09 anova.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# 한글폰트 출력 가능 코드\n",
    "from matplotlib import rc\n",
    "rc('font', family='AppleGothic') # Mac Os\n",
    "#rc('font', family='NanumGothic') # Windows Os\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 로컬 컴퓨터에 저장되어 있는 경우 상위폴더에 dataset 폴더 형성 후 데이터를 집어 넣으면 됨\n",
    "# path = '../dataset/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# csv 파일 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 로컬 컴퓨터에 저장되어 있는 경우\n",
    "# data = pd.read_csv(path+'iris.csv')\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>target</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  target\n",
       "0             5.1          3.5           1.4          0.2       0\n",
       "1             4.9          3.0           1.4          0.2       0\n",
       "2             4.7          3.2           1.3          0.2       0\n",
       "3             4.6          3.1           1.5          0.2       0\n",
       "4             5.0          3.6           1.4          0.2       0\n",
       "..            ...          ...           ...          ...     ...\n",
       "145           6.7          3.0           5.2          2.3       2\n",
       "146           6.3          2.5           5.0          1.9       2\n",
       "147           6.5          3.0           5.2          2.0       2\n",
       "148           6.2          3.4           5.4          2.3       2\n",
       "149           5.9          3.0           5.1          1.8       2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/freejyb/fin_stats/main/dataset/iris.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   target        150 non-null    int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 6.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width      target\n",
       "count    150.000000   150.000000    150.000000   150.000000  150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333    1.000000\n",
       "std        0.828066     0.435866      1.765298     0.762238    0.819232\n",
       "min        4.300000     2.000000      1.000000     0.100000    0.000000\n",
       "25%        5.100000     2.800000      1.600000     0.300000    0.000000\n",
       "50%        5.800000     3.000000      4.350000     1.300000    1.000000\n",
       "75%        6.400000     3.300000      5.100000     1.800000    2.000000\n",
       "max        7.900000     4.400000      6.900000     2.500000    2.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 분산분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-1 일원분산분석 (One-way ANOVA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='target', y= 'sepal_width', data=data)\n",
    "plt.title('꽃 종류별 꽃 받침의 폭')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정규분포를 따른다는 귀무가설을 유의수준 5%에서 기각할 수 없습니다. 따라서 정규성을 충족합니다.\n",
      "정규분포를 따른다는 귀무가설을 유의수준 5%에서 기각할 수 없습니다. 따라서 정규성을 충족합니다.\n",
      "정규분포를 따른다는 귀무가설을 유의수준 5%에서 기각할 수 없습니다. 따라서 정규성을 충족합니다.\n"
     ]
    }
   ],
   "source": [
    "# 정규성 검정\n",
    "# 귀무가설 : 정규분포를 따른다.\n",
    "from  scipy.stats import shapiro\n",
    "\n",
    "for i in range(3):\n",
    "    p_value = shapiro(data.sepal_width[data.target==i])[1]\n",
    "    if p_value < 0.05:\n",
    "        print('정규분포를 따른다는 귀무가설을 유의수준 5%에서 기각할 수 있습니다. 따라서 정규성을 충족하지 못합니다.')\n",
    "    else:\n",
    "        print('정규분포를 따른다는 귀무가설을 유의수준 5%에서 기각할 수 없습니다. 따라서 정규성을 충족합니다.')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeveneResult(statistic=0.5902115655853319, pvalue=0.5555178984739075)\n",
      "BartlettResult(statistic=2.0910752014392338, pvalue=0.35150280041580323)\n"
     ]
    }
   ],
   "source": [
    "#등분산성 확인 - 레빈 검증 (귀무가설 : 등분산의 특징이 있다.)\n",
    "from scipy.stats import levene\n",
    "print(levene(data.sepal_width[data.target==0],\n",
    "             data.sepal_width[data.target==1],\n",
    "             data.sepal_width[data.target==2]))\n",
    "\n",
    "#등분산성 확인 - 바틀렛 검증 (귀무가설 : 등분산의 특징이 있다.)\n",
    "from scipy.stats import bartlett\n",
    "print(bartlett(data.sepal_width[data.target==0],\n",
    "               data.sepal_width[data.target==1],\n",
    "               data.sepal_width[data.target==2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA 분석 결과, 5%의 유의수준으로 모든 수준에서 모평균이 동일하다는 귀무가설을 기각할 수 있습니다. 따라서 위 집단의 모평균이 다르다고 말할 수 있습니다.\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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(target)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.344933</td>\n",
       "      <td>5.672467</td>\n",
       "      <td>49.16004</td>\n",
       "      <td>4.492017e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>147.0</td>\n",
       "      <td>16.962000</td>\n",
       "      <td>0.115388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              df     sum_sq   mean_sq         F        PR(>F)\n",
       "C(target)    2.0  11.344933  5.672467  49.16004  4.492017e-17\n",
       "Residual   147.0  16.962000  0.115388       NaN           NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 꽃 종류가 sepal_width에 어떤 영향을 미치는지 그룹별 변동과 그룹내 변동을 구하여 ANOVA를 시행한다.\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm, anova_single\n",
    "\n",
    "model = ols('sepal_width ~ C(target)', data).fit()\n",
    "\n",
    "anova_table = anova_lm(model)\n",
    "\n",
    "if anova_table['PR(>F)'][0] < 0.05:\n",
    "    print(f'ANOVA 분석 결과, 5%의 유의수준으로 모든 수준에서 모평균이 동일하다는 귀무가설을 기각할 수 있습니다. 따라서 위 집단의 모평균이 다르다고 말할 수 있습니다.')\n",
    "else:\n",
    "    print(f'ANOVA 분석 결과, 5%의 유의수준으로 모든 수준에서 모평균이 동일하다는 귀무가설을 기각할 수 없습니다.')\n",
    "    \n",
    "anova_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            sepal_width   R-squared:                       0.401\n",
      "Model:                            OLS   Adj. R-squared:                  0.393\n",
      "Method:                 Least Squares   F-statistic:                     49.16\n",
      "Date:                Tue, 15 Mar 2022   Prob (F-statistic):           4.49e-17\n",
      "Time:                        12:14:42   Log-Likelihood:                -49.366\n",
      "No. Observations:                 150   AIC:                             104.7\n",
      "Df Residuals:                     147   BIC:                             113.8\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          3.4280      0.048     71.359      0.000       3.333       3.523\n",
      "C(target)[T.1]    -0.6580      0.068     -9.685      0.000      -0.792      -0.524\n",
      "C(target)[T.2]    -0.4540      0.068     -6.683      0.000      -0.588      -0.320\n",
      "==============================================================================\n",
      "Omnibus:                        1.920   Durbin-Watson:                   1.879\n",
      "Prob(Omnibus):                  0.383   Jarque-Bera (JB):                1.632\n",
      "Skew:                           0.027   Prob(JB):                        0.442\n",
      "Kurtosis:                       3.508   Cond. No.                         3.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-2 이원분산분석 (Two-way ANOVA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(target)</th>\n",
       "      <td>0.888920</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.721152</td>\n",
       "      <td>1.032884e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>3.125004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.194491</td>\n",
       "      <td>4.867516e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>13.785343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.430978</td>\n",
       "      <td>1.073592e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.409029</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.344799</td>\n",
       "      <td>3.888826e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>13.556485</td>\n",
       "      <td>144.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sum_sq     df           F        PR(>F)\n",
       "C(target)      0.888920    2.0    4.721152  1.032884e-02\n",
       "sepal_width    3.125004    1.0   33.194491  4.867516e-08\n",
       "petal_length  13.785343    1.0  146.430978  1.073592e-23\n",
       "petal_width    0.409029    1.0    4.344799  3.888826e-02\n",
       "Residual      13.556485  144.0         NaN           NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_model=ols('sepal_length ~ C(target) + sepal_width + petal_length + petal_width', data).fit()\n",
    "anova_lm(multi_model, typ=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           sepal_length   R-squared:                       0.867\n",
      "Model:                            OLS   Adj. R-squared:                  0.863\n",
      "Method:                 Least Squares   F-statistic:                     188.3\n",
      "Date:                Tue, 15 Mar 2022   Prob (F-statistic):           2.67e-61\n",
      "Time:                        12:14:43   Log-Likelihood:                -32.558\n",
      "No. Observations:                 150   AIC:                             77.12\n",
      "Df Residuals:                     144   BIC:                             95.18\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          2.1713      0.280      7.760      0.000       1.618       2.724\n",
      "C(target)[T.1]    -0.7236      0.240     -3.013      0.003      -1.198      -0.249\n",
      "C(target)[T.2]    -1.0235      0.334     -3.067      0.003      -1.683      -0.364\n",
      "sepal_width        0.4959      0.086      5.761      0.000       0.326       0.666\n",
      "petal_length       0.8292      0.069     12.101      0.000       0.694       0.965\n",
      "petal_width       -0.3152      0.151     -2.084      0.039      -0.614      -0.016\n",
      "==============================================================================\n",
      "Omnibus:                        0.418   Durbin-Watson:                   1.966\n",
      "Prob(Omnibus):                  0.811   Jarque-Bera (JB):                0.572\n",
      "Skew:                          -0.060   Prob(JB):                        0.751\n",
      "Kurtosis:                       2.722   Cond. No.                         94.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(multi_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 판별분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 4)\n",
      "(112,)\n",
      "(38, 4)\n",
      "(38,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 훈련, 검정셋 분리\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(columns='target'), data['target'],\n",
    "                                                    test_size = 0.25, shuffle=True, random_state=2022)\n",
    "\n",
    "# 훈련, 검정셋의 행열 값 출력\n",
    "for i in [X_train, y_train, X_test, y_test]:\n",
    "    print(i.shape)\n",
    "\n",
    "# 판별분석 시행\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "lda.fit(X=X_train, y=y_train)\n",
    "lda_X_train = lda.transform(X_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 판별분석이 적용된 데이터에 대해 데이터프레임 형성\n",
    "lda_cols = [\"lda_1\", \"lda_2\"] \n",
    "df = pd.DataFrame(data=lda_X_train, columns=lda_cols) \n",
    "df[\"target\"] = y_train.values\n",
    "\n",
    "# 산점도를 그리기 위해 라벨값, 마커 모양 설정\n",
    "markers=[\"^\", \"s\", \"o\"] \n",
    "label_name = ['setosa', 'versicolor', 'virginica']\n",
    "\n",
    "# 산점도 그리기\n",
    "for i, marker in enumerate(markers): \n",
    "    x_data = df[df[\"target\"]==i][\"lda_1\"] \n",
    "    y_data = df[df[\"target\"]==i][\"lda_2\"] \n",
    "    plt.scatter(x_data, y_data, marker=marker, label=label_name[i]) \n",
    "    \n",
    "plt.legend(loc=\"upper right\") \n",
    "plt.xlabel(\"lda_1\") \n",
    "plt.ylabel(\"lda_2\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "혼동행렬\n",
      "[[13  0  0]\n",
      " [ 0 12  0]\n",
      " [ 0  1 12]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       0.92      1.00      0.96        12\n",
      "   virginica       1.00      0.92      0.96        13\n",
      "\n",
      "    accuracy                           0.97        38\n",
      "   macro avg       0.97      0.97      0.97        38\n",
      "weighted avg       0.98      0.97      0.97        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 라이브러리 호출\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# 판별분석 모델을 통해 X_test를 입력하여 y값 예측 시행\n",
    "y_pred = lda.predict(X_test)\n",
    "\n",
    "# 혼동행렬 생성\n",
    "cf = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 혼동행렬 및 결과 리포트 수행\n",
    "print(f'혼동행렬\\n{cf}\\n')\n",
    "print(classification_report(y_test, y_pred, target_names=['setosa', 'versicolor', 'virginica']))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3.9.4 64-bit",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}