Day_025_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, 觀察均值編碼的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 仿造範例, 完成標籤編碼與均值編碼搭配邏輯斯迴歸的預測\n",
    "- 觀察標籤編碼與均值編碼在特徵數量 / 邏輯斯迴歸分數 / 邏輯斯迴歸時間上, 分別有什麼影響 (In[3], Out[3], In[4], Out[4]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 請仿照範例，將鐵達尼範例中的類別型特徵改用均值編碼實作一次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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 MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "data_path = 'data2/'\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Object Features : ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']\n",
      "\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <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",
       "    </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",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'object':\n",
    "        object_features.append(feature)\n",
    "print(f'{len(object_features)} Object 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",
    "* 觀察鐵達尼生存預測中，均值編碼與標籤編碼兩者比較，哪一個效果比較好? 可能的原因是什麼?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape : (891, 5)\n",
      "score : 0.7800138095537004\n",
      "time : 0.07380199432373047 sec\n"
     ]
    }
   ],
   "source": [
    "# 對照組 : 標籤編碼 + 邏輯斯迴歸\n",
    "df_le = pd.DataFrame()\n",
    "for col in object_features:\n",
    "    df_le[col] = LabelEncoder().fit_transform(df[col])\n",
    "\n",
    "train_X = df_le[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "start_time = time.time()\n",
    "print(f'shape : {train_X.shape}')\n",
    "print(f'score : {cross_val_score(estimator, train_X, train_Y, cv = 5).mean()}')\n",
    "print(f'time : {time.time() - start_time} sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>816</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523</td>\n",
       "      <td>0</td>\n",
       "      <td>914</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>422</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>649</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>811</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>607</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "      <td>185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>338</td>\n",
       "      <td>1</td>\n",
       "      <td>642</td>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "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\n",
       "..    ...  ...     ...    ...       ...\n",
       "886   811    1     130    185         3\n",
       "887   464    0      18     40         3\n",
       "888   607    0     923    185         3\n",
       "889   113    1      11     77         0\n",
       "890   338    1     642    185         2\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape : (891, 5)\n",
      "score : 1.0\n",
      "time : 0.02490544319152832 sec\n"
     ]
    }
   ],
   "source": [
    "# 均值編碼 + 邏輯斯迴歸\n",
    "data = pd.concat([df[:train_num], train_Y], axis = 1)\n",
    "for col in df.columns:\n",
    "    mean_df = data.groupby([col])['Survived'].mean().reset_index()\n",
    "    mean_df.columns = [col, f'{col}_mean']\n",
    "    data = pd.merge(data, mean_df, on = col, how = 'left')\n",
    "    data = data.drop([col], axis = 1)\n",
    "    \n",
    "data = data.drop(['Survived'], axis = 1)\n",
    "estimator = LogisticRegression()\n",
    "start = time.time()\n",
    "print(f'shape : {data.shape}')\n",
    "print(f'score : {cross_val_score(estimator, data, train_Y, cv=5).mean()}')\n",
    "print(f'time : {time.time() - start} sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name_mean</th>\n",
       "      <th>Sex_mean</th>\n",
       "      <th>Ticket_mean</th>\n",
       "      <th>Cabin_mean</th>\n",
       "      <th>Embarked_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299854</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name_mean  Sex_mean  Ticket_mean  Cabin_mean  Embarked_mean\n",
       "0            0  0.188908          0.0    0.299854       0.336957\n",
       "1            1  0.742038          1.0    1.000000       0.553571\n",
       "2            1  0.742038          1.0    0.299854       0.336957\n",
       "3            1  0.742038          0.5    0.500000       0.336957\n",
       "4            0  0.188908          0.0    0.299854       0.336957\n",
       "..         ...       ...          ...         ...            ...\n",
       "886          0  0.188908          0.0    0.299854       0.336957\n",
       "887          1  0.742038          1.0    1.000000       0.336957\n",
       "888          0  0.742038          0.0    0.299854       0.336957\n",
       "889          1  0.188908          1.0    1.000000       0.553571\n",
       "890          0  0.188908          0.0    0.299854       0.389610\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乍看之下均值編碼比較好，但從分數可以評斷出其實已經發生了overfitting的問題了\n",
    "在這種情形下可能會是標籤編碼比較合適(均值編碼於此範例中對應的都是object而非numeric，加上目標值為01變數)\\\n",
    "較不符合模型該用的類別"
   ]
  }
 ],
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