - "- `labels` : Étiquettes pour la classification, utilisées pour calculer la perte (`CrossEntropy` ou `MSE` selon `num_labels`). \n", - "\n", - "**Sorties possibles :** \n", - "- `loss` : Perte (si `labels` est fourni). \n", - "- `logits` : Scores de classification (avant application de **Softmax**). \n", - "- `hidden_states` : États cachés de toutes les couches du modèle (si demandé). \n", - "- `attentions` : Poids des mécanismes d’attention (si demandé). \n", - "\n", - "### Analyse de sentiment de tweet\n", - "\n", - "Pour une **analyse de sentiment de tweet** avec **deux classes distinctes** (0 = négatif, 1 = positif), il faut utiliser **une classification simple** (**single-label classification**). \n", - "\n", - "- Chaque tweet appartient **exclusivement** à une seule catégorie (**négatif ou positif, mais pas les deux**). \n", - "- La classification **multi-étiquettes** est utilisée lorsque **plusieurs catégories peuvent s’appliquer simultanément** (ex : un tweet peut être à la fois « joyeux » et « informatif »). \n", - "\n", - "### 🔹 **Comment configurer le modèle pour une classification binaire ?**\n", - "\n", - "1. Charger un modèle pré-entraîné et indiquer `num_labels=2`.\n", - "2. Le modèle renverra 0 ou 1, indiquant si le tweet est négatif ou positif. \n", - "\n", - "Pour entraîner le modèle sur un dataset personnalisé (ex : tweets annotés manuellement), il faudra fine-tuner DistilBERT avec une **Cross-Entropy Loss** adaptée à la classification binaire.\n", - "\n", - "### Documentation\n", - "\n", - "- `distilbert-base-uncased` : https://huggingface.co/docs/transformers/model_doc/distilbert" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kVj7LqF-bZGq" - }, - "outputs": [], - "source": [ - "# FONCTION PRINCIPALE D'ENTRAÎNEMENT\n", - "def train_bert_sentiment(data_path, model_name=\"distilbert-base-uncased\", batch_size=4, epochs=3, sample_size=20000):\n", - " \"\"\"\n", - " Fonction principale pour l'entraînement du modèle DistilBERT sur une tâche d'analyse de sentiments.\n", - " \"\"\"\n", - "\n", - " # Définir les paramètres\n", - " params = {\n", - " 'model_name': model_name,\n", - " 'batch_size': batch_size,\n", - " 'learning_rate': 2e-5,\n", - " 'epochs': epochs,\n", - " 'max_length': 128,\n", - " 'sample_size': sample_size\n", - " }\n", - "\n", - " # Charger les données\n", - " print(\"Chargement du dataset...\")\n", - " column_names = ['target', 'ids', 'date', 'flag', 'user', 'text']\n", - "\n", - " try:\n", - " raw_data = pd.read_csv(data_path, encoding='utf-8', names=column_names)\n", - " except UnicodeDecodeError:\n", - " print(\"Lecture du fichier en latin-1...\")\n", - " raw_data = pd.read_csv(data_path, encoding='latin-1', names=column_names)\n", - "\n", - " # Préparer les données\n", - " print(\"Préparation des données...\")\n", - " data_splits = prepare_data(raw_data, sample_size=sample_size)\n", - "\n", - " # Initialiser le tokenizer et le modèle\n", - " print(\"Initialisation du modèle DistilBERT...\")\n", - " tokenizer = DistilBertTokenizer.from_pretrained(model_name)\n", - " model = DistilBertForSequenceClassification.from_pretrained(\n", - " model_name,\n", - " num_labels=2 # Sentiment binaire (0 = négatif, 1 = positif)\n", - " )\n", - "\n", - " # Ajustement du batch size selon la mémoire GPU\n", - " adjusted_batch_size = min(8, batch_size)\n", - " print(f\"Batch size ajusté à {adjusted_batch_size} pour économiser la mémoire GPU.\")\n", - "\n", - " # Création des datasets et des dataloaders\n", - " train_dataset = TweetDataset(data_splits['train']['texts'], data_splits['train']['labels'], tokenizer)\n", - " val_dataset = TweetDataset(data_splits['val']['texts'], data_splits['val']['labels'], tokenizer)\n", - " test_dataset = TweetDataset(data_splits['test']['texts'], data_splits['test']['labels'], tokenizer)\n", - "\n", - " train_loader = DataLoader(train_dataset, batch_size=adjusted_batch_size, shuffle=True)\n", - " val_loader = DataLoader(val_dataset, batch_size=adjusted_batch_size * 2)\n", - " test_loader = DataLoader(test_dataset, batch_size=adjusted_batch_size * 2)\n", - "\n", - " # Détection du device (GPU/CPU)\n", - " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - " print(f\"Utilisation de : {device}\")\n", - "\n", - " # Déplacement du modèle sur le device\n", - " model.to(device)\n", - "\n", - " # Entraînement du modèle\n", - " print(\"Début de l'entraînement...\")\n", - "\n", - " # Ajustement de l'accumulation de gradients\n", - " gradient_accumulation_steps = max(1, 16 // adjusted_batch_size) # Éviter des valeurs trop élevées\n", - " print(f\"Accumulation de gradient ajustée à {gradient_accumulation_steps} étapes.\")\n", - "\n", - " history, metrics = train_model(\n", - " model,\n", - " train_loader,\n", - " val_loader,\n", - " test_loader,\n", - " device,\n", - " epochs=epochs,\n", - " gradient_accumulation_steps=gradient_accumulation_steps\n", - " )\n", - "\n", - " # Enregistrement du modèle dans MLflow\n", - " print(\"Enregistrement du modèle dans MLflow...\")\n", - " run_id = log_model_to_mlflow(model, tokenizer, model_name, metrics, history, params)\n", - "\n", - " # Tracer et enregistrer l'historique d'entraînement\n", - " plot_training_history(history, model_name, run_id)\n", - "\n", - " # Retourner le modèle entraîné et le tokenizer\n", - " return {\n", - " 'model': model,\n", - " 'tokenizer': tokenizer,\n", - " 'metrics': metrics,\n", - " 'run_id': run_id\n", - " }\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "f4c7033b02a2493085d1ac0cfac9cccf", - "3d5883b5f0bb4098bda8599622824948", - "3502027e67de4c36b46688597314fb2e", - "4db20c46a69043fdb7d29ccaa5f03e12", - "eb20692f8413420f9ca58df09b2d5feb", - "e47e179020f54e5b84017148d09480e9", - "e23da7120310469798127dfbf039919e", - "44325b25ca5f41d2a5ff31e6cdcf179f", - "b6aead1ab91f4ad1a903009b95c39036", - "2eadd184b69d41a696fed5c7bc5b1843", - "a52850e715624d3986a292dca1e6fa62", - "2bd37c30fbe74e3eb9f7d0a660b94772", - "5a904bdfc6e3488b963f9952ed7936c9", - "63840ffc38eb4932b39d00edfcbe5f15", - 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latin-1...\n", - "Préparation des données...\n", - "Prétraitement des tweets...\n", - "Initialisation du modèle DistilBERT...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f4c7033b02a2493085d1ac0cfac9cccf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/48.0 [00:00" - 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Entraîner le modèle\n", - "bert_pack = train_bert_sentiment(\n", - " data_path=csv_file_path,\n", - " model_name=\"distilbert-base-uncased\",\n", - " batch_size=16,\n", - " epochs=20,\n", - " sample_size=100000\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gYK8Th3rT76h" - }, - "source": [ - "## Chargement du modèle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yYPiB5A8N7l_" - }, - "outputs": [], - "source": [ - "# FONCTION POUR CHARGER LE MODÈLE ENTRAINÉ\n", - "def load_bert_model(model_base_dir=\"content/bert-model\"):\n", - " \"\"\"\n", - " Charge le modèle BERT entraîné, le tokenizer et le preprocessor\n", - " à partir de l'arborescence des artefacts sauvegardés.\n", - "\n", - " Args:\n", - " model_base_dir (str): Répertoire de base contenant tous les artefacts du modèle\n", - " (par défaut: \"content/bert-model\")\n", - "\n", - " Returns:\n", - " dict: Un dictionnaire contenant le modèle, le tokenizer et la fonction de prétraitement\n", - " \"\"\"\n", - " # Définir les chemins des différents artefacts\n", - " model_dir = os.path.join(model_base_dir, \"model\")\n", - " tokenizer_dir = os.path.join(model_base_dir, \"tokenizer\")\n", - " preprocessor_dir = os.path.join(model_base_dir, \"preprocessor\")\n", - " preprocessor_file = os.path.join(preprocessor_dir, \"preprocessor.pkl\")\n", - "\n", - " # Charger le modèle\n", - " try:\n", - " model = DistilBertForSequenceClassification.from_pretrained(model_dir)\n", - " print(f\"Modèle chargé avec succès depuis '{model_dir}'\")\n", - " except Exception as e:\n", - " print(f\"Erreur lors du chargement du modèle : {e}\")\n", - " raise\n", - "\n", - " # Charger le tokenizer\n", - " try:\n", - " tokenizer = DistilBertTokenizer.from_pretrained(tokenizer_dir)\n", - " print(f\"Tokenizer chargé avec succès depuis '{tokenizer_dir}'\")\n", - " except Exception as e:\n", - " print(f\"Erreur lors du chargement du tokenizer : {e}\")\n", - " raise\n", - "\n", - " # Charger la fonction de prétraitement\n", - " try:\n", - " with open(preprocessor_file, \"rb\") as f_in:\n", - " preprocess_tweet_for_bert = pickle.load(f_in)\n", - " print(f\"Fonction 'preprocess_tweet_for_bert' chargée avec succès depuis '{preprocessor_file}'\")\n", - " except FileNotFoundError:\n", - " print(f\"Erreur : Fichier preprocessor introuvable à l'emplacement '{preprocessor_file}'.\")\n", - " preprocess_tweet_for_bert = None\n", - " except Exception as e:\n", - " print(f\"Erreur lors du chargement de la fonction de prétraitement : {e}\")\n", - " preprocess_tweet_for_bert = None\n", - "\n", - " # Vérifier si on a bien chargé la fonction de prétraitement\n", - " if preprocess_tweet_for_bert is None:\n", - " print(\"ATTENTION: Fonction de prétraitement non chargée. Les prédictions pourraient être affectées.\")\n", - "\n", - " # Création d'un pack pour faciliter l'utilisation\n", - " model_pack = {\n", - " 'model': model,\n", - " 'tokenizer': tokenizer,\n", - " 'preprocess': preprocess_tweet_for_bert\n", - " }\n", - "\n", - " return model_pack" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tB3Uiz5pT76h" - }, - "source": [ - "## Fonction de prédiction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RkFU6mWxT76h" - }, - "outputs": [], - "source": [ - "# FONCTION DE PRÉDICTION\n", - "def predict_sentiment(tweet, model_pack):\n", - " \"\"\"\n", - " Prédit le sentiment d'un tweet à l'aide du modèle BERT\n", - " \"\"\"\n", - " model = model_pack['model']\n", - " tokenizer = model_pack['tokenizer']\n", - " preprocess = model_pack['preprocess']\n", - "\n", - " # Prétraitement du tweet\n", - " processed_tweet = preprocess(tweet)\n", - "\n", - " # Tokenisation\n", - " encoding = tokenizer(\n", - " processed_tweet,\n", - " return_tensors=\"pt\",\n", - " truncation=True,\n", - " padding=True,\n", - " max_length=128\n", - " )\n", - "\n", - " # Détection du device\n", - " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - " model = model.to(device)\n", - "\n", - " # Passer au mode évaluation\n", - " model.eval()\n", - "\n", - " # Déplacer les tenseurs sur le device\n", - " input_ids = encoding['input_ids'].to(device)\n", - " attention_mask = encoding['attention_mask'].to(device)\n", - "\n", - " # Prédiction\n", - " with torch.no_grad():\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - " probabilities = torch.nn.functional.softmax(logits, dim=1)\n", - "\n", - " # Récupérer le sentiment prédit et le score de confiance\n", - " predicted_class = torch.argmax(probabilities, dim=1).item()\n", - " confidence_score = probabilities[0][predicted_class].item()\n", - " raw_score = probabilities[0][1].item() # Score de la classe positive\n", - "\n", - " # Convertir en étiquette lisible\n", - " sentiment = \"Positif\" if predicted_class == 1 else \"Négatif\"\n", - "\n", - " # Résultat\n", - " result = {\n", - " 'sentiment': sentiment,\n", - " 'confidence': confidence_score,\n", - " 'raw_score': raw_score\n", - " }\n", - "\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_tNu9-3tT76h" - }, - "source": [ - "## Test sur un ensemble de tweets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ZFMlwStUT76h" - }, - "outputs": [], - "source": [ - "# FONCTION DE TEST SUR ENSEMBLE DE TWEETS\n", - "def test_model_on_examples(model_pack, test_tweets):\n", - " \"\"\"\n", - " Teste le modèle sur une liste de tweets et retourne un DataFrame avec les résultats des prédictions.\n", - " \"\"\"\n", - " results = {\n", - " \"Positif\": 0,\n", - " \"Négatif\": 0\n", - " }\n", - "\n", - " # Créer un DataFrame pour stocker les résultats\n", - " predictions_data = []\n", - "\n", - " for i, tweet in enumerate(test_tweets):\n", - " # Utiliser la fonction predict_sentiment avec model_pack\n", - " result = predict_sentiment(tweet, model_pack)\n", - "\n", - " # Ajouter l'emoji approprié pour le sentiment\n", - " emoji = \"💚\" if result['sentiment'] == \"Positif\" else \"❌\"\n", - "\n", - " # Ajouter les données au DataFrame\n", - " predictions_data.append({\n", - " \"Tweet\": tweet,\n", - " \"Sentiment\": result['sentiment'],\n", - " \"Emoji\": emoji,\n", - " \"Score de confiance\": result['confidence'],\n", - " \"Score brut\": result['raw_score'],\n", - " \"Index\": i+1\n", - " })\n", - "\n", - " # Comptage des résultats\n", - " results[result['sentiment']] += 1\n", - "\n", - " # Créer le DataFrame\n", - " df_predictions = pd.DataFrame(predictions_data)\n", - "\n", - " # Afficher un résumé\n", - " print(\"\\nTest du modèle sur les exemples:\")\n", - " print(\"=\"*50)\n", - " print(f\"Résumé: {len(test_tweets)} tweets analysés\")\n", - " print(f\"Tweets positifs: {results['Positif']} ({(results['Positif']/len(test_tweets)*100):.1f}%)\")\n", - " print(f\"Tweets négatifs: {results['Négatif']} ({(results['Négatif']/len(test_tweets)*100):.1f}%)\")\n", - "\n", - " # Sauvegarder les résultats dans un fichier CSV\n", - " os.makedirs('content/bert-model', exist_ok=True)\n", - " df_predictions.to_csv(\"content/bert-model/predictions_results.csv\", index=False)\n", - "\n", - " # Retourner le DataFrame\n", - " return df_predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 1102, - "status": "ok", - "timestamp": 1752355022988, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "Tkb9WDu-T76h", - "outputId": "0f07d599-7592-4f49-a40f-eb8b22ec0359" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modèle chargé avec succès depuis 'content/bert-model/model'\n", - "Tokenizer chargé avec succès depuis 'content/bert-model/tokenizer'\n", - "Fonction 'preprocess_tweet_for_bert' chargée avec succès depuis 'content/bert-model/preprocessor/preprocessor.pkl'\n", - "\n", - "Test du modèle sur les exemples:\n", - "==================================================\n", - "Résumé: 15 tweets analysés\n", - "Tweets positifs: 7 (46.7%)\n", - "Tweets négatifs: 8 (53.3%)\n", - "Analyse de sentiment terminée! Résultats disponibles dans content/bert-model/predictions_results.csv\n" - ] - } - ], - "source": [ - "# Liste améliorée de tweets pour tester le modèle\n", - "test_tweets_improved = [\n", - " # Tweets positifs avec différentes caractéristiques\n", - " \"I absolutely love flying with @AirParadis! Their service is exceptional and the staff is always so friendly :) #bestairline #travel\",\n", - " \"I just landed after an amazing flight... The crew was truly fantastic and the food was delicious! Can't wait to fly with them again :D\",\n", - " \"Wow! You have to try @AirParadis. Best. Flight. Ever. Their new seats are incredibly comfortable. http://airparadis.com/newseats #travel #happy #AirParadis\",\n", - " \"It was my first time flying business class, and I’m completely blown away!!! The service is definitely worth it... @AirParadis never lets me down :)\",\n", - " \"What an incredible flight experience with @AirParadis! Everything was perfect from takeoff to landing. Would do it again in a heartbeat! #travel #satisfaction\",\n", - "\n", - " # Tweets négatifs avec différentes caractéristiques\n", - " \"Honestly, my last flight with @AirParadis was disappointing. The flight was delayed without any notice. #disappointment\",\n", - " \"I had high hopes for @AirParadis, but the seat was uncomfortable and the staff seemed overwhelmed during the flight. Not the best experience.\",\n", - " \"So frustrated with @AirParadis this time. The staff was unresponsive and it ruined our trip. #nothappy\",\n", - " \"The flight I booked with @AirParadis was canceled at the last minute. Very disappointing and no adequate communication!\",\n", - " \"The quality of service with @AirParadis has declined. I expected better. It left a sour taste. #disappointed\",\n", - "\n", - " # Tweets mixtes et nuancés\n", - " \"To be honest, @AirParadis has significantly improved their service since last year! Not perfect yet, but they are making an effort... #progress\",\n", - " \"We were really looking forward to our trip, but our flight was delayed... at least the team was very apologetic and provided us with complimentary drinks! @AirParadis\",\n", - " \"The flight with @AirParadis had great moments, but the delay somewhat tarnished the experience. I'll give them another chance!\",\n", - " \"Overall, @AirParadis has good service, but I had a few minor issues with the check-in process. It could be better! #mixedfeelings\",\n", - " \"While our flight was pleasant, the lack of communication regarding the schedule was a bit frustrating. @AirParadis can do better. #nuanced\"\n", - "]\n", - "\n", - "# Charger le modèle entraîné\n", - "model_pack = load_bert_model()\n", - "\n", - "# Tester le modèle sur les exemples\n", - "df_results = test_model_on_examples(model_pack, test_tweets_improved)\n", - "\n", - "print(\"Analyse de sentiment terminée! Résultats disponibles dans content/bert-model/predictions_results.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 519 - }, - "executionInfo": { - "elapsed": 69, - "status": "ok", - "timestamp": 1752355023061, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "XIHAcDH3E1rH", - "outputId": "9982e8d8-6530-4b66-9a4c-1ac8f158b260" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df_results\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Tweet\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"The quality of service with @AirParadis has declined. I expected better. It left a sour taste. #disappointed\",\n \"We were really looking forward to our trip, but our flight was delayed... at least the team was very apologetic and provided us with complimentary drinks! @AirParadis\",\n \"I absolutely love flying with @AirParadis! Their service is exceptional and the staff is always so friendly :) #bestairline #travel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentiment\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"N\\u00e9gatif\",\n \"Positif\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Emoji\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"\\u274c\",\n \"\\ud83d\\udc9a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score de confiance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.14419873334810537,\n \"min\": 0.5295969843864441,\n \"max\": 0.9885379076004028,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.9871659874916077,\n 0.5295969843864441\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score brut\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4223161774401945,\n \"min\": 0.011462084017693996,\n \"max\": 0.9652082920074463,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.012833991087973118,\n 0.4704030156135559\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 1,\n \"max\": 15,\n \"num_unique_values\": 15,\n \"samples\": [\n 10,\n 12\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "df_results" - }, - "text/html": [ - "\n", - "
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TweetSentimentEmojiScore de confianceScore brutIndex
0I absolutely love flying with @AirParadis! The...Positif💚0.9415390.9415391
1I just landed after an amazing flight... The c...Positif💚0.9495520.9495522
2Wow! You have to try @AirParadis. Best. Flight...Positif💚0.9601810.9601813
3It was my first time flying business class, an...Négatif❌0.9500300.0499704
4What an incredible flight experience with @Air...Positif💚0.9652080.9652085
5Honestly, my last flight with @AirParadis was ...Négatif❌0.9874600.0125396
6I had high hopes for @AirParadis, but the seat...Négatif❌0.9844690.0155317
7So frustrated with @AirParadis this time. The ...Négatif❌0.9756840.0243168
8The flight I booked with @AirParadis was cance...Négatif❌0.9885380.0114629
9The quality of service with @AirParadis has de...Négatif❌0.9871660.01283410
10To be honest, @AirParadis has significantly im...Positif💚0.9086420.90864211
11We were really looking forward to our trip, bu...Négatif❌0.5295970.47040312
12The flight with @AirParadis had great moments,...Négatif❌0.6826780.31732213
13Overall, @AirParadis has good service, but I h...Positif💚0.7129990.71299914
14While our flight was pleasant, the lack of com...Positif💚0.7426260.74262615
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Négatif ❌ \n", - "13 Overall, @AirParadis has good service, but I h... Positif 💚 \n", - "14 While our flight was pleasant, the lack of com... Positif 💚 \n", - "\n", - " Score de confiance Score brut Index \n", - "0 0.941539 0.941539 1 \n", - "1 0.949552 0.949552 2 \n", - "2 0.960181 0.960181 3 \n", - "3 0.950030 0.049970 4 \n", - "4 0.965208 0.965208 5 \n", - "5 0.987460 0.012539 6 \n", - "6 0.984469 0.015531 7 \n", - "7 0.975684 0.024316 8 \n", - "8 0.988538 0.011462 9 \n", - "9 0.987166 0.012834 10 \n", - "10 0.908642 0.908642 11 \n", - "11 0.529597 0.470403 12 \n", - "12 0.682678 0.317322 13 \n", - "13 0.712999 0.712999 14 \n", - "14 0.742626 0.742626 15 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 536 - }, - "executionInfo": { - "elapsed": 607, - "status": "ok", - "timestamp": 1752355023660, - "user": { - "displayName": "Grégoire", - "userId": "01249298206117462766" - }, - "user_tz": -120 - }, - "id": "-mJxfVLiGfTL", - "outputId": "94c492a5-6077-461e-90a8-45097869c7d6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modèle chargé avec succès depuis 'content/bert-model/model'\n", - "Tokenizer chargé avec succès depuis 'content/bert-model/tokenizer'\n", - "Fonction 'preprocess_tweet_for_bert' chargée avec succès depuis 'content/bert-model/preprocessor/preprocessor.pkl'\n", - "\n", - "Test du modèle sur les exemples:\n", - "==================================================\n", - "Résumé: 10 tweets analysés\n", - "Tweets positifs: 5 (50.0%)\n", - "Tweets négatifs: 5 (50.0%)\n", - "Analyse de sentiment terminée! Vérifiez si les résultats sont dans content/bert-model/predictions_results.csv\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df_results\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Tweet\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"@AirParadis let me down. Flight canceled for no valid reason, no solution offered. Zero service. I'm disgusted. \\ud83d\\ude24 #Abandoned\",\n \"PERFECT flight with @AirParadis today. Amazing staff, clean plane, arrived early. What more could you ask for? \\u2b50\\u2b50\\u2b50\\u2b50\\u2b50 #HappyCustomer\",\n \"AVOID @AirParadis! The worst customer service I've ever seen. Rude, incompetent, and they lost my luggage! 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TweetSentimentEmojiScore de confianceScore brutIndex
0@AirParadis offers absolutely FLAWLESS service...Positif💚0.9642950.9642951
1PERFECT flight with @AirParadis today. Amazing...Positif💚0.9353710.9353712
2I'm totally a fan of @AirParadis! Every flight...Positif💚0.9763770.9763773
3Extraordinary experience in business class on ...Positif💚0.9690830.9690834
4@AirParadis saved my vacation! After a cancell...Positif💚0.9118610.9118615
5AVOID @AirParadis! The worst customer service ...Négatif❌0.9780220.0219786
6DISASTROUS flight with @AirParadis. 5-hour del...Négatif❌0.9769900.0230107
7How can @AirParadis dare to charge for seats t...Négatif❌0.9771530.0228478
8@AirParadis let me down. Flight canceled for n...Négatif❌0.9860050.0139959
9The food served on @AirParadis was simply ined...Négatif❌0.6343080.36569210
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\n" - ], - "text/plain": [ - " Tweet Sentiment Emoji \\\n", - "0 @AirParadis offers absolutely FLAWLESS service... Positif 💚 \n", - "1 PERFECT flight with @AirParadis today. Amazing... Positif 💚 \n", - "2 I'm totally a fan of @AirParadis! Every flight... Positif 💚 \n", - "3 Extraordinary experience in business class on ... Positif 💚 \n", - "4 @AirParadis saved my vacation! After a cancell... Positif 💚 \n", - "5 AVOID @AirParadis! The worst customer service ... Négatif ❌ \n", - "6 DISASTROUS flight with @AirParadis. 5-hour del... Négatif ❌ \n", - "7 How can @AirParadis dare to charge for seats t... Négatif ❌ \n", - "8 @AirParadis let me down. Flight canceled for n... Négatif ❌ \n", - "9 The food served on @AirParadis was simply ined... Négatif ❌ \n", - "\n", - " Score de confiance Score brut Index \n", - "0 0.964295 0.964295 1 \n", - "1 0.935371 0.935371 2 \n", - "2 0.976377 0.976377 3 \n", - "3 0.969083 0.969083 4 \n", - "4 0.911861 0.911861 5 \n", - "5 0.978022 0.021978 6 \n", - "6 0.976990 0.023010 7 \n", - "7 0.977153 0.022847 8 \n", - "8 0.986005 0.013995 9 \n", - "9 0.634308 0.365692 10 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Liste améliorée de tweets pour tester le modèle\n", - "unambiguous_tweets = [\n", - " # --- Strongly Positive ---\n", - " \"@AirParadis offers absolutely FLAWLESS service! Best airline, period. I recommend it 1000%! 😍 #DreamTrip\",\n", - " \"PERFECT flight with @AirParadis today. Amazing staff, clean plane, arrived early. What more could you ask for? ⭐⭐⭐⭐⭐ #HappyCustomer\",\n", - " \"I'm totally a fan of @AirParadis! Every flight is a pleasure. Comfort, kindness, punctuality... they have it all! Thank you! 😊✈️\",\n", - " \"Extraordinary experience in business class on @AirParadis. Worth every penny. Luxury and attention to detail. WELL DONE! 👏 #LuxuryTravel\",\n", - " \"@AirParadis saved my vacation! After a cancellation with another airline, they immediately found me a seat. Top-notch customer service! 🙏 #Saved\",\n", - "\n", - " # --- Strongly Negative ---\n", - " \"AVOID @AirParadis! The worst customer service I've ever seen. Rude, incompetent, and they lost my luggage! An absolute disgrace. 😠 #AirlineToAvoid\",\n", - " \"DISASTROUS flight with @AirParadis. 5-hour delay, no information, unfriendly staff. NEVER flying with them again. 🤬 #TripRuined\",\n", - " \"How can @AirParadis dare to charge for seats that are so dirty and uncomfortable? It's outright theft. Horrible airline. 👎 #Scam\",\n", - " \"@AirParadis let me down. Flight canceled for no valid reason, no solution offered. Zero service. I'm disgusted. 😤 #Abandoned\",\n", - " \"The food served on @AirParadis was simply inedible. Cold, tasteless... I've never eaten anything so bad on a flight. 🤢 #AirplaneFood\"\n", - "]\n", - "\n", - "# Charger le modèle entraîné\n", - "# Assurez-vous que la fonction load_bert_model est définie et fonctionnelle\n", - "model_pack = load_bert_model()\n", - "\n", - "# Tester le modèle sur les exemples\n", - "# Assurez-vous que la fonction test_model_on_examples est définie,\n", - "# prend le model_pack et la liste de tweets, et renvoie un DataFrame\n", - "df_results = test_model_on_examples(model_pack, unambiguous_tweets)\n", - "\n", - "# Afficher un message indiquant où trouver les résultats (si sauvegardés par la fonction)\n", - "print(\"Analyse de sentiment terminée! Vérifiez si les résultats sont dans content/bert-model/predictions_results.csv\")\n", - "\n", - "# Optionnel : Afficher les premières lignes du DataFrame résultant\n", - "df_results" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hkxoyNm_t6YG" - }, - "source": [ - "# Enregistrement de dépendances" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "HjaFxsm3t9eP" - }, - "outputs": [], - "source": [ - "!pip freeze > requirements_bert.txt" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/requirements_analyse_exploratoire.txt b/notebooks/requirements_analyse_exploratoire.txt deleted file mode 100644 index 195c74f..0000000 --- a/notebooks/requirements_analyse_exploratoire.txt +++ /dev/null @@ -1,634 +0,0 @@ -absl-py==1.4.0 -accelerate==1.8.1 -aiofiles==24.1.0 -aiohappyeyeballs==2.6.1 -aiohttp==3.11.15 -aiosignal==1.3.2 -alabaster==1.0.0 -albucore==0.0.24 -albumentations==2.0.8 -ale-py==0.11.1 -altair==5.5.0 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==4.9.0 -argon2-cffi==25.1.0 -argon2-cffi-bindings==21.2.0 -array_record==0.7.2 -arviz==0.21.0 -astropy==7.1.0 -astropy-iers-data==0.2025.6.30.0.39.40 -astunparse==1.6.3 -atpublic==5.1 -attrs==25.3.0 -audioread==3.0.1 -autograd==1.8.0 -babel==2.17.0 -backcall==0.2.0 -backports.tarfile==1.2.0 -beautifulsoup4==4.13.4 -betterproto==2.0.0b6 -bigframes==2.8.0 -bigquery-magics==0.9.0 -bleach==6.2.0 -blinker==1.9.0 -blis==1.3.0 -blobfile==3.0.0 -blosc2==3.5.0 -bokeh==3.7.3 -Bottleneck==1.4.2 -bqplot==0.12.45 -branca==0.8.1 -build==1.2.2.post1 -CacheControl==0.14.3 -cachetools==5.5.2 -catalogue==2.0.10 -certifi==2025.6.15 -cffi==1.17.1 -chardet==5.2.0 -charset-normalizer==3.4.2 -chex==0.1.89 -clarabel==0.11.1 -click==8.2.1 -cloudpathlib==0.21.1 -cloudpickle==3.1.1 -cmake==3.31.6 -cmdstanpy==1.2.5 -colorcet==3.1.0 -colorlover==0.3.0 -colour==0.1.5 -community==1.0.0b1 -confection==0.1.5 -cons==0.4.6 -contourpy==1.3.2 -cramjam==2.10.0 -cryptography==43.0.3 -cuda-python==12.6.2.post1 -cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -cudf-polars-cu12==25.2.2 -cufflinks==0.17.3 -cuml-cu12==25.2.1 -cupy-cuda12x==13.3.0 -curl_cffi==0.11.4 -cuvs-cu12==25.2.1 -cvxopt==1.3.2 -cvxpy==1.6.6 -cycler==0.12.1 -cyipopt==1.5.0 -cymem==2.0.11 -Cython==3.0.12 -dask==2024.12.1 -dask-cuda==25.2.0 -dask-cudf-cu12==25.2.2 -dask-expr==1.1.21 -dataproc-spark-connect==0.7.5 -datascience==0.17.6 -datasets==2.14.4 -db-dtypes==1.4.3 -dbus-python==1.2.18 -debugpy==1.8.0 -decorator==4.4.2 -defusedxml==0.7.1 -diffusers==0.34.0 -dill==0.3.7 -distributed==2024.12.1 -distributed-ucxx-cu12==0.42.0 -distro==1.9.0 -dlib==19.24.6 -dm-tree==0.1.9 -docstring_parser==0.16 -docutils==0.21.2 -dopamine_rl==4.1.2 -duckdb==1.2.2 -earthengine-api==1.5.22 -easydict==1.13 -editdistance==0.8.1 -eerepr==0.1.2 -einops==0.8.1 -en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85 -entrypoints==0.4 -et_xmlfile==2.0.0 -etils==1.12.2 -etuples==0.3.9 -Farama-Notifications==0.0.4 -fastai==2.7.19 -fastapi==0.115.14 -fastcore==1.7.29 -fastdownload==0.0.7 -fastjsonschema==2.21.1 -fastprogress==1.0.3 -fastrlock==0.8.3 -ffmpy==0.6.0 -filelock==3.18.0 -firebase-admin==6.9.0 -Flask==3.1.1 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.19.7 -fonttools==4.58.4 -frozendict==2.4.6 -frozenlist==1.7.0 -fsspec==2025.3.2 -future==1.0.0 -gast==0.6.0 -gcsfs==2025.3.2 -GDAL==3.8.4 -gdown==5.2.0 -geemap==0.35.3 -geocoder==1.38.1 -geographiclib==2.0 -geopandas==1.0.1 -geopy==2.4.1 -gin-config==0.5.0 -gitdb==4.0.12 -GitPython==3.1.44 -glob2==0.7 -google==2.0.3 -google-ai-generativelanguage==0.6.15 -google-api-core==2.25.1 -google-api-python-client==2.174.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.100.0 -google-cloud-bigquery==3.34.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-core==2.4.3 -google-cloud-dataproc==5.20.0 -google-cloud-datastore==2.21.0 -google-cloud-firestore==2.21.0 -google-cloud-functions==1.20.4 -google-cloud-iam==2.19.1 -google-cloud-language==2.17.2 -google-cloud-resource-manager==1.14.2 -google-cloud-spanner==3.55.0 -google-cloud-storage==2.19.0 -google-cloud-translate==3.21.0 -google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz -google-crc32c==1.7.1 -google-genai==1.23.0 -google-generativeai==0.8.5 -google-pasta==0.2.0 -google-resumable-media==2.7.2 -googleapis-common-protos==1.70.0 -googledrivedownloader==1.1.0 -gradio==5.31.0 -gradio_client==1.10.1 -graphviz==0.21 -greenlet==3.2.3 -groovy==0.1.2 -grpc-google-iam-v1==0.14.2 -grpc-interceptor==0.15.4 -grpcio==1.73.1 -grpcio-status==1.71.2 -grpclib==0.4.8 -gspread==6.2.1 -gspread-dataframe==4.0.0 -gym==0.25.2 -gym-notices==0.0.8 -gymnasium==1.2.0 -h11==0.16.0 -h2==4.2.0 -h5netcdf==1.6.3 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.5 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.75 -holoviews==1.21.0 -hpack==4.1.0 -html5lib==1.1 -httpcore==1.0.9 -httpimport==1.4.1 -httplib2==0.22.0 -httpx==0.28.1 -huggingface-hub==0.33.1 -humanize==4.12.3 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.13.0 -immutabledict==4.2.1 -importlib_metadata==8.7.0 -importlib_resources==6.5.2 -imutils==0.5.4 -inflect==7.5.0 -iniconfig==2.1.0 -intel-cmplr-lib-ur==2025.2.0 -intel-openmp==2025.2.0 -ipyevents==2.0.2 -ipyfilechooser==0.6.0 -ipykernel==6.17.1 -ipyleaflet==0.20.0 -ipyparallel==8.8.0 -ipython==7.34.0 -ipython-genutils==0.2.0 -ipython-sql==0.5.0 -ipytree==0.2.2 -ipywidgets==7.7.1 -itsdangerous==2.2.0 -jaraco.classes==3.4.0 -jaraco.context==6.0.1 -jaraco.functools==4.2.1 -jax==0.5.2 -jax-cuda12-pjrt==0.5.1 -jax-cuda12-plugin==0.5.1 -jaxlib==0.5.1 -jeepney==0.9.0 -jieba==0.42.1 -Jinja2==3.1.6 -jiter==0.10.0 -joblib==1.5.1 -jsonpatch==1.33 -jsonpickle==4.1.1 -jsonpointer==3.0.0 -jsonschema==4.24.0 -jsonschema-specifications==2025.4.1 -jupyter-client==6.1.12 -jupyter-console==6.1.0 -jupyter-leaflet==0.20.0 -jupyter-server==1.16.0 -jupyter_core==5.8.1 -jupyter_kernel_gateway @ git+https://github.com/googlecolab/kernel_gateway@b134e9945df25c2dcb98ade9129399be10788671 -jupyterlab_pygments==0.3.0 -jupyterlab_widgets==3.0.15 -jupytext==1.17.2 -kaggle==1.7.4.5 -kagglehub==0.3.12 -keras==3.8.0 -keras-hub==0.18.1 -keras-nlp==0.18.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.8 -langchain==0.3.26 -langchain-core==0.3.67 -langchain-text-splitters==0.3.8 -langcodes==3.5.0 -langsmith==0.4.4 -language_data==1.3.0 -launchpadlib==1.10.16 -lazr.restfulclient==0.14.4 -lazr.uri==1.0.6 -lazy_loader==0.4 -libclang==18.1.1 -libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-25.2.1-py3-none-manylinux_2_28_x86_64.whl -libcugraph-cu12==25.2.0 -libcuml-cu12==25.2.1 -libcuvs-cu12==25.2.1 -libkvikio-cu12==25.2.1 -libpysal==4.13.0 -libraft-cu12==25.2.0 -librosa==0.11.0 -libucx-cu12==1.18.1 -libucxx-cu12==0.42.0 -lightgbm @ file:///tmp/lightgbm/LightGBM/dist/lightgbm-4.5.0-py3-none-linux_x86_64.whl -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -logical-unification==0.4.6 -lxml==5.4.0 -Mako==1.1.3 -marisa-trie==1.2.1 -Markdown==3.8.2 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -miniKanren==1.0.3 -missingno==0.5.2 -mistune==3.1.3 -mizani==0.13.5 -mkl==2025.0.1 -ml-dtypes==0.4.1 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.1.1 -multidict==6.6.3 -multipledispatch==1.0.0 -multiprocess==0.70.15 -multitasking==0.0.11 -murmurhash==1.0.13 -music21==9.3.0 -namex==0.1.0 -narwhals==1.45.0 -natsort==8.4.0 -nbclassic==1.3.1 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -ndindex==1.10.0 -nest-asyncio==1.6.0 -networkx==3.5 -nibabel==5.3.2 -nltk==3.9.1 -notebook==6.5.7 -notebook_shim==0.2.4 -numba==0.60.0 -numba-cuda==0.2.0 -numexpr==2.11.0 -numpy==2.0.2 -nvidia-cublas-cu12==12.5.3.2 -nvidia-cuda-cupti-cu12==12.5.82 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.5.82 -nvidia-cuda-runtime-cu12==12.5.82 -nvidia-cudnn-cu12==9.3.0.75 -nvidia-cufft-cu12==11.2.3.61 -nvidia-curand-cu12==10.3.6.82 -nvidia-cusolver-cu12==11.6.3.83 -nvidia-cusparse-cu12==12.5.1.3 -nvidia-cusparselt-cu12==0.6.2 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.21.5 -nvidia-nvcomp-cu12==4.2.0.11 -nvidia-nvjitlink-cu12==12.5.82 -nvidia-nvtx-cu12==12.4.127 -nvtx==0.2.12 -nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-25.2.0-py3-none-any.whl -oauth2client==4.1.3 -oauthlib==3.3.1 -omegaconf==2.3.0 -openai==1.93.0 -opencv-contrib-python==4.11.0.86 -opencv-python==4.11.0.86 -opencv-python-headless==4.11.0.86 -openpyxl==3.1.5 -opt_einsum==3.4.0 -optax==0.2.5 -optree==0.16.0 -orbax-checkpoint==0.11.16 -orjson==3.10.18 -osqp==1.0.4 -packaging==24.2 -pandas==2.2.2 -pandas-datareader==0.10.0 -pandas-gbq==0.29.1 -pandas-stubs==2.2.2.240909 -pandocfilters==1.5.1 -panel==1.7.2 -param==2.2.1 -parso==0.8.4 -parsy==2.1 -partd==1.4.2 -pathlib==1.0.1 -patsy==1.0.1 -peewee==3.18.1 -peft==0.15.2 -pexpect==4.9.0 -pickleshare==0.7.5 -pillow==11.2.1 -platformdirs==4.3.8 -plotly==5.24.1 -plotnine==0.14.6 -pluggy==1.6.0 -ply==3.11 -polars==1.21.0 -pooch==1.8.2 -portpicker==1.5.2 -preshed==3.0.10 -prettytable==3.16.0 -proglog==0.1.12 -progressbar2==4.5.0 -prometheus_client==0.22.1 -promise==2.3 -prompt_toolkit==3.0.51 -propcache==0.3.2 -prophet==1.1.7 -proto-plus==1.26.1 -protobuf==5.29.5 -psutil==5.9.5 -psycopg2==2.9.10 -ptyprocess==0.7.0 -py-cpuinfo==9.0.0 -py4j==0.10.9.7 -pyarrow==18.1.0 -pyasn1==0.6.1 -pyasn1_modules==0.4.2 -pycairo==1.28.0 -pycocotools==2.0.10 -pycparser==2.22 -pycryptodomex==3.23.0 -pydantic==2.11.7 -pydantic_core==2.33.2 -pydata-google-auth==1.9.1 -pydot==3.0.4 -pydotplus==2.0.2 -PyDrive==1.3.1 -PyDrive2==1.21.3 -pydub==0.25.1 -pyerfa==2.0.1.5 -pygame==2.6.1 -pygit2==1.18.0 -Pygments==2.19.2 -PyGObject==3.42.0 -PyJWT==2.10.1 -pylibcudf-cu12 @ https://pypi.nvidia.com/pylibcudf-cu12/pylibcudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -pylibcugraph-cu12==25.2.0 -pylibraft-cu12==25.2.0 -pymc==5.23.0 -pymystem3==0.2.0 -pynndescent==0.5.13 -pynvjitlink-cu12==0.7.0 -pynvml==12.0.0 -pyogrio==0.11.0 -pyomo==6.9.2 -PyOpenGL==3.1.9 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.1 -pyproject_hooks==1.2.0 -pyshp==2.3.1 -PySocks==1.7.1 -pyspark==3.5.1 -pytensor==2.31.5 -pytest==8.3.5 -python-apt==0.0.0 -python-box==7.3.2 -python-dateutil==2.9.0.post0 -python-louvain==0.16 -python-multipart==0.0.20 -python-slugify==8.0.4 -python-snappy==0.7.3 -python-utils==3.9.1 -pytz==2025.2 -pyviz_comms==3.0.6 -PyWavelets==1.8.0 -PyYAML==6.0.2 -pyzmq==24.0.1 -raft-dask-cu12==25.2.0 -rapids-dask-dependency==25.2.0 -ratelim==0.1.6 -referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 -requests-oauthlib==2.0.0 -requests-toolbelt==1.0.0 -requirements-parser==0.9.0 -rich==13.9.4 -rmm-cu12==25.2.0 -roman-numerals-py==3.1.0 -rpds-py==0.26.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.1 -safehttpx==0.1.6 -safetensors==0.5.3 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.15.3 -scooby==0.10.1 -scs==3.2.7.post2 -seaborn==0.13.2 -SecretStorage==3.3.3 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==4.1.0 -sentencepiece==0.2.0 -sentry-sdk==2.32.0 -setproctitle==1.3.6 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.4.9 -six==1.17.0 -sklearn-compat==0.1.3 -sklearn-pandas==2.2.0 -slicer==0.0.8 -smart_open==7.3.0 -smmap==5.0.2 -sniffio==1.3.1 -snowballstemmer==3.0.1 -sortedcontainers==2.4.0 -soundfile==0.13.1 -soupsieve==2.7 -soxr==0.5.0.post1 -spacy==3.8.7 -spacy-legacy==3.0.12 -spacy-loggers==1.0.5 -spanner-graph-notebook==1.1.7 -Sphinx==8.2.3 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.41 -sqlglot==25.20.2 -sqlparse==0.5.3 -srsly==2.5.1 -stanio==0.5.1 -starlette==0.46.2 -statsmodels==0.14.4 -stringzilla==3.12.5 -stumpy==1.13.0 -sympy==1.13.1 -tables==3.10.2 -tabulate==0.9.0 -tbb==2022.2.0 -tblib==3.1.0 -tcmlib==1.4.0 -tenacity==8.5.0 -tensorboard==2.18.0 -tensorboard-data-server==0.7.2 -tensorflow==2.18.0 -tensorflow-datasets==4.9.9 -tensorflow-hub==0.16.1 -tensorflow-io-gcs-filesystem==0.37.1 -tensorflow-metadata==1.17.2 -tensorflow-probability==0.25.0 -tensorflow-text==2.18.1 -tensorflow_decision_forests==1.11.0 -tensorstore==0.1.74 -termcolor==3.1.0 -terminado==0.18.1 -text-unidecode==1.3 -textblob==0.19.0 -tf-slim==1.1.0 -tf_keras==2.18.0 -thinc==8.3.6 -threadpoolctl==3.6.0 -tifffile==2025.6.11 -tiktoken==0.9.0 -timm==1.0.16 -tinycss2==1.4.0 -tokenizers==0.21.2 -toml==0.10.2 -tomlkit==0.13.3 -toolz==0.12.1 -torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchao==0.10.0 -torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp311-cp311-linux_x86_64.whl -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==4.53.0 -treelite==4.4.1 -treescope==0.1.9 -triton==3.2.0 -tsfresh==0.21.0 -tweepy==4.15.0 -typeguard==4.4.4 -typer==0.16.0 -types-pytz==2025.2.0.20250516 -types-setuptools==80.9.0.20250529 -typing-inspection==0.4.1 -typing_extensions==4.14.0 -tzdata==2025.2 -tzlocal==5.3.1 -uc-micro-py==1.0.3 -ucx-py-cu12==0.42.0 -ucxx-cu12==0.42.0 -umap-learn==0.5.8 -umf==0.11.0 -uritemplate==4.2.0 -urllib3==2.4.0 -uvicorn==0.35.0 -vega-datasets==0.9.0 -wadllib==1.3.6 -wandb==0.20.1 -wasabi==1.1.3 -wcwidth==0.2.13 -weasel==0.4.1 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -websockets==15.0.1 -Werkzeug==3.1.3 -widgetsnbextension==3.6.10 -wordcloud==1.9.4 -wrapt==1.17.2 -wurlitzer==3.1.1 -xarray==2025.3.1 -xarray-einstats==0.9.1 -xgboost==2.1.4 -xlrd==2.0.2 -xxhash==3.5.0 -xyzservices==2025.4.0 -yarl==1.20.1 -ydf==0.12.0 -yellowbrick==1.5 -yfinance==0.2.64 -zict==3.0.0 -zipp==3.23.0 -zstandard==0.23.0 diff --git a/notebooks/requirements_bert.txt b/notebooks/requirements_bert.txt deleted file mode 100644 index 9df0e3b..0000000 --- a/notebooks/requirements_bert.txt +++ /dev/null @@ -1,651 +0,0 @@ -absl-py==1.4.0 -accelerate==1.8.1 -aiofiles==24.1.0 -aiohappyeyeballs==2.6.1 -aiohttp==3.11.15 -aiosignal==1.4.0 -alabaster==1.0.0 -albucore==0.0.24 -albumentations==2.0.8 -ale-py==0.11.1 -alembic==1.16.4 -altair==5.5.0 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==4.9.0 -argon2-cffi==25.1.0 -argon2-cffi-bindings==21.2.0 -array_record==0.7.2 -arviz==0.21.0 -astropy==7.1.0 -astropy-iers-data==0.2025.7.7.0.39.39 -astunparse==1.6.3 -atpublic==5.1 -attrs==25.3.0 -audioread==3.0.1 -autograd==1.8.0 -babel==2.17.0 -backcall==0.2.0 -backports.tarfile==1.2.0 -beautifulsoup4==4.13.4 -betterproto==2.0.0b6 -bigframes==2.8.0 -bigquery-magics==0.10.1 -bleach==6.2.0 -blinker==1.9.0 -blis==1.3.0 -blobfile==3.0.0 -blosc2==3.5.1 -bokeh==3.7.3 -boto3==1.39.4 -botocore==1.39.4 -Bottleneck==1.4.2 -bqplot==0.12.45 -branca==0.8.1 -build==1.2.2.post1 -CacheControl==0.14.3 -cachetools==5.5.2 -catalogue==2.0.10 -certifi==2025.7.9 -cffi==1.17.1 -chardet==5.2.0 -charset-normalizer==3.4.2 -chex==0.1.89 -clarabel==0.11.1 -click==8.2.1 -cloudpathlib==0.21.1 -cloudpickle==3.1.1 -cmake==3.31.6 -cmdstanpy==1.2.5 -colorcet==3.1.0 -colorlover==0.3.0 -colour==0.1.5 -community==1.0.0b1 -confection==0.1.5 -cons==0.4.6 -contourpy==1.3.2 -cramjam==2.10.0 -cryptography==43.0.3 -cuda-python==12.6.2.post1 -cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -cudf-polars-cu12==25.2.2 -cufflinks==0.17.3 -cuml-cu12==25.2.1 -cupy-cuda12x==13.3.0 -curl_cffi==0.11.4 -cuvs-cu12==25.2.1 -cvxopt==1.3.2 -cvxpy==1.6.6 -cycler==0.12.1 -cyipopt==1.5.0 -cymem==2.0.11 -Cython==3.0.12 -dask==2024.12.1 -dask-cuda==25.2.0 -dask-cudf-cu12==25.2.2 -dask-expr==1.1.21 -databricks-sdk==0.58.0 -dataproc-spark-connect==0.8.2 -datascience==0.17.6 -datasets==2.14.4 -db-dtypes==1.4.3 -dbus-python==1.2.18 -debugpy==1.8.0 -decorator==4.4.2 -defusedxml==0.7.1 -diffusers==0.34.0 -dill==0.3.7 -distributed==2024.12.1 -distributed-ucxx-cu12==0.42.0 -distro==1.9.0 -dlib==19.24.6 -dm-tree==0.1.9 -docker==7.1.0 -docstring_parser==0.16 -docutils==0.21.2 -dopamine_rl==4.1.2 -dotenv==0.9.9 -duckdb==1.2.2 -earthengine-api==1.5.23 -easydict==1.13 -editdistance==0.8.1 -eerepr==0.1.2 -einops==0.8.1 -en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85 -entrypoints==0.4 -et_xmlfile==2.0.0 -etils==1.12.2 -etuples==0.3.9 -Farama-Notifications==0.0.4 -fastai==2.7.19 -fastapi==0.116.0 -fastcore==1.7.29 -fastdownload==0.0.7 -fastjsonschema==2.21.1 -fastprogress==1.0.3 -fastrlock==0.8.3 -ffmpy==0.6.0 -filelock==3.18.0 -firebase-admin==6.9.0 -Flask==3.1.1 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.19.7 -fonttools==4.58.5 -frozendict==2.4.6 -frozenlist==1.7.0 -fsspec==2025.3.2 -future==1.0.0 -gast==0.6.0 -gcsfs==2025.3.2 -GDAL==3.8.4 -gdown==5.2.0 -geemap==0.35.3 -geocoder==1.38.1 -geographiclib==2.0 -geopandas==1.0.1 -geopy==2.4.1 -gin-config==0.5.0 -gitdb==4.0.12 -GitPython==3.1.44 -glob2==0.7 -google==2.0.3 -google-ai-generativelanguage==0.6.15 -google-api-core==2.25.1 -google-api-python-client==2.176.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.102.0 -google-cloud-bigquery==3.34.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-core==2.4.3 -google-cloud-dataproc==5.21.0 -google-cloud-datastore==2.21.0 -google-cloud-firestore==2.21.0 -google-cloud-functions==1.20.4 -google-cloud-iam==2.19.1 -google-cloud-language==2.17.2 -google-cloud-resource-manager==1.14.2 -google-cloud-spanner==3.55.0 -google-cloud-storage==2.19.0 -google-cloud-translate==3.21.1 -google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz -google-crc32c==1.7.1 -google-genai==1.24.0 -google-generativeai==0.8.5 -google-pasta==0.2.0 -google-resumable-media==2.7.2 -googleapis-common-protos==1.70.0 -googledrivedownloader==1.1.0 -gradio==5.31.0 -gradio_client==1.10.1 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -graphviz==0.21 -greenlet==3.2.3 -groovy==0.1.2 -grpc-google-iam-v1==0.14.2 -grpc-interceptor==0.15.4 -grpcio==1.73.1 -grpcio-status==1.71.2 -grpclib==0.4.8 -gspread==6.2.1 -gspread-dataframe==4.0.0 -gunicorn==23.0.0 -gym==0.25.2 -gym-notices==0.0.8 -gymnasium==1.2.0 -h11==0.16.0 -h2==4.2.0 -h5netcdf==1.6.3 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.5 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.76 -holoviews==1.21.0 -hpack==4.1.0 -html5lib==1.1 -httpcore==1.0.9 -httpimport==1.4.1 -httplib2==0.22.0 -httpx==0.28.1 -huggingface-hub==0.33.2 -humanize==4.12.3 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.13.0 -immutabledict==4.2.1 -importlib_metadata==8.7.0 -importlib_resources==6.5.2 -imutils==0.5.4 -inflect==7.5.0 -iniconfig==2.1.0 -intel-cmplr-lib-ur==2025.2.0 -intel-openmp==2025.2.0 -ipyevents==2.0.2 -ipyfilechooser==0.6.0 -ipykernel==6.17.1 -ipyleaflet==0.20.0 -ipyparallel==8.8.0 -ipython==7.34.0 -ipython-genutils==0.2.0 -ipython-sql==0.5.0 -ipytree==0.2.2 -ipywidgets==7.7.1 -itsdangerous==2.2.0 -jaraco.classes==3.4.0 -jaraco.context==6.0.1 -jaraco.functools==4.2.1 -jax==0.5.2 -jax-cuda12-pjrt==0.5.1 -jax-cuda12-plugin==0.5.1 -jaxlib==0.5.1 -jeepney==0.9.0 -jieba==0.42.1 -Jinja2==3.1.6 -jiter==0.10.0 -jmespath==1.0.1 -joblib==1.5.1 -jsonpatch==1.33 -jsonpickle==4.1.1 -jsonpointer==3.0.0 -jsonschema==4.24.0 -jsonschema-specifications==2025.4.1 -jupyter-client==6.1.12 -jupyter-console==6.1.0 -jupyter-leaflet==0.20.0 -jupyter-server==1.16.0 -jupyter_core==5.8.1 -jupyter_kernel_gateway @ git+https://github.com/googlecolab/kernel_gateway@b134e9945df25c2dcb98ade9129399be10788671 -jupyterlab_pygments==0.3.0 -jupyterlab_widgets==3.0.15 -jupytext==1.17.2 -kaggle==1.7.4.5 -kagglehub==0.3.12 -keras==3.8.0 -keras-hub==0.18.1 -keras-nlp==0.18.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.8 -langchain==0.3.26 -langchain-core==0.3.68 -langchain-text-splitters==0.3.8 -langcodes==3.5.0 -langsmith==0.4.4 -language_data==1.3.0 -launchpadlib==1.10.16 -lazr.restfulclient==0.14.4 -lazr.uri==1.0.6 -lazy_loader==0.4 -libclang==18.1.1 -libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-25.2.1-py3-none-manylinux_2_28_x86_64.whl -libcugraph-cu12==25.2.0 -libcuml-cu12==25.2.1 -libcuvs-cu12==25.2.1 -libkvikio-cu12==25.2.1 -libpysal==4.13.0 -libraft-cu12==25.2.0 -librosa==0.11.0 -libucx-cu12==1.18.1 -libucxx-cu12==0.42.0 -lightgbm @ file:///tmp/lightgbm/LightGBM/dist/lightgbm-4.5.0-py3-none-linux_x86_64.whl -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -logical-unification==0.4.6 -lxml==5.4.0 -Mako==1.1.3 -marisa-trie==1.2.1 -Markdown==3.8.2 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -miniKanren==1.0.3 -missingno==0.5.2 -mistune==3.1.3 -mizani==0.13.5 -mkl==2025.0.1 -ml-dtypes==0.4.1 -mlflow==3.1.1 -mlflow-skinny==3.1.1 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.1.1 -multidict==6.6.3 -multipledispatch==1.0.0 -multiprocess==0.70.15 -multitasking==0.0.11 -murmurhash==1.0.13 -music21==9.3.0 -namex==0.1.0 -narwhals==1.46.0 -natsort==8.4.0 -nbclassic==1.3.1 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -ndindex==1.10.0 -nest-asyncio==1.6.0 -networkx==3.5 -nibabel==5.3.2 -nltk==3.9.1 -notebook==6.5.7 -notebook_shim==0.2.4 -numba==0.60.0 -numba-cuda==0.2.0 -numexpr==2.11.0 -numpy==2.0.2 -nvidia-cublas-cu12==12.5.3.2 -nvidia-cuda-cupti-cu12==12.5.82 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.5.82 -nvidia-cuda-runtime-cu12==12.5.82 -nvidia-cudnn-cu12==9.3.0.75 -nvidia-cufft-cu12==11.2.3.61 -nvidia-curand-cu12==10.3.6.82 -nvidia-cusolver-cu12==11.6.3.83 -nvidia-cusparse-cu12==12.5.1.3 -nvidia-cusparselt-cu12==0.6.2 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.21.5 -nvidia-nvcomp-cu12==4.2.0.11 -nvidia-nvjitlink-cu12==12.5.82 -nvidia-nvtx-cu12==12.4.127 -nvtx==0.2.12 -nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-25.2.0-py3-none-any.whl -oauth2client==4.1.3 -oauthlib==3.3.1 -omegaconf==2.3.0 -openai==1.93.3 -opencv-contrib-python==4.11.0.86 -opencv-python==4.11.0.86 -opencv-python-headless==4.12.0.88 -openpyxl==3.1.5 -opentelemetry-api==1.35.0 -opentelemetry-sdk==1.35.0 -opentelemetry-semantic-conventions==0.56b0 -opt_einsum==3.4.0 -optax==0.2.5 -optree==0.16.0 -orbax-checkpoint==0.11.16 -orjson==3.10.18 -osqp==1.0.4 -packaging==24.2 -pandas==2.2.2 -pandas-datareader==0.10.0 -pandas-gbq==0.29.1 -pandas-stubs==2.2.2.240909 -pandocfilters==1.5.1 -panel==1.7.3 -param==2.2.1 -parso==0.8.4 -parsy==2.1 -partd==1.4.2 -pathlib==1.0.1 -patsy==1.0.1 -peewee==3.18.2 -peft==0.16.0 -pexpect==4.9.0 -pickleshare==0.7.5 -pillow==11.2.1 -platformdirs==4.3.8 -plotly==5.24.1 -plotnine==0.14.6 -pluggy==1.6.0 -ply==3.11 -polars==1.21.0 -pooch==1.8.2 -portpicker==1.5.2 -preshed==3.0.10 -prettytable==3.16.0 -proglog==0.1.12 -progressbar2==4.5.0 -prometheus_client==0.22.1 -promise==2.3 -prompt_toolkit==3.0.51 -propcache==0.3.2 -prophet==1.1.7 -proto-plus==1.26.1 -protobuf==5.29.5 -psutil==5.9.5 -psycopg2==2.9.10 -ptyprocess==0.7.0 -py-cpuinfo==9.0.0 -py4j==0.10.9.7 -pyarrow==18.1.0 -pyasn1==0.6.1 -pyasn1_modules==0.4.2 -pycairo==1.28.0 -pycocotools==2.0.10 -pycparser==2.22 -pycryptodomex==3.23.0 -pydantic==2.11.7 -pydantic_core==2.33.2 -pydata-google-auth==1.9.1 -pydot==3.0.4 -pydotplus==2.0.2 -PyDrive==1.3.1 -PyDrive2==1.21.3 -pydub==0.25.1 -pyerfa==2.0.1.5 -pygame==2.6.1 -pygit2==1.18.0 -Pygments==2.19.2 -PyGObject==3.42.0 -PyJWT==2.10.1 -pylibcudf-cu12 @ https://pypi.nvidia.com/pylibcudf-cu12/pylibcudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -pylibcugraph-cu12==25.2.0 -pylibraft-cu12==25.2.0 -pymc==5.23.0 -pymystem3==0.2.0 -pynndescent==0.5.13 -pynvjitlink-cu12==0.7.0 -pynvml==12.0.0 -pyogrio==0.11.0 -pyomo==6.9.2 -PyOpenGL==3.1.9 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.1 -pyproject_hooks==1.2.0 -pyshp==2.3.1 -PySocks==1.7.1 -pyspark==3.5.1 -pytensor==2.31.7 -pytest==8.3.5 -python-apt==0.0.0 -python-box==7.3.2 -python-dateutil==2.9.0.post0 -python-dotenv==1.1.1 -python-louvain==0.16 -python-multipart==0.0.20 -python-slugify==8.0.4 -python-snappy==0.7.3 -python-utils==3.9.1 -pytz==2025.2 -pyviz_comms==3.0.6 -PyWavelets==1.8.0 -PyYAML==6.0.2 -pyzmq==24.0.1 -raft-dask-cu12==25.2.0 -rapids-dask-dependency==25.2.0 -ratelim==0.1.6 -referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 -requests-oauthlib==2.0.0 -requests-toolbelt==1.0.0 -requirements-parser==0.9.0 -rich==13.9.4 -rmm-cu12==25.2.0 -roman-numerals-py==3.1.0 -rpds-py==0.26.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.2 -s3transfer==0.13.0 -safehttpx==0.1.6 -safetensors==0.5.3 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.15.3 -scooby==0.10.1 -scs==3.2.7.post2 -seaborn==0.13.2 -SecretStorage==3.3.3 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==4.1.0 -sentencepiece==0.2.0 -sentry-sdk==2.32.0 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.5.0 -six==1.17.0 -sklearn-compat==0.1.3 -sklearn-pandas==2.2.0 -slicer==0.0.8 -smart_open==7.3.0.post1 -smmap==5.0.2 -sniffio==1.3.1 -snowballstemmer==3.0.1 -sortedcontainers==2.4.0 -soundfile==0.13.1 -soupsieve==2.7 -soxr==0.5.0.post1 -spacy==3.8.7 -spacy-legacy==3.0.12 -spacy-loggers==1.0.5 -spanner-graph-notebook==1.1.7 -Sphinx==8.2.3 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.41 -sqlglot==25.20.2 -sqlparse==0.5.3 -srsly==2.5.1 -stanio==0.5.1 -starlette==0.46.2 -statsmodels==0.14.5 -stringzilla==3.12.5 -stumpy==1.13.0 -sympy==1.13.1 -tables==3.10.2 -tabulate==0.9.0 -tbb==2022.2.0 -tblib==3.1.0 -tcmlib==1.4.0 -tenacity==8.5.0 -tensorboard==2.18.0 -tensorboard-data-server==0.7.2 -tensorflow==2.18.0 -tensorflow-datasets==4.9.9 -tensorflow-hub==0.16.1 -tensorflow-io-gcs-filesystem==0.37.1 -tensorflow-metadata==1.17.2 -tensorflow-probability==0.25.0 -tensorflow-text==2.18.1 -tensorflow_decision_forests==1.11.0 -tensorstore==0.1.74 -termcolor==3.1.0 -terminado==0.18.1 -text-unidecode==1.3 -textblob==0.19.0 -tf-slim==1.1.0 -tf_keras==2.18.0 -thinc==8.3.6 -threadpoolctl==3.6.0 -tifffile==2025.6.11 -tiktoken==0.9.0 -timm==1.0.16 -tinycss2==1.4.0 -tokenizers==0.21.2 -toml==0.10.2 -tomlkit==0.13.3 -toolz==0.12.1 -torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchao==0.10.0 -torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp311-cp311-linux_x86_64.whl -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==4.53.1 -treelite==4.4.1 -treescope==0.1.9 -triton==3.2.0 -tsfresh==0.21.0 -tweepy==4.15.0 -typeguard==4.4.4 -typer==0.16.0 -types-pytz==2025.2.0.20250516 -types-setuptools==80.9.0.20250529 -typing-inspection==0.4.1 -typing_extensions==4.14.1 -tzdata==2025.2 -tzlocal==5.3.1 -uc-micro-py==1.0.3 -ucx-py-cu12==0.42.0 -ucxx-cu12==0.42.0 -umap-learn==0.5.9.post2 -umf==0.11.0 -uritemplate==4.2.0 -urllib3==2.4.0 -uvicorn==0.35.0 -vega-datasets==0.9.0 -wadllib==1.3.6 -wandb==0.21.0 -wasabi==1.1.3 -wcwidth==0.2.13 -weasel==0.4.1 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -websockets==15.0.1 -Werkzeug==3.1.3 -widgetsnbextension==3.6.10 -wordcloud==1.9.4 -wrapt==1.17.2 -wurlitzer==3.1.1 -xarray==2025.3.1 -xarray-einstats==0.9.1 -xgboost==2.1.4 -xlrd==2.0.2 -xxhash==3.5.0 -xyzservices==2025.4.0 -yarl==1.20.1 -ydf==0.12.0 -yellowbrick==1.5 -yfinance==0.2.65 -zict==3.0.0 -zipp==3.23.0 -zstandard==0.23.0 diff --git a/notebooks/requirements_modele_avance.txt b/notebooks/requirements_modele_avance.txt deleted file mode 100644 index 285b7c0..0000000 --- a/notebooks/requirements_modele_avance.txt +++ /dev/null @@ -1,653 +0,0 @@ -absl-py==1.4.0 -accelerate==1.8.1 -aiofiles==24.1.0 -aiohappyeyeballs==2.6.1 -aiohttp==3.11.15 -aiosignal==1.3.2 -alabaster==1.0.0 -albucore==0.0.24 -albumentations==2.0.8 -ale-py==0.11.1 -alembic==1.16.2 -altair==5.5.0 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==4.9.0 -argon2-cffi==25.1.0 -argon2-cffi-bindings==21.2.0 -array_record==0.7.2 -arviz==0.21.0 -astropy==7.1.0 -astropy-iers-data==0.2025.6.30.0.39.40 -astunparse==1.6.3 -atpublic==5.1 -attrs==25.3.0 -audioread==3.0.1 -autograd==1.8.0 -babel==2.17.0 -backcall==0.2.0 -backports.tarfile==1.2.0 -beautifulsoup4==4.13.4 -betterproto==2.0.0b6 -bigframes==2.8.0 -bigquery-magics==0.9.0 -bleach==6.2.0 -blinker==1.9.0 -blis==1.3.0 -blobfile==3.0.0 -blosc2==3.5.0 -bokeh==3.7.3 -boto3==1.39.3 -botocore==1.39.3 -Bottleneck==1.4.2 -bqplot==0.12.45 -branca==0.8.1 -build==1.2.2.post1 -CacheControl==0.14.3 -cachetools==5.5.2 -catalogue==2.0.10 -certifi==2025.6.15 -cffi==1.17.1 -chardet==5.2.0 -charset-normalizer==3.4.2 -chex==0.1.89 -clarabel==0.11.1 -click==8.2.1 -cloudpathlib==0.21.1 -cloudpickle==3.1.1 -cmake==3.31.6 -cmdstanpy==1.2.5 -colorcet==3.1.0 -colorlover==0.3.0 -colour==0.1.5 -community==1.0.0b1 -confection==0.1.5 -cons==0.4.6 -contourpy==1.3.2 -cramjam==2.10.0 -cryptography==43.0.3 -cuda-python==12.6.2.post1 -cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -cudf-polars-cu12==25.2.2 -cufflinks==0.17.3 -cuml-cu12==25.2.1 -cupy-cuda12x==13.3.0 -curl_cffi==0.11.4 -cuvs-cu12==25.2.1 -cvxopt==1.3.2 -cvxpy==1.6.6 -cycler==0.12.1 -cyipopt==1.5.0 -cymem==2.0.11 -Cython==3.0.12 -dask==2024.12.1 -dask-cuda==25.2.0 -dask-cudf-cu12==25.2.2 -dask-expr==1.1.21 -databricks-sdk==0.57.0 -dataproc-spark-connect==0.7.5 -datascience==0.17.6 -datasets==2.14.4 -db-dtypes==1.4.3 -dbus-python==1.2.18 -debugpy==1.8.0 -decorator==4.4.2 -defusedxml==0.7.1 -diffusers==0.34.0 -dill==0.3.7 -distributed==2024.12.1 -distributed-ucxx-cu12==0.42.0 -distro==1.9.0 -dlib==19.24.6 -dm-tree==0.1.9 -docker==7.1.0 -docstring_parser==0.16 -docutils==0.21.2 -dopamine_rl==4.1.2 -dotenv==0.9.9 -duckdb==1.2.2 -earthengine-api==1.5.22 -easydict==1.13 -editdistance==0.8.1 -eerepr==0.1.2 -einops==0.8.1 -en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85 -entrypoints==0.4 -et_xmlfile==2.0.0 -etils==1.12.2 -etuples==0.3.9 -Farama-Notifications==0.0.4 -fastai==2.7.19 -fastapi==0.115.14 -fastcore==1.7.29 -fastdownload==0.0.7 -fastjsonschema==2.21.1 -fastprogress==1.0.3 -fastrlock==0.8.3 -ffmpy==0.6.0 -filelock==3.18.0 -firebase-admin==6.9.0 -Flask==3.1.1 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.19.7 -fonttools==4.58.4 -frozendict==2.4.6 -frozenlist==1.7.0 -fsspec==2025.3.2 -future==1.0.0 -gast==0.6.0 -gcsfs==2025.3.2 -GDAL==3.8.4 -gdown==5.2.0 -geemap==0.35.3 -gensim==4.3.3 -geocoder==1.38.1 -geographiclib==2.0 -geopandas==1.0.1 -geopy==2.4.1 -gin-config==0.5.0 -gitdb==4.0.12 -GitPython==3.1.44 -glob2==0.7 -google==2.0.3 -google-ai-generativelanguage==0.6.15 -google-api-core==2.25.1 -google-api-python-client==2.174.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.100.0 -google-cloud-bigquery==3.34.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-core==2.4.3 -google-cloud-dataproc==5.20.0 -google-cloud-datastore==2.21.0 -google-cloud-firestore==2.21.0 -google-cloud-functions==1.20.4 -google-cloud-iam==2.19.1 -google-cloud-language==2.17.2 -google-cloud-resource-manager==1.14.2 -google-cloud-spanner==3.55.0 -google-cloud-storage==2.19.0 -google-cloud-translate==3.21.0 -google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz -google-crc32c==1.7.1 -google-genai==1.23.0 -google-generativeai==0.8.5 -google-pasta==0.2.0 -google-resumable-media==2.7.2 -googleapis-common-protos==1.70.0 -googledrivedownloader==1.1.0 -gradio==5.31.0 -gradio_client==1.10.1 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -graphviz==0.21 -greenlet==3.2.3 -groovy==0.1.2 -grpc-google-iam-v1==0.14.2 -grpc-interceptor==0.15.4 -grpcio==1.73.1 -grpcio-status==1.71.2 -grpclib==0.4.8 -gspread==6.2.1 -gspread-dataframe==4.0.0 -gunicorn==23.0.0 -gym==0.25.2 -gym-notices==0.0.8 -gymnasium==1.2.0 -h11==0.16.0 -h2==4.2.0 -h5netcdf==1.6.3 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.5 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.75 -holoviews==1.21.0 -hpack==4.1.0 -html5lib==1.1 -httpcore==1.0.9 -httpimport==1.4.1 -httplib2==0.22.0 -httpx==0.28.1 -huggingface-hub==0.33.1 -humanize==4.12.3 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.13.0 -immutabledict==4.2.1 -importlib_metadata==8.7.0 -importlib_resources==6.5.2 -imutils==0.5.4 -inflect==7.5.0 -iniconfig==2.1.0 -intel-cmplr-lib-ur==2025.2.0 -intel-openmp==2025.2.0 -ipyevents==2.0.2 -ipyfilechooser==0.6.0 -ipykernel==6.17.1 -ipyleaflet==0.20.0 -ipyparallel==8.8.0 -ipython==7.34.0 -ipython-genutils==0.2.0 -ipython-sql==0.5.0 -ipytree==0.2.2 -ipywidgets==7.7.1 -itsdangerous==2.2.0 -jaraco.classes==3.4.0 -jaraco.context==6.0.1 -jaraco.functools==4.2.1 -jax==0.5.2 -jax-cuda12-pjrt==0.5.1 -jax-cuda12-plugin==0.5.1 -jaxlib==0.5.1 -jeepney==0.9.0 -jieba==0.42.1 -Jinja2==3.1.6 -jiter==0.10.0 -jmespath==1.0.1 -joblib==1.5.1 -jsonpatch==1.33 -jsonpickle==4.1.1 -jsonpointer==3.0.0 -jsonschema==4.24.0 -jsonschema-specifications==2025.4.1 -jupyter-client==6.1.12 -jupyter-console==6.1.0 -jupyter-leaflet==0.20.0 -jupyter-server==1.16.0 -jupyter_core==5.8.1 -jupyter_kernel_gateway @ git+https://github.com/googlecolab/kernel_gateway@b134e9945df25c2dcb98ade9129399be10788671 -jupyterlab_pygments==0.3.0 -jupyterlab_widgets==3.0.15 -jupytext==1.17.2 -kaggle==1.7.4.5 -kagglehub==0.3.12 -keras==3.8.0 -keras-hub==0.18.1 -keras-nlp==0.18.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.8 -langchain==0.3.26 -langchain-core==0.3.67 -langchain-text-splitters==0.3.8 -langcodes==3.5.0 -langsmith==0.4.4 -language_data==1.3.0 -launchpadlib==1.10.16 -lazr.restfulclient==0.14.4 -lazr.uri==1.0.6 -lazy_loader==0.4 -libclang==18.1.1 -libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-25.2.1-py3-none-manylinux_2_28_x86_64.whl -libcugraph-cu12==25.2.0 -libcuml-cu12==25.2.1 -libcuvs-cu12==25.2.1 -libkvikio-cu12==25.2.1 -libpysal==4.13.0 -libraft-cu12==25.2.0 -librosa==0.11.0 -libucx-cu12==1.18.1 -libucxx-cu12==0.42.0 -lightgbm @ file:///tmp/lightgbm/LightGBM/dist/lightgbm-4.5.0-py3-none-linux_x86_64.whl -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -logical-unification==0.4.6 -lxml==5.4.0 -Mako==1.1.3 -marisa-trie==1.2.1 -Markdown==3.8.2 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -miniKanren==1.0.3 -missingno==0.5.2 -mistune==3.1.3 -mizani==0.13.5 -mkl==2025.0.1 -ml-dtypes==0.4.1 -mlflow==3.1.1 -mlflow-skinny==3.1.1 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.1.1 -multidict==6.6.3 -multipledispatch==1.0.0 -multiprocess==0.70.15 -multitasking==0.0.11 -murmurhash==1.0.13 -music21==9.3.0 -namex==0.1.0 -narwhals==1.45.0 -natsort==8.4.0 -nbclassic==1.3.1 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -ndindex==1.10.0 -nest-asyncio==1.6.0 -networkx==3.5 -nibabel==5.3.2 -nltk==3.9.1 -notebook==6.5.7 -notebook_shim==0.2.4 -numba==0.60.0 -numba-cuda==0.2.0 -numexpr==2.11.0 -numpy==1.26.4 -nvidia-cublas-cu12==12.5.3.2 -nvidia-cuda-cupti-cu12==12.5.82 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.5.82 -nvidia-cuda-runtime-cu12==12.5.82 -nvidia-cudnn-cu12==9.3.0.75 -nvidia-cufft-cu12==11.2.3.61 -nvidia-curand-cu12==10.3.6.82 -nvidia-cusolver-cu12==11.6.3.83 -nvidia-cusparse-cu12==12.5.1.3 -nvidia-cusparselt-cu12==0.6.2 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.21.5 -nvidia-nvcomp-cu12==4.2.0.11 -nvidia-nvjitlink-cu12==12.5.82 -nvidia-nvtx-cu12==12.4.127 -nvtx==0.2.12 -nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-25.2.0-py3-none-any.whl -oauth2client==4.1.3 -oauthlib==3.3.1 -omegaconf==2.3.0 -openai==1.93.0 -opencv-contrib-python==4.11.0.86 -opencv-python==4.11.0.86 -opencv-python-headless==4.11.0.86 -openpyxl==3.1.5 -opentelemetry-api==1.34.1 -opentelemetry-sdk==1.34.1 -opentelemetry-semantic-conventions==0.55b1 -opt_einsum==3.4.0 -optax==0.2.5 -optree==0.16.0 -orbax-checkpoint==0.11.16 -orjson==3.10.18 -osqp==1.0.4 -packaging==24.2 -pandas==2.2.2 -pandas-datareader==0.10.0 -pandas-gbq==0.29.1 -pandas-stubs==2.2.2.240909 -pandocfilters==1.5.1 -panel==1.7.2 -param==2.2.1 -parso==0.8.4 -parsy==2.1 -partd==1.4.2 -pathlib==1.0.1 -patsy==1.0.1 -peewee==3.18.1 -peft==0.15.2 -pexpect==4.9.0 -pickleshare==0.7.5 -pillow==11.2.1 -platformdirs==4.3.8 -plotly==5.24.1 -plotnine==0.14.6 -pluggy==1.6.0 -ply==3.11 -polars==1.21.0 -pooch==1.8.2 -portpicker==1.5.2 -preshed==3.0.10 -prettytable==3.16.0 -proglog==0.1.12 -progressbar2==4.5.0 -prometheus_client==0.22.1 -promise==2.3 -prompt_toolkit==3.0.51 -propcache==0.3.2 -prophet==1.1.7 -proto-plus==1.26.1 -protobuf==5.29.5 -psutil==5.9.5 -psycopg2==2.9.10 -ptyprocess==0.7.0 -py-cpuinfo==9.0.0 -py4j==0.10.9.7 -pyarrow==18.1.0 -pyasn1==0.6.1 -pyasn1_modules==0.4.2 -pycairo==1.28.0 -pycocotools==2.0.10 -pycparser==2.22 -pycryptodomex==3.23.0 -pydantic==2.11.7 -pydantic_core==2.33.2 -pydata-google-auth==1.9.1 -pydot==3.0.4 -pydotplus==2.0.2 -PyDrive==1.3.1 -PyDrive2==1.21.3 -pydub==0.25.1 -pyerfa==2.0.1.5 -pygame==2.6.1 -pygit2==1.18.0 -Pygments==2.19.2 -PyGObject==3.42.0 -PyJWT==2.10.1 -pylibcudf-cu12 @ https://pypi.nvidia.com/pylibcudf-cu12/pylibcudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -pylibcugraph-cu12==25.2.0 -pylibraft-cu12==25.2.0 -pymc==5.23.0 -pymystem3==0.2.0 -pynndescent==0.5.13 -pynvjitlink-cu12==0.7.0 -pynvml==12.0.0 -pyogrio==0.11.0 -pyomo==6.9.2 -PyOpenGL==3.1.9 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.1 -pyproject_hooks==1.2.0 -pyshp==2.3.1 -PySocks==1.7.1 -pyspark==3.5.1 -pytensor==2.31.5 -pytest==8.3.5 -python-apt==0.0.0 -python-box==7.3.2 -python-dateutil==2.9.0.post0 -python-dotenv==1.1.1 -python-louvain==0.16 -python-multipart==0.0.20 -python-slugify==8.0.4 -python-snappy==0.7.3 -python-utils==3.9.1 -pytz==2025.2 -pyviz_comms==3.0.6 -PyWavelets==1.8.0 -PyYAML==6.0.2 -pyzmq==24.0.1 -raft-dask-cu12==25.2.0 -rapids-dask-dependency==25.2.0 -ratelim==0.1.6 -referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 -requests-oauthlib==2.0.0 -requests-toolbelt==1.0.0 -requirements-parser==0.9.0 -rich==13.9.4 -rmm-cu12==25.2.0 -roman-numerals-py==3.1.0 -rpds-py==0.26.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.1 -s3transfer==0.13.0 -safehttpx==0.1.6 -safetensors==0.5.3 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.13.1 -scooby==0.10.1 -scs==3.2.7.post2 -seaborn==0.13.2 -SecretStorage==3.3.3 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==4.1.0 -sentencepiece==0.2.0 -sentry-sdk==2.32.0 -setproctitle==1.3.6 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.4.9 -six==1.17.0 -sklearn-compat==0.1.3 -sklearn-pandas==2.2.0 -slicer==0.0.8 -smart_open==7.3.0 -smmap==5.0.2 -sniffio==1.3.1 -snowballstemmer==3.0.1 -sortedcontainers==2.4.0 -soundfile==0.13.1 -soupsieve==2.7 -soxr==0.5.0.post1 -spacy==3.8.7 -spacy-legacy==3.0.12 -spacy-loggers==1.0.5 -spanner-graph-notebook==1.1.7 -Sphinx==8.2.3 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.41 -sqlglot==25.20.2 -sqlparse==0.5.3 -srsly==2.5.1 -stanio==0.5.1 -starlette==0.46.2 -statsmodels==0.14.4 -stringzilla==3.12.5 -stumpy==1.13.0 -sympy==1.13.1 -tables==3.10.2 -tabulate==0.9.0 -tbb==2022.2.0 -tblib==3.1.0 -tcmlib==1.4.0 -tenacity==8.5.0 -tensorboard==2.18.0 -tensorboard-data-server==0.7.2 -tensorflow==2.18.0 -tensorflow-datasets==4.9.9 -tensorflow-hub==0.16.1 -tensorflow-io-gcs-filesystem==0.37.1 -tensorflow-metadata==1.17.2 -tensorflow-probability==0.25.0 -tensorflow-text==2.18.1 -tensorflow_decision_forests==1.11.0 -tensorstore==0.1.74 -termcolor==3.1.0 -terminado==0.18.1 -text-unidecode==1.3 -textblob==0.19.0 -tf-slim==1.1.0 -tf_keras==2.18.0 -thinc==8.3.6 -threadpoolctl==3.6.0 -tifffile==2025.6.11 -tiktoken==0.9.0 -timm==1.0.16 -tinycss2==1.4.0 -tokenizers==0.21.2 -toml==0.10.2 -tomlkit==0.13.3 -toolz==0.12.1 -torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchao==0.10.0 -torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp311-cp311-linux_x86_64.whl -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==4.53.0 -treelite==4.4.1 -treescope==0.1.9 -triton==3.2.0 -tsfresh==0.21.0 -tweepy==4.15.0 -typeguard==4.4.4 -typer==0.16.0 -types-pytz==2025.2.0.20250516 -types-setuptools==80.9.0.20250529 -typing-inspection==0.4.1 -typing_extensions==4.14.0 -tzdata==2025.2 -tzlocal==5.3.1 -uc-micro-py==1.0.3 -ucx-py-cu12==0.42.0 -ucxx-cu12==0.42.0 -umap-learn==0.5.8 -umf==0.11.0 -uritemplate==4.2.0 -urllib3==2.4.0 -uvicorn==0.35.0 -vega-datasets==0.9.0 -wadllib==1.3.6 -wandb==0.20.1 -wasabi==1.1.3 -wcwidth==0.2.13 -weasel==0.4.1 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -websockets==15.0.1 -Werkzeug==3.1.3 -widgetsnbextension==3.6.10 -wordcloud==1.9.4 -wrapt==1.17.2 -wurlitzer==3.1.1 -xarray==2025.3.1 -xarray-einstats==0.9.1 -xgboost==2.1.4 -xlrd==2.0.2 -xxhash==3.5.0 -xyzservices==2025.4.0 -yarl==1.20.1 -ydf==0.12.0 -yellowbrick==1.5 -yfinance==0.2.64 -zict==3.0.0 -zipp==3.23.0 -zstandard==0.23.0 diff --git a/notebooks/requirements_modele_simple.txt b/notebooks/requirements_modele_simple.txt deleted file mode 100644 index 1b861e7..0000000 --- a/notebooks/requirements_modele_simple.txt +++ /dev/null @@ -1,652 +0,0 @@ -absl-py==1.4.0 -accelerate==1.8.1 -aiofiles==24.1.0 -aiohappyeyeballs==2.6.1 -aiohttp==3.11.15 -aiosignal==1.3.2 -alabaster==1.0.0 -albucore==0.0.24 -albumentations==2.0.8 -ale-py==0.11.1 -alembic==1.16.2 -altair==5.5.0 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==4.9.0 -argon2-cffi==25.1.0 -argon2-cffi-bindings==21.2.0 -array_record==0.7.2 -arviz==0.21.0 -astropy==7.1.0 -astropy-iers-data==0.2025.6.30.0.39.40 -astunparse==1.6.3 -atpublic==5.1 -attrs==25.3.0 -audioread==3.0.1 -autograd==1.8.0 -babel==2.17.0 -backcall==0.2.0 -backports.tarfile==1.2.0 -beautifulsoup4==4.13.4 -betterproto==2.0.0b6 -bigframes==2.8.0 -bigquery-magics==0.9.0 -bleach==6.2.0 -blinker==1.9.0 -blis==1.3.0 -blobfile==3.0.0 -blosc2==3.5.0 -bokeh==3.7.3 -boto3==1.39.3 -botocore==1.39.3 -Bottleneck==1.4.2 -bqplot==0.12.45 -branca==0.8.1 -build==1.2.2.post1 -CacheControl==0.14.3 -cachetools==5.5.2 -catalogue==2.0.10 -certifi==2025.6.15 -cffi==1.17.1 -chardet==5.2.0 -charset-normalizer==3.4.2 -chex==0.1.89 -clarabel==0.11.1 -click==8.2.1 -cloudpathlib==0.21.1 -cloudpickle==3.1.1 -cmake==3.31.6 -cmdstanpy==1.2.5 -colorcet==3.1.0 -colorlover==0.3.0 -colour==0.1.5 -community==1.0.0b1 -confection==0.1.5 -cons==0.4.6 -contourpy==1.3.2 -cramjam==2.10.0 -cryptography==43.0.3 -cuda-python==12.6.2.post1 -cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -cudf-polars-cu12==25.2.2 -cufflinks==0.17.3 -cuml-cu12==25.2.1 -cupy-cuda12x==13.3.0 -curl_cffi==0.11.4 -cuvs-cu12==25.2.1 -cvxopt==1.3.2 -cvxpy==1.6.6 -cycler==0.12.1 -cyipopt==1.5.0 -cymem==2.0.11 -Cython==3.0.12 -dask==2024.12.1 -dask-cuda==25.2.0 -dask-cudf-cu12==25.2.2 -dask-expr==1.1.21 -databricks-sdk==0.57.0 -dataproc-spark-connect==0.7.5 -datascience==0.17.6 -datasets==2.14.4 -db-dtypes==1.4.3 -dbus-python==1.2.18 -debugpy==1.8.0 -decorator==4.4.2 -defusedxml==0.7.1 -diffusers==0.34.0 -dill==0.3.7 -distributed==2024.12.1 -distributed-ucxx-cu12==0.42.0 -distro==1.9.0 -dlib==19.24.6 -dm-tree==0.1.9 -docker==7.1.0 -docstring_parser==0.16 -docutils==0.21.2 -dopamine_rl==4.1.2 -dotenv==0.9.9 -duckdb==1.2.2 -earthengine-api==1.5.22 -easydict==1.13 -editdistance==0.8.1 -eerepr==0.1.2 -einops==0.8.1 -en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85 -entrypoints==0.4 -et_xmlfile==2.0.0 -etils==1.12.2 -etuples==0.3.9 -Farama-Notifications==0.0.4 -fastai==2.7.19 -fastapi==0.115.14 -fastcore==1.7.29 -fastdownload==0.0.7 -fastjsonschema==2.21.1 -fastprogress==1.0.3 -fastrlock==0.8.3 -ffmpy==0.6.0 -filelock==3.18.0 -firebase-admin==6.9.0 -Flask==3.1.1 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.19.7 -fonttools==4.58.4 -frozendict==2.4.6 -frozenlist==1.7.0 -fsspec==2025.3.2 -future==1.0.0 -gast==0.6.0 -gcsfs==2025.3.2 -GDAL==3.8.4 -gdown==5.2.0 -geemap==0.35.3 -geocoder==1.38.1 -geographiclib==2.0 -geopandas==1.0.1 -geopy==2.4.1 -gin-config==0.5.0 -gitdb==4.0.12 -GitPython==3.1.44 -glob2==0.7 -google==2.0.3 -google-ai-generativelanguage==0.6.15 -google-api-core==2.25.1 -google-api-python-client==2.174.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.100.0 -google-cloud-bigquery==3.34.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-core==2.4.3 -google-cloud-dataproc==5.20.0 -google-cloud-datastore==2.21.0 -google-cloud-firestore==2.21.0 -google-cloud-functions==1.20.4 -google-cloud-iam==2.19.1 -google-cloud-language==2.17.2 -google-cloud-resource-manager==1.14.2 -google-cloud-spanner==3.55.0 -google-cloud-storage==2.19.0 -google-cloud-translate==3.21.0 -google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz -google-crc32c==1.7.1 -google-genai==1.23.0 -google-generativeai==0.8.5 -google-pasta==0.2.0 -google-resumable-media==2.7.2 -googleapis-common-protos==1.70.0 -googledrivedownloader==1.1.0 -gradio==5.31.0 -gradio_client==1.10.1 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -graphviz==0.21 -greenlet==3.2.3 -groovy==0.1.2 -grpc-google-iam-v1==0.14.2 -grpc-interceptor==0.15.4 -grpcio==1.73.1 -grpcio-status==1.71.2 -grpclib==0.4.8 -gspread==6.2.1 -gspread-dataframe==4.0.0 -gunicorn==23.0.0 -gym==0.25.2 -gym-notices==0.0.8 -gymnasium==1.2.0 -h11==0.16.0 -h2==4.2.0 -h5netcdf==1.6.3 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.5 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.75 -holoviews==1.21.0 -hpack==4.1.0 -html5lib==1.1 -httpcore==1.0.9 -httpimport==1.4.1 -httplib2==0.22.0 -httpx==0.28.1 -huggingface-hub==0.33.1 -humanize==4.12.3 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.13.0 -immutabledict==4.2.1 -importlib_metadata==8.7.0 -importlib_resources==6.5.2 -imutils==0.5.4 -inflect==7.5.0 -iniconfig==2.1.0 -intel-cmplr-lib-ur==2025.2.0 -intel-openmp==2025.2.0 -ipyevents==2.0.2 -ipyfilechooser==0.6.0 -ipykernel==6.17.1 -ipyleaflet==0.20.0 -ipyparallel==8.8.0 -ipython==7.34.0 -ipython-genutils==0.2.0 -ipython-sql==0.5.0 -ipytree==0.2.2 -ipywidgets==7.7.1 -itsdangerous==2.2.0 -jaraco.classes==3.4.0 -jaraco.context==6.0.1 -jaraco.functools==4.2.1 -jax==0.5.2 -jax-cuda12-pjrt==0.5.1 -jax-cuda12-plugin==0.5.1 -jaxlib==0.5.1 -jeepney==0.9.0 -jieba==0.42.1 -Jinja2==3.1.6 -jiter==0.10.0 -jmespath==1.0.1 -joblib==1.5.1 -jsonpatch==1.33 -jsonpickle==4.1.1 -jsonpointer==3.0.0 -jsonschema==4.24.0 -jsonschema-specifications==2025.4.1 -jupyter-client==6.1.12 -jupyter-console==6.1.0 -jupyter-leaflet==0.20.0 -jupyter-server==1.16.0 -jupyter_core==5.8.1 -jupyter_kernel_gateway @ git+https://github.com/googlecolab/kernel_gateway@b134e9945df25c2dcb98ade9129399be10788671 -jupyterlab_pygments==0.3.0 -jupyterlab_widgets==3.0.15 -jupytext==1.17.2 -kaggle==1.7.4.5 -kagglehub==0.3.12 -keras==3.8.0 -keras-hub==0.18.1 -keras-nlp==0.18.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.8 -langchain==0.3.26 -langchain-core==0.3.67 -langchain-text-splitters==0.3.8 -langcodes==3.5.0 -langsmith==0.4.4 -language_data==1.3.0 -launchpadlib==1.10.16 -lazr.restfulclient==0.14.4 -lazr.uri==1.0.6 -lazy_loader==0.4 -libclang==18.1.1 -libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-25.2.1-py3-none-manylinux_2_28_x86_64.whl -libcugraph-cu12==25.2.0 -libcuml-cu12==25.2.1 -libcuvs-cu12==25.2.1 -libkvikio-cu12==25.2.1 -libpysal==4.13.0 -libraft-cu12==25.2.0 -librosa==0.11.0 -libucx-cu12==1.18.1 -libucxx-cu12==0.42.0 -lightgbm @ file:///tmp/lightgbm/LightGBM/dist/lightgbm-4.5.0-py3-none-linux_x86_64.whl -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -logical-unification==0.4.6 -lxml==5.4.0 -Mako==1.1.3 -marisa-trie==1.2.1 -Markdown==3.8.2 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -miniKanren==1.0.3 -missingno==0.5.2 -mistune==3.1.3 -mizani==0.13.5 -mkl==2025.0.1 -ml-dtypes==0.4.1 -mlflow==3.1.1 -mlflow-skinny==3.1.1 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.1.1 -multidict==6.6.3 -multipledispatch==1.0.0 -multiprocess==0.70.15 -multitasking==0.0.11 -murmurhash==1.0.13 -music21==9.3.0 -namex==0.1.0 -narwhals==1.45.0 -natsort==8.4.0 -nbclassic==1.3.1 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -ndindex==1.10.0 -nest-asyncio==1.6.0 -networkx==3.5 -nibabel==5.3.2 -nltk==3.9.1 -notebook==6.5.7 -notebook_shim==0.2.4 -numba==0.60.0 -numba-cuda==0.2.0 -numexpr==2.11.0 -numpy==2.0.2 -nvidia-cublas-cu12==12.5.3.2 -nvidia-cuda-cupti-cu12==12.5.82 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.5.82 -nvidia-cuda-runtime-cu12==12.5.82 -nvidia-cudnn-cu12==9.3.0.75 -nvidia-cufft-cu12==11.2.3.61 -nvidia-curand-cu12==10.3.6.82 -nvidia-cusolver-cu12==11.6.3.83 -nvidia-cusparse-cu12==12.5.1.3 -nvidia-cusparselt-cu12==0.6.2 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.21.5 -nvidia-nvcomp-cu12==4.2.0.11 -nvidia-nvjitlink-cu12==12.5.82 -nvidia-nvtx-cu12==12.4.127 -nvtx==0.2.12 -nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-25.2.0-py3-none-any.whl -oauth2client==4.1.3 -oauthlib==3.3.1 -omegaconf==2.3.0 -openai==1.93.0 -opencv-contrib-python==4.11.0.86 -opencv-python==4.11.0.86 -opencv-python-headless==4.11.0.86 -openpyxl==3.1.5 -opentelemetry-api==1.34.1 -opentelemetry-sdk==1.34.1 -opentelemetry-semantic-conventions==0.55b1 -opt_einsum==3.4.0 -optax==0.2.5 -optree==0.16.0 -orbax-checkpoint==0.11.16 -orjson==3.10.18 -osqp==1.0.4 -packaging==24.2 -pandas==2.2.2 -pandas-datareader==0.10.0 -pandas-gbq==0.29.1 -pandas-stubs==2.2.2.240909 -pandocfilters==1.5.1 -panel==1.7.2 -param==2.2.1 -parso==0.8.4 -parsy==2.1 -partd==1.4.2 -pathlib==1.0.1 -patsy==1.0.1 -peewee==3.18.1 -peft==0.15.2 -pexpect==4.9.0 -pickleshare==0.7.5 -pillow==11.2.1 -platformdirs==4.3.8 -plotly==5.24.1 -plotnine==0.14.6 -pluggy==1.6.0 -ply==3.11 -polars==1.21.0 -pooch==1.8.2 -portpicker==1.5.2 -preshed==3.0.10 -prettytable==3.16.0 -proglog==0.1.12 -progressbar2==4.5.0 -prometheus_client==0.22.1 -promise==2.3 -prompt_toolkit==3.0.51 -propcache==0.3.2 -prophet==1.1.7 -proto-plus==1.26.1 -protobuf==5.29.5 -psutil==5.9.5 -psycopg2==2.9.10 -ptyprocess==0.7.0 -py-cpuinfo==9.0.0 -py4j==0.10.9.7 -pyarrow==18.1.0 -pyasn1==0.6.1 -pyasn1_modules==0.4.2 -pycairo==1.28.0 -pycocotools==2.0.10 -pycparser==2.22 -pycryptodomex==3.23.0 -pydantic==2.11.7 -pydantic_core==2.33.2 -pydata-google-auth==1.9.1 -pydot==3.0.4 -pydotplus==2.0.2 -PyDrive==1.3.1 -PyDrive2==1.21.3 -pydub==0.25.1 -pyerfa==2.0.1.5 -pygame==2.6.1 -pygit2==1.18.0 -Pygments==2.19.2 -PyGObject==3.42.0 -PyJWT==2.10.1 -pylibcudf-cu12 @ https://pypi.nvidia.com/pylibcudf-cu12/pylibcudf_cu12-25.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -pylibcugraph-cu12==25.2.0 -pylibraft-cu12==25.2.0 -pymc==5.23.0 -pymystem3==0.2.0 -pynndescent==0.5.13 -pynvjitlink-cu12==0.7.0 -pynvml==12.0.0 -pyogrio==0.11.0 -pyomo==6.9.2 -PyOpenGL==3.1.9 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.1 -pyproject_hooks==1.2.0 -pyshp==2.3.1 -PySocks==1.7.1 -pyspark==3.5.1 -pytensor==2.31.5 -pytest==8.3.5 -python-apt==0.0.0 -python-box==7.3.2 -python-dateutil==2.9.0.post0 -python-dotenv==1.1.1 -python-louvain==0.16 -python-multipart==0.0.20 -python-slugify==8.0.4 -python-snappy==0.7.3 -python-utils==3.9.1 -pytz==2025.2 -pyviz_comms==3.0.6 -PyWavelets==1.8.0 -PyYAML==6.0.2 -pyzmq==24.0.1 -raft-dask-cu12==25.2.0 -rapids-dask-dependency==25.2.0 -ratelim==0.1.6 -referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 -requests-oauthlib==2.0.0 -requests-toolbelt==1.0.0 -requirements-parser==0.9.0 -rich==13.9.4 -rmm-cu12==25.2.0 -roman-numerals-py==3.1.0 -rpds-py==0.26.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.1 -s3transfer==0.13.0 -safehttpx==0.1.6 -safetensors==0.5.3 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.15.3 -scooby==0.10.1 -scs==3.2.7.post2 -seaborn==0.13.2 -SecretStorage==3.3.3 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==4.1.0 -sentencepiece==0.2.0 -sentry-sdk==2.32.0 -setproctitle==1.3.6 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.4.9 -six==1.17.0 -sklearn-compat==0.1.3 -sklearn-pandas==2.2.0 -slicer==0.0.8 -smart_open==7.3.0 -smmap==5.0.2 -sniffio==1.3.1 -snowballstemmer==3.0.1 -sortedcontainers==2.4.0 -soundfile==0.13.1 -soupsieve==2.7 -soxr==0.5.0.post1 -spacy==3.8.7 -spacy-legacy==3.0.12 -spacy-loggers==1.0.5 -spanner-graph-notebook==1.1.7 -Sphinx==8.2.3 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.41 -sqlglot==25.20.2 -sqlparse==0.5.3 -srsly==2.5.1 -stanio==0.5.1 -starlette==0.46.2 -statsmodels==0.14.4 -stringzilla==3.12.5 -stumpy==1.13.0 -sympy==1.13.1 -tables==3.10.2 -tabulate==0.9.0 -tbb==2022.2.0 -tblib==3.1.0 -tcmlib==1.4.0 -tenacity==8.5.0 -tensorboard==2.18.0 -tensorboard-data-server==0.7.2 -tensorflow==2.18.0 -tensorflow-datasets==4.9.9 -tensorflow-hub==0.16.1 -tensorflow-io-gcs-filesystem==0.37.1 -tensorflow-metadata==1.17.2 -tensorflow-probability==0.25.0 -tensorflow-text==2.18.1 -tensorflow_decision_forests==1.11.0 -tensorstore==0.1.74 -termcolor==3.1.0 -terminado==0.18.1 -text-unidecode==1.3 -textblob==0.19.0 -tf-slim==1.1.0 -tf_keras==2.18.0 -thinc==8.3.6 -threadpoolctl==3.6.0 -tifffile==2025.6.11 -tiktoken==0.9.0 -timm==1.0.16 -tinycss2==1.4.0 -tokenizers==0.21.2 -toml==0.10.2 -tomlkit==0.13.3 -toolz==0.12.1 -torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchao==0.10.0 -torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp311-cp311-linux_x86_64.whl -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==4.53.0 -treelite==4.4.1 -treescope==0.1.9 -triton==3.2.0 -tsfresh==0.21.0 -tweepy==4.15.0 -typeguard==4.4.4 -typer==0.16.0 -types-pytz==2025.2.0.20250516 -types-setuptools==80.9.0.20250529 -typing-inspection==0.4.1 -typing_extensions==4.14.0 -tzdata==2025.2 -tzlocal==5.3.1 -uc-micro-py==1.0.3 -ucx-py-cu12==0.42.0 -ucxx-cu12==0.42.0 -umap-learn==0.5.8 -umf==0.11.0 -uritemplate==4.2.0 -urllib3==2.4.0 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