D43 探索性資料分析(EDA)_從資料中選取好的特徵 7.特徵選取_作業.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 今天的作業\n",
    "在鐵達尼資料集中，以Titanic_train.csv 中，首先將有遺失值的數值刪除，試著將課堂中所學的方法應用上去。\n",
    "* Q1: 目標變數為 Survived，以 Pclass, Age, SibSp, Parch, Fare,Sex, Embarked 為特徵，試著用今天教授的包裝法，搭配課程所教的 SVC，試著排出其餘特徵的重要性。\n"
   ]
  },
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   "metadata": {
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1558,
     "status": "ok",
     "timestamp": 1578021044012,
     "user": {
      "displayName": "Mora chen",
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    "id": "yz28_IgkYdBW",
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "C:\\Users\\suipi\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.1\n",
      "1.1.1\n"
     ]
    }
   ],
   "source": [
    "# library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "import sklearn\n",
    "print(sklearn.__version__)\n",
    "#如果只有 0.19 記得要更新至 最新版本\n",
    "print(pd.__version__)\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 特徵選取會用到的函數\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.datasets import make_friedman1\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.svm import SVC\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n",
    "print(df_train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   PassengerId    891 non-null    int64  \n",
      " 1   Survived       891 non-null    int64  \n",
      " 2   Pclass         891 non-null    int64  \n",
      " 3   Name           891 non-null    object \n",
      " 4   Sex            891 non-null    object \n",
      " 5   Age            714 non-null    float64\n",
      " 6   SibSp          891 non-null    int64  \n",
      " 7   Parch          891 non-null    int64  \n",
      " 8   Ticket         891 non-null    object \n",
      " 9   Fare           891 non-null    float64\n",
      " 10  Cabin          204 non-null    object \n",
      " 11  Embarked       889 non-null    object \n",
      " 12  Survived_cate  891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(6)\n",
      "memory usage: 90.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#1.產稱一個新的變數 Survived_cate ，資料型態傳換成類別型態      \n",
    "#2.把題目中的 Survived 用 Survived_cate 來做分析    \n",
    "df_train['Survived_cate']=df_train['Survived']\n",
    "df_train['Survived_cate']=df_train['Survived_cate'].astype('object')\n",
    "print(df_train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived_cate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked  \\\n",
       "1          1       1  female  38.0      1      0  71.2833   C85        C   \n",
       "3          1       1  female  35.0      1      0  53.1000  C123        S   \n",
       "6          0       1    male  54.0      0      0  51.8625   E46        S   \n",
       "10         1       3  female   4.0      1      1  16.7000    G6        S   \n",
       "11         1       1  female  58.0      0      0  26.5500  C103        S   \n",
       "\n",
       "   Survived_cate  \n",
       "1              1  \n",
       "3              1  \n",
       "6              0  \n",
       "10             1  \n",
       "11             1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183, 10)\n"
     ]
    }
   ],
   "source": [
    "#我們先把遺失值刪除\n",
    "## 取出資料後，把遺失值刪除\n",
    "complete_data=df_train.dropna()\n",
    "\n",
    "#排除 Ｎame 當特徵，先刪除每一個人都是獨特的資料，先檢視其他變數\n",
    "complete_data=complete_data.drop(['Name','Ticket','PassengerId'], axis=1)\n",
    "display(complete_data.head(5))\n",
    "\n",
    "print(complete_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 Numeric Features : ['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_features = []\n",
    "for dtype, feature in zip(complete_data.dtypes, complete_data.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 category Features : ['Sex', 'Cabin', 'Embarked', 'Survived_cate']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cat_features = []\n",
    "for dtype, feature in zip(complete_data.dtypes, complete_data.columns):\n",
    "    if dtype == 'object':\n",
    "        cat_features.append(feature)\n",
    "print(f'{len(cat_features)} category Features : {cat_features}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定義 x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>female</td>\n",
       "      <td>C</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",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass   Age  SibSp  Parch     Fare     Sex Embarked\n",
       "1        1  38.0      1      0  71.2833  female        C\n",
       "3        1  35.0      1      0  53.1000  female        S\n",
       "6        1  54.0      0      0  51.8625    male        S\n",
       "10       3   4.0      1      1  16.7000  female        S\n",
       "11       1  58.0      0      0  26.5500  female        S"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1     1\n",
       "3     1\n",
       "6     0\n",
       "10    1\n",
       "11    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=complete_data[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare','Sex', 'Embarked']]\n",
    "y=complete_data['Survived']\n",
    "display(x.head(5))\n",
    "display(y.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: 目標變數為 Survived，試著用今天教授的包裝法，搭配課程所教的 SVC，試著排出其餘特徵的重要性!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#離散要轉換成數值，['Sex', 'Embarked']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#執行 包裝法\n",
    "\n"
   ]
  }
 ],
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