Day_023_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, 觀察降低偏態的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察使用log1p降偏態時, 對於分布與迴歸分數的影響 (In[6], Out[6])\n",
    "- 修正區塊中的資料問題後, 觀察以box-cox降偏態, 對於分布與迴歸分數的影響 (In[7], Out[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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 = '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 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": 2,
     "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(f'{len(num_features)} Numeric Features : {num_features}\\n')\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": 11,
   "metadata": {},
   "outputs": [
    {
     "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",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.distplot(df['Fare'][:train_num])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7015378821166279"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.710545477371163"
      ]
     },
     "execution_count": 13,
     "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": [
    "分布較為集中，但並沒有說有好很多，以結果分數來看稍佳"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1309.000000\n",
       "mean       33.270043\n",
       "std        51.747063\n",
       "min         0.000000\n",
       "25%         7.895800\n",
       "50%        14.454200\n",
       "75%        31.275000\n",
       "max       512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fixed['Fare'].describe()\n",
    "sns.distplot(df_fixed['Fare'][:train_num])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7094218818655451"
      ]
     },
     "execution_count": 22,
     "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'] = df_fixed['Fare'] + 1\n",
    "df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "原始數據因為最小值 = 0, 只要透過 + 1 即可避免運算錯誤"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}