Day_021_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測 \n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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>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\n",
    "from sklearn.preprocessing import MinMaxScaler\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": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Numeric Features : ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
      "\n"
     ]
    },
    {
     "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare\n",
       "0       3  22.0      1      0   7.2500\n",
       "1       1  38.0      1      0  71.2833\n",
       "2       3  26.0      0      0   7.9250\n",
       "3       1  35.0      1      0  53.1000\n",
       "4       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print('{} Numeric Features : {}\\n'.format(len(num_features), num_features))\n",
    "\n",
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(0)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18348828>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 顯示 Fare 與目標值的散佈圖\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.distplot(df['Fare'][:train_num])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7016289882888318"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算基礎分數\n",
    "df_mm = MMEncoder.fit_transform(df)\n",
    "train_X = df_mm[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業1 \n",
    "* 試著在鐵達尼的票價 (Fare) 欄位中使用對數去偏 (log1p) , 結果是否更好?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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"
    },
    {
     "data": {
      "text/plain": [
       "0.7106367963253961"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 Fare 取 log1p 後, 看散佈圖, 並計算分數\n",
    "df_fixed = copy.deepcopy(df)\n",
    "\"\"\"\n",
    "Your Code Here, transform df_fixed['Fare'] with log1p\n",
    "\"\"\"\n",
    "df_fixed['Fare'] = np.log1p(df_fixed['Fare'])\n",
    "sns.distplot(df_fixed['Fare'][:train_num])\n",
    "plt.show()\n",
    "\n",
    "df_fixed = MMEncoder.fit_transform(df_fixed)\n",
    "train_X = df_fixed[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業2\n",
    "* 最後的 boxcox 區塊直接執行會造成錯誤, 起因為輸入值有負值, 請問如何修正後可以使用 boxcox? (Hint : 試圖修正資料)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "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"
    },
    {
     "data": {
      "text/plain": [
       "0.7095195488169852"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 Fare 取 boxcox 後, 看散佈圖, 並計算分數 (執行會有 error, 請試圖修正)\n",
    "from scipy import stats\n",
    "df_fixed = copy.deepcopy(df)\n",
    "\"\"\"\n",
    "Your Code Here, fix the error\n",
    "\"\"\"\n",
    "df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'] + 1)[0]\n",
    "sns.distplot(df_fixed['Fare'][:train_num])\n",
    "plt.show()\n",
    "\n",
    "df_fixed = MMEncoder.fit_transform(df_fixed)\n",
    "train_X = df_fixed[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}