03_의사결정트리_서울 지역 다중 분류하기.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "district_dict_list = [\n",
    "            {'district': 'Gangseo-gu', 'latitude': 37.551000, 'longitude': 126.849500, 'label':'Gangseo'},\n",
    "            {'district': 'Yangcheon-gu', 'latitude': 37.52424, 'longitude': 126.855396, 'label':'Gangseo'},\n",
    "            {'district': 'Guro-gu', 'latitude': 37.4954, 'longitude': 126.8874, 'label':'Gangseo'},\n",
    "            {'district': 'Geumcheon-gu', 'latitude': 37.4519, 'longitude': 126.9020, 'label':'Gangseo'},\n",
    "            {'district': 'Mapo-gu', 'latitude': 37.560229, 'longitude': 126.908728, 'label':'Gangseo'},\n",
    "            \n",
    "            {'district': 'Gwanak-gu', 'latitude': 37.487517, 'longitude': 126.915065, 'label':'Gangnam'},\n",
    "            {'district': 'Dongjak-gu', 'latitude': 37.5124, 'longitude': 126.9393, 'label':'Gangnam'},\n",
    "            {'district': 'Seocho-gu', 'latitude': 37.4837, 'longitude': 127.0324, 'label':'Gangnam'},\n",
    "            {'district': 'Gangnam-gu', 'latitude': 37.5172, 'longitude': 127.0473, 'label':'Gangnam'},\n",
    "            {'district': 'Songpa-gu', 'latitude': 37.503510, 'longitude': 127.117898, 'label':'Gangnam'},\n",
    "   \n",
    "            {'district': 'Yongsan-gu', 'latitude': 37.532561, 'longitude': 127.008605, 'label':'Gangbuk'},\n",
    "            {'district': 'Jongro-gu', 'latitude': 37.5730, 'longitude': 126.9794, 'label':'Gangbuk'},\n",
    "            {'district': 'Seongbuk-gu', 'latitude': 37.603979, 'longitude': 127.056344, 'label':'Gangbuk'},\n",
    "            {'district': 'Nowon-gu', 'latitude': 37.6542, 'longitude': 127.0568, 'label':'Gangbuk'},\n",
    "            {'district': 'Dobong-gu', 'latitude': 37.6688, 'longitude': 127.0471, 'label':'Gangbuk'},\n",
    "     \n",
    "            {'district': 'Seongdong-gu', 'latitude': 37.557340, 'longitude': 127.041667, 'label':'Gangdong'},\n",
    "            {'district': 'Dongdaemun-gu', 'latitude': 37.575759, 'longitude': 127.025288, 'label':'Gangdong'},\n",
    "            {'district': 'Gwangjin-gu', 'latitude': 37.557562, 'longitude': 127.083467, 'label':'Gangdong'},\n",
    "            {'district': 'Gangdong-gu', 'latitude': 37.554194, 'longitude': 127.151405, 'label':'Gangdong'},\n",
    "            {'district': 'Jungrang-gu', 'latitude': 37.593684, 'longitude': 127.090384, 'label':'Gangdong'}\n",
    "         ]\n",
    "\n",
    "train_df = pd.DataFrame(district_dict_list)\n",
    "train_df = train_df[['district', 'longitude', 'latitude', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dong_dict_list = [\n",
    "            {'dong': 'Gaebong-dong', 'latitude': 37.489853, 'longitude': 126.854547, 'label':'Gangseo'},\n",
    "            {'dong': 'Gochuk-dong', 'latitude': 37.501394, 'longitude': 126.859245, 'label':'Gangseo'},\n",
    "            {'dong': 'Hwagok-dong', 'latitude': 37.537759, 'longitude': 126.847951, 'label':'Gangseo'},\n",
    "            {'dong': 'Banghwa-dong', 'latitude': 37.575817, 'longitude': 126.815719, 'label':'Gangseo'},\n",
    "            {'dong': 'Sangam-dong', 'latitude': 37.577039, 'longitude': 126.891620, 'label':'Gangseo'},\n",
    "            \n",
    "            {'dong': 'Nonhyun-dong', 'latitude': 37.508838, 'longitude': 127.030720, 'label':'Gangnam'},\n",
    "            {'dong': 'Daechi-dong', 'latitude': 37.501163, 'longitude': 127.057193, 'label':'Gangnam'},\n",
    "            {'dong': 'Seocho-dong', 'latitude': 37.486401, 'longitude': 127.018281, 'label':'Gangnam'},\n",
    "            {'dong': 'Bangbae-dong', 'latitude': 37.483279, 'longitude': 126.988194, 'label':'Gangnam'},\n",
    "            {'dong': 'Dogok-dong', 'latitude': 37.492896, 'longitude': 127.043159, 'label':'Gangnam'},\n",
    "    \n",
    "            {'dong': 'Pyoungchang-dong', 'latitude': 37.612129, 'longitude': 126.975724, 'label':'Gangbuk'},\n",
    "            {'dong': 'Sungbuk-dong', 'latitude': 37.597916, 'longitude': 126.998067, 'label':'Gangbuk'},\n",
    "            {'dong': 'Ssangmoon-dong', 'latitude': 37.648094, 'longitude': 127.030421, 'label':'Gangbuk'},\n",
    "            {'dong': 'Ui-dong', 'latitude': 37.648446, 'longitude': 127.011396, 'label':'Gangbuk'},\n",
    "            {'dong': 'Samcheong-dong', 'latitude': 37.591109, 'longitude': 126.980488, 'label':'Gangbuk'},\n",
    "    \n",
    "            {'dong': 'Hwayang-dong', 'latitude': 37.544234, 'longitude': 127.071648, 'label':'Gangdong'},\n",
    "            {'dong': 'Gui-dong', 'latitude': 37.543757, 'longitude': 127.086803, 'label':'Gangdong'},\n",
    "            {'dong': 'Neung-dong', 'latitude': 37.553102, 'longitude': 127.080248, 'label':'Gangdong'},\n",
    "            {'dong': 'Amsa-dong', 'latitude': 37.552370, 'longitude': 127.127124, 'label':'Gangdong'},\n",
    "            {'dong': 'Chunho-dong', 'latitude': 37.547436, 'longitude': 127.137382, 'label':'Gangdong'}\n",
    "         ]\n",
    "\n",
    "test_df = pd.DataFrame(dong_dict_list)\n",
    "test_df = test_df[['dong', 'longitude', 'latitude', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gangbuk     5\n",
       "Gangnam     5\n",
       "Gangseo     5\n",
       "Gangdong    5\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gangbuk     5\n",
       "Gangnam     5\n",
       "Gangseo     5\n",
       "Gangdong    5\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>district</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gangseo-gu</td>\n",
       "      <td>126.849500</td>\n",
       "      <td>37.551000</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yangcheon-gu</td>\n",
       "      <td>126.855396</td>\n",
       "      <td>37.524240</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guro-gu</td>\n",
       "      <td>126.887400</td>\n",
       "      <td>37.495400</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Geumcheon-gu</td>\n",
       "      <td>126.902000</td>\n",
       "      <td>37.451900</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mapo-gu</td>\n",
       "      <td>126.908728</td>\n",
       "      <td>37.560229</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       district   longitude   latitude    label\n",
       "0    Gangseo-gu  126.849500  37.551000  Gangseo\n",
       "1  Yangcheon-gu  126.855396  37.524240  Gangseo\n",
       "2       Guro-gu  126.887400  37.495400  Gangseo\n",
       "3  Geumcheon-gu  126.902000  37.451900  Gangseo\n",
       "4       Mapo-gu  126.908728  37.560229  Gangseo"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'district visualizaiton in 2d plane')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 622x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('longitude', 'latitude', data= train_df, fit_reg= False,\n",
    "          scatter_kws= {'s': 150},\n",
    "          markers= ['o', 'x', '+', '*'],\n",
    "          hue = 'label')\n",
    "plt.title('district visualizaiton in 2d plane')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['district'], axis= 1, inplace= True)\n",
    "test_df.drop(['dong'], axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_df[['longitude', 'latitude']]\n",
    "y_train = train_df[['label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = test_df[['longitude', 'latitude']]\n",
    "y_test = test_df[['label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import numpy as np\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Q\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "y_encoded = le.fit_transform(y_train)\n",
    "clf = tree.DecisionTreeClassifier(random_state= 35).fit(x_train, y_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_decision_surface(clf, x, y):\n",
    "    x_min = x.longitude.min() - 0.01\n",
    "    x_max = x.longitude.max() + 0.01\n",
    "    y_min = x.latitude.min() - 0.01\n",
    "    y_max = x.latitude.max() + 0.01\n",
    "    \n",
    "    n_classes = len(le.classes_)\n",
    "    plot_colors = 'rywb'\n",
    "    plot_step = 0.001\n",
    "    \n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "    \n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    cs = plt.contourf(xx, yy, Z, cmap = plt.cm.RdYlBu)\n",
    "    \n",
    "    for i, color in zip(range(n_classes), plot_colors):\n",
    "        idx = np.where(y == i)\n",
    "        plt.scatter(x.loc[idx].longitude, x.loc[idx].latitude,\n",
    "                   c= color, label= le.classes_[i],\n",
    "                   cmap= plt.cm.RdYlBu, edgecolor= 'black', s= 200)\n",
    "        \n",
    "    # 챠트 제목\n",
    "    plt.title(\"Decision surface of a decision tree\",fontsize=16)\n",
    "    # 챠트 기호 설명\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=14)\n",
    "    # x축의 이름과 폰트 크기 설정\n",
    "    plt.xlabel('longitude',fontsize=16)\n",
    "    # y축의 이름과 폰트 크기 설정\n",
    "    plt.ylabel('latitude',fontsize=16)\n",
    "    # 챠트 크기 설정\n",
    "    plt.rcParams[\"figure.figsize\"] = [7,5]\n",
    "    # 챠트 폰트 크기 설정\n",
    "    plt.rcParams[\"font.size\"] = 14\n",
    "    # x축 좌표상의 폰트 크기 설정\n",
    "    plt.rcParams[\"xtick.labelsize\"] = 14\n",
    "    # y축 좌표상의 폰트 크기 설정\n",
    "    plt.rcParams[\"ytick.labelsize\"] = 14\n",
    "\n",
    "    # 챠트 그리기\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_decision_surface(clf, x_train, y_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(max_depth= 4,\n",
    "                                 min_samples_split= 2,\n",
    "                                 min_samples_leaf= 2,\n",
    "                                 random_state= 70).fit(x_train, y_encoded.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_decision_surface(clf, x_train, y_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  1.0\n"
     ]
    }
   ],
   "source": [
    "print('accuracy : ', str(accuracy_score(y_test.values.ravel(), le.classes_[pred])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparsion = pd.DataFrame({'predicion': le.classes_[pred],\n",
    "                           'ground_truth': y_test.values.ravel()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>predicion</th>\n",
       "      <th>ground_truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gangseo</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gangseo</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gangseo</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gangseo</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gangseo</td>\n",
       "      <td>Gangseo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gangnam</td>\n",
       "      <td>Gangnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gangnam</td>\n",
       "      <td>Gangnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gangnam</td>\n",
       "      <td>Gangnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gangnam</td>\n",
       "      <td>Gangnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gangnam</td>\n",
       "      <td>Gangnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gangbuk</td>\n",
       "      <td>Gangbuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gangbuk</td>\n",
       "      <td>Gangbuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gangbuk</td>\n",
       "      <td>Gangbuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gangbuk</td>\n",
       "      <td>Gangbuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gangbuk</td>\n",
       "      <td>Gangbuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Gangdong</td>\n",
       "      <td>Gangdong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Gangdong</td>\n",
       "      <td>Gangdong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gangdong</td>\n",
       "      <td>Gangdong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Gangdong</td>\n",
       "      <td>Gangdong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gangdong</td>\n",
       "      <td>Gangdong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicion ground_truth\n",
       "0    Gangseo      Gangseo\n",
       "1    Gangseo      Gangseo\n",
       "2    Gangseo      Gangseo\n",
       "3    Gangseo      Gangseo\n",
       "4    Gangseo      Gangseo\n",
       "5    Gangnam      Gangnam\n",
       "6    Gangnam      Gangnam\n",
       "7    Gangnam      Gangnam\n",
       "8    Gangnam      Gangnam\n",
       "9    Gangnam      Gangnam\n",
       "10   Gangbuk      Gangbuk\n",
       "11   Gangbuk      Gangbuk\n",
       "12   Gangbuk      Gangbuk\n",
       "13   Gangbuk      Gangbuk\n",
       "14   Gangbuk      Gangbuk\n",
       "15  Gangdong     Gangdong\n",
       "16  Gangdong     Gangdong\n",
       "17  Gangdong     Gangdong\n",
       "18  Gangdong     Gangdong\n",
       "19  Gangdong     Gangdong"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparsion"
   ]
  }
 ],
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