titanicxgb-hinge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#filling missing values\n",
    "train[\"Age\"] = train[\"Age\"].fillna(30)\n",
    "train[\"Embarked\"].fillna(\"S\", inplace=True)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform non-numerical labels to numerical labels\n",
    "encoder = LabelEncoder()\n",
    "for col in ('Sex','Embarked'):\n",
    "    train['Sex'] = encoder.fit_transform(train['Sex'])\n",
    "    train['Embarked'] = encoder.fit_transform(train['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    891 non-null    int64  \n",
      " 1   Sex       891 non-null    int64  \n",
      " 2   Age       891 non-null    float64\n",
      " 3   SibSp     891 non-null    int64  \n",
      " 4   Parch     891 non-null    int64  \n",
      " 5   Fare      891 non-null    float64\n",
      " 6   Embarked  891 non-null    int64  \n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 48.9 KB\n"
     ]
    }
   ],
   "source": [
    "#Features\n",
    "train_x = train.drop([\"PassengerId\",\"Name\",\"Ticket\",\"Cabin\",\"Survived\"], axis=1)\n",
    "#label\n",
    "train_y = train[\"Survived\"]\n",
    "train_x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose only the data that contains survivors\n",
    "survived_label = train.groupby(['Survived'])\n",
    "alive = survived_label.get_group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose only the data that contains dead people\n",
    "dead = survived_label.get_group(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "1       1    0  38.0      1      0  71.2833         0\n",
       "2       3    0  26.0      0      0   7.9250         2\n",
       "3       1    0  35.0      1      0  53.1000         2\n",
       "8       3    0  27.0      0      2  11.1333         2\n",
       "9       2    0  14.0      1      0  30.0708         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ALIVE\n",
    "#FEATURES\n",
    "alive_x = alive.drop([\"PassengerId\",\"Name\",\"Ticket\",\"Cabin\",\"Survived\"], axis=1)\n",
    "#label\n",
    "alive_y = alive[\"Survived\"]\n",
    "alive_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0       3    1  22.0      1      0   7.2500         2\n",
       "4       3    1  35.0      0      0   8.0500         2\n",
       "5       3    1  30.0      0      0   8.4583         1\n",
       "6       1    1  54.0      0      0  51.8625         2\n",
       "7       3    1   2.0      3      1  21.0750         2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DEAD\n",
    "#FEATURES\n",
    "dead_x = dead.drop([\"PassengerId\",\"Name\",\"Ticket\",\"Cabin\",\"Survived\"], axis=1)\n",
    "#label\n",
    "dead_y = dead[\"Survived\"]\n",
    "dead_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sampling the data 80% train 20% test\n",
    "#GENERAL\n",
    "train_x, test_x, train_y, test_y = train_test_split(train_x, train_y, \n",
    "                                                  test_size=0.2, \n",
    "                                                  random_state=1)\n",
    "\n",
    "#ALIVE\n",
    "train_alive_x, test_alive_x, train_alive_y, test_alive_y = train_test_split(alive_x, alive_y, \n",
    "                                                  test_size=0.2, \n",
    "                                                  random_state=1)\n",
    "#DEAD\n",
    "train_dead_x, test_dead_x, train_dead_y, test_dead_y = train_test_split(dead_x, dead_y, \n",
    "                                                  test_size=0.2, \n",
    "                                                  random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.2 %\n",
      "\n",
      "Predictions [0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "#XGBClassifier scikit-learn API \n",
    "model = xgb.XGBClassifier(objective = 'binary:hinge', max_depth = 6, learning_rate=0.3)\n",
    "#fit = training\n",
    "model.fit(train_x, train_y)\n",
    "#predictions\n",
    "predict_alive = model.predict(test_alive_x)\n",
    "print(np.round(accuracy_score(test_alive_y,predict_alive)*100, 2),'%')\n",
    "print('\\nPredictions', predict_alive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_prob = model.predict_proba(test_alive_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting prob values to list\n",
    "df_prob = pd.DataFrame(predict_prob)\n",
    "s_prob_alive = df_prob[df_prob.columns[1]] \n",
    "list_prob_alive = s_prob_alive.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_alive = plt.hist(list_prob_alive, range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting prob values to list\n",
    "predict_dead = model.predict(test_dead_x)\n",
    "predict_prob_dead = model.predict_proba(test_dead_x)\n",
    "df_prob_dead = pd.DataFrame(predict_prob_dead)\n",
    "s_prob_dead = df_prob_dead[df_prob_dead.columns[1]] \n",
    "list_prob_dead = s_prob_dead.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_dead = plt.hist(list_prob_dead, range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Results using XGB')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list_prob_alive, range=(0,1), alpha=0.8,label='Alive')\n",
    "plt.hist(list_prob_dead, range=(0,1), alpha=0.7, label='Dead')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Probability of being alive')\n",
    "plt.ylabel('Events')\n",
    "plt.title('Results using XGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CREEMOS DATOS ALEADORIOS Y ORDENEMOLOS DE MENOR A MAYOR\n",
    "\n",
    "list_prob_alive = sorted(list_prob_alive)\n",
    "list_prob_dead = sorted(list_prob_dead)\n",
    "\n",
    "#LISTAS DONDE SE GUARDARAN LOS VALORES DE EFICIENCIA Y BACK_REJECTION\n",
    "eficiency = []\n",
    "background_rejection = []\n",
    "\n",
    "#ITERAMOS SOBRE UN FLOAT QUE VA DE 0 A 100 EN 1000 PASOS\n",
    "for cut in np.linspace(0,1, 50):\n",
    "    \n",
    "    #INICIALIZAMOS EL VALOR DE VIVOS ACEPTADOS Y MUERTOS RECHAZADOS\n",
    "    #PUES EN EL CORTE = 0 ACEPTAMOS TODOS LOS 1s Y RECHAZAMOS TODOS LOS 0s\n",
    "    vivos_aceptados = len(list_prob_alive)\n",
    "    muertos_rechazados = 0\n",
    "\n",
    "\n",
    "    for i, v in enumerate(list_prob_alive):\n",
    "        # YA QUE LAS LISTAS ESTAN ORDENADAS BASTA CON CHECAR EL PRIMER VALOR QUE \n",
    "        # SEA MAYOR QUE EL CORTE\n",
    "        if cut<=v:\n",
    "            vivos_aceptados-=i\n",
    "            break\n",
    "        #EN EL CASO DE QUE EL CORTE SEA MAYOR QUE TODAS LAS PROBS CONSIDERA QUE \n",
    "        # NINGUN VIVO FUE ACEPTADO\n",
    "        if i==len(list_prob_alive)-1:\n",
    "            vivos_aceptados=0\n",
    "\n",
    "    # ANALOGAMENTE PARA LOS MUERTOS        \n",
    "    for i, m in enumerate(list_prob_dead):\n",
    "        if cut<=m:\n",
    "            muertos_rechazados+=i\n",
    "            break\n",
    "        if i==len(list_prob_dead)-1:\n",
    "            muertos_rechazados=len(list_prob_dead)\n",
    "    \n",
    "    #ALMACENA EN LA LISTA\n",
    "    eficiency.append(vivos_aceptados/len(list_prob_alive))\n",
    "    background_rejection.append(muertos_rechazados/len(list_prob_dead))\n",
    "\n",
    "    neural = plt.plot(eficiency, background_rejection)\n",
    "    plt.xlabel('Signal efficiency')\n",
    "    plt.ylabel('Background rejection')\n",
    "    plt.title('XGB ROC curve')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "xgb",
   "language": "python",
   "name": "xgb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}