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"id": "aGDo4_KJWxlR",
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"id": "aGDo4_KJWxlR",
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"text": [
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"Chargement du modèle depuis le run 7d52e808227f468da7f733265280fc02...\n"
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"model_id": "9ecc775347df487bb63a5e1b2dcc68b7",
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"version_major": 2,
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:urllib3.connectionpool:Connection pool is full, discarding connection: minio-api.greg-madman-nas.duckdns.org. Connection pool size: 10\n",
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"WARNING:urllib3.connectionpool:Connection pool is full, discarding connection: minio-api.greg-madman-nas.duckdns.org. Connection pool size: 10\n",
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"WARNING:urllib3.connectionpool:Connection pool is full, discarding connection: minio-api.greg-madman-nas.duckdns.org. Connection pool size: 10\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Modèle chargé avec succès : RandomForestClassifier\n",
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"Type de vectoriseur utilisé dans ce run : bow\n",
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"Artefacts disponibles :\n",
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" - confusion_matrix.png\n",
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" - cv_results.csv\n",
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" - cv_results_max_depth.png\n",
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" - cv_results_min_samples_split.png\n",
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" - cv_results_n_estimators.png\n",
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" - roc_curve.png\n",
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" - vectorizer_BoW.pkl\n",
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"Vectoriseur de type BoW/CountVectorizer trouvé : vectorizer_BoW.pkl\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "91b8f1667ff54011bd98fd748a0a91c6",
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"version_major": 2,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Vectoriseur téléchargé dans : /tmp/tmpgfjoxqet/vectorizer_BoW.pkl\n",
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"Vectoriseur chargé avec succès : CountVectorizer\n",
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"Modèle et vectoriseur sauvegardés avec succès dans content/simple-model/best\n",
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"Chemins des fichiers :\n",
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" - Modèle : /content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks/content/simple-model/best/RandomForestClassifier_model.pkl\n",
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" - Vectoriseur : /content/drive/MyDrive/Colab_Notebooks/Project_7/notebooks/content/simple-model/best/CountVectorizer_vectorizer.pkl\n"
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]
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}
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],
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"source": [
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"source": [
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"# Charger et sauvegarder le meilleur modèle et son vectoriseur\n",
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"# Charger et sauvegarder le meilleur modèle et son vectoriseur\n",
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"best_model, vectorizer = load_and_save_best_model(run_id=best_model_row['Run ID'])"
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"best_model, vectorizer = load_and_save_best_model(run_id=best_model_row['Run ID'])"
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