0311_표준화.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 표준화 - 표준점수와 편차값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29da6202bc8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "import platform\n",
    "\n",
    "#매킨토시의 경우\n",
    "if platform.system() == 'Darwin':\n",
    "    rc('font', family='AppleGothic')\n",
    "#윈도우의 경우\n",
    "elif platform.system() == 'Windows':\n",
    "    font_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\n",
    "    rc('font', family=font_name)\n",
    "\n",
    "data = pd.read_csv('./data/student.csv', index_col='이름', encoding='cp949')\n",
    "\n",
    "#1. 인덱스를 기본으로 막대 그래프 그리기\n",
    "data.plot(kind='bar')\n",
    "\n",
    "#2. 표준화 작업하기 (각 열의 평균과 표준편차 구하기)\n",
    "kormean, korstd = data['국어'].mean(), data['국어'].std()\n",
    "engmean, engstd = data['영어'].mean(), data['영어'].std()\n",
    "mathmean, mathstd = data['수학'].mean(), data['수학'].std()\n",
    "\n",
    "#3. 표준점수를 계산한 열 추가해주기\n",
    "#cf. 0이면 중간. 1.0이면 상하위 15%. 2.0이면 상하위 1.1%\n",
    "data['국어표준점수'] = (data['국어']-kormean)/korstd\n",
    "data['영어표준점수'] = (data['영어']-engmean)/engstd\n",
    "data['수학표준점수'] = (data['수학']-mathmean)/mathstd\n",
    "data\n",
    "\n",
    "#4. 편차값 계산 하기 10*표준값 + 50\n",
    "data['국어편차값'] = (data['국어표준점수']*10+50)\n",
    "data['영어편차값'] = (data['영어표준점수']*10+50)\n",
    "data['수학편차값'] = (data['수학표준점수']*10+50)\n",
    "data\n",
    "\n",
    "#5. 1번보다 표준화시킨 4번 점수로 그래프를 다시 그리는게 과목별, 과목간 비교가 쉬울것\n",
    "data[['국어편차값','영어편차값','수학편차값']].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 정규화 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": 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>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>국어표준점수</th>\n",
       "      <th>영어표준점수</th>\n",
       "      <th>수학표준점수</th>\n",
       "      <th>국어편차값</th>\n",
       "      <th>영어편차값</th>\n",
       "      <th>수학편차값</th>\n",
       "      <th>국어정규화1</th>\n",
       "      <th>국어정규화2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이름</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>박지영</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>55</td>\n",
       "      <td>0.616320</td>\n",
       "      <td>0.554321</td>\n",
       "      <td>-0.941716</td>\n",
       "      <td>56.163201</td>\n",
       "      <td>55.543208</td>\n",
       "      <td>40.582843</td>\n",
       "      <td>0.927835</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>김태함</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>-0.176091</td>\n",
       "      <td>-0.386979</td>\n",
       "      <td>0.399335</td>\n",
       "      <td>48.239085</td>\n",
       "      <td>46.130213</td>\n",
       "      <td>53.993351</td>\n",
       "      <td>0.721649</td>\n",
       "      <td>0.649351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>김효섭</td>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "      <td>76</td>\n",
       "      <td>0.695561</td>\n",
       "      <td>1.024971</td>\n",
       "      <td>0.184767</td>\n",
       "      <td>56.955613</td>\n",
       "      <td>60.249706</td>\n",
       "      <td>51.847670</td>\n",
       "      <td>0.948454</td>\n",
       "      <td>0.935065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>임경희</td>\n",
       "      <td>76</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "      <td>0.061632</td>\n",
       "      <td>0.742581</td>\n",
       "      <td>0.828471</td>\n",
       "      <td>50.616320</td>\n",
       "      <td>57.425807</td>\n",
       "      <td>58.284714</td>\n",
       "      <td>0.783505</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>권혁진</td>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "      <td>83</td>\n",
       "      <td>0.893664</td>\n",
       "      <td>0.648451</td>\n",
       "      <td>0.560261</td>\n",
       "      <td>58.936642</td>\n",
       "      <td>56.484508</td>\n",
       "      <td>55.602612</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>하혜진</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>97</td>\n",
       "      <td>0.220114</td>\n",
       "      <td>0.601386</td>\n",
       "      <td>1.311250</td>\n",
       "      <td>52.201143</td>\n",
       "      <td>56.013858</td>\n",
       "      <td>63.112497</td>\n",
       "      <td>0.824742</td>\n",
       "      <td>0.779221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>이준원</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>-2.157120</td>\n",
       "      <td>-2.034253</td>\n",
       "      <td>-1.746346</td>\n",
       "      <td>28.428796</td>\n",
       "      <td>29.657471</td>\n",
       "      <td>32.536538</td>\n",
       "      <td>0.206186</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>윤정웅</td>\n",
       "      <td>50</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>-0.968503</td>\n",
       "      <td>-0.998823</td>\n",
       "      <td>-0.995358</td>\n",
       "      <td>40.314970</td>\n",
       "      <td>40.011766</td>\n",
       "      <td>40.046423</td>\n",
       "      <td>0.515464</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>주시현</td>\n",
       "      <td>95</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>0.814423</td>\n",
       "      <td>-0.151654</td>\n",
       "      <td>0.399335</td>\n",
       "      <td>58.144230</td>\n",
       "      <td>48.483462</td>\n",
       "      <td>53.993351</td>\n",
       "      <td>0.979381</td>\n",
       "      <td>0.974026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     국어  영어  수학    국어표준점수    영어표준점수    수학표준점수      국어편차값      영어편차값  \\\n",
       "이름                                                                    \n",
       "박지영  90  85  55  0.616320  0.554321 -0.941716  56.163201  55.543208   \n",
       "김태함  70  65  80 -0.176091 -0.386979  0.399335  48.239085  46.130213   \n",
       "김효섭  92  95  76  0.695561  1.024971  0.184767  56.955613  60.249706   \n",
       "임경희  76  89  88  0.061632  0.742581  0.828471  50.616320  57.425807   \n",
       "권혁진  97  87  83  0.893664  0.648451  0.560261  58.936642  56.484508   \n",
       "하혜진  80  86  97  0.220114  0.601386  1.311250  52.201143  56.013858   \n",
       "이준원  20  30  40 -2.157120 -2.034253 -1.746346  28.428796  29.657471   \n",
       "윤정웅  50  52  54 -0.968503 -0.998823 -0.995358  40.314970  40.011766   \n",
       "주시현  95  70  80  0.814423 -0.151654  0.399335  58.144230  48.483462   \n",
       "\n",
       "         수학편차값    국어정규화1    국어정규화2  \n",
       "이름                                  \n",
       "박지영  40.582843  0.927835  0.909091  \n",
       "김태함  53.993351  0.721649  0.649351  \n",
       "김효섭  51.847670  0.948454  0.935065  \n",
       "임경희  58.284714  0.783505  0.727273  \n",
       "권혁진  55.602612  1.000000  1.000000  \n",
       "하혜진  63.112497  0.824742  0.779221  \n",
       "이준원  32.536538  0.206186  0.000000  \n",
       "윤정웅  40.046423  0.515464  0.389610  \n",
       "주시현  53.993351  0.979381  0.974026  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. 최대값으로 나누어서 값을 저장 - 정규화\n",
    "#정규화 방법 1 : 자기값을 해당 컬럼의 최대값으로 나누기\n",
    "#정규화 방법 2 : 자기 값 - 최소값 / (최대값 - 최소값)\n",
    "\n",
    "data['국어정규화1'] = data['국어']/data['국어'].max()\n",
    "data['국어정규화2'] = (data['국어']-data['국어'].min())/(data['국어'].max()-data['국어'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 사이킷런을 이용한 Scailing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.6537062 ]\n",
      " [-0.1867732 ]\n",
      " [ 0.73775414]\n",
      " [ 0.06537062]\n",
      " [ 0.94787399]\n",
      " [ 0.2334665 ]\n",
      " [-2.28797169]\n",
      " [-1.0272526 ]\n",
      " [ 0.86382605]]\n",
      "1.2335811384723961e-17\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "#하는 방법은 다 똑같고 메소드 이름만 다르다.\n",
    "scaler = preprocessing.StandardScaler() # << 이 StandardScaler대신 다른 표준화 방법 이름을 갖다 넣음 된다.\n",
    "\n",
    "result = scaler.fit_transform(data[['국어']].values)\n",
    "#왜 대괄호가 두개인가???\n",
    "# >> 머신러닝의 데이터들은 모두 array를 이용하는데, data['국어']는 1차원 데이터가 된다. \n",
    "\n",
    "print(result) #표준화 값을 구한 것\n",
    "print(np.mean(result)) #0인지 확인\n",
    "print(np.std(result)) #1인지 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 거리행렬을 이용한 표준화 - 유클리드, 맨하튼, 최대값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.72701315 0.68662353]\n",
      " [0.73279349 0.6804511 ]\n",
      " [0.6956733  0.7183583 ]\n",
      " [0.64938292 0.76046158]\n",
      " [0.74443789 0.66769172]\n",
      " [0.68110394 0.73218674]\n",
      " [0.5547002  0.83205029]\n",
      " [0.69310872 0.72083306]\n",
      " [0.80505584 0.59319904]]\n",
      "[[0.51428571 0.48571429]\n",
      " [0.51851852 0.48148148]\n",
      " [0.49197861 0.50802139]\n",
      " [0.46060606 0.53939394]\n",
      " [0.52717391 0.47282609]\n",
      " [0.48192771 0.51807229]\n",
      " [0.4        0.6       ]\n",
      " [0.49019608 0.50980392]\n",
      " [0.57575758 0.42424242]]\n",
      "[[1.         0.94444444]\n",
      " [1.         0.92857143]\n",
      " [0.96842105 1.        ]\n",
      " [0.85393258 1.        ]\n",
      " [1.         0.89690722]\n",
      " [0.93023256 1.        ]\n",
      " [0.66666667 1.        ]\n",
      " [0.96153846 1.        ]\n",
      " [1.         0.73684211]]\n"
     ]
    }
   ],
   "source": [
    "matrix = data[['국어','영어']].values\n",
    "matrix\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# 유클리드 거리로 표준화 하는 객체 생성하기. 맨하튼을 쓸거면 l1. \n",
    "norm = preprocessing.Normalizer(norm='l2')\n",
    "print(norm.transform(matrix))\n",
    "\n",
    "#맨하튼 거리로 계산\n",
    "norm = preprocessing.Normalizer(norm='l1')\n",
    "print(norm.transform(matrix))\n",
    "\n",
    "#최대값으로 계산\n",
    "norm = preprocessing.Normalizer(norm='max')\n",
    "print(norm.transform(matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다중공선성 이슈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.00000e+01, 8.50000e+01, 8.10000e+03, 7.65000e+03, 7.22500e+03,\n",
       "        7.29000e+05, 6.88500e+05, 6.50250e+05, 6.14125e+05],\n",
       "       [7.00000e+01, 6.50000e+01, 4.90000e+03, 4.55000e+03, 4.22500e+03,\n",
       "        3.43000e+05, 3.18500e+05, 2.95750e+05, 2.74625e+05],\n",
       "       [9.20000e+01, 9.50000e+01, 8.46400e+03, 8.74000e+03, 9.02500e+03,\n",
       "        7.78688e+05, 8.04080e+05, 8.30300e+05, 8.57375e+05],\n",
       "       [7.60000e+01, 8.90000e+01, 5.77600e+03, 6.76400e+03, 7.92100e+03,\n",
       "        4.38976e+05, 5.14064e+05, 6.01996e+05, 7.04969e+05],\n",
       "       [9.70000e+01, 8.70000e+01, 9.40900e+03, 8.43900e+03, 7.56900e+03,\n",
       "        9.12673e+05, 8.18583e+05, 7.34193e+05, 6.58503e+05],\n",
       "       [8.00000e+01, 8.60000e+01, 6.40000e+03, 6.88000e+03, 7.39600e+03,\n",
       "        5.12000e+05, 5.50400e+05, 5.91680e+05, 6.36056e+05],\n",
       "       [2.00000e+01, 3.00000e+01, 4.00000e+02, 6.00000e+02, 9.00000e+02,\n",
       "        8.00000e+03, 1.20000e+04, 1.80000e+04, 2.70000e+04],\n",
       "       [5.00000e+01, 5.20000e+01, 2.50000e+03, 2.60000e+03, 2.70400e+03,\n",
       "        1.25000e+05, 1.30000e+05, 1.35200e+05, 1.40608e+05],\n",
       "       [9.50000e+01, 7.00000e+01, 9.02500e+03, 6.65000e+03, 4.90000e+03,\n",
       "        8.57375e+05, 6.31750e+05, 4.65500e+05, 3.43000e+05]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial = preprocessing.PolynomialFeatures(degree=2, include_bias=False)\n",
    "result = polynomial.fit_transform(matrix)\n",
    "result\n",
    "\n",
    "polynomial = preprocessing.PolynomialFeatures(degree=3, include_bias=False)\n",
    "result = polynomial.fit_transform(matrix)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100 200]\n",
      " [300 150]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_function_transformer.py:97: FutureWarning: The default validate=True will be replaced by validate=False in 0.22.\n",
      "  \"validate=False in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[200, 300],\n",
       "       [400, 250]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.array([[100,200], [300,150]])\n",
    "print(matrix)\n",
    "\n",
    "def intconvert(x):\n",
    "    return x + 100\n",
    "\n",
    "#matrix 데이터를 정수로 변환하기\n",
    "transformer = preprocessing.FunctionTransformer(intconvert)\n",
    "matrix = transformer.transform(matrix)\n",
    "matrix"
   ]
  }
 ],
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