{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c5d059b-ed8a-4e2e-9420-25890f648895",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_46791/2472232159.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n",
      "/tmp/ipykernel_46791/2472232159.py:7: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql('select * from data_safeidx', con=engine)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import psycopg2 as pg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "engine = pg.connect(\"dbname='safeidx' user='fbk_mpba' host='172.104.247.67' port='5432' password='fbk2024$'\")\n",
    "df = pd.read_sql('select * from data_safeidx', con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03aa2a04-93fa-469e-a678-685cacdebd6c",
   "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>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>town</th>\n",
       "      <th>province</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>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>novice</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>SIKLOS</td>\n",
       "      <td></td>\n",
       "      <td>F</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>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>MALMO</td>\n",
       "      <td></td>\n",
       "      <td>M</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>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>CALDARO</td>\n",
       "      <td>BZ</td>\n",
       "      <td>F</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>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>LINZ</td>\n",
       "      <td></td>\n",
       "      <td>M</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>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>RUSAVA</td>\n",
       "      <td></td>\n",
       "      <td>M</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  difficulty             cause     town province gender equipment helmet  \\\n",
       "0     novice        fall_alone   SIKLOS               F       ski   None   \n",
       "1   advanced        fall_alone    MALMO               M       ski   None   \n",
       "2   advanced        fall_alone  CALDARO       BZ      F       ski   None   \n",
       "3   advanced  collision_person     LINZ               M       ski   None   \n",
       "4   advanced  collision_person   RUSAVA               M       ski   None   \n",
       "\n",
       "               destination   diagnosis india   age          country  \\\n",
       "0  hospital_emergency_room  distortion  None  32.0         Ungheria   \n",
       "1  hospital_emergency_room      bruise  None  32.0           Svezia   \n",
       "2                 domicile       other  None  12.0           Italia   \n",
       "3  hospital_emergency_room      bruise  None  58.0          Austria   \n",
       "4                    other      bruise  None  25.0  Repubblica Ceca   \n",
       "\n",
       "  injury_side injury_general_location evacuation_vehicles  \n",
       "0           L             lower_limbs              [akja]  \n",
       "1           R           skull_or_face              [akja]  \n",
       "2           R                    None        [snowmobile]  \n",
       "3           R             lower_limbs        [snowmobile]  \n",
       "4           L             lower_limbs             [other]  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "babc2e8b-1030-4e8a-aa41-6d2a788959a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ev = set({})\n",
    "for i,row in df.iterrows():\n",
    "    ev = ev.union(set(row.evacuation_vehicles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8d6cc1c-f4f5-44ec-8652-b135963452ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in ev:\n",
    "    df[c] = False\n",
    "for i,row in df.iterrows():\n",
    "    for c in row.evacuation_vehicles:\n",
    "        df.loc[i,c] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "758c3317-1b02-4aed-b94d-7b6998d23797",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['town','province','evacuation_vehicles'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adadc0dc-9d6e-4277-8956-d1d4b2492e7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "33617e77-7c2b-41a3-96c0-8930aa5ac869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.3808e+04, 0.0000e+00, 8.8100e+02, 0.0000e+00, 0.0000e+00,\n",
       "        3.3690e+03, 0.0000e+00, 1.5200e+02, 0.0000e+00, 1.1000e+01]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 14464 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled = df[~pd.isna(df.india)]\n",
    "plt.hist(labeled.india)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8c139d9-bf61-45ec-9da1-7eaf4ff754b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_46791/382759161.py: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",
      "  labeled['age'] =  labeled['age'].astype(np.float32).fillna(np.nan)\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py:4: 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",
      "  labeled[c] =  labeled[c].fillna('None').astype('category')\n",
      "/tmp/ipykernel_46791/382759161.py: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",
      "  labeled.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "labeled['age'] =  labeled['age'].astype(np.float32).fillna(np.nan)\n",
    "for c in labeled.columns:\n",
    "    if c!='age':\n",
    "        labeled[c] =  labeled[c].fillna('None').astype('category')\n",
    "labeled.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fabf354e-f39e-4cde-af84-c65a277d309a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( labeled.drop(columns=['india']),\n",
    "                                                    labeled.india, test_size=0.33, random_state=0,stratify=labeled.india)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cf6cf5d8-d43e-499e-98a5-65ecd0b8ccda",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train,y_train, test_size=0.33, random_state=0,stratify=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1453965-f927-4edc-ad3b-421997d62268",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "w.columns = ['class','p']\n",
    "w.p = np.sqrt(w.p.sum())/w.p\n",
    "weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "774ad570-a60b-475b-80cf-4f9b9949cc9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#weight_train = (1-y_train.values.astype(int))*5+1 ## peso classi unbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1fcc5234-abad-459a-9420-810833657796",
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier, Pool\n",
    "\n",
    "train_data = Pool(data=X_train,\n",
    "                  label=y_train,\n",
    "                 cat_features=[c for c in X_train.columns if c!='age'])\n",
    "valid_data = Pool(data=X_valid,\n",
    "                  label=y_valid,cat_features=[c for c in X_train.columns if c!='age']\n",
    "                  )\n",
    "model = CatBoostClassifier(iterations=1000)\n",
    "\n",
    "model.fit(train_data,eval_set=valid_data,verbose=False,early_stopping_rounds=100)\n",
    "preds_class = model.predict(valid_data,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8df84007-110b-4f31-bc7d-53e7d5c9a178",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_class_valid = model.predict(valid_data)\n",
    "preds_class_train= model.predict(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "55627ec4-fd24-4815-98d4-d8462bbfdd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,matthews_corrcoef,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "877e914d-44d4-4299-8d3c-24c4fc353317",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "776395d9-a8e2-4fda-90e1-11d6dbe80de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 145   40    2    0    0]\n",
      " [  98 2388  555    2    0]\n",
      " [   1  211  512   20    0]\n",
      " [   0    3   21   10    0]\n",
      " [   0    1    0    0    1]]\n",
      "0.4797388666004143\n",
      "0.7620947630922693\n",
      "########################################\n",
      "[[ 349   27    3    0    0]\n",
      " [ 194 5033  946    4    0]\n",
      " [   6  274 1213   17    0]\n",
      " [   0    3   12   53    0]\n",
      " [   1    0    0    0    4]]\n",
      "0.6100405041372103\n",
      "0.8172994225334808\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_valid,preds_class_valid))\n",
    "print(matthews_corrcoef(y_valid,preds_class_valid))\n",
    "print(accuracy_score(y_valid,preds_class_valid))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train,preds_class_train))\n",
    "print(matthews_corrcoef(y_train,preds_class_train))\n",
    "print(accuracy_score(y_train,preds_class_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "34c397bc-529a-4c52-b30e-957b28021200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 123   63    1    0    0]\n",
      " [  26 2826  191    0    0]\n",
      " [   1  440  297    6    0]\n",
      " [   0    4   25    5    0]\n",
      " [   0    2    0    0    0]]\n",
      "0.4591984245284171\n",
      "0.8107231920199501\n",
      "########################################\n",
      "[[ 376    3    0    0    0]\n",
      " [   0 6123   54    0    0]\n",
      " [   0  261 1249    0    0]\n",
      " [   0    0    0   68    0]\n",
      " [   0    0    0    0    5]]\n",
      "0.8974734086657309\n",
      "0.9609288610394398\n"
     ]
    }
   ],
   "source": [
    "##try with xgboost\n",
    "import xgboost as xgb\n",
    "\n",
    "# Create regression matrices\n",
    "dtrain = xgb.DMatrix(X_train,y_train.apply(lambda x:x.replace('i','')).astype(int), enable_categorical=True,feature_names=list(X_train.columns.values))#,weight=weight_train)\n",
    "dvalid = xgb.DMatrix(X_valid,y_valid.apply(lambda x:x.replace('i','')).astype(int), enable_categorical=True,feature_names=list(X_train.columns.values))\n",
    "\n",
    "params = {\"objective\": \"multi:softprob\", \"num_class\": 5}\n",
    "n = 1000\n",
    "\n",
    "results = xgb.train(\n",
    "   params, dtrain,\n",
    "\n",
    "   num_boost_round=n,\n",
    "  evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "    verbose_eval=False,\n",
    "    early_stopping_rounds=100\n",
    "  # metrics=[\"mlogloss\", \"auc\", \"merror\"],\n",
    ")\n",
    "preds_class_valid = results.predict(dvalid)\n",
    "preds_class_train= results.predict(dtrain)\n",
    "print(confusion_matrix(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1ec2f8d4-e56a-4978-b3e1-696ab3bec1df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4010, 24, 5)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "pred = results.predict(dvalid, output_margin=True,)\n",
    "\n",
    "explainer = shap.TreeExplainer(results,feature_names=list(X_train.columns.values))\n",
    "explanation = explainer(dvalid)\n",
    "\n",
    "shap_values = explanation.values\n",
    "shap_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e31317a7-3257-405f-9762-bf8d77699176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 122404 characters]",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.beeswarm(explanation[:,:,2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8ccca6dc-5bca-45c4-8ea1-b21a7ae8b433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['diagnosis', 'destination', 'age', 'helicopter',\n",
       "       'injury_general_location', 'difficulty', 'cause', 'country',\n",
       "       'injury_side', 'ski_lift'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals= np.abs(shap_values).mean(0).mean(1)\n",
    "feature_importance = pd.DataFrame(list(zip(X_train.columns,vals)),columns=['col_name','feature_importance_vals'])\n",
    "feature_importance.sort_values(by=['feature_importance_vals'],ascending=False,inplace=True)\n",
    "feature_importance.col_name[0:10].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d3fa6df7-1056-401f-bea8-aa724ff46613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 118   67    2    0    0]\n",
      " [  30 2794  217    2    0]\n",
      " [   0  435  300    9    0]\n",
      " [   0    5   24    5    0]\n",
      " [   0    2    0    0    0]]\n",
      "0.43952307639594157\n",
      "0.8022443890274314\n",
      "########################################\n",
      "[[ 371    8    0    0    0]\n",
      " [   1 6091   85    0    0]\n",
      " [   0  353 1156    1    0]\n",
      " [   0    0    0   68    0]\n",
      " [   0    0    0    0    5]]\n",
      "0.8543147994467417\n",
      "0.9449563828480158\n"
     ]
    }
   ],
   "source": [
    "##try with xgboost\n",
    "import xgboost as xgb\n",
    "X_train_small = X_train[feature_importance.col_name[0:10].values]\n",
    "X_valid_small = X_valid[feature_importance.col_name[0:10].values]\n",
    "\n",
    "# Create regression matrices\n",
    "dtrain = xgb.DMatrix(X_train_small,y_train.apply(lambda x:x.replace('i','')).astype(int), enable_categorical=True,feature_names=list(X_train_small.columns.values))#,weight=weight_train)\n",
    "dvalid = xgb.DMatrix(X_valid_small,y_valid.apply(lambda x:x.replace('i','')).astype(int), enable_categorical=True,feature_names=list(X_train_small.columns.values))\n",
    "\n",
    "params = {\"objective\": \"multi:softprob\", \"num_class\": 5}\n",
    "n = 1000\n",
    "\n",
    "results = xgb.train(\n",
    "   params, dtrain,\n",
    "\n",
    "   num_boost_round=n,\n",
    "  evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "    verbose_eval=False,\n",
    "    early_stopping_rounds=100\n",
    "  # metrics=[\"mlogloss\", \"auc\", \"merror\"],\n",
    ")\n",
    "preds_class_valid = results.predict(dvalid)\n",
    "preds_class_train= results.predict(dtrain)\n",
    "print(confusion_matrix(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c79de7d-8336-4d07-9571-79be5f92c381",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5120687b-02ed-42ee-80ca-543fe6aa540d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=True, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None, num_class=5,\n",
       "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=True, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None, num_class=5,\n",
       "              num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=True, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None, num_class=5,\n",
       "              num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgb.XGBClassifier(n_estimators=1000,objective=\"multi:softprob\",num_class=5,enable_categorical=True)\n",
    "model.fit(X_train, y_train.apply(lambda x:x.replace('i','')).astype(int), early_stopping_rounds=100,eval_set = [(X_valid, y_valid.apply(lambda x:x.replace('i','')).astype(int))],verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9cdb16a1-0761-4e3d-9214-6fef6ced875a",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_class_valid = model.predict(X_valid)\n",
    "preds_class_train= model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7c980af9-65ce-4472-806b-2e45295eb86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_class_valid.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "65228d59-e4c2-411d-8535-b36c15a55bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 125   61    1    0    0]\n",
      " [  15 2885  143    0    0]\n",
      " [   1  440  296    7    0]\n",
      " [   1    5   24    4    0]\n",
      " [   0    1    0    0    1]]\n",
      "0.49681030860342146\n",
      "0.8256857855361596\n",
      "########################################\n",
      "[[ 294   84    1    0    0]\n",
      " [  18 5995  164    0    0]\n",
      " [   1  739  770    0    0]\n",
      " [   0    9   18   41    0]\n",
      " [   1    0    1    0    3]]\n",
      "0.6440004170345552\n",
      "0.8727116353360364\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid))\n",
    "print(matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid))\n",
    "print(accuracy_score(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train))\n",
    "print(matthews_corrcoef(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train))\n",
    "print(accuracy_score(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train))"
   ]
  }
 ],
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