{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66148399-693d-4d63-9be0-5716dcc9f334",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "plot a pretty confusion matrix with seaborn\n",
    "Created on Mon Jun 25 14:17:37 2018\n",
    "@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC\n",
    "REFerences:\n",
    "  https://www.mathworks.com/help/nnet/ref/plotconfusion.html\n",
    "  https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report\n",
    "  https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python\n",
    "  https://www.programcreek.com/python/example/96197/seaborn.heatmap\n",
    "  https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054\n",
    "  http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n",
    "\"\"\"\n",
    "import sys\n",
    "sys.path.append('../') ##accrocchio\n",
    "from src.utils import retrive_data,split\n",
    "import pickle\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from optuna.visualization import plot_param_importances,plot_edf,plot_optimization_history,plot_intermediate_values,plot_parallel_coordinate,plot_edf\n",
    "\n",
    "import matplotlib.font_manager as fm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "from matplotlib.collections import QuadMesh\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score, matthews_corrcoef,confusion_matrix\n",
    "\n",
    "def get_new_fig(fn, figsize=[9, 9]):\n",
    "    \"\"\"Init graphics\"\"\"\n",
    "    fig1 = plt.figure(fn, figsize)\n",
    "    ax1 = fig1.gca()  # Get Current Axis\n",
    "    ax1.cla()  # clear existing plot\n",
    "    return fig1, ax1\n",
    "\n",
    "\n",
    "def configcell_text_and_colors(\n",
    "    array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0\n",
    "):\n",
    "    \"\"\"\n",
    "    config cell text and colors\n",
    "    and return text elements to add and to dell\n",
    "    @TODO: use fmt\n",
    "    \"\"\"\n",
    "    text_add = []\n",
    "    text_del = []\n",
    "    cell_val = array_df[lin][col]\n",
    "    tot_all = array_df[-1][-1]\n",
    "    per = (float(cell_val) / tot_all) * 100\n",
    "    curr_column = array_df[:, col]\n",
    "    ccl = len(curr_column)\n",
    "\n",
    "    # last line  and/or last column\n",
    "    if (col == (ccl - 1)) or (lin == (ccl - 1)):\n",
    "        # tots and percents\n",
    "        if cell_val != 0:\n",
    "            if (col == ccl - 1) and (lin == ccl - 1):\n",
    "                tot_rig = 0\n",
    "                for i in range(array_df.shape[0] - 1):\n",
    "                    tot_rig += array_df[i][i]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            elif col == ccl - 1:\n",
    "                tot_rig = array_df[lin][lin]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            elif lin == ccl - 1:\n",
    "                tot_rig = array_df[col][col]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            per_err = 100 - per_ok\n",
    "        else:\n",
    "            per_ok = per_err = 0\n",
    "\n",
    "        per_ok_s = [\"%.2f%%\" % (per_ok), \"100%\"][per_ok == 100]\n",
    "\n",
    "        # text to DEL\n",
    "        text_del.append(oText)\n",
    "\n",
    "        # text to ADD\n",
    "        font_prop = fm.FontProperties(weight=\"bold\", size=fz)\n",
    "        text_kwargs = dict(\n",
    "            color=\"w\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            gid=\"sum\",\n",
    "            fontproperties=font_prop,\n",
    "        )\n",
    "        lis_txt = [\"%d\" % (cell_val), per_ok_s, \"%.2f%%\" % (per_err)]\n",
    "        lis_kwa = [text_kwargs]\n",
    "        dic = text_kwargs.copy()\n",
    "        dic[\"color\"] = \"g\"\n",
    "        lis_kwa.append(dic)\n",
    "        dic = text_kwargs.copy()\n",
    "        dic[\"color\"] = \"r\"\n",
    "        lis_kwa.append(dic)\n",
    "        lis_pos = [\n",
    "            (oText._x, oText._y - 0.3),\n",
    "            (oText._x, oText._y),\n",
    "            (oText._x, oText._y + 0.3),\n",
    "        ]\n",
    "        for i in range(len(lis_txt)):\n",
    "            newText = dict(\n",
    "                x=lis_pos[i][0],\n",
    "                y=lis_pos[i][1],\n",
    "                text=lis_txt[i],\n",
    "                kw=lis_kwa[i],\n",
    "            )\n",
    "            text_add.append(newText)\n",
    "\n",
    "        # set background color for sum cells (last line and last column)\n",
    "        carr = [0.27, 0.30, 0.27, 1.0]\n",
    "        if (col == ccl - 1) and (lin == ccl - 1):\n",
    "            carr = [0.17, 0.20, 0.17, 1.0]\n",
    "        facecolors[posi] = carr\n",
    "\n",
    "    else:\n",
    "        if per > 0:\n",
    "            txt = \"%s\\n%.2f%%\" % (cell_val, per)\n",
    "        else:\n",
    "            if show_null_values == 0:\n",
    "                txt = \"\"\n",
    "            elif show_null_values == 1:\n",
    "                txt = \"0\"\n",
    "            else:\n",
    "                txt = \"0\\n0.0%\"\n",
    "        oText.set_text(txt)\n",
    "\n",
    "        # main diagonal\n",
    "        if col == lin:\n",
    "            # set color of the textin the diagonal to white\n",
    "            oText.set_color(\"w\")\n",
    "            # set background color in the diagonal to blue\n",
    "            facecolors[posi] = [0.35, 0.8, 0.55, 1.0]\n",
    "        else:\n",
    "            oText.set_color(\"r\")\n",
    "\n",
    "    return text_add, text_del\n",
    "\n",
    "\n",
    "def insert_totals(df_cm):\n",
    "    \"\"\"insert total column and line (the last ones)\"\"\"\n",
    "    sum_col = []\n",
    "    for c in df_cm.columns:\n",
    "        sum_col.append(df_cm[c].sum())\n",
    "    sum_lin = []\n",
    "    for item_line in df_cm.iterrows():\n",
    "        sum_lin.append(item_line[1].sum())\n",
    "    df_cm[\"sum_lin\"] = sum_lin\n",
    "    sum_col.append(np.sum(sum_lin))\n",
    "    df_cm.loc[\"sum_col\"] = sum_col\n",
    "\n",
    "\n",
    "def pp_matrix(\n",
    "    df_cm,\n",
    "    annot=True,\n",
    "    cmap=\"Oranges\",\n",
    "    fmt=\".2f\",\n",
    "    fz=11,\n",
    "    lw=0.5,\n",
    "    cbar=False,\n",
    "    figsize=[8, 8],\n",
    "    show_null_values=0,\n",
    "    pred_val_axis=\"y\",\n",
    "):\n",
    "    \"\"\"\n",
    "    print conf matrix with default layout (like matlab)\n",
    "    params:\n",
    "      df_cm          dataframe (pandas) without totals\n",
    "      annot          print text in each cell\n",
    "      cmap           Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:\n",
    "      fz             fontsize\n",
    "      lw             linewidth\n",
    "      pred_val_axis  where to show the prediction values (x or y axis)\n",
    "                      'col' or 'x': show predicted values in columns (x axis) instead lines\n",
    "                      'lin' or 'y': show predicted values in lines   (y axis)\n",
    "    \"\"\"\n",
    "    if pred_val_axis in (\"col\", \"x\"):\n",
    "        xlbl = \"Predicted\"\n",
    "        ylbl = \"Actual\"\n",
    "    else:\n",
    "        xlbl = \"Actual\"\n",
    "        ylbl = \"Predicted\"\n",
    "        df_cm = df_cm.T\n",
    "\n",
    "    # create \"Total\" column\n",
    "    insert_totals(df_cm)\n",
    "\n",
    "    # this is for print allways in the same window\n",
    "    fig, ax1 = get_new_fig(\"Conf matrix default\", figsize)\n",
    "\n",
    "    ax = sn.heatmap(\n",
    "        df_cm,\n",
    "        annot=annot,\n",
    "        annot_kws={\"size\": fz},\n",
    "        linewidths=lw,\n",
    "        ax=ax1,\n",
    "        cbar=cbar,\n",
    "        cmap=cmap,\n",
    "        linecolor=\"w\",\n",
    "        fmt=fmt,\n",
    "    )\n",
    "\n",
    "    # set ticklabels rotation\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, fontsize=10)\n",
    "    ax.set_yticklabels(ax.get_yticklabels(), rotation=25, fontsize=10)\n",
    "\n",
    "    # Turn off all the ticks\n",
    "    for t in ax.xaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "    for t in ax.yaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "\n",
    "    # face colors list\n",
    "    quadmesh = ax.findobj(QuadMesh)[0]\n",
    "    facecolors = quadmesh.get_facecolors()\n",
    "\n",
    "    # iter in text elements\n",
    "    array_df = np.array(df_cm.to_records(index=False).tolist())\n",
    "    text_add = []\n",
    "    text_del = []\n",
    "    posi = -1  # from left to right, bottom to top.\n",
    "    for t in ax.collections[0].axes.texts:  # ax.texts:\n",
    "        pos = np.array(t.get_position()) - [0.5, 0.5]\n",
    "        lin = int(pos[1])\n",
    "        col = int(pos[0])\n",
    "        posi += 1\n",
    "\n",
    "        # set text\n",
    "        txt_res = configcell_text_and_colors(\n",
    "            array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values\n",
    "        )\n",
    "\n",
    "        text_add.extend(txt_res[0])\n",
    "        text_del.extend(txt_res[1])\n",
    "\n",
    "    # remove the old ones\n",
    "    for item in text_del:\n",
    "        item.remove()\n",
    "    # append the new ones\n",
    "    for item in text_add:\n",
    "        ax.text(item[\"x\"], item[\"y\"], item[\"text\"], **item[\"kw\"])\n",
    "\n",
    "    # titles and legends\n",
    "    ax.set_title(\"Confusion matrix\")\n",
    "    ax.set_xlabel(xlbl)\n",
    "    ax.set_ylabel(ylbl)\n",
    "    plt.tight_layout()  # set layout slim\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def pp_matrix_from_data(\n",
    "    y_test,\n",
    "    predictions,\n",
    "    columns=None,\n",
    "    annot=True,\n",
    "    cmap=\"Oranges\",\n",
    "    fmt=\".2f\",\n",
    "    fz=11,\n",
    "    lw=0.5,\n",
    "    cbar=False,\n",
    "    figsize=[8, 8],\n",
    "    show_null_values=0,\n",
    "    pred_val_axis=\"lin\",\n",
    "):\n",
    "    \"\"\"\n",
    "    plot confusion matrix function with y_test (actual values) and predictions (predic),\n",
    "    whitout a confusion matrix yet\n",
    "    \"\"\"\n",
    "    from pandas import DataFrame\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    # data\n",
    "    if not columns:\n",
    "        from string import ascii_uppercase\n",
    "\n",
    "        columns = [\n",
    "            \"class %s\" % (i)\n",
    "            for i in list(ascii_uppercase)[0 : len(np.unique(y_test))]\n",
    "        ]\n",
    "\n",
    "    confm = confusion_matrix(y_test, predictions)\n",
    "    df_cm = DataFrame(confm, index=columns, columns=columns)\n",
    "    pp_matrix(\n",
    "        df_cm,\n",
    "        fz=fz,\n",
    "        cmap=cmap,\n",
    "        figsize=figsize,\n",
    "        show_null_values=show_null_values,\n",
    "        pred_val_axis=pred_val_axis,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d6e04c2-37bb-4d9c-8ab6-63e93ec29fb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f779a954-7c90-48ad-badc-402f65be389d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   class          p\n",
      "0      0   0.203424\n",
      "1      1   0.013596\n",
      "2      2   0.056109\n",
      "3      3   1.130661\n",
      "4      4  16.206137\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#you can put these parameters in the args but here I keep it simpler\n",
    "num_boost_round = 600\n",
    "SKI_AREA_TEST= 'Klausberg'\n",
    "SEASON_TEST_SKIAREA = 'Kronplatz'\n",
    "SEASON_TEST_YEAR= 2023\n",
    "weight_type = 'sqrt' \n",
    "## get the data\n",
    "labeled,labeled_small,to_remove,evacuations = retrive_data(reload_data=False,threshold_under_represented=0.5,path='/home/agobbi/Projects/PID/datanalytics/PID/src')\n",
    "\n",
    "\n",
    "#split the data\n",
    "dataset,dataset_test = split(labeled_small  ,\n",
    "                            SKI_AREA_TEST= SKI_AREA_TEST,\n",
    "                            SEASON_TEST_SKIAREA = SEASON_TEST_SKIAREA,\n",
    "                            SEASON_TEST_YEAR= SEASON_TEST_YEAR,\n",
    "                            use_smote = False,\n",
    "                            weight_type = weight_type )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "654af94f-e6e8-4dce-baab-fbf63c426c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = labeled.groupby('india').age.count().reset_index().rename(columns={'age':'Nsamples'})\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "\n",
    "plt.bar(aa.india, aa.Nsamples,log=True)\n",
    "plt.xlabel('India')\n",
    "plt.ylabel('Counts (log)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('counts.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92dff3bc-68ac-4765-83ed-8ef449e3af80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "#plot_edf(study)\n",
    "#plot_optimization_history(study)\n",
    "with open('../src/best_params.pkl','rb') as f:\n",
    "    params_final,feat_imp,best_model,study = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e270ae7a-5a18-4452-949a-61694b8bfee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "plot_param_importances(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d00b19ca-4466-4239-98c3-4cd155b4965a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_parallel_coordinate(study)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d8e7c975-6cd3-4c8f-909a-12ba49037015",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../src/best_params_and_final_model.pkl','rb') as f:\n",
    "    tot,bst_FS,FS = pickle.load(f)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "960c03f9-0bb2-4881-8d9a-549454e07f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(15.2, 0.7068054959209963, 'ACC:0.64\\nMCC:0.38')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(tot.FS, tot.acc,'o-', label='accuracy')\n",
    "plt.plot(tot.FS, tot.mcc,'o-', label='MCC')\n",
    "plt.xlabel('N features')\n",
    "plt.ylabel('Score')\n",
    "bacc = tot.acc[tot.FS==FS].values[0]\n",
    "bmcc = tot.mcc[tot.FS==FS].values[0]\n",
    "plt.vlines(x=FS, ymin=0, ymax=tot.acc.max()*1.2,color='gray',linestyles='dotted')\n",
    "plt.text(x=FS+0.2, y=tot.acc.max()*1.1, s=f'ACC:{round(bacc,2)}\\nMCC:{round(bmcc,2)}',fontdict={'size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dfefae5-2a8c-4d06-9ae7-330ffc1c5aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "600a9d2a-e278-45a7-936a-b675bbc2aded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
       " [Text(0, 0, 'age'),\n",
       "  Text(1, 0, 'diagnosis'),\n",
       "  Text(2, 0, 'injury_general_location'),\n",
       "  Text(3, 0, 'country'),\n",
       "  Text(4, 0, 'difficulty'),\n",
       "  Text(5, 0, 'destination'),\n",
       "  Text(6, 0, 'cause'),\n",
       "  Text(7, 0, 'injury_side'),\n",
       "  Text(8, 0, 'gender'),\n",
       "  Text(9, 0, 'snowmobile'),\n",
       "  Text(10, 0, 'ambulance'),\n",
       "  Text(11, 0, 'helicopter'),\n",
       "  Text(12, 0, 'equipment'),\n",
       "  Text(13, 0, 'indipendently'),\n",
       "  Text(14, 0, 'akja'),\n",
       "  Text(15, 0, 'ski_lift'),\n",
       "  Text(16, 0, 'snowmobile_sled'),\n",
       "  Text(17, 0, 'helmet'),\n",
       "  Text(18, 0, 'skiarea_ambulance')])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (12,5))\n",
    "plt.bar(feat_imp.index,feat_imp.values,)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "#plt.vlines(x=FS, ymin=0, ymax=300,color='gray',linestyles='dotted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ff1fe4f-970a-4484-a0f6-731c0128d52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT ON THE TEST SKI AREA mcc=0.38970728651001324, acc=0.7561455260570304, \n",
      "cm=array([[  39,   26,    2,    0,    0],\n",
      "       [  34, 1278,  245,    0,    0],\n",
      "       [   0,  170,  220,    6,    0],\n",
      "       [   0,    0,   12,    1,    0],\n",
      "       [   0,    0,    1,    0,    0]])\n",
      "RESULT ON THE TEST SKI SEASON mcc=0.46928939502481826, acc=0.7517178195144297, cm=array([[  50,   15,    3,    0,    1],\n",
      "       [  21, 1268,  375,    1,    1],\n",
      "       [   0,  102,  320,   12,    0],\n",
      "       [   0,    0,   11,    2,    0],\n",
      "       [   0,    0,    0,    0,    1]])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "dtest_FS = xgb.DMatrix(dataset_test.X_test_area[bst_FS.feature_names],dataset_test.y_test_area,enable_categorical=True,)\n",
    "dtest_season_FS = xgb.DMatrix(dataset_test.X_test_season[bst_FS.feature_names],dataset_test.y_test_season,enable_categorical=True,)\n",
    "preds_class_test = bst_FS.predict(dtest_FS)\n",
    "preds_class_test_season = bst_FS.predict(dtest_season_FS)\n",
    "\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI AREA {mcc=}, {acc=}, \\n{cm=}')\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI SEASON {mcc=}, {acc=}, {cm=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "422dc553-950c-4af9-a5b7-e482badb7755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT ON THE TEST SKI AREA mcc=0.36210533676802603, acc=0.7404129793510325, \n",
      "cm=array([[  41,   25,    1,    0,    0],\n",
      "       [  62, 1257,  236,    1,    1],\n",
      "       [   1,  179,  203,   13,    0],\n",
      "       [   0,    0,    8,    5,    0],\n",
      "       [   0,    0,    1,    0,    0]])\n",
      "RESULT ON THE TEST SKI SEASON mcc=0.4349525919582487, acc=0.7329363261566652, cm=array([[  53,   13,    3,    0,    0],\n",
      "       [  43, 1246,  373,    2,    2],\n",
      "       [   0,  112,  298,   24,    0],\n",
      "       [   0,    0,   11,    2,    0],\n",
      "       [   0,    0,    0,    0,    1]])\n"
     ]
    }
   ],
   "source": [
    "dtest_FS = xgb.DMatrix(dataset_test.X_test_area[bst_FS.feature_names],dataset_test.y_test_area,enable_categorical=True,)\n",
    "dtest_season_FS = xgb.DMatrix(dataset_test.X_test_season[bst_FS.feature_names],dataset_test.y_test_season,enable_categorical=True,)\n",
    "preds_class_test = bst_FS.predict(dtest_FS)\n",
    "preds_class_test_season = bst_FS.predict(dtest_season_FS)\n",
    "\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI AREA {mcc=}, {acc=}, \\n{cm=}')\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI SEASON {mcc=}, {acc=}, {cm=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e2cac2f-b414-45ed-8e26-3fb9786e73aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1032, 1503, 2070]),)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(preds_class_test_season.argmax(1)==4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "585102a0-0942-48b1-a4d1-ff84393fd70f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2bb3ef03-c2fa-4102-ae70-fb11dae3e042",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_106563/989477338.py:65: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "  per_ok_s = [\"%.2f%%\" % (per_ok), \"100%\"][per_ok == 100]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "pp_matrix_from_data(dataset_test.y_test_area,preds_class_test.argmax(1),cmap = 'cividis', columns=[f'india{i}' for i in range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73463369-7ffa-4481-a1cf-b8c6bc7142ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_106563/989477338.py:65: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "  per_ok_s = [\"%.2f%%\" % (per_ok), \"100%\"][per_ok == 100]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_matrix_from_data(dataset_test.y_test_season,preds_class_test_season.argmax(1),cmap = 'cividis', columns=[f'india{i}' for i in range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33ad200c-1064-4152-bfeb-c679f7966772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>indipendently</th>\n",
       "      <th>snowmobile_sled</th>\n",
       "      <th>akja</th>\n",
       "      <th>privat_helicopter</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "      <th>other</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2023</td>\n",
       "      <td>advanced</td>\n",
       "      <td>other</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9030</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>illness</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18060</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2024</td>\n",
       "      <td>advanced</td>\n",
       "      <td>other</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      skiarea_name  season difficulty    cause gender equipment helmet  \\\n",
       "6362     Kronplatz    2023   advanced    other      M       ski   True   \n",
       "9030     Kronplatz    2024       easy  illness      M      None   True   \n",
       "18060    Kronplatz    2024   advanced    other      M       ski   True   \n",
       "\n",
       "      destination diagnosis  india  ...  indipendently snowmobile_sled   akja  \\\n",
       "6362        other      None      0  ...          False           False   True   \n",
       "9030        other     other      1  ...          False           False  False   \n",
       "18060       other     other      4  ...          False           False   True   \n",
       "\n",
       "      privat_helicopter snowmobile skiarea_ambulance helicopter  \\\n",
       "6362              False      False             False      False   \n",
       "9030              False       True             False      False   \n",
       "18060             False      False             False       True   \n",
       "\n",
       "      offroad_vehicle  other    car  \n",
       "6362            False  False  False  \n",
       "9030            False  False  False  \n",
       "18060           False   True  False  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test.X_test_season.iloc[np.where(preds_class_test_season.argmax(1)==4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ae3d4c40-3b60-4895-964d-8856d02cf645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 184, 1032, 2070])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(preds_class_test_season.argmax(1)==4)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa91b66b-2094-4469-895f-9c59adad7eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6362     0\n",
       "9030     1\n",
       "18060    4\n",
       "Name: india, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test.y_test_season.iloc[np.where(preds_class_test_season.argmax(1)==4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "013a5151-06ab-48af-abb7-4c02d0065bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skiarea_name               Kronplatz\n",
       "season                          2024\n",
       "difficulty                      easy\n",
       "cause                        illness\n",
       "gender                             M\n",
       "equipment                       None\n",
       "helmet                          True\n",
       "destination                    other\n",
       "diagnosis                      other\n",
       "india                              1\n",
       "age                             64.0\n",
       "country                       Italia\n",
       "injury_side                     None\n",
       "injury_general_location         None\n",
       "ambulance                      False\n",
       "ski_lift                        True\n",
       "quad                           False\n",
       "indipendently                  False\n",
       "snowmobile_sled                False\n",
       "akja                           False\n",
       "privat_helicopter              False\n",
       "snowmobile                      True\n",
       "skiarea_ambulance              False\n",
       "helicopter                     False\n",
       "offroad_vehicle                False\n",
       "other                          False\n",
       "car                            False\n",
       "Name: 9030, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test.X_test_season.iloc[np.where(preds_class_test_season.argmax(1)==4)].iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60c9ebdc-943c-4461-a0ee-9196227d1d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "\n",
    "explainer = shap.TreeExplainer(bst_FS,feature_names=bst_FS.feature_names)\n",
    "shap_values = explainer.shap_values(dtest_season_FS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fe1306f0-83b8-4438-87e4-08e7a63b6e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42.0, 0.8, 0.2, 0.0, 57.0]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.round(preds_class_test_season[184]*100,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f438ab81-aa6b-40ef-ba9f-72f2419c151f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5c6e71ae-5dc4-49ee-a3a4-f7ae55a51750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='i7Q925GXM6RKWK7NUZJWP'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": -1.1636028289794922, \"outValue\": 0.4295194149017334, \"link\": \"identity\", \"featureNames\": [\"age\", \"diagnosis\", \"country\", \"difficulty\", \"injury_general_location\", \"cause\", \"destination\", \"equipment\", \"injury_side\", \"snowmobile\", \"gender\", \"indipendently\", \"helmet\", \"akja\", \"ski_lift\", \"ambulance\", \"snowmobile_sled\", \"helicopter\", \"other\", \"skiarea_ambulance\"], \"features\": {\"0\": {\"effect\": 0.4104311168193817, \"value\": \"\"}, \"1\": {\"effect\": 0.8114210963249207, \"value\": \"\"}, \"2\": {\"effect\": 0.06270141154527664, \"value\": \"\"}, \"3\": {\"effect\": -1.5315330028533936, \"value\": \"\"}, \"4\": {\"effect\": -0.022503014653921127, \"value\": \"\"}, \"5\": {\"effect\": -0.20787297189235687, \"value\": \"\"}, \"6\": {\"effect\": 2.2462408542633057, \"value\": \"\"}, \"7\": {\"effect\": 0.2167215347290039, \"value\": \"\"}, \"8\": {\"effect\": 0.030627980828285217, \"value\": \"\"}, \"9\": {\"effect\": -0.4790402352809906, \"value\": \"\"}, \"10\": {\"effect\": 0.07920225709676743, \"value\": \"\"}, \"11\": {\"effect\": 0.11538670212030411, \"value\": \"\"}, \"12\": {\"effect\": 0.03300907835364342, \"value\": \"\"}, \"13\": {\"effect\": -0.0023419498465955257, \"value\": \"\"}, \"14\": {\"effect\": 0.010712329298257828, \"value\": \"\"}, \"15\": {\"effect\": 0.002213317435234785, \"value\": \"\"}, \"16\": {\"effect\": -0.0884682834148407, \"value\": \"\"}, \"17\": {\"effect\": -0.08531501144170761, \"value\": \"\"}, \"18\": {\"effect\": 0.0022041318006813526, \"value\": \"\"}, \"19\": {\"effect\": -0.010675014927983284, \"value\": \"\"}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('i7Q925GXM6RKWK7NUZJWP')\n",
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       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7f1e8624ca10>"
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   ],
   "source": [
    "i=4\n",
    "shap.plots.force(explainer.expected_value[i], shap_values[i][1032],feature_names=bst_FS.feature_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e65a1cd7-8f29-4709-86f9-cb9eacfa4e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = dataset.X_train\n",
    "xx['india'] = dataset.y_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "16ef4703-3d5f-480a-bf1d-15c96cba0f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['other']\n",
       "Categories (7, object): ['None', 'domicile', 'hospital_emergency_room', 'other', 'private_clinic', 'public_clinic', 'traumacenter']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.destination[xx.india==4].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "45c04ea6-8ebe-4b9c-938a-fb13647aaa38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>injury_side</th>\n",
       "      <th>...</th>\n",
       "      <th>snowmobile_sled</th>\n",
       "      <th>akja</th>\n",
       "      <th>privat_helicopter</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "      <th>other</th>\n",
       "      <th>car</th>\n",
       "      <th>india</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>75.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>intermediate</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15824</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Italia</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3159</th>\n",
       "      <td>intermediate</td>\n",
       "      <td>illness</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Repubblica Ceca</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>None</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11525</th>\n",
       "      <td>advanced</td>\n",
       "      <td>other</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         difficulty             cause gender equipment helmet destination  \\\n",
       "2473   intermediate        fall_alone      M       ski   True       other   \n",
       "2517   intermediate  collision_person      M       ski   True       other   \n",
       "15824          None              None      M      None   None       other   \n",
       "3159   intermediate           illness      M       ski  False       other   \n",
       "10356          None        fall_alone      M      None   True       other   \n",
       "11525      advanced             other      M       ski   True       other   \n",
       "\n",
       "      diagnosis   age          country injury_side  ... snowmobile_sled  \\\n",
       "2473      other  75.0           Italia        None  ...            True   \n",
       "2517       None  47.0         Slovenia        None  ...           False   \n",
       "15824      None  62.0           Italia        None  ...           False   \n",
       "3159      other  68.0  Repubblica Ceca        None  ...            True   \n",
       "10356     other  21.0           Italia        None  ...           False   \n",
       "11525     other  81.0           Italia        None  ...           False   \n",
       "\n",
       "        akja privat_helicopter snowmobile skiarea_ambulance helicopter  \\\n",
       "2473   False             False      False              True      False   \n",
       "2517   False             False       True             False      False   \n",
       "15824  False             False      False             False       True   \n",
       "3159   False             False      False             False      False   \n",
       "10356  False             False       True             False       True   \n",
       "11525   True             False      False             False      False   \n",
       "\n",
       "      offroad_vehicle  other    car india  \n",
       "2473            False  False  False     4  \n",
       "2517            False  False  False     4  \n",
       "15824           False  False  False     4  \n",
       "3159            False  False  False     4  \n",
       "10356           False  False  False     4  \n",
       "11525           False  False  False     4  \n",
       "\n",
       "[6 rows x 25 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx[xx.india==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ceb5b308-1260-4426-ad05-4251bb45c5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############Check inference part\n",
    "import os\n",
    "with open('../src/data.pkl','rb') as f:\n",
    "    df = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f35925ae-f963-4028-b949-9eceffedb9be",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>dateandtime</th>\n",
       "      <th>skiarea_id</th>\n",
       "      <th>skiarea_name</th>\n",
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       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>town</th>\n",
       "      <th>...</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>injury_side</th>\n",
       "      <th>injury_general_location</th>\n",
       "      <th>evacuation_vehicles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-12 10:02:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampeago</td>\n",
       "      <td>12</td>\n",
       "      <td>602</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>novice</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>SIKLOS</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>distortion</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Ungheria</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-12 11:02:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampeago</td>\n",
       "      <td>12</td>\n",
       "      <td>662</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>MALMO</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Svezia</td>\n",
       "      <td>R</td>\n",
       "      <td>skull_or_face</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-14 14:15:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampeago</td>\n",
       "      <td>14</td>\n",
       "      <td>855</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>CALDARO</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-16 10:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampeago</td>\n",
       "      <td>16</td>\n",
       "      <td>600</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>LINZ</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Austria</td>\n",
       "      <td>R</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-16 10:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampeago</td>\n",
       "      <td>16</td>\n",
       "      <td>600</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>RUSAVA</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>other</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Repubblica Ceca</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[other]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150102</th>\n",
       "      <td>2024-02-04 13:35:00+00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Speikboden</td>\n",
       "      <td>35</td>\n",
       "      <td>815</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Dettmannsdorf OT Dettmannsdorf - Kölzow</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>i2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Germania</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[snowmobile_sled, helicopter]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150103</th>\n",
       "      <td>2024-01-27 10:50:00+00:00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Bardonecchia</td>\n",
       "      <td>27</td>\n",
       "      <td>650</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Milano</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>wound</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>L</td>\n",
       "      <td>upper_limbs</td>\n",
       "      <td>[indipendently]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150104</th>\n",
       "      <td>2024-02-04 14:52:00+00:00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Klausberg</td>\n",
       "      <td>35</td>\n",
       "      <td>892</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>traumacenter</td>\n",
       "      <td>other</td>\n",
       "      <td>i2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Germania</td>\n",
       "      <td>L</td>\n",
       "      <td>upper_limbs</td>\n",
       "      <td>[car, ski_lift, snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150105</th>\n",
       "      <td>2024-02-04 08:04:00+00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Moena Lusia</td>\n",
       "      <td>35</td>\n",
       "      <td>484</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Rimini</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>R</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[ski_lift, snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150106</th>\n",
       "      <td>2024-02-04 15:40:00+00:00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Porta Vescovo - Arabba</td>\n",
       "      <td>35</td>\n",
       "      <td>940</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Busto Garolfo</td>\n",
       "      <td>...</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>distortion</td>\n",
       "      <td>None</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150107 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      dateandtime  skiarea_id            skiarea_name  \\\n",
       "0       2009-01-12 10:02:00+00:00         NaN                Pampeago   \n",
       "1       2009-01-12 11:02:00+00:00         NaN                Pampeago   \n",
       "2       2009-01-14 14:15:00+00:00         NaN                Pampeago   \n",
       "3       2009-01-16 10:00:00+00:00         NaN                Pampeago   \n",
       "4       2009-01-16 10:00:00+00:00         NaN                Pampeago   \n",
       "...                           ...         ...                     ...   \n",
       "150102  2024-02-04 13:35:00+00:00        76.0              Speikboden   \n",
       "150103  2024-01-27 10:50:00+00:00        84.0            Bardonecchia   \n",
       "150104  2024-02-04 14:52:00+00:00        77.0               Klausberg   \n",
       "150105  2024-02-04 08:04:00+00:00        61.0             Moena Lusia   \n",
       "150106  2024-02-04 15:40:00+00:00        65.0  Porta Vescovo - Arabba   \n",
       "\n",
       "        day_of_year  minute_of_day  year  season    difficulty  \\\n",
       "0                12            602  2009    2009        novice   \n",
       "1                12            662  2009    2009      advanced   \n",
       "2                14            855  2009    2009      advanced   \n",
       "3                16            600  2009    2009      advanced   \n",
       "4                16            600  2009    2009      advanced   \n",
       "...             ...            ...   ...     ...           ...   \n",
       "150102           35            815  2024    2024  intermediate   \n",
       "150103           27            650  2024    2024          easy   \n",
       "150104           35            892  2024    2024          easy   \n",
       "150105           35            484  2024    2024  intermediate   \n",
       "150106           35            940  2024    2024  intermediate   \n",
       "\n",
       "                   cause                                     town  ...  \\\n",
       "0             fall_alone                                   SIKLOS  ...   \n",
       "1             fall_alone                                    MALMO  ...   \n",
       "2             fall_alone                                  CALDARO  ...   \n",
       "3       collision_person                                     LINZ  ...   \n",
       "4       collision_person                                   RUSAVA  ...   \n",
       "...                  ...                                      ...  ...   \n",
       "150102        fall_alone  Dettmannsdorf OT Dettmannsdorf - Kölzow  ...   \n",
       "150103        fall_alone                                   Milano  ...   \n",
       "150104  collision_person                               Düsseldorf  ...   \n",
       "150105        fall_alone                                   Rimini  ...   \n",
       "150106        fall_alone                            Busto Garolfo  ...   \n",
       "\n",
       "       equipment helmet              destination   diagnosis india   age  \\\n",
       "0            ski   None  hospital_emergency_room  distortion  None  32.0   \n",
       "1            ski   None  hospital_emergency_room      bruise  None  32.0   \n",
       "2            ski   None                 domicile       other  None  12.0   \n",
       "3            ski   None  hospital_emergency_room      bruise  None  58.0   \n",
       "4            ski   None                    other      bruise  None  25.0   \n",
       "...          ...    ...                      ...         ...   ...   ...   \n",
       "150102       ski   True  hospital_emergency_room       other    i2  37.0   \n",
       "150103       ski   True                     None       wound  None  33.0   \n",
       "150104       ski   True             traumacenter       other    i2  66.0   \n",
       "150105       ski   True                 domicile      bruise  None  14.0   \n",
       "150106       ski   True                 domicile  distortion  None  58.0   \n",
       "\n",
       "                country  injury_side injury_general_location  \\\n",
       "0              Ungheria            L             lower_limbs   \n",
       "1                Svezia            R           skull_or_face   \n",
       "2                Italia            R                    None   \n",
       "3               Austria            R             lower_limbs   \n",
       "4       Repubblica Ceca            L             lower_limbs   \n",
       "...                 ...          ...                     ...   \n",
       "150102         Germania            L             lower_limbs   \n",
       "150103           Italia            L             upper_limbs   \n",
       "150104         Germania            L             upper_limbs   \n",
       "150105           Italia            R             lower_limbs   \n",
       "150106           Italia            L             lower_limbs   \n",
       "\n",
       "                  evacuation_vehicles  \n",
       "0                              [akja]  \n",
       "1                              [akja]  \n",
       "2                        [snowmobile]  \n",
       "3                        [snowmobile]  \n",
       "4                             [other]  \n",
       "...                               ...  \n",
       "150102  [snowmobile_sled, helicopter]  \n",
       "150103                [indipendently]  \n",
       "150104    [car, ski_lift, snowmobile]  \n",
       "150105         [ski_lift, snowmobile]  \n",
       "150106                         [akja]  \n",
       "\n",
       "[150107 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9e564cb2-e87c-4579-a8c3-4ec8c78c80ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd68dd8dbb0>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.power(dataset.weight_train,2.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20c8703e-d784-48cb-804a-030a652c4c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['akja']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.evacuation_vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "773bedfc-2dee-4e12-ba76-2e59d2afd909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skiarea_name                    Kronplatz\n",
       "season                               2018\n",
       "difficulty                           easy\n",
       "cause                          fall_alone\n",
       "town                            Piottello\n",
       "province                           Milano\n",
       "gender                                  F\n",
       "equipment                             ski\n",
       "helmet                               True\n",
       "destination                private_clinic\n",
       "diagnosis                      distortion\n",
       "india                                  i1\n",
       "age                                  45.0\n",
       "country                            Italia\n",
       "injury_side                             R\n",
       "injury_general_location       lower_limbs\n",
       "evacuation_vehicles          [snowmobile]\n",
       "Name: 13031, dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~pd.isnull(df.india)].iloc[123]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "03aa0bd6-19fd-4c79-bcd2-5576fa346be5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open('../service/app/metadata.pkl','rb') as f:\n",
    "    to_remove,use_small,evacuations,encoders = pickle.load(f)\n",
    "    \n",
    "    \n",
    "i=123\n",
    "df.drop(columns=['dateandtime','skiarea_id','day_of_year','minute_of_day','year'], inplace=True, errors='ignore')\n",
    "data = df[~pd.isnull(df.india)].iloc[i:i+1]\n",
    "evacuation = data.evacuation_vehicles.values[0]\n",
    "if isinstance(evacuation,str):\n",
    "    evacuation = [evacuation]\n",
    "for c in evacuations:\n",
    "    data[c] = False\n",
    "for c in evacuation:\n",
    "    data[c] = True\n",
    "\n",
    "for c in evacuation:\n",
    "    if c not in evacuations:\n",
    "        data['other'] = True\n",
    "        break\n",
    "\n",
    "data.drop(columns=['town','province','evacuation_vehicles'],inplace=True, errors='ignore')\n",
    "\n",
    "\n",
    "data['age'] =  data['age'].astype(np.float32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for c in data.columns:\n",
    "    if c not in ['india','age','season','skiarea_name','destination']:\n",
    "        data[c] = data[c].astype('str')  \n",
    "if use_small:\n",
    "    for c in to_remove.keys():\n",
    "        for k in to_remove[c]:\n",
    "            data.loc[data[c]==k,c] = 'other'\n",
    "for c in data.columns:\n",
    "    if c not in ['age','season','skiarea_name','india']:\n",
    "        data[c] =  data[c].fillna('None')\n",
    "        if use_small:\n",
    "            data[c] = pd.Categorical( encoders['small'][c].transform(data[c]), categories=list(range(len(encoders['small'][c].classes_))), ordered=False)\n",
    "        else:\n",
    "            data[c] = pd.Categorical( encoders['normal'][c].transform(data[c]), categories=list(range(len(encoders['normal'][c].classes_))), ordered=False)\n",
    "         \n",
    "\n",
    "bst_FS = xgb.Booster()\n",
    "bst_FS.load_model(\"../service/app/model.json\")\n",
    "with open('../service/app/best_params_and_final_model.pkl','rb') as f:\n",
    "    tot,bst_FS,FS = pickle.load(f)  \n",
    "    \n",
    "dtest_FS = xgb.DMatrix(data[bst_FS.feature_names],enable_categorical=True)\n",
    "preds = bst_FS.predict(dtest_FS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8f64bbfe-9b66-45a0-abcb-dbe5d7782334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>ski_lift</th>\n",
       "      <th>ambulance</th>\n",
       "      <th>indipendently</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>akja</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "      <th>other</th>\n",
       "      <th>snowmobile_sled</th>\n",
       "      <th>car</th>\n",
       "      <th>quad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13031</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>i1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      skiarea_name  season difficulty cause gender equipment helmet  \\\n",
       "13031    Kronplatz    2018          2     3      1         2      2   \n",
       "\n",
       "      destination diagnosis india  ...  ski_lift ambulance indipendently  \\\n",
       "13031           6         9    i1  ...         0         0             0   \n",
       "\n",
       "      snowmobile akja offroad_vehicle other snowmobile_sled car quad  \n",
       "13031          1    0               0     0               0   0    0  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "02e77ff0-546a-4de7-891c-7fe809e007ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>age</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>injury_general_location</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>country</th>\n",
       "      <th>destination</th>\n",
       "      <th>cause</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>injury_side</th>\n",
       "      <th>equipment</th>\n",
       "      <th>gender</th>\n",
       "      <th>ambulance</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>akja</th>\n",
       "      <th>indipendently</th>\n",
       "      <th>snowmobile_sled</th>\n",
       "      <th>helmet</th>\n",
       "      <th>ski_lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13031</th>\n",
       "      <td>45.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age diagnosis injury_general_location difficulty country destination  \\\n",
       "13031  45.0         9                       1          2       7           6   \n",
       "\n",
       "      cause helicopter injury_side equipment gender ambulance snowmobile akja  \\\n",
       "13031     3          0           2         2      1         0          1    0   \n",
       "\n",
       "      indipendently snowmobile_sled helmet ski_lift  \n",
       "13031             0               0      2        0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[bst_FS.feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5856226f-6776-42c4-a618-5460eb1211eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skiarea_name                    Kronplatz\n",
       "season                               2018\n",
       "difficulty                           easy\n",
       "cause                          fall_alone\n",
       "town                            Piottello\n",
       "province                           Milano\n",
       "gender                                  F\n",
       "equipment                             ski\n",
       "helmet                               True\n",
       "destination                private_clinic\n",
       "diagnosis                      distortion\n",
       "india                                  i1\n",
       "age                                  45.0\n",
       "country                            Italia\n",
       "injury_side                             R\n",
       "injury_general_location       lower_limbs\n",
       "evacuation_vehicles          [snowmobile]\n",
       "Name: 13031, dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df[~pd.isnull(df.india)].iloc[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ea0a39c9-06ba-43e4-96c0-cb76713a0bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skiarea_name               Kronplatz\n",
       "season                          2018\n",
       "difficulty                         2\n",
       "cause                              3\n",
       "gender                             1\n",
       "equipment                          2\n",
       "helmet                             2\n",
       "destination                        6\n",
       "diagnosis                          9\n",
       "india                             i1\n",
       "age                             45.0\n",
       "country                            7\n",
       "injury_side                        2\n",
       "injury_general_location            1\n",
       "privat_helicopter                  0\n",
       "helicopter                         0\n",
       "skiarea_ambulance                  0\n",
       "ski_lift                           0\n",
       "ambulance                          0\n",
       "indipendently                      0\n",
       "snowmobile                         1\n",
       "akja                               0\n",
       "offroad_vehicle                    0\n",
       "other                              0\n",
       "snowmobile_sled                    0\n",
       "car                                0\n",
       "quad                               0\n",
       "Name: 13031, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "57be1bf4-4716-44db-b5c2-ab97efe0f92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00761119, 0.718736  , 0.2629358 , 0.00976286, 0.00095411]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "da8d0b45-0b61-4c78-b9cb-06e17ca155b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
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       "      <th>destination</th>\n",
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       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>privat_helicopter</th>\n",
       "      <th>other</th>\n",
       "      <th>ski_lift</th>\n",
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       "      <th>snowmobile_sled</th>\n",
       "      <th>car</th>\n",
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       "      <th>snowmobile</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>None</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>F</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>easy</td>\n",
       "      <td>illness</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>wound</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>fracture</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18414</th>\n",
       "      <td>Drei Zinnen</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>dislocation</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18415</th>\n",
       "      <td>Obereggen</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>trauma_crane</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18416</th>\n",
       "      <td>Speikboden</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>M</td>\n",
       "      <td>snowboard</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>concussion</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18417</th>\n",
       "      <td>Speikboden</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18418</th>\n",
       "      <td>Klausberg</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>traumacenter</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18330 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      skiarea_name  season    difficulty             cause gender  equipment  \\\n",
       "0        Kronplatz    2018          None        fall_alone      F        ski   \n",
       "1        Kronplatz    2018          easy           illness      M        ski   \n",
       "2        Kronplatz    2018      advanced        fall_alone      M        ski   \n",
       "3        Kronplatz    2018      advanced        fall_alone      M        ski   \n",
       "4        Kronplatz    2018  intermediate        fall_alone      M        ski   \n",
       "...            ...     ...           ...               ...    ...        ...   \n",
       "18414  Drei Zinnen    2024  intermediate        fall_alone      M        ski   \n",
       "18415    Obereggen    2024  intermediate  collision_person      M        ski   \n",
       "18416   Speikboden    2024  intermediate  collision_person      M  snowboard   \n",
       "18417   Speikboden    2024  intermediate        fall_alone      F        ski   \n",
       "18418    Klausberg    2024          easy  collision_person      F        ski   \n",
       "\n",
       "      helmet              destination     diagnosis  india  ...  \\\n",
       "0       True  hospital_emergency_room    distortion      1  ...   \n",
       "1       True                 domicile         other      0  ...   \n",
       "2       True  hospital_emergency_room         wound      1  ...   \n",
       "3       True  hospital_emergency_room      fracture      1  ...   \n",
       "4       True  hospital_emergency_room         other      1  ...   \n",
       "...      ...                      ...           ...    ...  ...   \n",
       "18414   True  hospital_emergency_room   dislocation      2  ...   \n",
       "18415   True  hospital_emergency_room  trauma_crane      1  ...   \n",
       "18416   True  hospital_emergency_room    concussion      2  ...   \n",
       "18417   True  hospital_emergency_room         other      2  ...   \n",
       "18418   True             traumacenter         other      2  ...   \n",
       "\n",
       "       skiarea_ambulance privat_helicopter  other ski_lift   akja  \\\n",
       "0                  False             False  False    False  False   \n",
       "1                  False             False  False     True  False   \n",
       "2                  False             False  False    False   True   \n",
       "3                  False             False  False    False   True   \n",
       "4                  False             False  False    False   True   \n",
       "...                  ...               ...    ...      ...    ...   \n",
       "18414              False             False  False    False  False   \n",
       "18415              False             False  False    False  False   \n",
       "18416              False             False  False    False  False   \n",
       "18417              False             False  False    False  False   \n",
       "18418              False             False  False     True  False   \n",
       "\n",
       "      snowmobile_sled    car   quad snowmobile offroad_vehicle  \n",
       "0               False  False  False       True           False  \n",
       "1               False  False  False      False           False  \n",
       "2               False  False  False      False           False  \n",
       "3               False  False  False      False           False  \n",
       "4               False  False  False      False           False  \n",
       "...               ...    ...    ...        ...             ...  \n",
       "18414            True  False  False      False           False  \n",
       "18415           False  False  False       True           False  \n",
       "18416           False  False  False      False           False  \n",
       "18417            True  False  False      False           False  \n",
       "18418           False   True  False       True           False  \n",
       "\n",
       "[18330 rows x 27 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "edcd37a4-50de-4e43-a97d-c2ec1cf13c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder().fit([1,2,3,'a',1,2,3,'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1b8dd296-ae02-4034-a583-ed1ea02b9ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'a':[1,2,3,'a',1,2,3,'a']})\n",
    "data['a'] = data['a'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c140eb7c-69aa-4aca-995a-606b0033b966",
   "metadata": {},
   "outputs": [
    {
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