anomalies (4) (1).ipynb

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  "metadata": {
    "colab": {
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Fusion de données"
      ],
      "metadata": {
        "id": "C-r-Y1IP75qS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Lecture des données\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "merged_df = pd.read_csv(\"new_data_concat.csv\")"
      ],
      "metadata": {
        "id": "28s7iHPj8DAN",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Taille de la base de données\n",
        "len(merged_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "vSj_Cwtm9J-d",
        "outputId": "ecbc45b8-a642-420a-b156-2399fd4ae696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5751809"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrer les lignes avec des quantités d'actes et des montants de dépense positifs\n",
        "merged_df = merged_df[\n",
        "    (merged_df['Quantité d\\'acte - Prestation seule (pas presta. de réf.)'] > 0) &\n",
        "    (merged_df['Montant de la dépense - Prestation seule'] > 0)\n",
        "]"
      ],
      "metadata": {
        "id": "TDB5Wj4p2pF1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Quelques variables rajoutés (en se basant sur les indicateurs de fraudes)\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Calcul du Délai prescription-facturation (en mois)\n",
        "merged_df['Délai prescription-facturation'] = (\n",
        "    (merged_df['Année de remboursement'] - merged_df['Année de prescription']) * 12 +\n",
        "    (merged_df['Mois de remboursement'] - merged_df['Mois de prescription'])\n",
        ")\n",
        "\n",
        "# Calcul des remboursements par mois\n",
        "remboursements_par_mois = merged_df.groupby(\n",
        "    ['N° PS exécutant Statistique', 'Année de remboursement', 'Mois de remboursement']\n",
        ")['Nombre de bénéficiaires'].sum().reset_index(name='Bénéficiaires par mois')\n",
        "merged_df = merged_df.merge(remboursements_par_mois, on=['N° PS exécutant Statistique', 'Année de remboursement', 'Mois de remboursement'])\n",
        "\n",
        "# Calcul des dépenses par mois\n",
        "depenses_par_mois = merged_df.groupby(\n",
        "    ['N° PS exécutant Statistique', 'Année de remboursement', 'Mois de remboursement']\n",
        ")['Montant de la dépense - Prestation seule'].sum().reset_index(name='Dépenses par mois')\n",
        "# Fusion des dépenses par mois dans le DataFrame original\n",
        "merged_df = merged_df.merge(depenses_par_mois, on=['N° PS exécutant Statistique', 'Année de remboursement', 'Mois de remboursement'])\n",
        "\n",
        "\n",
        "# Calcul de la proportion de jeunes pour chaque 'N° PS exécutant Statistique'\n",
        "proportion_jeunes = merged_df.groupby('N° PS exécutant Statistique')['Age du bénéficiaire'].apply(lambda x: (x < 18).mean())\n",
        "merged_df = merged_df.merge(proportion_jeunes.rename('Proportion jeunes'), on='N° PS exécutant Statistique')\n",
        "\n",
        "# Ajouter une colonne binaire 'Age supérieur à 18'\n",
        "merged_df['Age supérieur à 18'] = (merged_df['Age du bénéficiaire'] > 18).astype(int)"
      ],
      "metadata": {
        "id": "hdVp6zbs8DDj",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Erreurs de saisie (délais)\n",
        "# Filtrer les données pour retirer les délais supérieurs à 8 mois\n",
        "merged_df = merged_df[merged_df['Délai prescription-facturation'] <= 8]"
      ],
      "metadata": {
        "id": "G2jySn9w3TkV",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Nombre moyen de prescripteurs par établissement\n",
        "merged_df['Prescripteurs par établissement'] = merged_df.groupby('Département du cabinet principal du PS exécutant')['N° PS prescripteur Statistique'].transform('nunique')"
      ],
      "metadata": {
        "id": "6ceEBoPTlG5n",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Proportion des prescripteurs par établissement\n",
        "total_prescripteurs = merged_df.groupby('Département du cabinet principal du PS exécutant')['N° PS prescripteur Statistique'].transform('nunique')\n",
        "merged_df['Proportion prescripteurs par établissement'] = 1 / total_prescripteurs"
      ],
      "metadata": {
        "id": "EN4Df55Po-Lo",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Moyenne d'âge par établissement\n",
        "merged_df['Moyenne âge par établissement'] = (\n",
        "    merged_df.groupby('N° PS exécutant Statistique')['Age du bénéficiaire']\n",
        "    .transform('mean')\n",
        ")"
      ],
      "metadata": {
        "id": "cxShJ-BbpKwW",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Spécialités 'MEDECINE GENERALE' et 'OTO RHINO-LARYNGOLOGIE'\n",
        "merged_df = merged_df[\n",
        "    merged_df[\"Libellé spécialité/nat. activité du PS prescripteur\"].isin([\"MEDECINE GENERALE\", \"OTO RHINO-LARYNGOLOGIE\"])\n",
        "]\n",
        "\n",
        "# Compter le nombre d'occurrences de chaque spécialité par établissement\n",
        "counts = merged_df.groupby(['N° PS exécutant Statistique',\n",
        "                              \"Libellé spécialité/nat. activité du PS prescripteur\"]).size().reset_index(name='count')\n",
        "\n",
        "# Obtenir le nombre total pour chaque établissement\n",
        "total_counts = merged_df.groupby('N° PS exécutant Statistique').size().reset_index(name='total')\n",
        "\n",
        "# Fusionner les deux DataFrame pour avoir une base de calcul\n",
        "proportion_df = pd.merge(counts, total_counts, on='N° PS exécutant Statistique')\n",
        "\n",
        "# Calculer la proportion pour chaque spécialité\n",
        "proportion_df['proportion_med_gen'] = proportion_df.apply(lambda x: x['count'] / x['total'] if x['Libellé spécialité/nat. activité du PS prescripteur'] == \"MEDECINE GENERALE\" else 0, axis=1)\n",
        "proportion_df['proportion_orl'] = proportion_df.apply(lambda x: x['count'] / x['total'] if x['Libellé spécialité/nat. activité du PS prescripteur'] == \"OTO RHINO-LARYNGOLOGIE\" else 0, axis=1)\n",
        "\n",
        "# Agréger pour chaque établissement (somme par établissement)\n",
        "proportion_aggregated = proportion_df.groupby('N° PS exécutant Statistique')[['proportion_med_gen', 'proportion_orl']].sum().reset_index()\n",
        "\n",
        "# Fusionner les proportions calculées avec le DataFrame original\n",
        "merged_df = pd.merge(merged_df, proportion_aggregated, on='N° PS exécutant Statistique', how='left')"
      ],
      "metadata": {
        "id": "F9dqYPGDBLdT",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['proportion_med_gen'] = merged_df['proportion_med_gen'] * 100\n",
        "merged_df['proportion_orl'] = merged_df['proportion_orl'] * 100\n",
        "merged_df['difference_proportions'] = merged_df['proportion_orl']-merged_df['proportion_med_gen']"
      ],
      "metadata": {
        "id": "DQ3uimHJCcAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Distances entre régions"
      ],
      "metadata": {
        "id": "IMS4XxLr5ZXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Code régions\n",
        "\n",
        "REGIONS = {\n",
        "    'Auvergne-Rhône-Alpes': ['01', '03', '07', '15', '26', '38', '42', '43', '63', '69', '73', '74'],\n",
        "    'Bourgogne-Franche-Comté': ['21', '25', '39', '58', '70', '71', '89', '90'],\n",
        "    'Bretagne': ['35', '22', '56', '29'],\n",
        "    'Centre-Val de Loire': ['18', '28', '36', '37', '41', '45'],\n",
        "    'Corse': ['200', '201'],\n",
        "    'Grand Est': ['08', '10', '51', '52', '54', '55', '57', '67', '68', '88'],\n",
        "    'Guadeloupe': ['971'],\n",
        "    'Guyane': ['973'],\n",
        "    'Hauts-de-France': ['02', '59', '60', '62', '80'],\n",
        "    'Île-de-France': ['75', '77', '78', '91', '92', '93', '94', '95'],\n",
        "    'La Réunion': ['974'],\n",
        "    'Martinique': ['972'],\n",
        "    'Normandie': ['14', '27', '50', '61', '76'],\n",
        "    'Nouvelle-Aquitaine': ['16', '17', '19', '23', '24', '33', '40', '47', '64', '79', '86', '87'],\n",
        "    'Occitanie': ['09', '11', '12', '30', '31', '32', '34', '46', '48', '65', '66', '81', '82'],\n",
        "    'Pays de la Loire': ['44', '49', '53', '72', '85'],\n",
        "    'Provence-Alpes-Côte d\\'Azur': ['04', '05', '06', '13', '83', '84'],\n",
        "}\n",
        "\n",
        "DEPARTMENTS = {\n",
        "    '1': 'Ain',\n",
        "    '2': 'Aisne',\n",
        "    '3': 'Allier',\n",
        "    '4': 'Alpes-de-Haute-Provence',\n",
        "    '5': 'Hautes-Alpes',\n",
        "    '6': 'Alpes-Maritimes',\n",
        "    '7': 'Ardèche',\n",
        "    '8': 'Ardennes',\n",
        "    '9': 'Ariège',\n",
        "    '10': 'Aube',\n",
        "    '11': 'Aude',\n",
        "    '12': 'Aveyron',\n",
        "    '13': 'Bouches-du-Rhône',\n",
        "    '14': 'Calvados',\n",
        "    '15': 'Cantal',\n",
        "    '16': 'Charente',\n",
        "    '17': 'Charente-Maritime',\n",
        "    '18': 'Cher',\n",
        "    '19': 'Corrèze',\n",
        "    '200': 'Corse-du-Sud',\n",
        "    '201': 'Haute-Corse',\n",
        "    '21': 'Côte-d\\'Or',\n",
        "    '22': 'Côtes-d\\'Armor',\n",
        "    '23': 'Creuse',\n",
        "    '24': 'Dordogne',\n",
        "    '25': 'Doubs',\n",
        "    '26': 'Drôme',\n",
        "    '27': 'Eure',\n",
        "    '28': 'Eure-et-Loir',\n",
        "    '29': 'Finistère',\n",
        "    '30': 'Gard',\n",
        "    '31': 'Haute-Garonne',\n",
        "    '32': 'Gers',\n",
        "    '33': 'Gironde',\n",
        "    '34': 'Hérault',\n",
        "    '35': 'Ille-et-Vilaine',\n",
        "    '36': 'Indre',\n",
        "    '37': 'Indre-et-Loire',\n",
        "    '38': 'Isère',\n",
        "    '39': 'Jura',\n",
        "    '40': 'Landes',\n",
        "    '41': 'Loir-et-Cher',\n",
        "    '42': 'Loire',\n",
        "    '43': 'Haute-Loire',\n",
        "    '44': 'Loire-Atlantique',\n",
        "    '45': 'Loiret',\n",
        "    '46': 'Lot',\n",
        "    '47': 'Lot-et-Garonne',\n",
        "    '48': 'Lozère',\n",
        "    '49': 'Maine-et-Loire',\n",
        "    '50': 'Manche',\n",
        "    '51': 'Marne',\n",
        "    '52': 'Haute-Marne',\n",
        "    '53': 'Mayenne',\n",
        "    '54': 'Meurthe-et-Moselle',\n",
        "    '55': 'Meuse',\n",
        "    '56': 'Morbihan',\n",
        "    '57': 'Moselle',\n",
        "    '58': 'Nièvre',\n",
        "    '59': 'Nord',\n",
        "    '60': 'Oise',\n",
        "    '61': 'Orne',\n",
        "    '62': 'Pas-de-Calais',\n",
        "    '63': 'Puy-de-Dôme',\n",
        "    '64': 'Pyrénées-Atlantiques',\n",
        "    '65': 'Hautes-Pyrénées',\n",
        "    '66': 'Pyrénées-Orientales',\n",
        "    '67': 'Bas-Rhin',\n",
        "    '68': 'Haut-Rhin',\n",
        "    '69': 'Rhône',\n",
        "    '70': 'Haute-Saône',\n",
        "    '71': 'Saône-et-Loire',\n",
        "    '72': 'Sarthe',\n",
        "    '73': 'Savoie',\n",
        "    '74': 'Haute-Savoie',\n",
        "    '75': 'Paris',\n",
        "    '76': 'Seine-Maritime',\n",
        "    '77': 'Seine-et-Marne',\n",
        "    '78': 'Yvelines',\n",
        "    '79': 'Deux-Sèvres',\n",
        "    '80': 'Somme',\n",
        "    '81': 'Tarn',\n",
        "    '82': 'Tarn-et-Garonne',\n",
        "    '83': 'Var',\n",
        "    '84': 'Vaucluse',\n",
        "    '85': 'Vendée',\n",
        "    '86': 'Vienne',\n",
        "    '87': 'Haute-Vienne',\n",
        "    '88': 'Vosges',\n",
        "    '89': 'Yonne',\n",
        "    '90': 'Territoire de Belfort',\n",
        "    '91': 'Essonne',\n",
        "    '92': 'Hauts-de-Seine',\n",
        "    '93': 'Seine-Saint-Denis',\n",
        "    '94': 'Val-de-Marne',\n",
        "    '95': 'Val-d\\'Oise',\n",
        "    '971': 'Guadeloupe',\n",
        "    '972': 'Martinique',\n",
        "    '973': 'Guyane',\n",
        "    '974': 'La Réunion',\n",
        "    '976': 'Mayotte',\n",
        "}"
      ],
      "metadata": {
        "id": "p6X8wuAll4F4",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Mappage des régions\n",
        "def get_region(department):\n",
        "    for region, departments in REGIONS.items():\n",
        "        if department in departments:\n",
        "            return region\n",
        "    return None\n",
        "\n",
        "# Département de l'exécutant\n",
        "merged_df[\"Région du PS exécutant\"] = merged_df[\"Département d'exercice du PS exécutant\"].apply(get_region)\n",
        "# Région du bénéficiaire\n",
        "merged_df[\"Région du bénéficiaire\"] = merged_df[\"Département du bénéficiaire\"].apply(get_region)"
      ],
      "metadata": {
        "id": "cs8rEgNymk34",
        "collapsed": true,
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Données localisation\n",
        "\n",
        "coord_region = pd.read_csv('points-extremes-des-departements-metropolitains-de-france.csv', sep=',')\n",
        "# Latitude et longitude moyenne\n",
        "coord_region['Latitude moyenne'] = (\n",
        "    (coord_region['Latitude la plus au nord'] + coord_region['Latitude la plus au sud']) / 2\n",
        ")\n",
        "coord_region['Longitude moyenne'] = (\n",
        "    (coord_region['Longitude la plus à l’est'] + coord_region['Longitude la plus à l’ouest']) / 2\n",
        ")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "bXxdCihO5w2D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "departments_benef = coord_region.rename(columns={\n",
        "    'Departement': 'Département du bénéficiaire',\n",
        "    'Latitude moyenne': 'Latitude bénéficiaire',\n",
        "    'Longitude moyenne': 'Longitude bénéficiaire'\n",
        "})\n",
        "departments_execut = coord_region.rename(columns={\n",
        "    'Departement': 'Département d\\'exercice du PS exécutant',\n",
        "    'Latitude moyenne': 'Latitude exécutant',\n",
        "    'Longitude moyenne': 'Longitude exécutant'\n",
        "})"
      ],
      "metadata": {
        "id": "rQAM06TP6Cs6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2A'] = \"200\"\n",
        "departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2B'] = \"201\"\n",
        "\n",
        "departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2A'] = \"200\"\n",
        "departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2B'] = \"201\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ZQoK6M_t6Mxa",
        "outputId": "cf7f3e81-60a7-4691-82a6-516472f815f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-7b24c48e1a90>:1: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
            "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
            "A typical example is when you are setting values in a column of a DataFrame, like:\n",
            "\n",
            "df[\"col\"][row_indexer] = value\n",
            "\n",
            "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "  departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2A'] = \"200\"\n",
            "<ipython-input-15-7b24c48e1a90>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2A'] = \"200\"\n",
            "<ipython-input-15-7b24c48e1a90>:2: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
            "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
            "A typical example is when you are setting values in a column of a DataFrame, like:\n",
            "\n",
            "df[\"col\"][row_indexer] = value\n",
            "\n",
            "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "  departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2B'] = \"201\"\n",
            "<ipython-input-15-7b24c48e1a90>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  departments_benef['Département du bénéficiaire'][departments_benef['Département du bénéficiaire']== '2B'] = \"201\"\n",
            "<ipython-input-15-7b24c48e1a90>:4: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
            "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
            "A typical example is when you are setting values in a column of a DataFrame, like:\n",
            "\n",
            "df[\"col\"][row_indexer] = value\n",
            "\n",
            "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "  departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2A'] = \"200\"\n",
            "<ipython-input-15-7b24c48e1a90>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2A'] = \"200\"\n",
            "<ipython-input-15-7b24c48e1a90>:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
            "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
            "A typical example is when you are setting values in a column of a DataFrame, like:\n",
            "\n",
            "df[\"col\"][row_indexer] = value\n",
            "\n",
            "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "  departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2B'] = \"201\"\n",
            "<ipython-input-15-7b24c48e1a90>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  departments_execut['Département d\\'exercice du PS exécutant'][departments_execut['Département d\\'exercice du PS exécutant']== '2B'] = \"201\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "departments_benef['Département du bénéficiaire'] = departments_benef['Département du bénéficiaire'].astype(int)\n",
        "departments_execut['Département d\\'exercice du PS exécutant']= departments_execut['Département d\\'exercice du PS exécutant'].astype(int)"
      ],
      "metadata": {
        "id": "jAJ97vGj6VbD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = merged_df.merge(departments_benef, on='Département du bénéficiaire', how='left')\n",
        "merged_df = merged_df.merge(departments_execut, on='Département d\\'exercice du PS exécutant', how='left')"
      ],
      "metadata": {
        "id": "SKkvdAOX6buo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title calcul la distance beneficiare - etablissement\n",
        "def haversine(lat1, lon1, lat2, lon2):\n",
        "    R = 6371  # Rayon de la Terre en km\n",
        "    lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])\n",
        "    dlat = lat2 - lat1\n",
        "    dlon = lon2 - lon1\n",
        "    a = np.sin(dlat / 2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2)**2\n",
        "    c = 2 * np.arcsin(np.sqrt(a))\n",
        "    return R * c\n",
        "\n",
        "# Appliquer la fonction vectorisée\n",
        "merged_df['Distance benef ex (km)'] = haversine(\n",
        "    merged_df['Latitude bénéficiaire'],\n",
        "    merged_df['Longitude bénéficiaire'],\n",
        "    merged_df['Latitude exécutant'],\n",
        "    merged_df['Longitude exécutant']\n",
        ")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "_t-53Cyk8rAR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Distributions des distances\n",
        "merged_df['Distance benef ex (km)'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "collapsed": true,
        "id": "USGMrMq781-G",
        "outputId": "e22593e9-fa07-4fcd-e782-970aa6e312a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Distance benef ex (km)\n",
              "0.000000      3433801\n",
              "9.209367        15152\n",
              "7.951008        13657\n",
              "12.650608       10670\n",
              "54.799652        8254\n",
              "               ...   \n",
              "447.013197          1\n",
              "552.906542          1\n",
              "979.745351          1\n",
              "76.773308           1\n",
              "238.535363          1\n",
              "Name: count, Length: 3649, dtype: int64"
            ],
            "text/html": [
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              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Distance benef ex (km)</th>\n",
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              "  </thead>\n",
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              "      <td>10670</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title  Filtre sur les distances\n",
        "merged_df['Distance benef ex (km)'] = merged_df['Distance benef ex (km)'].apply(\n",
        "    lambda x: 0 if x < 50 else x * 1000\n",
        ")"
      ],
      "metadata": {
        "id": "O0RHi5YI9xlw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Modele de détéction d'**anomalies**\n",
        "\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import pandas as pd\n",
        "\n",
        "# Sélection des colonnes pertinentes\n",
        "features = [\n",
        "    'Délai prescription-facturation',\n",
        "    'Bénéficiaires par mois',\n",
        "    'Dépenses par mois',\n",
        "    'Quantité d\\'acte - Prestation seule (pas presta. de réf.)',\n",
        "    'Montant de la dépense - Prestation seule',\n",
        "    'Proportion jeunes',\n",
        "    'Age supérieur à 18',\n",
        "    'Prescripteurs par établissement',\n",
        "    'Moyenne âge par établissement',\n",
        "    'proportion_med_gen',\n",
        "    'proportion_orl',\n",
        "    'difference_proportions',\n",
        "    'Distance benef ex (km)'\n",
        "]\n",
        "\n",
        "# Remplir les valeurs manquantes avec la moyenne de chaque colonne (si nécessaire)\n",
        "merged_df[features] = merged_df[features].fillna(merged_df[features].mean())\n",
        "\n",
        "# Standardisation des données\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(merged_df[features])\n",
        "\n",
        "# Application de l'Isolation Forest\n",
        "model = IsolationForest(contamination=0.01, random_state=42)\n",
        "merged_df['Fraude_anomalie'] = model.fit_predict(X_scaled)\n"
      ],
      "metadata": {
        "id": "48GRzhtf8DHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Vue générale des résultats\n",
        "# Les anomalies seront marquées par -1, et les données normales par 1\n",
        "merged_df['Fraude_anomalie'] = merged_df['Fraude_anomalie'].map({1: 0, -1: 1})  # 0 pour normal, 1 pour fraude\n",
        "\n",
        "# Affichage du nombre d'anomalies détectées\n",
        "print(f\"Nombre d'anomalies détectées : {merged_df['Fraude_anomalie'].sum()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glxQkv07CmwM",
        "outputId": "c43eec00-06c6-450b-f5ab-edc458236aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nombre d'anomalies détectées : 39423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Délai prescription-facturation (généralement courtes pour les fraudes!)\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Comparaison des moyennes des variables pour les anomalies et les normales\n",
        "anomalies = merged_df[merged_df['Fraude_anomalie'] == 1]\n",
        "normales = merged_df[merged_df['Fraude_anomalie'] == 0]\n",
        "\n",
        "# Calcul des moyennes\n",
        "mean_anomalies = anomalies['Délai prescription-facturation'].mean()\n",
        "mean_normales = normales['Délai prescription-facturation'].mean()\n",
        "\n",
        "# Préparer les données pour le graphique\n",
        "categories = ['Anomalies', 'Normales']\n",
        "means = [mean_anomalies, mean_normales]\n",
        "\n",
        "# Création du graphique barh\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Choisir un jeu de couleurs stylé\n",
        "colors = ['#1f77b4', '#ff7f0e']\n",
        "\n",
        "# Tracer le graphique en barh\n",
        "bars = plt.barh(categories, means, color=colors, edgecolor='black', linewidth=1.2)\n",
        "\n",
        "# Ajouter des labels sur les barres\n",
        "for bar in bars:\n",
        "    plt.text(bar.get_width() + 0.05, bar.get_y() + bar.get_height()/2,\n",
        "             f'{bar.get_width():.2f}', va='center', ha='left', fontsize=12, color='black')\n",
        "\n",
        "# Ajouter des titres et des labels\n",
        "plt.xlabel(\"Moyenne du Délai prescription-facturation\", fontsize=14)\n",
        "plt.ylabel(\"Catégories\", fontsize=14)\n",
        "plt.title(\"Comparaison des moyennes du Délai prescription-facturation\", fontsize=16, fontweight='bold')\n",
        "\n",
        "# Personnaliser les axes et l'apparence\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "\n",
        "# Ajouter un fond et des bordures stylisées\n",
        "plt.gca().set_facecolor('whitesmoke')\n",
        "plt.gca().spines['top'].set_linewidth(1.5)\n",
        "plt.gca().spines['top'].set_color('gray')\n",
        "plt.gca().spines['right'].set_linewidth(1.5)\n",
        "plt.gca().spines['right'].set_color('gray')\n",
        "\n",
        "# Afficher le graphique\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "72fbMRkDCq7a",
        "outputId": "1e918c52-3e3b-4f4c-d266-e8260f9c97bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Départements fréquents dans les anomalies détectées\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Comptage des départements les plus fréquents dans anomalies\n",
        "top_departments = anomalies['Département d\\'exercice du PS exécutant'].value_counts().head(5)\n",
        "\n",
        "# Création du donut chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.pie(top_departments, labels=top_departments.index, autopct='%1.1f%%', startangle=90, wedgeprops={'width': 0.3})\n",
        "\n",
        "# Ajouter un cercle au centre pour créer l'effet donut\n",
        "centre_circle = plt.Circle((0, 0), 0.50, color='white', fc='white', lw=0)\n",
        "plt.gca().add_artist(centre_circle)\n",
        "\n",
        "# Ajouter un titre\n",
        "plt.title(\"Répartition des départements les plus fréquents dans les anomalies\", fontsize=14)\n",
        "\n",
        "# Afficher le graphique\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "qN6St5XlEDp3",
        "outputId": "346882ae-0c50-4734-a682-71c119456ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title departement_to_region\n",
        "departement_to_region = {\n",
        "    '01': 'Auvergne-Rhône-Alpes',\n",
        "    '02': 'Hauts-de-France',\n",
        "    '03': 'Auvergne-Rhône-Alpes',\n",
        "    '04': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '05': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '06': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '07': 'Auvergne-Rhône-Alpes',\n",
        "    '08': 'Grand Est',\n",
        "    '09': 'Occitanie',\n",
        "    '10': 'Grand Est',\n",
        "    '11': 'Occitanie',\n",
        "    '12': 'Occitanie',\n",
        "    '13': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '14': 'Normandie',\n",
        "    '15': 'Auvergne-Rhône-Alpes',\n",
        "    '16': 'Nouvelle-Aquitaine',\n",
        "    '17': 'Nouvelle-Aquitaine',\n",
        "    '18': 'Centre-Val de Loire',\n",
        "    '19': 'Nouvelle-Aquitaine',\n",
        "    '20': 'Corse',\n",
        "    '21': 'Bourgogne-Franche-Comté',\n",
        "    '22': 'Bretagne',\n",
        "    '23': 'Nouvelle-Aquitaine',\n",
        "    '24': 'Nouvelle-Aquitaine',\n",
        "    '25': 'Bourgogne-Franche-Comté',\n",
        "    '26': 'Auvergne-Rhône-Alpes',\n",
        "    '27': 'Normandie',\n",
        "    '28': 'Centre-Val de Loire',\n",
        "    '29': 'Bretagne',\n",
        "    '30': 'Occitanie',\n",
        "    '31': 'Occitanie',\n",
        "    '32': 'Occitanie',\n",
        "    '33': 'Nouvelle-Aquitaine',\n",
        "    '34': 'Occitanie',\n",
        "    '35': 'Bretagne',\n",
        "    '36': 'Centre-Val de Loire',\n",
        "    '37': 'Centre-Val de Loire',\n",
        "    '38': 'Auvergne-Rhône-Alpes',\n",
        "    '39': 'Bourgogne-Franche-Comté',\n",
        "    '40': 'Nouvelle-Aquitaine',\n",
        "    '41': 'Centre-Val de Loire',\n",
        "    '42': 'Auvergne-Rhône-Alpes',\n",
        "    '43': 'Auvergne-Rhône-Alpes',\n",
        "    '44': 'Pays de la Loire',\n",
        "    '45': 'Centre-Val de Loire',\n",
        "    '46': 'Occitanie',\n",
        "    '47': 'Nouvelle-Aquitaine',\n",
        "    '48': 'Occitanie',\n",
        "    '49': 'Pays de la Loire',\n",
        "    '50': 'Normandie',\n",
        "    '51': 'Grand Est',\n",
        "    '52': 'Grand Est',\n",
        "    '53': 'Pays de la Loire',\n",
        "    '54': 'Grand Est',\n",
        "    '55': 'Grand Est',\n",
        "    '56': 'Bretagne',\n",
        "    '57': 'Grand Est',\n",
        "    '58': 'Bourgogne-Franche-Comté',\n",
        "    '59': 'Hauts-de-France',\n",
        "    '60': 'Hauts-de-France',\n",
        "    '61': 'Normandie',\n",
        "    '62': 'Hauts-de-France',\n",
        "    '63': 'Auvergne-Rhône-Alpes',\n",
        "    '64': 'Nouvelle-Aquitaine',\n",
        "    '65': 'Occitanie',\n",
        "    '66': 'Occitanie',\n",
        "    '67': 'Grand Est',\n",
        "    '68': 'Grand Est',\n",
        "    '69': 'Auvergne-Rhône-Alpes',\n",
        "    '70': 'Bourgogne-Franche-Comté',\n",
        "    '71': 'Bourgogne-Franche-Comté',\n",
        "    '72': 'Pays de la Loire',\n",
        "    '73': 'Auvergne-Rhône-Alpes',\n",
        "    '74': 'Auvergne-Rhône-Alpes',\n",
        "    '75': 'Île-de-France',\n",
        "    '76': 'Normandie',\n",
        "    '77': 'Île-de-France',\n",
        "    '78': 'Île-de-France',\n",
        "    '79': 'Nouvelle-Aquitaine',\n",
        "    '80': 'Hauts-de-France',\n",
        "    '81': 'Occitanie',\n",
        "    '82': 'Occitanie',\n",
        "    '83': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '84': 'Provence-Alpes-Côte d\\'Azur',\n",
        "    '85': 'Pays de la Loire',\n",
        "    '86': 'Nouvelle-Aquitaine',\n",
        "    '87': 'Nouvelle-Aquitaine',\n",
        "    '88': 'Grand Est',\n",
        "    '89': 'Bourgogne-Franche-Comté',\n",
        "    '90': 'Bourgogne-Franche-Comté',\n",
        "    '91': 'Île-de-France',\n",
        "    '92': 'Île-de-France',\n",
        "    '93': 'Île-de-France',\n",
        "    '94': 'Île-de-France',\n",
        "    '95': 'Île-de-France',\n",
        "    '96': 'Outre-mer',\n",
        "    '97': 'Outre-mer',\n",
        "    '98': 'Outre-mer',\n",
        "    '99': 'Outre-mer',\n",
        "}\n",
        "\n",
        "# Comptage des départements les plus fréquents dans anomalies\n",
        "top_departments = anomalies['Département d\\'exercice du PS exécutant'].value_counts().head(10)\n",
        "\n",
        "# Mappage des départements vers les régions\n",
        "top_departments_regions = top_departments.index.astype(str).map(departement_to_region)\n",
        "\n",
        "# Comptage des régions les plus fréquentes dans anomalies (après mappage)\n",
        "region_counts = top_departments_regions.value_counts()\n",
        "\n",
        "# Mappage des départements aux régions\n",
        "department_to_region = top_departments.index.to_series().map(departement_to_region)\n",
        "\n",
        "# Calcul de la somme des pourcentages des départements dans chaque région\n",
        "region_percentage = top_departments.groupby(top_departments_regions).sum() / top_departments.sum() * 100\n",
        "\n",
        "# Création du barh chart pour les régions\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Choisir un jeu de couleurs stylé\n",
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
        "\n",
        "# Tracer le graphique en barh\n",
        "bars = region_percentage.plot(kind='barh', color=colors, edgecolor='black', linewidth=1.2)\n",
        "\n",
        "# Ajouter des labels sur les barres\n",
        "for index, value in enumerate(region_percentage):\n",
        "    # Ajouter l'étiquette dans la barre, avec la somme des pourcentages des départements\n",
        "    plt.text(value + 0.1, index, f'{value:.1f}%', va='center', ha='left', fontsize=12, color='black')\n",
        "\n",
        "# Ajouter des titres et des labels\n",
        "plt.xlabel(\"Somme des pourcentages des départements\", fontsize=14)\n",
        "plt.ylabel(\"Régions\", fontsize=14)\n",
        "plt.title(\"Répartition des régions les plus fréquentes dans les anomalies\", fontsize=16, fontweight='bold')\n",
        "\n",
        "# Personnaliser les axes et l'apparence\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "\n",
        "# Ajouter un fond et des bordures stylisées\n",
        "plt.gca().set_facecolor('whitesmoke')\n",
        "plt.gca().spines['top'].set_linewidth(1.5)\n",
        "plt.gca().spines['top'].set_color('gray')\n",
        "plt.gca().spines['right'].set_linewidth(1.5)\n",
        "plt.gca().spines['right'].set_color('gray')\n",
        "\n",
        "# Afficher le graphique\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "cSFaMCrhGbJx",
        "outputId": "8f3e06a9-bc88-4dcf-d93a-22eb3fb5d914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Exemple de détéction (à vérifier)\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sélectionner l'établissement le plus fréquent dans les anomalies et les normales\n",
        "etablissement_anomalie_id = anomalies['N° PS exécutant Statistique'].value_counts().idxmax()  # L'établissement le plus fréquent dans les anomalies\n",
        "etablissement_normal_id = normales['N° PS exécutant Statistique'].value_counts().idxmax()  # L'établissement le plus fréquent dans les normales\n",
        "\n",
        "# Filtrer les données pour ces deux établissements\n",
        "anomalies_etablissement = anomalies[anomalies['N° PS exécutant Statistique'] == etablissement_anomalie_id]\n",
        "normales_etablissement = normales[normales['N° PS exécutant Statistique'] == etablissement_normal_id]\n",
        "\n",
        "# Calculer les remboursements totaux par mois pour anomalies et normales\n",
        "anomalies_mois = anomalies_etablissement.groupby(['Année de remboursement', 'Mois de remboursement'])['Dépenses par mois'].sum().reset_index()\n",
        "normales_mois = normales_etablissement.groupby(['Année de remboursement', 'Mois de remboursement'])['Dépenses par mois'].sum().reset_index()\n",
        "\n",
        "# Créer un graphique\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Tracer les remboursements pour l'établissement avec anomalies\n",
        "sns.lineplot(data=anomalies_mois, x='Mois de remboursement', y='Dépenses par mois', label=f'Anomalies ({etablissement_anomalie_id})', color='red', marker='o')\n",
        "\n",
        "# Tracer les remboursements pour l'établissement normal\n",
        "sns.lineplot(data=normales_mois, x='Mois de remboursement', y='Dépenses par mois', label=f'Normales ({etablissement_normal_id})', color='blue', marker='o')\n",
        "\n",
        "# Ajouter des détails au graphique\n",
        "plt.title(f'Dépenses par mois : {etablissement_anomalie_id} (Anomalies) vs {etablissement_normal_id} (Normales)')\n",
        "plt.xlabel('Mois')\n",
        "plt.ylabel('Dépenses')\n",
        "plt.legend(title='Catégorie', loc='upper left')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "\n",
        "# Afficher le graphique\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Affichage des établissements sélectionnés pour confirmation\n",
        "print(f\"Établissement le plus fréquent dans les anomalies : {etablissement_anomalie_id}\")\n",
        "print(f\"Établissement le plus fréquent dans les normales : {etablissement_normal_id}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "R1ugqxY4Gluo",
        "outputId": "ee627775-7d40-4ee1-81ab-d180bf718c53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Établissement le plus fréquent dans les anomalies : A92495305\n",
            "Établissement le plus fréquent dans les normales : A90494835\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anomalies['Distance benef ex (km)']= anomalies['Distance benef ex (km)']/1000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6L4dZmeYBdQo",
        "outputId": "a1f57e0b-621c-4d90-a4b2-d2cdd13bfb16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-55-e2ce125c0013>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  anomalies['Distance benef ex (km)']= anomalies['Distance benef ex (km)']/1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anomalies['Distance benef ex (km)'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "F87L3hgZBUTa",
        "outputId": "af56b53d-3266-40c7-ca9e-d00763341123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Distance benef ex (km)\n",
              "0.000000      14559\n",
              "231.466946     1835\n",
              "220.716056     1114\n",
              "53.216835       671\n",
              "158.388352      636\n",
              "              ...  \n",
              "385.784311        1\n",
              "89.542321         1\n",
              "507.087990        1\n",
              "61.180216         1\n",
              "675.699308        1\n",
              "Name: count, Length: 1025, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Distance benef ex (km)</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.000000</th>\n",
              "      <td>14559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231.466946</th>\n",
              "      <td>1835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220.716056</th>\n",
              "      <td>1114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53.216835</th>\n",
              "      <td>671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158.388352</th>\n",
              "      <td>636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>385.784311</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89.542321</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507.087990</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61.180216</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675.699308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1025 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution des distances dans les anomalies\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(anomalies['Distance benef ex (km)'], bins=30, kde=True, color='orange')\n",
        "plt.title(\"Distribution des distances (anomalies)\", fontsize=16)\n",
        "plt.xlabel(\"Distance benef ex (km)\", fontsize=12)\n",
        "plt.ylabel(\"Nombre d'occurrences\", fontsize=12)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "XcQB5AEa_v2x",
        "outputId": "bd4186e2-7e4c-42b9-8d29-b72d5c6c68e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lime.lime_tabular import LimeTabularExplainer\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Assurez-vous que 'features' correspond exactement à la taille de 'X_scaled'\n",
        "features = [\n",
        "    'Délai prescription-facturation',\n",
        "    'Bénéficiaires par mois',\n",
        "    'Dépenses par mois',\n",
        "    'Quantité d\\'acte - Prestation seule (pas presta. de réf.)',\n",
        "    'Montant de la dépense - Prestation seule',\n",
        "    'Proportion jeunes',\n",
        "    'Age supérieur à 18',\n",
        "    'Prescripteurs par établissement',\n",
        "    'Moyenne âge par établissement',\n",
        "    'proportion_med_gen',\n",
        "    'proportion_orl',\n",
        "    'difference_proportions',\n",
        "    'Distance benef ex (km)'\n",
        "]\n",
        "\n",
        "# Vérifiez que la taille de X_scaled est bien la même que la longueur de features\n",
        "print(\"Shape de X_scaled:\", X_scaled.shape)\n",
        "print(\"Longueur de features:\", len(features))\n",
        "\n",
        "# Filtrer les anomalies et les données normalisées\n",
        "anomalies_scaled = X_scaled[merged_df['Fraude_anomalie'] == 1]\n",
        "anomalies_df = merged_df[merged_df['Fraude_anomalie'] == 1]\n",
        "\n",
        "# Vérification si des anomalies existent\n",
        "if anomalies_scaled.shape[0] == 0:\n",
        "    print(\"Aucune anomalie détectée.\")\n",
        "else:\n",
        "    # Création de l'explainer LIME avec les données des anomalies uniquement\n",
        "    explainer = LimeTabularExplainer(\n",
        "        training_data=X_scaled,  # Utilisation de l'ensemble de données standardisé pour l'explication\n",
        "        mode=\"classification\",  # Mode classification\n",
        "        feature_names=features,  # Vérifiez que cette liste correspond au nombre de colonnes dans X_scaled\n",
        "        discretize_continuous=True,\n",
        "        random_state=42\n",
        "    )\n",
        "\n",
        "    # Sélection d'une anomalie spécifique (par exemple, la première anomalie)\n",
        "    observation_index = 0  # Vous pouvez ajuster cet index pour explorer d'autres anomalies\n",
        "    observation = anomalies_scaled[observation_index].reshape(1, -1)\n",
        "\n",
        "    # Fonction de prédiction pour LIME\n",
        "    def predict_proba(X):\n",
        "        try:\n",
        "            # Retourner les probabilités directement si disponibles\n",
        "            if hasattr(model, \"predict_proba\"):\n",
        "                return model.predict_proba(X)\n",
        "            # Sinon, calculer des probabilités à partir des scores de décision\n",
        "            elif hasattr(model, \"decision_function\"):\n",
        "                scores = model.decision_function(X)\n",
        "                probas = (scores - scores.min()) / (scores.max() - scores.min())\n",
        "                return np.vstack([1 - probas, probas]).T\n",
        "            else:\n",
        "                raise AttributeError(\"Le modèle doit implémenter 'predict_proba' ou 'decision_function'.\")\n",
        "        except Exception as e:\n",
        "            print(f\"Erreur lors de la génération des probabilités : {e}\")\n",
        "            return None\n",
        "\n",
        "    # Générer une explication pour l'observation\n",
        "    exp = explainer.explain_instance(\n",
        "        data_row=observation.flatten(),  # Observation à expliquer\n",
        "        predict_fn=predict_proba,  # Fonction de prédiction\n",
        "        num_features=13)\n",
        "\n",
        "    # Afficher les contributions des caractéristiques\n",
        "    feature_contributions = exp.as_list()\n",
        "\n",
        "    # Extraire les noms des caractéristiques et leurs contributions\n",
        "    feature_names, contributions = zip(*feature_contributions)\n",
        "\n",
        "    # Création du barplot\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    colors = ['green' if value > 0 else 'red' for value in contributions]  # Vert : contribution positive, rouge : négative\n",
        "    plt.barh(feature_names, contributions, color=colors)\n",
        "\n",
        "    # Ajouter les labels et le titre\n",
        "    plt.xlabel(\"Contribution à la prédiction\", fontsize=12)\n",
        "    plt.ylabel(\"Caractéristiques\", fontsize=12)\n",
        "    plt.title(f\"Contributions des caractéristiques à une anomalie (Observation {observation_index})\", fontsize=16)\n",
        "    plt.gca().invert_yaxis()  # Inverser l'axe Y\n",
        "    plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.7)\n",
        "\n",
        "    # Afficher le graphe\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "IcUgkPRE9pmB",
        "outputId": "7a18ad70-3ea7-452d-f0f1-a5b063b50e1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape de X_scaled: (3942359, 13)\n",
            "Longueur de features: 13\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anomalies.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "-J6LlbEaE0Ls",
        "outputId": "4baa31a9-91e0-45c8-ceec-5e19bc971b08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Année de remboursement  Mois de remboursement  \\\n",
              "956                    2021                      4   \n",
              "\n",
              "    N° PS prescripteur Statistique  \\\n",
              "956                      A03334694   \n",
              "\n",
              "    Libellé spécialité/nat. activité du PS prescripteur  \\\n",
              "956                                  MEDECINE GENERALE    \n",
              "\n",
              "     Département du cabinet principal du PS Prescripteur  \\\n",
              "956                                                  2     \n",
              "\n",
              "     Année de prescription  Mois de prescription N° PS exécutant Statistique  \\\n",
              "956                   2021                     4                   A92495305   \n",
              "\n",
              "     Catégorie du PS exécutant  \\\n",
              "956                          2   \n",
              "\n",
              "     Département du cabinet principal du PS exécutant  ...  \\\n",
              "956                                                91  ...   \n",
              "\n",
              "     Latitude bénéficiaire  Longitude bénéficiaire  \\\n",
              "956              49.453534                3.608257   \n",
              "\n",
              "     Latitude la plus au nord_y  Latitude la plus au sud_y  \\\n",
              "956                   48.776102                  48.284689   \n",
              "\n",
              "     Longitude la plus à l’est_y  Longitude la plus à l’ouest_y  \\\n",
              "956                     2.585374                        1.91492   \n",
              "\n",
              "     Latitude exécutant  Longitude exécutant  Distance benef ex (km)  \\\n",
              "956           48.530395             2.250147              142.668612   \n",
              "\n",
              "     Fraude_anomalie  \n",
              "956                1  \n",
              "\n",
              "[1 rows x 49 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-853156e4-d09f-4bfc-9873-b4f3d1fff51d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Année de remboursement</th>\n",
              "      <th>Mois de remboursement</th>\n",
              "      <th>N° PS prescripteur Statistique</th>\n",
              "      <th>Libellé spécialité/nat. activité du PS prescripteur</th>\n",
              "      <th>Département du cabinet principal du PS Prescripteur</th>\n",
              "      <th>Année de prescription</th>\n",
              "      <th>Mois de prescription</th>\n",
              "      <th>N° PS exécutant Statistique</th>\n",
              "      <th>Catégorie du PS exécutant</th>\n",
              "      <th>Département du cabinet principal du PS exécutant</th>\n",
              "      <th>...</th>\n",
              "      <th>Latitude bénéficiaire</th>\n",
              "      <th>Longitude bénéficiaire</th>\n",
              "      <th>Latitude la plus au nord_y</th>\n",
              "      <th>Latitude la plus au sud_y</th>\n",
              "      <th>Longitude la plus à l’est_y</th>\n",
              "      <th>Longitude la plus à l’ouest_y</th>\n",
              "      <th>Latitude exécutant</th>\n",
              "      <th>Longitude exécutant</th>\n",
              "      <th>Distance benef ex (km)</th>\n",
              "      <th>Fraude_anomalie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>956</th>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>A03334694</td>\n",
              "      <td>MEDECINE GENERALE</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>A92495305</td>\n",
              "      <td>2</td>\n",
              "      <td>91</td>\n",
              "      <td>...</td>\n",
              "      <td>49.453534</td>\n",
              "      <td>3.608257</td>\n",
              "      <td>48.776102</td>\n",
              "      <td>48.284689</td>\n",
              "      <td>2.585374</td>\n",
              "      <td>1.91492</td>\n",
              "      <td>48.530395</td>\n",
              "      <td>2.250147</td>\n",
              "      <td>142.668612</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 49 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-853156e4-d09f-4bfc-9873-b4f3d1fff51d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-853156e4-d09f-4bfc-9873-b4f3d1fff51d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-853156e4-d09f-4bfc-9873-b4f3d1fff51d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "anomalies"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: anomalies to csv\n",
        "\n",
        "anomalies.to_csv('anomalies.csv', index=False)\n",
        "from google.colab import files\n",
        "files.download('anomalies.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "4j3ro_SCF9FP",
        "outputId": "eacc5fd4-1b8e-4166-e8e9-47ddab179971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_669013cb-227f-4dc2-bb2c-27b34753c0a7\", \"anomalies.csv\", 17406004)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}