AI_Class/007/decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree (의사결정 나무)\n",
    "\n",
    "의사결정나무는 분류나무(classification tree)와 회귀나무(regression tree)로 구분된다.\n",
    "\n",
    "분류나무의 경우 상위노드에서 가지분할을 수행할 때, 분류(기준)변수와 분류 기준값의 선택 방법으로 카이제곱 통계량(Chi-square statistic)의 $p$-값, 지니 지수(Gini index), 엔트로피 지수(entropy index) 등이 사용된다.\n",
    "\n",
    "선택된 기준에 의해 분할이 일어날 때, 카이제곱통계량의 $p$-값은 그 값이 작을수록 자식노드간의 이질성이 큼을 나타내며, 자식노드에서의 지니 지수나 엔트로피 지수는 그 값이 클수록 자식노드 내의 이질성이 큼을 의미한다. \n",
    "\n",
    "따라서 이 값들이 가장 작아지는 방향으로 가지분할을 수행하게 된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropy\n",
    "\n",
    "불순도(Impurity)란 해당 범주 안에 서로 다른 데이터가 얼마나 섞여 있는지를 의미한다.\n",
    "\n",
    "엔트로피(Entropy)는 불순도(Impurity)를 수치적으로 나타낸 척도이다.\n",
    "\n",
    "$ 0 <= E <= 1$\n",
    "\n",
    "![007-5](https://user-images.githubusercontent.com/63298243/118453947-c073e280-b732-11eb-8cb6-dde7acb7f87d.png)\n",
    "\n",
    "+ p<sub>i</sub> = 한 영역 안에 존재하는 데이터 가운데 범주 i에 속하는 데이터의 비율\n",
    "\n",
    "아래의 그림에서 Entropy의 값이 클수록 이질적이며 순수도(purity)가 낮다고 할 수 있다.\n",
    "\n",
    "![007-9](https://user-images.githubusercontent.com/63298243/118514821-bde3ae00-b76f-11eb-93b8-999f9849b75e.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information gain\n",
    "Information gain = entropy(parent) - [weighted average] entropy(children)\n",
    "\n",
    "정보 획득을 최대화하는 방향으로 학습이 진행된다.\n",
    "\n",
    "반대로 말하면 Entropy가 작아지는 방향으로 진행된다.\n",
    "\n",
    "어느 feature의 어느 분기점에서 정보 획득이 최대화되는지 판단을 해서 분기가 진행된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![007-6](https://user-images.githubusercontent.com/63298243/118457569-d8e4fc80-b734-11eb-9250-11afca6c7859.JPG)\n",
    "\n",
    "![007-7](https://user-images.githubusercontent.com/63298243/118457572-da162980-b734-11eb-83a1-a9488ea3b1e7.JPG)\n",
    "\n",
    "![007-8](https://user-images.githubusercontent.com/63298243/118457580-daaec000-b734-11eb-90ea-659449d6bad3.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pruning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가지분할(split)은 나무의 가지를 생성하는 과정을, \n",
    "\n",
    "가지치기(pruning)는 생성된 가지를 잘라내어 모형을 단순화하는 과정을 말한다\n",
    "\n",
    "<img width=\"631\" alt=\"007-3\" src=\"https://user-images.githubusercontent.com/63298243/118453089-c917e900-b731-11eb-9f43-f009f088710d.png\">\n",
    "\n",
    "<img width=\"814\" alt=\"007-4\" src=\"https://user-images.githubusercontent.com/63298243/125481434-67472353-589f-4195-b614-4a5acb7a9ef6.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "유방암 진단 데이터셋\n",
    "\n",
    "1.샘플 코드 번호 id 번호  \n",
    "2. 덩어리 두께 1-10  \n",
    "3. 셀 크기의 균일성 1-10  \n",
    "4. 세포 모양의 균일성 1-10  \n",
    "5. 한계 접착 1-10  \n",
    "6. 단일 상피 세포 크기 1-10   \n",
    "7. 드러난 핵세포량 1-10  \n",
    "8. 부드러운 염색질 1-10  \n",
    "9. 정상 핵소체 1-10  \n",
    "10. 유사 분열 1-10  \n",
    "\n",
    "클래스 분포:  \n",
    "양성: 458(65.5%)  \n",
    "악성: 241(34.5%)\n",
    "\n",
    "[직접 다운로드](https://www.cc.gatech.edu/projects/disl/VISTA/breast.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/\\\n",
    "breast-cancer-wisconsin/breast-cancer-wisconsin.data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(uci_path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['id','clump','cell_size','cell_shape', 'adhesion','epithlial', 'bare_nuclei','chromatin','normal_nucleoli', 'mitoses', 'class'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "null(nan) 데이터 처리와 object를 int형으로 변환해야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>clump</th>\n",
       "      <th>cell_size</th>\n",
       "      <th>cell_shape</th>\n",
       "      <th>adhesion</th>\n",
       "      <th>epithlial</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
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       "      <td>1016277</td>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "        id  clump  cell_size  cell_shape  adhesion  epithlial bare_nuclei  \\\n",
       "0  1000025      5          1           1         1          2           1   \n",
       "1  1002945      5          4           4         5          7          10   \n",
       "2  1015425      3          1           1         1          2           2   \n",
       "3  1016277      6          8           8         1          3           4   \n",
       "4  1017023      4          1           1         3          2           1   \n",
       "\n",
       "   chromatin  normal_nucleoli  mitoses  class  \n",
       "0          3                1        1      2  \n",
       "1          3                2        1      2  \n",
       "2          3                1        1      2  \n",
       "3          3                7        1      2  \n",
       "4          3                1        1      2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               699 non-null    int64 \n",
      " 1   clump            699 non-null    int64 \n",
      " 2   cell_size        699 non-null    int64 \n",
      " 3   cell_shape       699 non-null    int64 \n",
      " 4   adhesion         699 non-null    int64 \n",
      " 5   epithlial        699 non-null    int64 \n",
      " 6   bare_nuclei      699 non-null    object\n",
      " 7   chromatin        699 non-null    int64 \n",
      " 8   normal_nucleoli  699 non-null    int64 \n",
      " 9   mitoses          699 non-null    int64 \n",
      " 10  class            699 non-null    int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 0\n",
       "clump              0\n",
       "cell_size          0\n",
       "cell_shape         0\n",
       "adhesion           0\n",
       "epithlial          0\n",
       "bare_nuclei        0\n",
       "chromatin          0\n",
       "normal_nucleoli    0\n",
       "mitoses            0\n",
       "class              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '10', '2', '4', '3', '9', '7', '?', '5', '8', '6'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bare_nuclei'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bare_nuclei'].replace('?', np.nan, inplace=True)\n",
    "df.dropna(subset=['bare_nuclei'], axis=0, inplace=True)\n",
    "df['bare_nuclei'] = df['bare_nuclei'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(data = df.corr(), annot=True, fmt = '.2f', linewidths = .5, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "정확도가 98% 이상이 되도록 만들어주세요 !!!\n",
    "\n",
    "종속변수는 'class'입니다.\n",
    "\n",
    "모델은 아래를 참고하여 쓰면 됩니다.\n",
    "- DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "\n",
    "#### 파라미터 설명\n",
    "criterion : 분류척도\n",
    "\n",
    "max_depth : 가지치는 최대 깊이\n",
    "\n",
    "    - max_depth가 커질수록 모델 정확도는 높아질 수 있으나, Overfitting, 즉 실제 데이터에 대해서는 정확도가 떨어질 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['class']                      #종속 변수 Y\n",
    "X=df.drop(['class','id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model = DecisionTreeClassifier(criterion='entropy', max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.07317073170731 %\n"
     ]
    }
   ],
   "source": [
    "r_square = dt_model.score(X_test, y_test)\n",
    "print(r_square*100,\"%\",sep=\" \")"
   ]
  }
 ],
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