Data Preprocess8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a77daf08-b46a-458d-94cb-9521e703f895",
   "metadata": {},
   "source": [
    "# 차원 축소\n",
    "## 필요 라이브러리 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0ab0fbd-0756-4795-ab74-1075891c123b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler       #PCA 전 특성에 대한 스케일링\n",
    "from sklearn.decomposition import PCA   # 주성분 분석 패키지\n",
    "from sklearn.manifold import TSNE\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "if os.name == 'nt':\n",
    "    font_family = 'Malgun Gothic'\n",
    "\n",
    "sns.set(font=font_family, rc={'axes.unicode_minus' : False})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f5def3-6059-4647-9585-d9ca24f34a16",
   "metadata": {},
   "source": [
    "## 붓꽃 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a22bd3e1-b054-461f-bc40-8ab5c310f125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "# iris 데이터 로드\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3a27a45-d05d-4c90-9f6c-17f4e6cd920f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 붓꽃 데이터 특성 확인\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c54e1d-6d36-4de6-b023-57e7d5b3c8d6",
   "metadata": {},
   "source": [
    "## 데이터프레임 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c112af12-c731-4c2d-8c47-d8f43854add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 생성 및 준비\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "# sy = pd.Series(iris.target, drtpe='category')\n",
    "# sy = sy.cat.rename_categories(iris.target_names)\n",
    "# df['species'] = sy\n",
    "\n",
    "df['target'] = iris.target\n",
    "\n",
    "# 필터명 정의\n",
    "cols = iris.feature_names\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8438694-beed-4a27-88fc-3181249e71d8",
   "metadata": {},
   "source": [
    "## iris data 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f87a87d-aa0c-462a-b879-ae7b47b42c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setosa는 세모, versicolor는 네모, virginica는 동그라미로 표현\n",
    "markers = ['^', 's', 'o']\n",
    "\n",
    "# setosa의 target값은 0, versicolor의 target값은 1, virginica의 target값은 2.\n",
    "# 각 target별로 다른 shape으로 scatter plot\n",
    "\n",
    "for i, marker in enumerate(markers):\n",
    "    x_axis_data = df[df['target']==i]['sepal length (cm)']\n",
    "    y_axis_data = df[df['target']==i]['sepal width (cm)']\n",
    "    plt.scatter(x_axis_data, y_axis_data, marker=marker, label=iris.target_names[i])\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('sepal width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7d333c-7c7d-4f8f-8851-74ce0e847cc7",
   "metadata": {},
   "source": [
    "## 주성분 분석(PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c790b11-3296-4bf2-9275-292d2cac217f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 개별 속성을 함께 스케일링\n",
    "# PCA로 압축하기 전에 각 속성값을 동일한 스케일로 변환하는 것이 필요\n",
    "\n",
    "iris_scaled = StandardScaler().fit_transform(df.iloc[:, :-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b30aba4d-ea0c-4dd0-b3ea-97c0a4856f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 2)\n"
     ]
    }
   ],
   "source": [
    "# n_components : PCA로 변환할 차원의 수\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "# fit()과 transform()을 호출하여 PCA 변환 데이터 반환\n",
    "pca.fit(iris_scaled)\n",
    "iris_pca = pca.transform(iris_scaled)\n",
    "print(iris_pca.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e9780b9-3f57-4cdd-b70d-a205c78290f4",
   "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>pca_component_1</th>\n",
       "      <th>pca_component_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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pca_component_1  pca_component_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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA 변환된 데이터의 컬럼명을 각각 pca_component_1, pca_component_2로 명명\n",
    "pca_columns = ['pca_component_1', 'pca_component_2']\n",
    "irisDF_pca = pd.DataFrame(iris_pca, columns=pca_columns)\n",
    "irisDF_pca['target'] = iris.target\n",
    "irisDF_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6609798b-2015-4be5-a7f5-a3fec053f0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 원본 데이터와 PCA데이터로 분류 모델 실행\n",
    "# 랜덤포레스트 알고리즘 , 교차검증 수행\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "246dba34-02b8-4b91-8b5c-89c0cd1b3df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 데이터 교차 검증 정확도 : [0.98 0.94 0.96]\n",
      "원본 데이터 교차 검증 평균 정확도 : 0.96\n"
     ]
    }
   ],
   "source": [
    "rcf = RandomForestClassifier(random_state=156)\n",
    "scores = cross_val_score(rcf, iris.data, iris.target, scoring='accuracy', cv=3)\n",
    "print(f'원본 데이터 교차 검증 정확도 : {scores}')\n",
    "print(f'원본 데이터 교차 검증 평균 정확도 : {np.mean(scores)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f00f0677-7dc4-47d9-b0b6-5edf1991a821",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA 데이터 교차 검증 정확도 : [0.88 0.88 0.88]\n",
      "PCA 데이터 교차 검증 평균 정확도 : 0.88\n"
     ]
    }
   ],
   "source": [
    "pca_X = irisDF_pca[['pca_component_1', 'pca_component_2']]\n",
    "scores_pca = cross_val_score(rcf, pca_X, iris.target, scoring='accuracy', cv=3)\n",
    "print(f'PCA 데이터 교차 검증 정확도 : {scores_pca}')\n",
    "print(f'PCA 데이터 교차 검증 평균 정확도 : {np.mean(scores_pca)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bbcd99-53c4-493a-9c61-d002c5703bb4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90cf9b12-978f-4637-b531-6d6f311431e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
      "       'petal width (cm)', 'target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임 확인\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6fca995a-0fc9-49ae-9b16-7494c800613f",
   "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>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</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd3ad591-2ec9-4e98-b26f-384dc72737fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)    #FutureWarning 제거\n",
    "\n",
    "# class target 정보 제외\n",
    "train_df = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n",
    "\n",
    "# 2차원 t-SNE 임베딩\n",
    "tsne_np = TSNE(n_components=2).fit_transform(train_df)\n",
    "\n",
    "# numpy array -> DataFrame 변환\n",
    "tsne_df = pd.DataFrame(tsne_np, columns=['component 0', 'component 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c1c3457-d3f8-4b41-b2d2-690cda64b292",
   "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>component 0</th>\n",
       "      <th>component 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-17.337639</td>\n",
       "      <td>-6.689819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-14.995592</td>\n",
       "      <td>-5.872775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-14.675938</td>\n",
       "      <td>-6.906576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-14.410111</td>\n",
       "      <td>-6.668876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-17.361380</td>\n",
       "      <td>-7.164054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   component 0  component 1\n",
       "0   -17.337639    -6.689819\n",
       "1   -14.995592    -5.872775\n",
       "2   -14.675938    -6.906576\n",
       "3   -14.410111    -6.668876\n",
       "4   -17.361380    -7.164054"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c023ccf-cab5-4f2f-9e15-bc9861febdd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# class target 정보 불러오기\n",
    "tsne_df['target'] = df['target']\n",
    "\n",
    "# target별 분리\n",
    "tsne_df_0 = tsne_df[tsne_df['target'] == 0]\n",
    "tsne_df_1 = tsne_df[tsne_df['target'] == 1]\n",
    "tsne_df_2 = tsne_df[tsne_df['target'] == 2]\n",
    "\n",
    "# target별 시각화\n",
    "plt.scatter(tsne_df_0['component 0'], tsne_df_0['component 1'], color = 'blue', label = 'setosa')\n",
    "plt.scatter(tsne_df_1['component 0'], tsne_df_0['component 1'], color = 'red', label = 'versicolor')\n",
    "plt.scatter(tsne_df_2['component 0'], tsne_df_0['component 1'], color = 'green', label = 'virginica')\n",
    "\n",
    "plt.xlabel('component 0')\n",
    "plt.ylabel('component 1')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cec86601-9c53-41bd-bf0e-dc5e007b3263",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}