10-2 PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 라이브러리 호출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\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",
    "\n",
    "# 로컬 컴퓨터에 저장되어 있는 경우 상위폴더에 dataset 폴더 형성 후 데이터를 집어 넣으면 됨\n",
    "path = '../dataset/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 로컬 컴퓨터에 저장되어 있는 경우\n",
    "\n",
    "# data = pd.read_csv(path+'iris.csv', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width','target'])\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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": 3,
     "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": "markdown",
   "metadata": {},
   "source": [
    "# 1. 스케일러 적용 후 PCA 시행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width\n",
       "0     -0.900681     1.019004     -1.340227    -1.315444\n",
       "1     -1.143017    -0.131979     -1.340227    -1.315444\n",
       "2     -1.385353     0.328414     -1.397064    -1.315444\n",
       "3     -1.506521     0.098217     -1.283389    -1.315444\n",
       "4     -1.021849     1.249201     -1.340227    -1.315444"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 표준화 패키지 라이브러리 \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# feature, target 분류\n",
    "feature = data.drop(['target'], axis=1).values \n",
    "target = data['target'].values \n",
    "\n",
    "# 스케일러 적용 후 저장\n",
    "scale_feature = StandardScaler().fit_transform(feature)\n",
    "scaled_data =pd.DataFrame(scale_feature, columns=['sepal length', 'sepal width', 'petal length', 'petal width'])\n",
    "scaled_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pca_1</th>\n",
       "      <th>pca_2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.870503</td>\n",
       "      <td>0.386966</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.564580</td>\n",
       "      <td>-0.896687</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.521170</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.372788</td>\n",
       "      <td>1.011254</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>-0.024332</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pca_1     pca_2  target\n",
       "0   -2.264703  0.480027       0\n",
       "1   -2.080961 -0.674134       0\n",
       "2   -2.364229 -0.341908       0\n",
       "3   -2.299384 -0.597395       0\n",
       "4   -2.389842  0.646835       0\n",
       "..        ...       ...     ...\n",
       "145  1.870503  0.386966       2\n",
       "146  1.564580 -0.896687       2\n",
       "147  1.521170  0.269069       2\n",
       "148  1.372788  1.011254       2\n",
       "149  0.960656 -0.024332       2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA 기법 적용 (라이브러리)\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 주성분을 몇개로 할지 결정 (2개 주성분)\n",
    "pca = PCA(n_components=2)\n",
    "pca2_feature = pca.fit_transform(scale_feature)\n",
    "\n",
    "# 주성분으로 이루어진 데이터 프레임 구성\n",
    "pca2_data = pd.DataFrame(data=pca2_feature, columns = ['pca_1', 'pca_2'])\n",
    "# pca2_data\n",
    "\n",
    "# 새로운 데이터프레임 형성 (라벨값 업데이트)\n",
    "finalDf = pca2_data\n",
    "finalDf['target'] = data.target.values\n",
    "finalDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target, color 지정\n",
    "targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
    "colors = ['r', 'g', 'b']\n",
    "\n",
    "# 산점도 그래프 출력\n",
    "plt.figure(figsize = (8, 8))\n",
    "for target, color in zip(targets,colors):\n",
    "    plt.scatter(finalDf.loc[finalDf['target'] == target, 'pca_1']\n",
    "               , finalDf.loc[finalDf['target'] == target, 'pca_2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "    \n",
    "plt.xlabel('Principal Component 1', fontsize = 15)\n",
    "plt.ylabel('Principal Component 2', fontsize = 15)\n",
    "plt.title('2 component PCA', fontsize=20)\n",
    "plt.legend(targets)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1번째 주성분이 설명가능한 분산 값 :  0.72962\n",
      "2번째 주성분이 설명가능한 분산 값 :  0.22851\n",
      "2개 주성분이 설명하지 못하는 분산 값 :  0.04187\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 설명가능한 분산 (PCA 매서드에서 출력 가능)\n",
    "\n",
    "# 주성분 개수 설정\n",
    "n_components = 2\n",
    "# Instantiate PCA\n",
    "pca = PCA(n_components=n_components)\n",
    "\n",
    "# Determine transformed features\n",
    "pca2_feature = pca.fit_transform(scaled_data)\n",
    "\n",
    "# explained_variance_ratio를 사용하여 각 주성분이 설명가능한 분산을 지정함\n",
    "exp_var_pca = pca.explained_variance_ratio_\n",
    "\n",
    "for i in range(len(exp_var_pca)):\n",
    "    print(f'{i+1}번째 주성분이 설명가능한 분산 값 : {exp_var_pca[i] : .5f}')\n",
    "print(f'{n_components}개 주성분이 설명하지 못하는 분산 값 : {1-sum(exp_var_pca) : .5f}')\n",
    "\n",
    "# explained 값을 cumsum하여 step으로 표시함\n",
    "cum_sum_eigenvalues = np.cumsum(exp_var_pca)\n",
    "\n",
    "# 시각화\n",
    "plt.bar(range(0,len(exp_var_pca)), exp_var_pca, alpha=0.5, align='center', label='n번째 주성분이 설명가능한 분산')\n",
    "plt.step(range(0,len(cum_sum_eigenvalues)), cum_sum_eigenvalues, where='mid',label='축적된 설명가능한 분산')\n",
    "plt.xticks(range(0,len(exp_var_pca)))\n",
    "plt.ylabel('설명 가능한 분산 비율')\n",
    "plt.xlabel('주성분')\n",
    "plt.legend(bbox_to_anchor=(1.0, .2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 참고\n",
    "\n",
    "- interactive plot\n",
    "- 3D plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "legendgroup": "",
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   "source": [
    "# interactive plot 출력\n",
    "\n",
    "# 라이브러리 호출\n",
    "import plotly.express as px\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# feature 지정\n",
    "feature = data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
    "\n",
    "# PCA 기법 반영 (2차원으로 축소) 및 데이터 프레임 형성\n",
    "pca = PCA(n_components=2)\n",
    "components = pd.DataFrame(pca.fit_transform(feature))\n",
    "components.columns = ['pca_1', 'pca_2']\n",
    "\n",
    "# plotly 도표 생성\n",
    "fig = px.scatter(components, x='pca_1', y='pca_2', color=data['target'])\n",
    "fig.show()"
   ]
  },
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   "execution_count": 11,
   "metadata": {},
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   "source": [
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "feature = data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "components = pca.fit_transform(feature)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(components, x=0, y=1, z=2, color=data['target'],\n",
    "                    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "                    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'})\n",
    "fig.update_layout(height=500)\n",
    "fig.show()"
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