5_Prepocessing_實戰.ipynb

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    "# 資料前處理(Missing data, One-hot encoding, Feature Scaling)\n",
    "資料的品質、特徵的選取決定了機器學習的上限，模型(Model)只是逼近這個上限。<br>\n",
    "雖然在學術界總是以Model為主要討論對象，但實際上在業界80%的時間都是在對資料進行前處理，<br>\n",
    "包含了資料獲取、清理、特徵選擇、特徵處理…到這裡我們可以稍微了解資料前處理的重要性。<br>\n",
    "\n",
    "常見的資料前處理如下所示：<br>\n",
    "1.缺失值的處理<br>\n",
    "2.類別資料的處理（有序、無序）<br>\n",
    "3.資料特徵縮放<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from io import StringIO\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 缺失值的處理\n",
    "缺值主要處理方式有兩種：<br>\n",
    "*丟棄，如果資料量夠多<br>\n",
    "*補值<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_data = '''A,B,C,D,E\n",
    "            5.0,2.0,3.0,,6\n",
    "            1.0,6.0,,8.0,5\n",
    "            0.0,11.0,12.0,4.0,5\n",
    "            3.0,,3.0,5.0,\n",
    "            5.0,1.0,4.0,2.0,4\n",
    "           '''\n",
    "df = pd.read_csv(StringIO(csv_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('preText.csv')"
   ]
  },
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       "     A     B     C    D    E\n",
       "0  5.0   2.0   3.0  NaN  6.0\n",
       "1  1.0   6.0   NaN  8.0  5.0\n",
       "2  0.0  11.0  12.0  4.0  5.0\n",
       "3  3.0   NaN   3.0  5.0  NaN\n",
       "4  5.0   1.0   4.0  2.0  4.0"
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    "df"
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    "## Missing Data(空值資料處理)\n",
    "\n",
    "使用Pandas去空值的方法“dropna”，dropna預設只要任一欄位有空值，就會整筆刪掉。<br>\n",
    "可透過參數來調整，像是把how設為all，就是要全部為空才清掉，或是用subset指定當某一欄為空時才刪。"
   ]
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       "     A     B     C    D    E\n",
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       "     A     B     C    D    E\n",
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   "source": [
    "補值則使用fillna函式即可，依照以下範例即可補上固定值0、平均數、眾數、中位數…"
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       "     A     B     C    D    E\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     C    D    E\n",
       "0  5.0   2.0   3.0  NaN  6.0\n",
       "1  1.0   6.0   NaN  8.0  5.0\n",
       "2  0.0  11.0  12.0  4.0  5.0\n",
       "3  3.0   5.0   3.0  5.0  NaN\n",
       "4  5.0   1.0   4.0  2.0  4.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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       "     A     B     C    D    E\n",
       "0  5.0   2.0   3.0  4.5  6.0\n",
       "1  1.0   6.0   NaN  8.0  5.0\n",
       "2  0.0  11.0  12.0  4.0  5.0\n",
       "3  3.0   5.0   3.0  5.0  NaN\n",
       "4  5.0   1.0   4.0  2.0  4.0"
      ]
     },
     "execution_count": 23,
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     "output_type": "execute_result"
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   "execution_count": 24,
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       "     A     B     C    D    E\n",
       "0  5.0   2.0   3.0  4.5  6.0\n",
       "1  1.0   6.0   NaN  8.0  5.0\n",
       "2  0.0  11.0  12.0  4.0  5.0\n",
       "3  3.0   5.0   3.0  5.0  4.0\n",
       "4  5.0   1.0   4.0  2.0  4.0"
      ]
     },
     "execution_count": 24,
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     "output_type": "execute_result"
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   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2.Categorical Data(類別資料處理)\n",
    "由於要在空間中表示點，所有的特徵都需要是數值，因此如果是類別的資料，<br>\n",
    "像是XL,L,M,S,XS或是資料類別為Male, Female, Not Specified，就需要轉成數值才能在空間中來表示。<br>\n",
    "有序的類別資料通常是直接使用數值替換，比方說XL, L, M, S, XS雖然是類別的屬性但因為有大小順序的關係，可以用10, 7 , 5, 3, 1來替換。<br>如果是Male, Female, Not Specified因為這三種都是等價的關係因此需要找一個方法讓這三個屬性距離原點是相同距離，<br>\n",
    "One-hot encoding 就是解決這的問題的方法，首先會將Male, Female, Not Specified由Gender從成一個欄位拆成三個欄位，<br>\n",
    "因此編號1的使用者的屬性資料就是(1,0,0)編號2的使用者就是(0,1,0) 編號三個使用者就是(0,0,1)這三個使用者對於原點的距離都是1，就達成我們想要的結果了。<br>\n",
    "但One-hot encoding的方法只適合類別種類少的形況下，如果類別種類太多就會產生出一大堆的特徵，造成其他的問題（比方說維數災難）。"
   ]
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>M</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>L</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>XL</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price  classlabel\n",
       "0  green    M   10.1           1\n",
       "1    red    L   13.5           2\n",
       "2   blue   XL   15.3           1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(\n",
    "    [['green', 'M', 10.1, 1],\n",
    "    ['red', 'L', 13.5, 2],\n",
    "    ['blue', 'XL', 15.3, 1]]\n",
    ")\n",
    "df2.columns = ['color', 'size', 'price', 'classlabel']\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由於size是屬於有序的資料，我們只要稍微轉換為數值即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
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       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
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       "      <td>13.5</td>\n",
       "      <td>2</td>\n",
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       "      <td>blue</td>\n",
       "      <td>3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   color  size  price  classlabel\n",
       "0  green     1   10.1           1\n",
       "1    red     2   13.5           2\n",
       "2   blue     3   15.3           1"
      ]
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     "execution_count": 26,
     "metadata": {},
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "至於color我們就用onehot-encoding的方法來處理，在pandas裡面要使用<br>\n",
    "onehot-encoding使用get_dummies這個函式就可以了，範例如下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "   blue  green  red\n",
       "0     0      1    0\n",
       "1     0      0    1\n",
       "2     1      0    0"
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   "source": []
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   "outputs": [
    {
     "data": {
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       "      <td>15.3</td>\n",
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       "   size  price  classlabel\n",
       "0     1   10.1           1\n",
       "1     2   13.5           2\n",
       "2     3   15.3           1"
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   "execution_count": 30,
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   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>color_blue</th>\n",
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       "      <th>classlabel</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   color_blue  color_green  color_red  size  price  classlabel\n",
       "0           0            1          0     1   10.1           1\n",
       "1           0            0          1     2   13.5           2\n",
       "2           1            0          0     3   15.3           1"
      ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.資料特徵縮放\n",
    "## 資料正規化(normalization)\n",
    "最常見的Normalization為0–1區間縮放，經過Normalization之後資料的範圍會介在0~1之間，<br>\n",
    "原本的最大值變為1，最小值變為0，具體作法如下圖\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x^{(i)}_{norm}=\\frac{x^{(i)}-x_{min}}{x_{max}-x_{min}}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Math(r'x^{(i)}_{norm}=\\frac{x^{(i)}-x_{min}}{x_{max}-x_{min}}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target_names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    },
    {
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       "\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "\n",
       "   target_names  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from io import StringIO\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x = pd.DataFrame(iris['data'], columns=iris['feature_names'])\n",
    "print(\"target_names: \"+str(iris['target_names']))\n",
    "y = pd.DataFrame(iris['target'], columns=['target_names'])\n",
    "data = pd.concat([x,y], axis=1)\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料標準化(Standardization)\n",
    "經過Standardization資料的平均值會變為0, 標準差變為1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x^{(i)}_{std}=\\frac{x^{(i)}-\\mu_{x}}{\\sigma_{x}}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Math(r'x^{(i)}_{std}=\\frac{x^{(i)}-\\mu_{x}}{\\sigma_{x}}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target_names</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1.015602</td>\n",
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       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.166667</td>\n",
       "      <td>-0.131539</td>\n",
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       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.327318</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.097889</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.041667</td>\n",
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       "1           0.166667         -0.131539           0.067797          0.041667   \n",
       "2           0.111111          0.327318           0.050847          0.041667   \n",
       "3           0.083333          0.097889           0.084746          0.041667   \n",
       "4           0.194444          1.245030           0.067797          0.041667   \n",
       "\n",
       "   target_names  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
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   ],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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