Projet_IA5/work_old_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9aeaf08b-26da-4300-b4d4-e20e9b835876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /opt/conda/lib/python3.13/site-packages (4.2.0)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.13/site-packages (from datasets) (3.13.1)\n",
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      "Requirement already satisfied: pandas in /opt/conda/lib/python3.13/site-packages (from datasets) (2.3.3)\n",
      "Requirement already satisfied: requests>=2.32.2 in /opt/conda/lib/python3.13/site-packages (from datasets) (2.32.5)\n",
      "Requirement already satisfied: httpx<1.0.0 in /opt/conda/lib/python3.13/site-packages (from datasets) (0.28.1)\n",
      "Requirement already satisfied: tqdm>=4.66.3 in /opt/conda/lib/python3.13/site-packages (from datasets) (4.67.1)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.13/site-packages (from datasets) (3.6.0)\n",
      "Requirement already satisfied: multiprocess<0.70.17 in /opt/conda/lib/python3.13/site-packages (from datasets) (0.70.16)\n",
      "Requirement already satisfied: fsspec<=2025.9.0,>=2023.1.0 in /opt/conda/lib/python3.13/site-packages (from fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (2025.9.0)\n",
      "Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in /opt/conda/lib/python3.13/site-packages (from datasets) (0.35.3)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.13/site-packages (from datasets) (25.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.13/site-packages (from datasets) (6.0.3)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /opt/conda/lib/python3.13/site-packages (from fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (3.13.0)\n",
      "Requirement already satisfied: anyio in /opt/conda/lib/python3.13/site-packages (from httpx<1.0.0->datasets) (4.11.0)\n",
      "Requirement already satisfied: certifi in /opt/conda/lib/python3.13/site-packages (from httpx<1.0.0->datasets) (2025.10.5)\n",
      "Requirement already satisfied: httpcore==1.* in /opt/conda/lib/python3.13/site-packages (from httpx<1.0.0->datasets) (1.0.9)\n",
      "Requirement already satisfied: idna in /opt/conda/lib/python3.13/site-packages (from httpx<1.0.0->datasets) (3.10)\n",
      "Requirement already satisfied: h11>=0.16 in /opt/conda/lib/python3.13/site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.13/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.15.0)\n",
      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /opt/conda/lib/python3.13/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.1.10)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (2.6.1)\n",
      "Requirement already satisfied: aiosignal>=1.4.0 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (1.4.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (25.3.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (1.8.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (6.7.0)\n",
      "Requirement already satisfied: propcache>=0.2.0 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (0.4.1)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /opt/conda/lib/python3.13/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets) (1.22.0)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.13/site-packages (from requests>=2.32.2->datasets) (3.4.3)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.13/site-packages (from requests>=2.32.2->datasets) (2.5.0)\n",
      "Requirement already satisfied: sniffio>=1.1 in /opt/conda/lib/python3.13/site-packages (from anyio->httpx<1.0.0->datasets) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.13/site-packages (from pandas->datasets) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.13/site-packages (from pandas->datasets) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.13/site-packages (from pandas->datasets) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07d7e876-b3ba-40ed-b6d6-81683e7ee513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ec0afc0b89148f19b72ec8f82927298",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/145 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a68949f462cc462cac490123368d8e35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading dataset shards:   0%|          | 0/145 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ds = load_dataset(\"PleIAs/French-PD-Books\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c5400ed-494e-4bae-a540-71655b000319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extraire les années de publication\n",
    "years = ds['train']['date']\n",
    "\n",
    "# Nettoyer et convertir en entiers (certaines dates peuvent être manquantes ou non numériques)\n",
    "years_clean = [int(y) for y in years if str(y).isdigit()]\n",
    "\n",
    "# Créer l'histogramme\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(years_clean, bins=50, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Année de publication')\n",
    "plt.ylabel('Nombre de textes')\n",
    "plt.title('Répartition des textes dans le temps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91c483b8-ae5b-4f1d-9f8a-dffc8eea16c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def clean_text(example):\n",
    "    text = example[\"complete_text\"]\n",
    "    date = example.get(\"date\", None)\n",
    "\n",
    "    # Si la date contient un \"-\", on essaie d'extraire l'année connue (format \"1234-????\" ou \"????-1234\") ou moyenne des deux années\n",
    "    if \"-\" in str(date) and date is not None:\n",
    "        parts = str(date).split(\"-\")\n",
    "        if (parts[1].isdigit() and len(parts[1]) == 4) and parts[0] == \"????\":\n",
    "            date = str(parts[1])\n",
    "        else:\n",
    "            date = str(parts[0])\n",
    "\n",
    "    # 1. Retirer les numéros de page\n",
    "    text = re.sub(r\"[—\\-–]\\s*\\d+\\s*[—\\-–]\", \" \", text)\n",
    "    \n",
    "    # 2. Corriger les apostrophes et guillemets échappés\n",
    "    text = text.replace(\"\\\\'\", \"'\")\n",
    "    text = text.replace(\"\\\\\\\"\", \"\\\"\")\n",
    "    text = text.replace(\"\\\\n\", \" \")\n",
    "    text = text.replace(\"\\\\r\", \" \")\n",
    "    text = text.replace(\"\\\\t\", \" \")\n",
    "    \n",
    "    # 3. Corriger les mots coupés (pattern plus précis)\n",
    "    text = re.sub(r'([a-zàâäæçéèêëïîôùûüœ])\\s+([a-zàâäæçéèêëïîôùûüœ]{2,})', \n",
    "                  r'\\1\\2', text)\n",
    "    \n",
    "    # 4. Corriger les cas avec plusieurs espaces\n",
    "    text = re.sub(r'([a-zàâäæçéèêëïîôùûüœ])\\s{2,}([a-zàâäæçéèêëïîôùûüœ])', \n",
    "                  r'\\1\\2', text)\n",
    "    \n",
    "    # 5. Normaliser les espaces multiples\n",
    "    text = re.sub(r\"\\s+\", \" \", text)\n",
    "    \n",
    "    # 6. Nettoyer les caractères spéciaux\n",
    "    text = re.sub(r\"[^\\w\\s\\.,;:\\?!'\\-\\\"«»À-ÖØ-öø-ÿœŒ]\", \" \", text)\n",
    "    \n",
    "    # 7. Re-normaliser après nettoyage\n",
    "    text = re.sub(r\"\\s+\", \" \", text)\n",
    "    \n",
    "    # 8. Corriger la ponctuation\n",
    "    text = re.sub(r\"\\s+([,.\\?!;:])\", r\"\\1\", text)\n",
    "    text = re.sub(r\"([,.\\?!;:])\\s*([,.\\?!;:])\", r\"\\1\\2\", text)\n",
    "    \n",
    "    text = text.strip()\n",
    "    return {\"text\": text, \"date\": str(date)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d8bc092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le texte nettoyé semble correct.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = ds['train'][500]\n",
    "\n",
    "cleaned_example = clean_text(example)\n",
    "\n",
    "def verify_clean_text(original_example, cleaned_example):\n",
    "    # Vérifie que le texte nettoyé n'est pas vide\n",
    "    if not cleaned_example[\"text\"]:\n",
    "        print(\"Le texte nettoyé est vide.\")\n",
    "        return False\n",
    "\n",
    "    # Vérifie que la date est bien une année à 4 chiffres\n",
    "    if not (cleaned_example[\"date\"].isdigit() and len(cleaned_example[\"date\"]) == 4):\n",
    "        print(f\"Date nettoyée incorrecte: {cleaned_example['date']}\")\n",
    "        return False\n",
    "\n",
    "    # Vérifie que les caractères spéciaux indésirables ont été retirés\n",
    "    if re.search(r\"[^\\w\\s\\.,;:\\?!'\\-\\\"«»À-ÖØ-öø-ÿœŒ]\", cleaned_example[\"text\"]):\n",
    "        print(\"Caractères spéciaux indésirables présents dans le texte nettoyé.\")\n",
    "        return False\n",
    "\n",
    "    # Vérifie qu'il n'y a pas de séquences d'espaces multiples\n",
    "    if re.search(r\"\\s{2,}\", cleaned_example[\"text\"]):\n",
    "        print(\"Espaces multiples détectés dans le texte nettoyé.\")\n",
    "        return False\n",
    "\n",
    "    print(\"Le texte nettoyé semble correct.\")\n",
    "    return True\n",
    "\n",
    "# Exemple d'utilisation\n",
    "verify_clean_text(example, cleaned_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39e38bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1dd55cc88a34a7fb447d36646c86c4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_ds = ds['train'].shuffle(seed=42).select(range(50000)) # Pour accélérer le traitement\n",
    "\n",
    "cleaned_ds = reduced_ds.map(clean_text, remove_columns=reduced_ds.column_names)\n",
    "# cleaned_ds = ds['train'].map(clean_text, remove_columns=ds['train'].column_names, num_proc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "685a1521",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range(3):\n",
    "#    print(f\"\\nExemple {i+1}:\")\n",
    "#    print(cleaned_ds[i])\n",
    "#    print(cleaned_ds[i][\"text\"][:200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0474c7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a0b038366ca40c4bf0d80dbab30b8c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taille de l'ensemble d'entraînement : 40000 textes.\n",
      "Taille de l'ensemble de test : 10000 textes.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Créer les labels par tranches de 10 ans\n",
    "def create_decade_label(example):\n",
    "    \"\"\"Crée une étiquette numérique (ex: 1880) pour chaque décennie.\"\"\"\n",
    "    year = int(example['date'])\n",
    "    decade = (year // 10) * 10\n",
    "    return {\"decade\": decade}\n",
    "\n",
    "# Appliquer la fonction à votre dataset\n",
    "dataset_with_labels = cleaned_ds.map(create_decade_label)\n",
    "\n",
    "# Séparer en ensembles d'entraînement (80%) et de test (20%)\n",
    "train_test_split = dataset_with_labels.train_test_split(test_size=0.2, seed=42)\n",
    "train_dataset = train_test_split['train']\n",
    "test_dataset = train_test_split['test']\n",
    "\n",
    "# Extraire les textes et les labels pour une utilisation plus simple\n",
    "train_texts = [ex['text'] for ex in train_dataset]\n",
    "test_texts = [ex['text'] for ex in test_dataset]\n",
    "train_labels = [ex['decade'] for ex in train_dataset]\n",
    "test_labels = [ex['decade'] for ex in test_dataset]\n",
    "\n",
    "print(f\"Taille de l'ensemble d'entraînement : {len(train_texts)} textes.\")\n",
    "print(f\"Taille de l'ensemble de test : {len(test_texts)} textes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4acca337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorisation TF-IDF en cours...\n",
      "Shape des features TF-IDF (entraînement) : (40000, 10000)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import gc\n",
    "\n",
    "# Configuration du vectoriseur TF-IDF\n",
    "# max_features limite la taille du vocabulaire pour économiser la mémoire\n",
    "# min_df et max_df ignorent les mots trop rares ou trop fréquents\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_features=10000,\n",
    "    min_df=5,\n",
    "    max_df=0.8,\n",
    "    ngram_range=(1, 1) # On utilise uniquement des unigrammes (mots seuls)\n",
    ")\n",
    "\n",
    "print(\"Vectorisation TF-IDF en cours...\")\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(train_texts)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(test_texts)\n",
    "\n",
    "# Libérer de la mémoire\n",
    "del train_texts\n",
    "del test_texts\n",
    "gc.collect()\n",
    "\n",
    "print(f\"Shape des features TF-IDF (entraînement) : {X_train_tfidf.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f38c125",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Création des embeddings pour 40000 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5de39b5ab7ec4e2cb40fc8a96ab62ac9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/2500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Création des embeddings pour 10000 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "659c7b138aaf4f58a23cb0055aa03499",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/625 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape des embeddings (entraînement) : (40000, 768)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pip install sentence-transformers -q\n",
    "\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import numpy as np\n",
    "import random\n",
    "import gc\n",
    "\n",
    "def get_text_sample(text, selection_method=\"start_tokens\", n_words=500):\n",
    "    \"\"\"\n",
    "    Extrait un échantillon de texte en sélectionnant des mots (tokens)\n",
    "    du début, du milieu, de la fin, et de manière aléatoire.\n",
    "    \"\"\"\n",
    "    tokens = text.split()\n",
    "    \n",
    "    # Si le texte est trop court pour un échantillonnage significatif,\n",
    "    # on retourne le texte entier.\n",
    "    if len(tokens) < n_words * 4:\n",
    "        return text\n",
    "\n",
    "    if selection_method == \"start_tokens\":\n",
    "        # 1. 500 mots du début\n",
    "        sorted_tokens = tokens[:n_words]\n",
    "\n",
    "    if selection_method == \"end_tokens\":\n",
    "        # 2. 500 mots de la fin\n",
    "        sorted_tokens = tokens[-n_words:]\n",
    "\n",
    "    if selection_method == \"middle_tokens\":\n",
    "        # 3. 500 mots du milieu\n",
    "        middle_index = len(tokens) // 2\n",
    "        middle_start = max(0, middle_index - (n_words // 2))\n",
    "        sorted_tokens = tokens[middle_start : middle_start + n_words]\n",
    "\n",
    "    if selection_method == \"random_tokens\":\n",
    "        # 4. 500 mots aléatoires (en évitant les doublons avec les sections déjà extraites)\n",
    "        # On construit la liste des indices déjà utilisés\n",
    "        middle_index = len(tokens) // 2\n",
    "        middle_start = max(0, middle_index - (n_words // 2))\n",
    "        used_indices = set(range(n_words)) | set(range(len(tokens) - n_words, len(tokens))) | set(range(middle_start, middle_start + n_words))\n",
    "        # On crée la liste des indices restants disponibles\n",
    "        remaining_indices = [i for i in range(len(tokens)) if i not in used_indices]\n",
    "        \n",
    "        if len(remaining_indices) < n_words:\n",
    "            random_indices = remaining_indices\n",
    "        else:\n",
    "            random_indices = random.sample(remaining_indices, n_words)\n",
    "        \n",
    "        sorted_tokens = [tokens[i] for i in sorted(random_indices)] # trier pour garder un semblant d'ordre\n",
    "\n",
    "    return \" \".join(sorted_tokens)\n",
    "\n",
    "# Charger un modèle CamemBERT léger, spécialisé dans la création d'embeddings\n",
    "embedding_model = SentenceTransformer('dangvantuan/sentence-camembert-base')\n",
    "\n",
    "# Fonction pour créer les embeddings par lots (pour optimiser l'usage de la RAM)\n",
    "def create_embeddings(texts, selection_method=\"start_tokens\", batch_size=32):\n",
    "    # Créer des échantillons de texte représentatifs\n",
    "    sampled_texts = [get_text_sample(text, selection_method, n_words=500) for text in texts]\n",
    "    \n",
    "    print(f\"Création des embeddings pour {len(sampled_texts)} textes...\")\n",
    "    return embedding_model.encode(\n",
    "        sampled_texts,\n",
    "        batch_size=batch_size,\n",
    "        show_progress_bar=True,\n",
    "        convert_to_numpy=True,\n",
    "        normalize_embeddings=True\n",
    "    )\n",
    "\n",
    "# Recréer les listes de textes car elles ont été supprimées\n",
    "# Assurez-vous que les variables `train_dataset` et `test_dataset` sont bien définies en exécutant les cellules précédentes.\n",
    "train_texts = [ex['text'] for ex in train_dataset]\n",
    "test_texts = [ex['text'] for ex in test_dataset]\n",
    "\n",
    "# start_tokens, end_tokens, middle_tokens, random_tokens\n",
    "selection_method = \"start_tokens\"\n",
    "X_train_embedding = create_embeddings(train_texts, selection_method, batch_size=16)\n",
    "X_test_embedding = create_embeddings(test_texts, selection_method, batch_size=16)\n",
    "\n",
    "print(f\"Shape des embeddings (entraînement) : {X_train_embedding.shape}\")\n",
    "\n",
    "# Libérer de la mémoire\n",
    "del train_texts\n",
    "del test_texts\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dfafa732",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import log_loss, classification_report, accuracy_score\n",
    "import numpy as np # Assurez-vous que numpy est importé\n",
    "\n",
    "def train_with_loss_tracking(X_train, X_test, y_train, y_test, method_name, n_epochs=20):\n",
    "    \"\"\"\n",
    "    Entraîne un classifieur SGD, suit la perte à chaque époque,\n",
    "    et affiche les résultats ainsi qu'un graphique de la perte.\n",
    "    \"\"\"\n",
    "    print(f\"\\\\n{'='*50}\")\n",
    "    print(f\"Entraînement avec suivi de la perte ({method_name})\")\n",
    "    print(f\"{'='*50}\\\\n\")\n",
    "\n",
    "    classifier = SGDClassifier(\n",
    "        loss='log_loss', \n",
    "        random_state=42, \n",
    "        n_jobs=-1, \n",
    "        #learning_rate='constant',\n",
    "        #eta0=0.001\n",
    "    )\n",
    "    \n",
    "    train_loss_history = []\n",
    "    test_loss_history = []\n",
    "    \n",
    "    # --- CORRECTION 1 : Définir la liste complète de TOUS les labels ---\n",
    "    # On combine les labels du train et du test pour être sûr de n'en oublier aucun.\n",
    "    all_classes = np.unique(np.concatenate((y_train, y_test)))\n",
    "\n",
    "    print(f\"Début de l'entraînement pour {n_epochs} époques...\")\n",
    "    for epoch in range(n_epochs):\n",
    "        classifier.partial_fit(X_train, y_train, classes=all_classes)\n",
    "        \n",
    "        y_train_pred_proba = classifier.predict_proba(X_train)\n",
    "        y_test_pred_proba = classifier.predict_proba(X_test)\n",
    "        \n",
    "        # --- CORRECTION 2 : Passer `labels=all_classes` à log_loss ---\n",
    "        train_loss = log_loss(y_train, y_train_pred_proba, labels=all_classes)\n",
    "        test_loss = log_loss(y_test, y_test_pred_proba, labels=all_classes)\n",
    "        \n",
    "        train_loss_history.append(train_loss)\n",
    "        test_loss_history.append(test_loss)\n",
    "        \n",
    "        print(f\"Époque {epoch+1}/{n_epochs} - Perte entraînement: {train_loss:.4f} - Perte test: {test_loss:.4f}\")\n",
    "\n",
    "    # Visualisation de la fonction de perte\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(train_loss_history, label=\"Perte d'entraînement (Train Loss)\")\n",
    "    plt.plot(test_loss_history, label=\"Perte de validation (Test Loss)\")\n",
    "    plt.title(f\"Évolution de la fonction de perte ({method_name})\")\n",
    "    plt.xlabel(\"Époques\")\n",
    "    plt.ylabel(\"Log Loss\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    # Évaluation finale sur l'ensemble de test\n",
    "    print(\"\\\\n------ ÉVALUATION FINALE ------\")\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    print(f\"🎯 Accuracy finale: {accuracy:.4f}\\\\n\")\n",
    "    print(\"📄 Rapport de classification détaillé :\")\n",
    "    print(classification_report(y_test, y_pred, zero_division=0)) # Ajout de zero_division pour éviter les warnings\n",
    "    \n",
    "    return classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c366fc0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\n==================================================\n",
      "Entraînement avec suivi de la perte (Embeddings (CamemBERT))\n",
      "==================================================\\n\n",
      "Début de l'entraînement pour 50 époques...\n",
      "Époque 1/50 - Perte entraînement: 2.4359 - Perte test: 2.4721\n",
      "Époque 2/50 - Perte entraînement: 2.4084 - Perte test: 2.4434\n",
      "Époque 3/50 - Perte entraînement: 2.3990 - Perte test: 2.4334\n",
      "Époque 4/50 - Perte entraînement: 2.3940 - Perte test: 2.4281\n",
      "Époque 5/50 - Perte entraînement: 2.3907 - Perte test: 2.4247\n",
      "Époque 6/50 - Perte entraînement: 2.3883 - Perte test: 2.4222\n",
      "Époque 7/50 - Perte entraînement: 2.3865 - Perte test: 2.4204\n",
      "Époque 8/50 - Perte entraînement: 2.3852 - Perte test: 2.4191\n",
      "Époque 9/50 - Perte entraînement: 2.3841 - Perte test: 2.4180\n",
      "Époque 10/50 - Perte entraînement: 2.3832 - Perte test: 2.4172\n",
      "Époque 11/50 - Perte entraînement: 2.3826 - Perte test: 2.4165\n",
      "Époque 12/50 - Perte entraînement: 2.3820 - Perte test: 2.4160\n",
      "Époque 13/50 - Perte entraînement: 2.3815 - Perte test: 2.4156\n",
      "Époque 14/50 - Perte entraînement: 2.3811 - Perte test: 2.4152\n",
      "Époque 15/50 - Perte entraînement: 2.3808 - Perte test: 2.4149\n",
      "Époque 16/50 - Perte entraînement: 2.3805 - Perte test: 2.4146\n",
      "Époque 17/50 - Perte entraînement: 2.3803 - Perte test: 2.4144\n",
      "Époque 18/50 - Perte entraînement: 2.3800 - Perte test: 2.4142\n",
      "Époque 19/50 - Perte entraînement: 2.3798 - Perte test: 2.4140\n",
      "Époque 20/50 - Perte entraînement: 2.3796 - Perte test: 2.4138\n",
      "Époque 21/50 - Perte entraînement: 2.3795 - Perte test: 2.4137\n",
      "Époque 22/50 - Perte entraînement: 2.3793 - Perte test: 2.4135\n",
      "Époque 23/50 - Perte entraînement: 2.3792 - Perte test: 2.4134\n",
      "Époque 24/50 - Perte entraînement: 2.3792 - Perte test: 2.4134\n",
      "Époque 25/50 - Perte entraînement: 2.3790 - Perte test: 2.4132\n",
      "Époque 26/50 - Perte entraînement: 2.3788 - Perte test: 2.4131\n",
      "Époque 27/50 - Perte entraînement: 2.3789 - Perte test: 2.4132\n",
      "Époque 28/50 - Perte entraînement: 2.3787 - Perte test: 2.4130\n",
      "Époque 29/50 - Perte entraînement: 2.3787 - Perte test: 2.4130\n",
      "Époque 30/50 - Perte entraînement: 2.3785 - Perte test: 2.4128\n",
      "Époque 31/50 - Perte entraînement: 2.3786 - Perte test: 2.4129\n",
      "Époque 32/50 - Perte entraînement: 2.3784 - Perte test: 2.4127\n",
      "Époque 33/50 - Perte entraînement: 2.3784 - Perte test: 2.4127\n",
      "Époque 34/50 - Perte entraînement: 2.3783 - Perte test: 2.4127\n",
      "Époque 35/50 - Perte entraînement: 2.3782 - Perte test: 2.4125\n",
      "Époque 36/50 - Perte entraînement: 2.3783 - Perte test: 2.4126\n",
      "Époque 37/50 - Perte entraînement: 2.3782 - Perte test: 2.4126\n",
      "Époque 38/50 - Perte entraînement: 2.3780 - Perte test: 2.4124\n",
      "Époque 39/50 - Perte entraînement: 2.3780 - Perte test: 2.4125\n",
      "Époque 40/50 - Perte entraînement: 2.3782 - Perte test: 2.4126\n",
      "Époque 41/50 - Perte entraînement: 2.3779 - Perte test: 2.4124\n",
      "Époque 42/50 - Perte entraînement: 2.3778 - Perte test: 2.4123\n",
      "Époque 43/50 - Perte entraînement: 2.3779 - Perte test: 2.4123\n",
      "Époque 44/50 - Perte entraînement: 2.3781 - Perte test: 2.4125\n",
      "Époque 45/50 - Perte entraînement: 2.3779 - Perte test: 2.4123\n",
      "Époque 46/50 - Perte entraînement: 2.3777 - Perte test: 2.4121\n",
      "Époque 47/50 - Perte entraînement: 2.3776 - Perte test: 2.4121\n",
      "Époque 48/50 - Perte entraînement: 2.3777 - Perte test: 2.4122\n",
      "Époque 49/50 - Perte entraînement: 2.3780 - Perte test: 2.4124\n",
      "Époque 50/50 - Perte entraînement: 2.3778 - Perte test: 2.4123\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\n------ ÉVALUATION FINALE ------\n",
      "🎯 Accuracy finale: 0.2550\\n\n",
      "📄 Rapport de classification détaillé :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        1560       0.00      0.00      0.00         1\n",
      "        1570       0.00      0.00      0.00         2\n",
      "        1580       0.00      0.00      0.00         2\n",
      "        1590       0.00      0.00      0.00         1\n",
      "        1600       0.00      0.00      0.00        12\n",
      "        1610       0.00      0.00      0.00        27\n",
      "        1620       0.00      0.00      0.00        24\n",
      "        1630       0.20      0.04      0.07        23\n",
      "        1640       0.00      0.00      0.00        27\n",
      "        1650       0.00      0.00      0.00        27\n",
      "        1660       0.00      0.00      0.00        12\n",
      "        1670       0.00      0.00      0.00         7\n",
      "        1680       0.00      0.00      0.00        10\n",
      "        1690       0.00      0.00      0.00        25\n",
      "        1700       0.00      0.00      0.00        21\n",
      "        1710       0.00      0.00      0.00        31\n",
      "        1720       0.00      0.00      0.00        45\n",
      "        1730       0.00      0.00      0.00        52\n",
      "        1740       0.49      0.30      0.37       101\n",
      "        1750       0.33      0.01      0.02       123\n",
      "        1760       0.32      0.15      0.20       122\n",
      "        1770       0.07      0.06      0.07       119\n",
      "        1780       0.22      0.18      0.19       176\n",
      "        1790       0.35      0.39      0.37       213\n",
      "        1800       0.08      0.01      0.01       167\n",
      "        1810       0.25      0.07      0.11       308\n",
      "        1820       0.28      0.26      0.27       447\n",
      "        1830       0.27      0.17      0.21       534\n",
      "        1840       0.29      0.22      0.25       687\n",
      "        1850       0.20      0.22      0.21       783\n",
      "        1860       0.21      0.35      0.26      1036\n",
      "        1870       0.25      0.40      0.31      1063\n",
      "        1880       0.23      0.36      0.28      1017\n",
      "        1890       0.29      0.29      0.29       898\n",
      "        1900       0.29      0.35      0.32       767\n",
      "        1910       0.34      0.18      0.23       466\n",
      "        1920       0.29      0.04      0.07       265\n",
      "        1930       0.61      0.20      0.30       217\n",
      "        1940       0.00      0.00      0.00        34\n",
      "        1950       0.00      0.00      0.00        23\n",
      "        1960       0.00      0.00      0.00        20\n",
      "        1970       0.20      0.06      0.09        18\n",
      "        1980       0.00      0.00      0.00        23\n",
      "        1990       0.00      0.00      0.00        10\n",
      "        2000       0.00      0.00      0.00        14\n",
      "\n",
      "    accuracy                           0.26     10000\n",
      "   macro avg       0.14      0.10      0.10     10000\n",
      "weighted avg       0.26      0.26      0.24     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Entraîner et évaluer avec TF-IDF et suivi de la perte\n",
    "#model_tfidf_sgd = train_with_loss_tracking(\n",
    "#    X_train_tfidf, X_test_tfidf, \n",
    "#    train_labels, test_labels,\n",
    "#    \"TF-IDF\",\n",
    "#    n_epochs=30\n",
    "#)\n",
    "\n",
    "# Entraîner et évaluer avec Embeddings et suivi de la perte\n",
    "model_embedding_sgd = train_with_loss_tracking(\n",
    "    X_train_embedding, X_test_embedding,\n",
    "    train_labels, test_labels,\n",
    "    \"Embeddings (CamemBERT)\",\n",
    "    n_epochs=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "02f2c811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Interrogation d'un texte aléatoire ---\n",
      "📚 Titre : Les conserves de fruits pour la consommation familiale et pour la vente : la conservation des matières alimentaires dans les ménages, à la ferme et dans les coopératives agricoles\n",
      "🗓️  Date réelle : 1912\n",
      "----------------------------------------\n",
      "🤖 Prédiction (TF-IDF) : 1900s\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b376ef8bc3a4497917ecb41ebbd114e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🧠 Prédiction (Embeddings) : 1900s\n",
      "----------------------------------------\\n\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "\n",
    "def predict_random_text(test_dataset):\n",
    "    \"\"\"\n",
    "    Sélectionne un texte aléatoire dans le jeu de données, le prédit avec les deux modèles\n",
    "    et affiche une comparaison.\n",
    "    \"\"\"\n",
    "    # 1. Sélectionner un exemple aléatoire\n",
    "    random_index = random.randint(0, len(test_dataset) - 1)\n",
    "    random_example = test_dataset[random_index]\n",
    "    \n",
    "    title = random_example.get('title', 'Titre non disponible')\n",
    "    true_date = random_example.get('date', 'Date non disponible')\n",
    "    full_text = random_example.get('complete_text', '')\n",
    "\n",
    "    print(f\"--- Interrogation d'un texte aléatoire ---\")\n",
    "    print(f\"📚 Titre : {title}\")\n",
    "    print(f\"🗓️  Date réelle : {true_date}\")\n",
    "    print(\"-\" * 40)\n",
    "\n",
    "    # 2. Nettoyer le texte comme pour l'entraînement\n",
    "    # La fonction clean_text renvoie un dictionnaire, nous extrayons le texte\n",
    "    cleaned_text_dict = clean_text(random_example)\n",
    "    cleaned_text = cleaned_text_dict['text']\n",
    "    \n",
    "    # Le texte doit être dans une liste pour les transformateurs\n",
    "    text_to_predict = [cleaned_text]\n",
    "\n",
    "    # 3. Prédiction avec le modèle TF-IDF\n",
    "    #tfidf_features = tfidf_vectorizer.transform(text_to_predict)\n",
    "    #prediction_tfidf = model_tfidf_sgd.predict(tfidf_features)[0]\n",
    "    #print(f\"🤖 Prédiction (TF-IDF) : {prediction_tfidf}s\")\n",
    "\n",
    "    # 4. Prédiction avec le modèle Embeddings\n",
    "    embedding_features = create_embeddings(text_to_predict, batch_size=1)\n",
    "    prediction_embedding = model_embedding_sgd.predict(embedding_features)[0]\n",
    "    print(f\"🧠 Prédiction (Embeddings) : {prediction_embedding}s\")\n",
    "    print(f\"----------------------------------------\\\\n\")\n",
    "\n",
    "# --- Lancer le test ---\n",
    "# Utilisez ds['test'] qui contient les métadonnées comme le titre.\n",
    "predict_random_text(ds['train'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e5a99b09-681b-4a44-82f7-7c1ffe1ac84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Lancement de 20 essais de prédiction ---\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "472255ee1dbb4b95b14eb983f027b22c",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 1/20 | Réel : 1743 | Embeddings : 1820\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19269415e0cd4499bfb606d82042a57f",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 2/20 | Réel : 1910 | Embeddings : 1870\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de75adcf21f6484984a07e70e6bd05a8",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 3/20 | Réel : 1758 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1f2f611e9d049f5a8e8c73cc086df22",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 4/20 | Réel : 1894 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dd535bac4ec4e9fbc3dec4420885708",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 5/20 | Réel : 1621 | Embeddings : 1770\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7336da60a6544e79a29e485712273e39",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 6/20 | Réel : 1834 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af189c27c73d47ad829fb52812b85c73",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 7/20 | Réel : 1860 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41613bba41b548ef86a1dfc9ebf94081",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 8/20 | Réel : 1914 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b4db1579a1c483986ba743ff7badfda",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 9/20 | Réel : 1832 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdbdd3a3cc234f50afb5201c02f71712",
       "version_major": 2,
       "version_minor": 0
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Essai 10/20 | Réel : 1876 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce842d3378794e96a537af7a58acb383",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 11/20 | Réel : 1885 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a72024d894b45528e594f863b871049",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 12/20 | Réel : 1883 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fae7fb5e8e03448c80a373a571a00373",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 13/20 | Réel : 1879 | Embeddings : 1880\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f98f7e2e9a140e9b4619fa4a520dcbc",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Essai 14/20 | Réel : 1853 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f00a08f651474e5e944b9af348ec876e",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 15/20 | Réel : 1799 | Embeddings : 1840\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abfbd36b9d9c4c6f92617bf36cb2e253",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 16/20 | Réel : 1865 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc987afe6e21477b8332e2be97d326b2",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 17/20 | Réel : 1833 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4921ec5c3ef347d284a9b07989334cc0",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 18/20 | Réel : 1828 | Embeddings : 1860\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9103f891674540a6ad26fe130a907294",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 19/20 | Réel : 1828 | Embeddings : 1870\n",
      "Création des embeddings pour 1 textes...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ad5af296e384ca3b36ab85c305bfab0",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Essai 20/20 | Réel : 1872 | Embeddings : 1890\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def test_and_plot_predictions(test_dataset, num_trials=15):\n",
    "    \"\"\"\n",
    "    Sélectionne plusieurs textes aléatoires, prédit leur date avec deux modèles,\n",
    "    et affiche un graphique comparatif des résultats.\n",
    "    \n",
    "    Args:\n",
    "        test_dataset (list): La liste d'exemples à tester.\n",
    "        num_trials (int): Le nombre d'essais à effectuer.\n",
    "    \"\"\"\n",
    "    real_dates = []\n",
    "    tfidf_predictions = []\n",
    "    embedding_predictions = []\n",
    "\n",
    "    print(f\"--- Lancement de {num_trials} essais de prédiction ---\")\n",
    "\n",
    "    for i in range(num_trials):\n",
    "        # 1. Sélectionner un exemple aléatoire\n",
    "        random_example = random.choice(test_dataset)\n",
    "        \n",
    "        # Extrait la date et la convertit en entier pour le graphique\n",
    "        # On suppose que la date est au format 'YYYYs' (ex: '1990s')\n",
    "        try:\n",
    "            true_date_str = random_example.get('date', '0s')\n",
    "            true_date_int = int(true_date_str[:4]) # Convertit '1990s' en 1990\n",
    "        except (ValueError, IndexError):\n",
    "            print(f\"Avertissement : impossible de parser la date '{true_date_str}'. Essai ignoré.\")\n",
    "            continue\n",
    "\n",
    "        # 2. Nettoyer le texte\n",
    "        cleaned_text = clean_text(random_example)['text']\n",
    "        text_to_predict = [cleaned_text]\n",
    "\n",
    "        # 3. Prédiction avec le modèle TF-IDF\n",
    "        #tfidf_features = tfidf_vectorizer.transform(text_to_predict)\n",
    "        #prediction_tfidf = model_tfidf_sgd.predict(tfidf_features)[0]\n",
    "\n",
    "        # 4. Prédiction avec le modèle Embeddings\n",
    "        embedding_features = create_embeddings(text_to_predict, batch_size=1)\n",
    "        prediction_embedding = model_embedding_sgd.predict(embedding_features)[0]\n",
    "        \n",
    "        # 5. Stocker les résultats\n",
    "        real_dates.append(true_date_int)\n",
    "        #tfidf_predictions.append(prediction_tfidf)\n",
    "        embedding_predictions.append(prediction_embedding)\n",
    "        \n",
    "        #print(f\"Essai {i+1}/{num_trials} | Réel : {true_date_int} | TF-IDF : {prediction_tfidf} | Embeddings : {prediction_embedding}\")\n",
    "        print(f\"Essai {i+1}/{num_trials} | Réel : {true_date_int} | Embeddings : {prediction_embedding}\")\n",
    "\n",
    "    # 6. Générer le graphique\n",
    "    if not real_dates:\n",
    "        print(\"Aucune donnée à afficher.\")\n",
    "        return\n",
    "\n",
    "    trials = range(1, len(real_dates) + 1)\n",
    "    \n",
    "    plt.style.use('seaborn-v0_8-whitegrid') # Style visuel agréable\n",
    "    plt.figure(figsize=(14, 7)) # Taille de la figure\n",
    "\n",
    "    # Tracer chaque série de données\n",
    "    plt.plot(trials, real_dates, 'o-', color='green', label='Date Réelle', markersize=8)\n",
    "    #plt.plot(trials, tfidf_predictions, 's--', color='blue', label='Prédiction (TF-IDF)')\n",
    "    plt.plot(trials, embedding_predictions, '^:', color='red', label='Prédiction (Embeddings)')\n",
    "\n",
    "    # Ajouter les titres et les légendes\n",
    "    plt.title('Comparaison des Prédictions de Date sur Plusieurs Essais', fontsize=16)\n",
    "    plt.xlabel(\"Numéro de l'essai\", fontsize=12)\n",
    "    plt.ylabel(\"Date (Année)\", fontsize=12)\n",
    "    plt.xticks(trials) # Assure que chaque essai a une graduation sur l'axe X\n",
    "    plt.legend(fontsize=10)\n",
    "    plt.tight_layout() # Ajuste le graphique pour qu'il s'affiche bien\n",
    "\n",
    "    # Afficher le graphique\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# --- Lancer le test ---\n",
    "# Utilisez ds['train'] et spécifiez le nombre d'essais souhaités (ex: 20)\n",
    "test_and_plot_predictions(ds['train'], num_trials=20)"
   ]
  }
 ],
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