recap.ipynb

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   "source": [
    "# Récapitulatif de nos résultats\n",
    "\n",
    "Ce notebook a pour but de présenter nos travaux sur ce projet, et notamment les performances de différents modèles que nous avons utilisé."
   ]
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   "metadata": {},
   "outputs": [],
   "source": [
    "# pour que, quand une fonction de recap.py est changée, on ait pas besoin de relancer cette cellule\n",
    "# import autoreload\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "try:\n",
    "    from recap import *\n",
    "except:\n",
    "    from recap.recap import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "df_scrap, df_label, df_gen = load_df()"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## 1. Statistiques sur nos différentes bases de données"
   ]
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   "metadata": {},
   "source": [
    "### BDD générée par LLM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette BDD a été construite en utilisant le LLM `XWIN-b7` basé sur Llama-2. Ceci n'est qu'un extrait de notre base complète. "
   ]
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      "- Nombre de tweets : 5691\n",
      "- Nombre de sujets : 190\n"
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   "source": [
    "### BDD scrapée depuis Twitter"
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   "source": [
    "Cette BDD a été récupérée _via_ l'API de Twitter lorsqu'elle était encore gratuite."
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      "- Nombre de tweets : 90152\n",
      "- Nombre de sujets : 212\n"
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      "- Nombre de tweets : 298\n",
      "- Nombre de sujets : 154\n"
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             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
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             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Répartition par sentiments (BDD labelisée à la main)"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "x"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "sum of y"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_info(df_label, \"BDD labelisée à la main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Analyses des performances des LLM sur la BDD générée\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confiance moyenne du modèle pour le vrai topic : 0.81\n",
      "Nombre de prédictions où le meilleur topic (selon le modèle) est le vrai topic : 1143 (57.15%)\n",
      "Nombre de prédictions où le vrai topic a un score supérieur à 0.8 : 1505 (75.25%)\n"
     ]
    }
   ],
   "source": [
    "# évaluation des prédictions\n",
    "\n",
    "preds = pd.read_csv(os.path.join(\"results\",\"res_topic_2000_comp.csv\"))\n",
    "try:\n",
    "    preds.drop(columns=[\"Unnamed: 0.1\", \"Unnamed: 0\", \"index\"], inplace=True)\n",
    "except: pass\n",
    "\n",
    "def score_true_topic(truth, pred_labels, pred_scores):\n",
    "    \"\"\"\n",
    "    score = confiance du modèle pour le vrai topic\n",
    "    \"\"\"\n",
    "    # print(truth, pred_labels)\n",
    "    try:\n",
    "        return pred_scores[ pred_labels.index(truth) ]\n",
    "    except ValueError:\n",
    "        return 0\n",
    "\n",
    "\n",
    "def score_best_topic(truth, pred_labels, pred_scores):\n",
    "    \"\"\"\n",
    "    score = 1 si le topic avec le plus haut score est le même que le vrai topic\n",
    "            0 sinon\n",
    "    \"\"\"\n",
    "    try:\n",
    "        return truth == pred_labels[0]\n",
    "    except IndexError: # aucune prédiction (pas censé arriver !)\n",
    "        return 0\n",
    "def score_threshold_topic(truth, pred_labels, pred_scores, threshold=0.8):\n",
    "    \"\"\"\n",
    "    score = 1 si la confiance du modèle pour le vrai topic est supérieur au threshold\n",
    "            0 sinon\n",
    "    \"\"\"\n",
    "    try:\n",
    "        return pred_scores[ pred_labels.index(truth) ] > 0.8\n",
    "    except ValueError:\n",
    "        return 0\n",
    "    \n",
    "threshold = 0.8\n",
    "\n",
    "accu_true_topic = 0\n",
    "accu_best_topic = 0\n",
    "accu_thresh     = 0\n",
    "n=len(preds)\n",
    "preds[\"labels\"] = preds[\"labels\"].map(eval)\n",
    "preds[\"scores\"] = preds[\"scores\"].map(eval)\n",
    "for id,truth, pred_labels, pred_scores in preds[[\"topic\",\"labels\",\"scores\"]].itertuples(index=True, name=None):\n",
    "    # pred_labels = eval(pred_labels)\n",
    "    # pred_scores = eval(pred_scores)\n",
    "    accu_true_topic += score_true_topic(truth, pred_labels, pred_scores)\n",
    "    accu_best_topic += score_best_topic(truth, pred_labels, pred_scores)\n",
    "    accu_thresh += score_threshold_topic(truth, pred_labels, pred_scores, threshold)\n",
    "\n",
    "print(f\"Confiance moyenne du modèle pour le vrai topic : {accu_true_topic/n:.2f}\")\n",
    "print(f\"Nombre de prédictions où le meilleur topic (selon le modèle) est le vrai topic : \"\n",
    "      f\"{accu_best_topic} ({accu_best_topic/n:.2%})\")\n",
    "print(f\"Nombre de prédictions où le vrai topic a un score supérieur à {threshold} : \"\n",
    "      f\"{accu_thresh} ({accu_thresh/n:.2%})\")\n",
    "\n",
    "\n",
    "conf_par_sujet = {}\n",
    "for sujet in preds[\"topic\"].unique():\n",
    "    all_sujet = preds[preds[\"topic\"] == sujet]\n",
    "    conf_par_sujet[sujet] = sum(\n",
    "        score_best_topic(sujet, pred_labels, pred_scores) \n",
    "        for pred_labels, pred_scores in all_sujet[[\"labels\",\"scores\"]].itertuples(index=False,name=None)\n",
    "    ) / len(all_sujet)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy : 0.45491954629385384\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.62      0.35      0.45     18933\n",
      "         0.0       0.37      0.62      0.46     18980\n",
      "         1.0       0.52      0.39      0.45     18952\n",
      "\n",
      "    accuracy                           0.45     56865\n",
      "   macro avg       0.50      0.45      0.45     56865\n",
      "weighted avg       0.50      0.45      0.45     56865\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"results/res_gen_sent_3_classes.csv\")\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "df_tested = df[~df.sentiment_pred.isna()]\n",
    "df_tested[\"sentiment\"] = df_tested[\"label\"].map(\n",
    "    lambda x : 0.0 if x == \"neutre\" else 1.0 if x == \"positif\" else -1.0\n",
    ")\n",
    "\n",
    "\n",
    "# df_tested_no_neutral = df_tested[(df_tested[\"sentiment\"] != 0) & (df_tested[\"sentiment_pred\"] != 0]\n",
    "\n",
    "acc = accuracy_score(df_tested['sentiment'], df_tested['sentiment_pred'])\n",
    "print(f\"accuracy : {acc}\")\n",
    "\n",
    "cm = confusion_matrix(df_tested['sentiment'], df_tested['sentiment_pred'])\n",
    "\n",
    "plt.figure()\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cmap=\"Blues\",\n",
    "    xticklabels=[\"négatif\", \"neutre\", \"positif\"],\n",
    "    yticklabels=[\"négatif\", \"neutre\", \"positif\"],\n",
    ")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")\n",
    "plt.title(\"Confusion Matrix for Roberta on generated tweets\")\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(df_tested['sentiment'], df_tested['sentiment_pred']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Test semi-interractif de nos modèles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/pernas/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/pernas/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/pernas/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "Chargement du modèle twitter-xlm-roberta-base-sentiment-finetunned (peut prendre quelques minutes)\n",
      "Chargement du modèle camembert-base-xnli\n"
     ]
    }
   ],
   "source": [
    "# chargement du modèle\n",
    "from transformers import pipeline\n",
    "from preprocessing import preprocess_text\n",
    "\n",
    "print(\"Chargement du modèle twitter-xlm-roberta-base-sentiment-finetunned (peut prendre quelques minutes)\")\n",
    "try:\n",
    "    classifier_sentiment = pipeline(\"text-classification\", model=\"citizenlab/twitter-xlm-roberta-base-sentiment-finetunned\")\n",
    "except:\n",
    "    print(\"Il y a eu un problème, deuxième tentative avec from_pt=True\")\n",
    "    classifier_sentiment = pipeline(\"text-classification\", model=\"citizenlab/twitter-xlm-roberta-base-sentiment-finetunned\", model_kwargs={\"from_pt\":True})\n",
    "\n",
    "print(\"Chargement du modèle camembert-base-xnli\")\n",
    "\n",
    "topic_classifier = pipeline(\"zero-shot-classification\", model=\"mtheo/camembert-base-xnli\", multi_label=True)\n",
    "\n",
    "    # print(\"Il y a eu un problème, deuxième tentative avec from_pt=True\")\n",
    "    # topic_classifier = pipeline(\"zero-shot-classification\", model=\"mtheo/camembert-base-xnli\", device=0, multi_label=True, model_kwargs={\"from_pt\":True})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prédictions :\n",
      " - Sentiment : Neutral, confiance : 0.961\n",
      " - Topic dominant   : Actualités (confiance : 0.5454986691474915)\n",
      " - Topic secondaire : Entreprise (confiance : 0.40949079394340515)\n"
     ]
    }
   ],
   "source": [
    "# tweet (ou n'importe quel texte pas trop long)\n",
    "tweet = \"Le succès ne consiste pas à ne jamais faire d'erreur mais à ne jamais faire la même erreur deux fois\"\n",
    "\n",
    "# traitement...\n",
    "tweet = preprocess_text(tweet, stem_lemm=None)\n",
    "pred_sent = classifier_sentiment.predict(tweet)[0]\n",
    "pred_topic = topic_classifier([tweet], topics, multi_label=True, hypothesis_template=\"Ce tweet parle de {}.\")[0]\n",
    "sent = pred_sent[\"label\"]\n",
    "score_sent = pred_sent[\"score\"]\n",
    "topics = pred_topic[\"labels\"]\n",
    "topics_score = pred_topic[\"scores\"]\n",
    "\n",
    "print(\"Prédictions :\")\n",
    "print(f\" - Sentiment : {sent}, confiance : {score_sent:.3f}\")\n",
    "print(f\" - Topic dominant   : {topics[0]} (confiance : {topics_score[0]})\")\n",
    "print(f\" - Topic secondaire : {topics[1]} (confiance : {topics_score[1]})\")\n",
    "\n"
   ]
  }
 ],
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