03_01_Titanic_test/GBDT_Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load package# load \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cross_validation  import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set path\n",
    "import os\n",
    "default_path = \"/Users/francislin/Desktop/Machine_Learning_Workshop/Titanic/\"\n",
    "os.chdir(default_path)\n",
    "\n",
    "# skip interaction warning\n",
    "import warnings; \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Prepararion\n",
    "# read data\n",
    "train=pd.read_csv('train.csv',encoding='big5')\n",
    "test=pd.read_csv('test.csv',encoding='big5')\n",
    "submit=pd.read_csv('gender_submission.csv',encoding='big5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# append in ONE data\n",
    "data=train.append(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10ccb5d68>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Family_Size']=data['Parch']+data['SibSp']\n",
    "g=sns.FacetGrid(data,col='Survived')\n",
    "g.map(sns.distplot,'Family_Size',kde=True)#kde是否要折線圖的參數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
       "       'Jonkheer', 'Dona'], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#設法整理出「尊稱」這個因子\n",
    "data['Title1']=data['Name'].str.split('.',expand=True)[0]\n",
    "#expand=True是讓資料在dataframe展開欄位\n",
    "#1是選第二項的title\n",
    "data['Title1'].head(10)\n",
    "# 切出尊稱\n",
    "data['Title1']=data['Title1'].str.split(', ',expand=True)[1].str.strip()#[1]代表切出第2個'dlm'前面到第一個的字串\n",
    "data['Title1'].head(10)\n",
    "# 秀出dintinct後的title1\n",
    "data['Title1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master'], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合併成比較常出現的姓氏在Title2\n",
    "data['Title2']=''\n",
    "for idx, row in data.iterrows():  # 請務必記得加上idx，不然跑回圈的item會變成(idx, row)\n",
    "    Sex = row['Sex']\n",
    "    Title1=row['Title1']\n",
    "    if Title1=='Master':\n",
    "        Title2='Master'\n",
    "    elif Title1=='Mrs':\n",
    "        Title2='Mrs'\n",
    "    elif Title1=='Miss':\n",
    "        Title2='Miss'\n",
    "    elif Sex=='male':\n",
    "        Title2='Mr'\n",
    "    else:\n",
    "        Title2='Miss'\n",
    "    data.loc[idx,'Title2']=Title2 #???\n",
    "data['Title2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A5', 'PC', 'STONO2', 'X', 'PP', 'CA', 'SCParis', 'SCA4', 'A4',\n",
       "       'SP', 'SOC', 'WC', 'SOTONOQ', 'WEP', 'STONO', 'C', 'SCPARIS',\n",
       "       'SOP', 'Fa', 'LINE', 'FCC', 'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH',\n",
       "       'AS', 'SOPP', 'FC', 'SOTONO2', 'CASOTON', 'SCA3', 'STONOQ', 'AQ4',\n",
       "       'A', 'LP', 'AQ3'], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 票的前幾碼代號可能有意義，流水編沒有所以拿掉\n",
    "data['Ticket_info'] = data['Ticket'].apply(lambda x : x.replace(\".\",\"\").replace(\"/\",\"\").strip().split(' ')[0] if not x.isdigit() else 'X')\n",
    "data['Ticket_info'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin_info</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PassengerId\n",
       "Cabin_info             \n",
       "A                    22\n",
       "B                    65\n",
       "C                    94\n",
       "D                    46\n",
       "E                    41\n",
       "F                    21\n",
       "G                     5\n",
       "N                  1014\n",
       "T                     1"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 抓船艙第一碼英文\n",
    "# 送出kaggle成績沒有比較好，一堆空的也是合理\n",
    "data['Cabin_info']=data['Cabin'].str[:1]\n",
    "data['Cabin_info']=data['Cabin_info'].fillna('N')#空的補N\n",
    "data['Cabin_info'].unique()\n",
    "data.groupby('Cabin_info').count()\n",
    "data.groupby('Cabin_info').agg({'PassengerId':pd.Series.nunique})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#最多在S港上傳，填補遺漏值\n",
    "data['Embarked']=data['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fare遺漏的人數： 0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "print('Fare遺漏的人數：',data['Fare'].isnull().sum())\n",
    "#用平均填補遺漏值\n",
    "data['Fare']=data['Fare'].fillna(data['Fare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age遺漏的人數： 0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "print('Age遺漏的人數：',data['Age'].isnull().sum())\n",
    "#用中位數填補遺漏值\n",
    "data['Age']=data['Age'].fillna(data['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Title2</th>\n",
       "      <th>Ticket_info</th>\n",
       "      <th>Cabin_info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>A5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>PC</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>STONO2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Embarked     Fare  Parch  PassengerId  Pclass     Sex  SibSp  \\\n",
       "0  22.0        S   7.2500      0            1       3    male      1   \n",
       "1  38.0        C  71.2833      0            2       1  female      1   \n",
       "2  26.0        S   7.9250      0            3       3  female      0   \n",
       "\n",
       "   Survived  Family_Size Title2 Ticket_info Cabin_info  \n",
       "0       0.0            1     Mr          A5          N  \n",
       "1       1.0            1    Mrs          PC          C  \n",
       "2       1.0            0     Mr      STONO2          N  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#留下準備get_dummies的欄位\n",
    "for_dummy_data=data.drop(['Name','Cabin','Ticket','Title1'], axis=1)\n",
    "for_dummy_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_info_X</th>\n",
       "      <th>Cabin_info_A</th>\n",
       "      <th>Cabin_info_B</th>\n",
       "      <th>Cabin_info_C</th>\n",
       "      <th>Cabin_info_D</th>\n",
       "      <th>Cabin_info_E</th>\n",
       "      <th>Cabin_info_F</th>\n",
       "      <th>Cabin_info_G</th>\n",
       "      <th>Cabin_info_N</th>\n",
       "      <th>Cabin_info_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Parch  PassengerId  Pclass  SibSp  Survived  Family_Size  \\\n",
       "0  22.0   7.2500      0            1       3      1       0.0            1   \n",
       "1  38.0  71.2833      0            2       1      1       1.0            1   \n",
       "2  26.0   7.9250      0            3       3      0       1.0            0   \n",
       "\n",
       "   Embarked_C  Embarked_Q      ...       Ticket_info_X  Cabin_info_A  \\\n",
       "0           0           0      ...                   0             0   \n",
       "1           1           0      ...                   0             0   \n",
       "2           0           0      ...                   0             0   \n",
       "\n",
       "   Cabin_info_B  Cabin_info_C  Cabin_info_D  Cabin_info_E  Cabin_info_F  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             1             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "\n",
       "   Cabin_info_G  Cabin_info_N  Cabin_info_T  \n",
       "0             0             1             0  \n",
       "1             0             0             0  \n",
       "2             0             1             0  \n",
       "\n",
       "[3 rows x 63 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummies = pd.get_dummies(for_dummy_data)\n",
    "#columns可以指定哪些欄位要get_dummies\n",
    "data_dummies.head(3)\n",
    "#data= pd.merge(left=data, right=data_dummies, how='left', left_on='PassengerId', right_on='PassengerId',)\n",
    "#data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Fare', 'Parch', 'PassengerId', 'Pclass', 'SibSp', 'Survived',\n",
       "       'Family_Size', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Sex_female',\n",
       "       'Sex_male', 'Title2_Master', 'Title2_Miss', 'Title2_Mr', 'Title2_Mrs',\n",
       "       'Ticket_info_A', 'Ticket_info_A4', 'Ticket_info_A5', 'Ticket_info_AQ3',\n",
       "       'Ticket_info_AQ4', 'Ticket_info_AS', 'Ticket_info_C', 'Ticket_info_CA',\n",
       "       'Ticket_info_CASOTON', 'Ticket_info_FC', 'Ticket_info_FCC',\n",
       "       'Ticket_info_Fa', 'Ticket_info_LINE', 'Ticket_info_LP',\n",
       "       'Ticket_info_PC', 'Ticket_info_PP', 'Ticket_info_PPP', 'Ticket_info_SC',\n",
       "       'Ticket_info_SCA3', 'Ticket_info_SCA4', 'Ticket_info_SCAH',\n",
       "       'Ticket_info_SCOW', 'Ticket_info_SCPARIS', 'Ticket_info_SCParis',\n",
       "       'Ticket_info_SOC', 'Ticket_info_SOP', 'Ticket_info_SOPP',\n",
       "       'Ticket_info_SOTONO2', 'Ticket_info_SOTONOQ', 'Ticket_info_SP',\n",
       "       'Ticket_info_STONO', 'Ticket_info_STONO2', 'Ticket_info_STONOQ',\n",
       "       'Ticket_info_SWPP', 'Ticket_info_WC', 'Ticket_info_WEP',\n",
       "       'Ticket_info_X', 'Cabin_info_A', 'Cabin_info_B', 'Cabin_info_C',\n",
       "       'Cabin_info_D', 'Cabin_info_E', 'Cabin_info_F', 'Cabin_info_G',\n",
       "       'Cabin_info_N', 'Cabin_info_T'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndata['Sex'] = data['Sex'].astype('category').cat.codes\\ndata['Embarked'] = data['Embarked'].astype('category').cat.codes\\ndata['Pclass'] = data['Pclass'].astype('category').cat.codes\\ndata['Title2'] = data['Title2'].astype('category').cat.codes\\ndata['Ticket_info'] = data['Ticket_info'].astype('category').cat.codes\\ndata['Cabin_info'] = data['Cabin_info'].astype('category').cat.codes\\ndata.head(4)\\n\""
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#感覺是把文字類別轉成某種數字\n",
    "#get_dummies了所以不必這一cell\n",
    "'''\n",
    "data['Sex'] = data['Sex'].astype('category').cat.codes\n",
    "data['Embarked'] = data['Embarked'].astype('category').cat.codes\n",
    "data['Pclass'] = data['Pclass'].astype('category').cat.codes\n",
    "data['Title2'] = data['Title2'].astype('category').cat.codes\n",
    "data['Ticket_info'] = data['Ticket_info'].astype('category').cat.codes\n",
    "data['Cabin_info'] = data['Cabin_info'].astype('category').cat.codes\n",
    "data.head(4)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Fare', 'Parch', 'PassengerId', 'Pclass', 'SibSp', 'Survived',\n",
       "       'Family_Size', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Sex_female',\n",
       "       'Sex_male', 'Title2_Master', 'Title2_Miss', 'Title2_Mr', 'Title2_Mrs',\n",
       "       'Ticket_info_A', 'Ticket_info_A4', 'Ticket_info_A5', 'Ticket_info_AQ3',\n",
       "       'Ticket_info_AQ4', 'Ticket_info_AS', 'Ticket_info_C', 'Ticket_info_CA',\n",
       "       'Ticket_info_CASOTON', 'Ticket_info_FC', 'Ticket_info_FCC',\n",
       "       'Ticket_info_Fa', 'Ticket_info_LINE', 'Ticket_info_LP',\n",
       "       'Ticket_info_PC', 'Ticket_info_PP', 'Ticket_info_PPP', 'Ticket_info_SC',\n",
       "       'Ticket_info_SCA3', 'Ticket_info_SCA4', 'Ticket_info_SCAH',\n",
       "       'Ticket_info_SCOW', 'Ticket_info_SCPARIS', 'Ticket_info_SCParis',\n",
       "       'Ticket_info_SOC', 'Ticket_info_SOP', 'Ticket_info_SOPP',\n",
       "       'Ticket_info_SOTONO2', 'Ticket_info_SOTONOQ', 'Ticket_info_SP',\n",
       "       'Ticket_info_STONO', 'Ticket_info_STONO2', 'Ticket_info_STONOQ',\n",
       "       'Ticket_info_SWPP', 'Ticket_info_WC', 'Ticket_info_WEP',\n",
       "       'Ticket_info_X', 'Cabin_info_A', 'Cabin_info_B', 'Cabin_info_C',\n",
       "       'Cabin_info_D', 'Cabin_info_E', 'Cabin_info_F', 'Cabin_info_G',\n",
       "       'Cabin_info_N', 'Cabin_info_T'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 運用'Survived'欄位來判斷訓練集還是測試集\n",
    "dataTrain = data_dummies[pd.notnull(data_dummies['Survived'])].sort_values(by=[\"PassengerId\"])\n",
    "dataTest = data_dummies[~pd.notnull(data_dummies['Survived'])].sort_values(by=[\"PassengerId\"])\n",
    "dataTrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    dataTrain[['Age', 'Fare', 'Parch', 'PassengerId', 'Pclass','SibSp',\n",
    "       'Family_Size', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Sex_female',\n",
    "       'Sex_male', 'Title2_Master', 'Title2_Miss', 'Title2_Mr', 'Title2_Mrs',\n",
    "       'Ticket_info_A', 'Ticket_info_A4', 'Ticket_info_A5', 'Ticket_info_AQ3',\n",
    "       'Ticket_info_AQ4', 'Ticket_info_AS', 'Ticket_info_C', 'Ticket_info_CA',\n",
    "       'Ticket_info_CASOTON', 'Ticket_info_FC', 'Ticket_info_FCC',\n",
    "       'Ticket_info_Fa', 'Ticket_info_LINE', 'Ticket_info_LP',\n",
    "       'Ticket_info_PC', 'Ticket_info_PP', 'Ticket_info_PPP', 'Ticket_info_SC',\n",
    "       'Ticket_info_SCA3', 'Ticket_info_SCA4', 'Ticket_info_SCAH',\n",
    "       'Ticket_info_SCOW', 'Ticket_info_SCPARIS', 'Ticket_info_SCParis',\n",
    "       'Ticket_info_SOC', 'Ticket_info_SOP', 'Ticket_info_SOPP',\n",
    "       'Ticket_info_SOTONO2', 'Ticket_info_SOTONOQ', 'Ticket_info_SP',\n",
    "       'Ticket_info_STONO', 'Ticket_info_STONO2', 'Ticket_info_STONOQ',\n",
    "       'Ticket_info_SWPP', 'Ticket_info_WC', 'Ticket_info_WEP',\n",
    "       'Ticket_info_X', 'Cabin_info_A', 'Cabin_info_B', 'Cabin_info_C',\n",
    "       'Cabin_info_D', 'Cabin_info_E', 'Cabin_info_F', 'Cabin_info_G',\n",
    "       'Cabin_info_N', 'Cabin_info_T']], dataTrain[['Survived']], test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBDT = GradientBoostingClassifier(random_state=0,n_estimators=100, max_depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=0, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBDT.fit(X_train,y_train['Survived'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.907 \n",
      "\n",
      "Classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.90      0.96      0.93       381\n",
      "        1.0       0.93      0.83      0.87       242\n",
      "\n",
      "avg / total       0.91      0.91      0.91       623\n",
      " \n",
      "\n",
      "Confusion matrix\n",
      "[[365  16]\n",
      " [ 42 200]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True):\n",
    "    y_pred=clf.predict(X)   \n",
    "    if show_accuracy:\n",
    "        print (\"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y,y_pred)),\"\\n\")\n",
    "\n",
    "    if show_classification_report:\n",
    "        print (\"Classification report\")\n",
    "        print (metrics.classification_report(y,y_pred),\"\\n\")\n",
    "        \n",
    "    if show_confusion_matrix:\n",
    "        print (\"Confusion matrix\")\n",
    "        print (metrics.confusion_matrix(y,y_pred),\"\\n\")\n",
    "        \n",
    "measure_performance(X_train,y_train, GBDT, show_classification_report=True, show_confusion_matrix=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_info_X</th>\n",
       "      <th>Cabin_info_A</th>\n",
       "      <th>Cabin_info_B</th>\n",
       "      <th>Cabin_info_C</th>\n",
       "      <th>Cabin_info_D</th>\n",
       "      <th>Cabin_info_E</th>\n",
       "      <th>Cabin_info_F</th>\n",
       "      <th>Cabin_info_G</th>\n",
       "      <th>Cabin_info_N</th>\n",
       "      <th>Cabin_info_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.5</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age    Fare  Parch  PassengerId  Pclass  SibSp  Family_Size  Embarked_C  \\\n",
       "0  34.5  7.8292      0          892       3      0            0           0   \n",
       "1  47.0  7.0000      0          893       3      1            1           0   \n",
       "2  62.0  9.6875      0          894       2      0            0           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S      ...       Ticket_info_X  Cabin_info_A  \\\n",
       "0           1           0      ...                   1             0   \n",
       "1           0           1      ...                   1             0   \n",
       "2           1           0      ...                   1             0   \n",
       "\n",
       "   Cabin_info_B  Cabin_info_C  Cabin_info_D  Cabin_info_E  Cabin_info_F  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "\n",
       "   Cabin_info_G  Cabin_info_N  Cabin_info_T  \n",
       "0             0             1             0  \n",
       "1             0             1             0  \n",
       "2             0             1             0  \n",
       "\n",
       "[3 rows x 62 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTest=dataTest.drop('Survived', axis=1)\n",
    "dataTest.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict Test data\n",
    "#submit the data to kaggle\n",
    "GBDT_res =GBDT.predict(dataTest)\n",
    "submit['Survived'] = GBDT_res\n",
    "submit['Survived'] = submit['Survived'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/francislin/Desktop/Machine_Learning_Workshop/Titanic'"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit.to_csv('submit_to_kaggle.csv', index= False)\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一樣的x去做\n",
      "CART:Your submission scored 0.74641\n",
      "RF:Your submission scored 0.76076\n",
      "GBDT:Your submission scored 0.77511\n"
     ]
    }
   ],
   "source": [
    "print(\"一樣的x去做\")\n",
    "#print('\\n')\n",
    "print(\"CART:\"+\"Your submission scored 0.74641\")\n",
    "print(\"RF:\"+\"Your submission scored 0.76076\")\n",
    "print(\"GBDT:\"+\"Your submission scored 0.77511\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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