homework/Day_024_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測\n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在鐵達尼生存預測中, 觀察計數編碼與特徵雜湊的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 仿造範例, 完成計數編碼以及搭配邏輯斯迴歸的預測 (In[4], Out[4], In[5], Out[5]) \n",
    "- 仿造範例, 完成雜湊編碼, 以及計數編碼+雜湊編碼 搭配邏輯斯迴歸的預測 (In[6], Out[6], In[7], Out[7]) \n",
    "- 試著回答上述執行結果的觀察"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 參考範例，將鐵達尼的艙位代碼( 'Cabin' )欄位使用特徵雜湊 / 標籤編碼 / 目標均值編碼三種轉換後，  \n",
    "與其他數值型欄位一起預估生存機率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T03:19:15.176299Z",
     "start_time": "2019-05-13T03:19:13.669873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy, time\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "data_path = 'data/'\n",
    "df_train = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "df_test = pd.read_csv(data_path + 'titanic_test.csv')\n",
    "\n",
    "train_Y = df_train['Survived']\n",
    "ids = df_test['PassengerId']\n",
    "df_train = df_train.drop(['PassengerId', 'Survived'] , axis = 1)\n",
    "df_test = df_test.drop(['PassengerId'] , axis = 1)\n",
    "df = pd.concat([df_train,df_test])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T03:19:52.347551Z",
     "start_time": "2019-05-13T03:19:52.320758Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Numeric Features : ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
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       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  \\\n",
       "0                            Braund, Mr. Owen Harris    male   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                             Heikkinen, Miss. Laina  female   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4                           Allen, Mr. William Henry    male   \n",
       "\n",
       "             Ticket Cabin Embarked  \n",
       "0         A/5 21171  None        S  \n",
       "1          PC 17599   C85        C  \n",
       "2  STON/O2. 3101282  None        S  \n",
       "3            113803  C123        S  \n",
       "4            373450  None        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#只取類別值 (object) 型欄位, 存於 object_features 中\n",
    "object_features = []\n",
    "\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'object':\n",
    "        object_features.append(feature)\n",
    "\n",
    "print(f'{len(object_features)} Numeric Features : {object_features}\\n')\n",
    "\n",
    "# 只留類別型欄位\n",
    "df = df[object_features]\n",
    "df = df.fillna('None')\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 承上題，三者比較效果何者最好?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.780004837244799\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>155</td>\n",
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       "      <th>1</th>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>816</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523</td>\n",
       "      <td>0</td>\n",
       "      <td>914</td>\n",
       "      <td>185</td>\n",
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       "      <th>3</th>\n",
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       "      <td>70</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>649</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "   Name  Sex  Ticket  Cabin  Embarked\n",
       "0   155    1     720    185         3\n",
       "1   286    0     816    106         0\n",
       "2   523    0     914    185         3\n",
       "3   422    0      65     70         3\n",
       "4    22    1     649    185         3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 對照組 : 標籤編碼 + 邏輯斯迴歸\n",
    "df_temp = pd.DataFrame()\n",
    "\n",
    "for c in df.columns:\n",
    "    df_temp[c] = LabelEncoder().fit_transform(df[c])\n",
    "\n",
    "train_X = df_temp[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "\n",
    "print(cross_val_score(estimator, train_X, train_Y, cv = 5).mean())\n",
    "\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T03:24:46.088287Z",
     "start_time": "2019-05-13T03:24:46.056087Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Cabin_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>None</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>G6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>B96 B98</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>F2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>C22 C26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>F33</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>F4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "               Cabin  Cabin_Count\n",
       "185             None         1014\n",
       "80       C23 C25 C27            6\n",
       "184               G6            5\n",
       "47   B57 B59 B63 B66            5\n",
       "60           B96 B98            4\n",
       "180               F2            4\n",
       "117                D            4\n",
       "79           C22 C26            4\n",
       "181              F33            4\n",
       "183               F4            4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加上 'Cabin' 欄位的計數編碼\n",
    "count_df = df.groupby(['Cabin'])['Name'].agg({'Cabin_Count':'size'}).reset_index()\n",
    "\n",
    "df = pd.merge(df, count_df, on = ['Cabin'], how = 'left')\n",
    "count_df.sort_values(by = ['Cabin_Count'], ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T03:25:20.638989Z",
     "start_time": "2019-05-13T03:25:20.626056Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Count</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  \\\n",
       "0                            Braund, Mr. Owen Harris    male   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                             Heikkinen, Miss. Laina  female   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4                           Allen, Mr. William Henry    male   \n",
       "\n",
       "             Ticket Cabin Embarked  Cabin_Count  \n",
       "0         A/5 21171  None        S         1014  \n",
       "1          PC 17599   C85        C            2  \n",
       "2  STON/O2. 3101282  None        S         1014  \n",
       "3            113803  C123        S            2  \n",
       "4            373450  None        S         1014  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "df.head()"
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    "ExecuteTime": {
     "end_time": "2019-05-13T03:26:32.277213Z",
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     "text": [
      "0.7856230275549181\n"
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    {
     "name": "stderr",
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      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
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       "   Name  Sex  Ticket  Cabin  Embarked  Cabin_Count\n",
       "0   155    1     720    185         3         1014\n",
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Cabin'計數編碼 + 邏輯斯迴歸\n",
    "df_temp = pd.DataFrame()\n",
    "\n",
    "for c in object_features:\n",
    "    df_temp[c] = LabelEncoder().fit_transform(df[c])\n",
    "\n",
    "df_temp['Cabin_Count'] = df['Cabin_Count']\n",
    "train_X = df_temp[:train_num]\n",
    "\n",
    "estimator = LogisticRegression()\n",
    "\n",
    "print(cross_val_score(estimator, train_X, train_Y, cv = 5).mean())\n",
    "\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7833756946889959\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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       "   Name  Sex  Ticket  Cabin  Embarked  Cabin_Hash\n",
       "0   155    1     720    185         3           8\n",
       "1   286    0     816    106         0           1\n",
       "2   523    0     914    185         3           8\n",
       "3   422    0      65     70         3           2\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Cabin'特徵雜湊 + 邏輯斯迴歸\n",
    "df_temp = pd.DataFrame()\n",
    "\n",
    "for c in object_features:\n",
    "    df_temp[c] = LabelEncoder().fit_transform(df[c])\n",
    "\n",
    "df_temp['Cabin_Hash'] = df['Cabin'].map(lambda x:hash(x) % 10)\n",
    "train_X = df_temp[:train_num]\n",
    "\n",
    "estimator = LogisticRegression()\n",
    "\n",
    "print(cross_val_score(estimator, train_X, train_Y, cv = 5).mean())\n",
    "\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
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     "start_time": "2019-05-13T03:30:18.965320Z"
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   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7833947386806166\n"
     ]
    },
    {
     "name": "stderr",
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      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/jasonch/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
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       "   Name  Sex  Ticket  Cabin  Embarked  Cabin_Hash  Cabin_Count\n",
       "0   155    1     720    185         3           8         1014\n",
       "1   286    0     816    106         0           1            2\n",
       "2   523    0     914    185         3           8         1014\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Cabin'計數編碼 + 'Cabin'特徵雜湊 + 邏輯斯迴歸\n",
    "df_temp = pd.DataFrame()\n",
    "\n",
    "for c in object_features:\n",
    "    df_temp[c] = LabelEncoder().fit_transform(df[c])\n",
    "\n",
    "df_temp['Cabin_Hash'] = df['Cabin'].map(lambda x:hash(x) % 10)\n",
    "df_temp['Cabin_Count'] = df['Cabin_Count']\n",
    "\n",
    "train_X = df_temp[:train_num]\n",
    "\n",
    "estimator = LogisticRegression()\n",
    "\n",
    "print(cross_val_score(estimator, train_X, train_Y, cv = 5).mean())\n",
    "\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "ANS：計數編碼效果最好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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