Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abcf3916",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.cluster as skc\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.metrics import silhouette_samples\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75845887",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'E:\\\\学业\\\\IPS\\\\组会\\\\task_2\\\\03_trainingData_B1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb6902c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path,encoding='utf-8')as f:\n",
    "    data = np.loadtxt(path, dtype=str, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cea16fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(path, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecab3c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eced06e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.replace('*',-110)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35938485",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Codex\\AppData\\Local\\Temp\\ipykernel_20324\\2720751214.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  train_df['label']=0\n"
     ]
    }
   ],
   "source": [
    "train_df['label']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94f34d06",
   "metadata": {},
   "outputs": [
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       "        ID WAP001 WAP002 WAP003 WAP004 WAP005 WAP006 WAP007 WAP008 WAP009  \\\n",
       "0        1   -110   -110    -88   -110   -110   -110   -110   -110   -110   \n",
       "1        2   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "2        3   -110   -110    -88   -110   -110   -110   -110   -110   -110   \n",
       "3        4   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "4        5   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "5243  5244   -110   -110   -110    -88   -110   -110   -110    -86    -86   \n",
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       "5247  5248   -110   -110   -110    -90   -110   -110   -110    -89    -89   \n",
       "\n",
       "      ...   LATITUDE FLOOR BUILDINGID SPACEID RELATIVEPOSITION USERID PHONEID  \\\n",
       "0     ...  35.094452     1          1     218                2     11      13   \n",
       "1     ...  35.094452     1          1     218                2     11      13   \n",
       "2     ...  35.094452     1          1     218                2     11      13   \n",
       "3     ...  35.094452     1          1     218                2     11      13   \n",
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       "...   ...        ...   ...        ...     ...              ...    ...     ...   \n",
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       "5246  ...  91.295152     4          1     109                2      1      14   \n",
       "5247  ...  91.295152     4          1     109                2      1      14   \n",
       "\n",
       "       TIMESTAMP                              checkSum label  \n",
       "0     1369911162  1:-7685.72349999845:4864929.89029999     0  \n",
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       "...          ...                                   ...   ...  \n",
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       "5247  1371054766  4:-7587.10229999944:4864986.09099999     0  \n",
       "\n",
       "[5248 rows x 212 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "def80fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Codex\\AppData\\Local\\Temp\\ipykernel_20324\\207508054.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_df.label[(train_df['LONGITUDE']<60) & (train_df['LATITUDE']<60)] =1\n",
      "C:\\Users\\Codex\\AppData\\Local\\Temp\\ipykernel_20324\\207508054.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_df.label[(train_df['LONGITUDE']>60) & (train_df['LATITUDE']>60)] =0\n"
     ]
    }
   ],
   "source": [
    "#手动打标签，如果是坐标轴右上方的为标签1，左下方的为标签0\n",
    "\n",
    "train_df.label[(train_df['LONGITUDE']<60) & (train_df['LATITUDE']<60)] =1\n",
    "train_df.label[(train_df['LONGITUDE']>60) & (train_df['LATITUDE']>60)] =0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a752481",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>35.094452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369911162</td>\n",
       "      <td>1:-7685.72349999845:4864929.89029999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-87</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>...</td>\n",
       "      <td>35.094452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369911145</td>\n",
       "      <td>1:-7685.72349999845:4864929.89029999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-88</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>...</td>\n",
       "      <td>35.094452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369911148</td>\n",
       "      <td>1:-7685.72349999845:4864929.89029999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-87</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>...</td>\n",
       "      <td>35.094452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369911151</td>\n",
       "      <td>1:-7685.72349999845:4864929.89029999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-87</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>...</td>\n",
       "      <td>35.094452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369911155</td>\n",
       "      <td>1:-7685.72349999845:4864929.89029999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5243</th>\n",
       "      <td>5244</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-88</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-86</td>\n",
       "      <td>-86</td>\n",
       "      <td>...</td>\n",
       "      <td>91.295152</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1371054764</td>\n",
       "      <td>4:-7587.10229999944:4864986.09099999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5244</th>\n",
       "      <td>5245</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-88</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-86</td>\n",
       "      <td>-86</td>\n",
       "      <td>...</td>\n",
       "      <td>91.295152</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1371054765</td>\n",
       "      <td>4:-7587.10229999944:4864986.09099999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5245</th>\n",
       "      <td>5246</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-90</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-86</td>\n",
       "      <td>-86</td>\n",
       "      <td>...</td>\n",
       "      <td>91.295152</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1371054765</td>\n",
       "      <td>4:-7587.10229999944:4864986.09099999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5246</th>\n",
       "      <td>5247</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-90</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-86</td>\n",
       "      <td>-86</td>\n",
       "      <td>...</td>\n",
       "      <td>91.295152</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1371054765</td>\n",
       "      <td>4:-7587.10229999944:4864986.09099999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5247</th>\n",
       "      <td>5248</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-90</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-110</td>\n",
       "      <td>-89</td>\n",
       "      <td>-89</td>\n",
       "      <td>...</td>\n",
       "      <td>91.295152</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1371054766</td>\n",
       "      <td>4:-7587.10229999944:4864986.09099999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5248 rows × 212 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID WAP001 WAP002 WAP003 WAP004 WAP005 WAP006 WAP007 WAP008 WAP009  \\\n",
       "0        1   -110   -110    -88   -110   -110   -110   -110   -110   -110   \n",
       "1        2   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "2        3   -110   -110    -88   -110   -110   -110   -110   -110   -110   \n",
       "3        4   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "4        5   -110   -110    -87   -110   -110   -110   -110   -110   -110   \n",
       "...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "5243  5244   -110   -110   -110    -88   -110   -110   -110    -86    -86   \n",
       "5244  5245   -110   -110   -110    -88   -110   -110   -110    -86    -86   \n",
       "5245  5246   -110   -110   -110    -90   -110   -110   -110    -86    -86   \n",
       "5246  5247   -110   -110   -110    -90   -110   -110   -110    -86    -86   \n",
       "5247  5248   -110   -110   -110    -90   -110   -110   -110    -89    -89   \n",
       "\n",
       "      ...   LATITUDE FLOOR BUILDINGID SPACEID RELATIVEPOSITION USERID PHONEID  \\\n",
       "0     ...  35.094452     1          1     218                2     11      13   \n",
       "1     ...  35.094452     1          1     218                2     11      13   \n",
       "2     ...  35.094452     1          1     218                2     11      13   \n",
       "3     ...  35.094452     1          1     218                2     11      13   \n",
       "4     ...  35.094452     1          1     218                2     11      13   \n",
       "...   ...        ...   ...        ...     ...              ...    ...     ...   \n",
       "5243  ...  91.295152     4          1     109                2      1      14   \n",
       "5244  ...  91.295152     4          1     109                2      1      14   \n",
       "5245  ...  91.295152     4          1     109                2      1      14   \n",
       "5246  ...  91.295152     4          1     109                2      1      14   \n",
       "5247  ...  91.295152     4          1     109                2      1      14   \n",
       "\n",
       "       TIMESTAMP                              checkSum label  \n",
       "0     1369911162  1:-7685.72349999845:4864929.89029999     1  \n",
       "1     1369911145  1:-7685.72349999845:4864929.89029999     1  \n",
       "2     1369911148  1:-7685.72349999845:4864929.89029999     1  \n",
       "3     1369911151  1:-7685.72349999845:4864929.89029999     1  \n",
       "4     1369911155  1:-7685.72349999845:4864929.89029999     1  \n",
       "...          ...                                   ...   ...  \n",
       "5243  1371054764  4:-7587.10229999944:4864986.09099999     0  \n",
       "5244  1371054765  4:-7587.10229999944:4864986.09099999     0  \n",
       "5245  1371054765  4:-7587.10229999944:4864986.09099999     0  \n",
       "5246  1371054765  4:-7587.10229999944:4864986.09099999     0  \n",
       "5247  1371054766  4:-7587.10229999944:4864986.09099999     0  \n",
       "\n",
       "[5248 rows x 212 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "066ee727",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_features = scale(np.asarray(train_df.iloc[:,1:201]).astype(float), axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "766a0270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.18598672, -0.18598672,  3.40323073, ..., -0.18598672,\n",
       "        -0.18598672, -0.18598672],\n",
       "       [-0.18880777, -0.18880777,  3.31327191, ..., -0.18880777,\n",
       "        -0.18880777, -0.18880777],\n",
       "       [-0.1808101 , -0.1808101 ,  3.46856804, ..., -0.1808101 ,\n",
       "        -0.1808101 , -0.1808101 ],\n",
       "       ...,\n",
       "       [-0.30761921, -0.30761921, -0.30761921, ..., -0.30761921,\n",
       "        -0.30761921, -0.30761921],\n",
       "       [-0.30633779, -0.30633779, -0.30633779, ..., -0.30633779,\n",
       "        -0.30633779, -0.30633779],\n",
       "       [-0.30601396, -0.30601396, -0.30601396, ..., -0.30601396,\n",
       "        -0.30601396, -0.30601396]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45b16101",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_label = np.asarray(train_df.iloc[:,211:212])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5981c610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1],\n",
       "       ...,\n",
       "       [0],\n",
       "       [0],\n",
       "       [0]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f193cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(train_df_features,train_df_label,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "741df7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion = 'entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed1cbb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cbf20508",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "10542973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9949206349206349"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f9eee7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['WAP001', 'WAP002', 'WAP003', 'WAP004', 'WAP005', 'WAP006', 'WAP007',\n",
       "       'WAP008', 'WAP009', 'WAP010',\n",
       "       ...\n",
       "       'WAP191', 'WAP192', 'WAP193', 'WAP194', 'WAP195', 'WAP196', 'WAP197',\n",
       "       'WAP198', 'WAP199', 'WAP200'],\n",
       "      dtype='object', length=200)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns[1:201]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34dec081",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error: Source.gv: syntax error in line 2 near 'YaHei'\r\n"
     ]
    },
    {
     "ename": "CalledProcessError",
     "evalue": "Command '[WindowsPath('dot'), '-Kdot', '-Tpdf', '-O', 'Source.gv']' returned non-zero exit status 1. [stderr: b\"Error: Source.gv: syntax error in line 2 near 'YaHei'\\r\\n\"]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\backend\\execute.py:91\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 91\u001b[0m     \u001b[43mproc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_returncode\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\subprocess.py:460\u001b[0m, in \u001b[0;36mCompletedProcess.check_returncode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode:\n\u001b[1;32m--> 460\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstdout,\n\u001b[0;32m    461\u001b[0m                              \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr)\n",
      "\u001b[1;31mCalledProcessError\u001b[0m: Command '[WindowsPath('dot'), '-Kdot', '-Tpdf', '-O', 'Source.gv']' returned non-zero exit status 1.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "Input \u001b[1;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 20>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m#dot_data.replace('helvetica','\"Microsoft YaHei\"'), encoding='utf-8'\u001b[39;00m\n\u001b[0;32m     19\u001b[0m graph \u001b[38;5;241m=\u001b[39m graphviz\u001b[38;5;241m.\u001b[39mSource(dot_data\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhelvetica\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMicrosoft YaHei\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m), encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 20\u001b[0m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\rendering.py:185\u001b[0m, in \u001b[0;36mRender.view\u001b[1;34m(self, filename, directory, cleanup, quiet, quiet_view)\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[38;5;129m@_tools\u001b[39m\u001b[38;5;241m.\u001b[39mdeprecate_positional_args(supported_number\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mview\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    152\u001b[0m          filename: typing\u001b[38;5;241m.\u001b[39mUnion[os\u001b[38;5;241m.\u001b[39mPathLike, \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    155\u001b[0m          quiet: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    156\u001b[0m          quiet_view: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m    157\u001b[0m     \u001b[38;5;124;03m\"\"\"Save the source to file, open the rendered result in a viewer.\u001b[39;00m\n\u001b[0;32m    158\u001b[0m \n\u001b[0;32m    159\u001b[0m \u001b[38;5;124;03m    Convenience short-cut for running ``.render(view=True)``.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    183\u001b[0m \u001b[38;5;124;03m        and no way to retrieve the application's exit status.\u001b[39;00m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 185\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirectory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mview\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    186\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mcleanup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcleanup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquiet_view\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet_view\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\rendering.py:122\u001b[0m, in \u001b[0;36mRender.render\u001b[1;34m(self, filename, directory, view, cleanup, format, renderer, formatter, neato_no_op, quiet, quiet_view, outfile, engine, raise_if_result_exists, overwrite_source)\u001b[0m\n\u001b[0;32m    118\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave(filename, directory\u001b[38;5;241m=\u001b[39mdirectory, skip_existing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    120\u001b[0m args\u001b[38;5;241m.\u001b[39mappend(filepath)\n\u001b[1;32m--> 122\u001b[0m rendered \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_render(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cleanup:\n\u001b[0;32m    125\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdelete \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m'\u001b[39m, filepath)\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\_tools.py:171\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    162\u001b[0m     wanted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    163\u001b[0m                        \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m deprecated\u001b[38;5;241m.\u001b[39mitems())\n\u001b[0;32m    164\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe signature of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m will be reduced\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    165\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msupported_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m positional args\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    166\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(supported)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: pass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwanted\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    167\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m as keyword arg(s)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    168\u001b[0m                   stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    169\u001b[0m                   category\u001b[38;5;241m=\u001b[39mcategory)\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\backend\\rendering.py:324\u001b[0m, in \u001b[0;36mrender\u001b[1;34m(engine, format, filepath, renderer, formatter, neato_no_op, quiet, outfile, raise_if_result_exists, overwrite_filepath)\u001b[0m\n\u001b[0;32m    320\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mFileExistsError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput file exists: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mfspath(outfile)\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    322\u001b[0m cmd \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args\n\u001b[1;32m--> 324\u001b[0m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    325\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    326\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mquiet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    327\u001b[0m \u001b[43m                  \u001b[49m\u001b[43mcapture_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mfspath(outfile)\n",
      "File \u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\graphviz\\backend\\execute.py:93\u001b[0m, in \u001b[0;36mrun_check\u001b[1;34m(cmd, input_lines, encoding, quiet, **kwargs)\u001b[0m\n\u001b[0;32m     91\u001b[0m     proc\u001b[38;5;241m.\u001b[39mcheck_returncode()\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m---> 93\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(\u001b[38;5;241m*\u001b[39me\u001b[38;5;241m.\u001b[39margs)\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m proc\n",
      "\u001b[1;31mCalledProcessError\u001b[0m: Command '[WindowsPath('dot'), '-Kdot', '-Tpdf', '-O', 'Source.gv']' returned non-zero exit status 1. [stderr: b\"Error: Source.gv: syntax error in line 2 near 'YaHei'\\r\\n\"]"
     ]
    }
   ],
   "source": [
    "'''\n",
    "import graphviz\n",
    "feature_name = train_df.columns[1:201]\n",
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n",
    "                                ,random_state=30\n",
    "                                ,splitter=\"random\"\n",
    "                                ,max_depth=3\n",
    "                                ,min_samples_leaf=10\n",
    "                                ,min_samples_split=10\n",
    "                                )\n",
    "\n",
    "clf = clf.fit(X_train, y_train)\n",
    "dot_data = tree.export_graphviz(clf\n",
    "                                ,feature_names= feature_name\n",
    "                                ,class_names=[\"左上角\",\"右下角\"]\n",
    "                                ,filled=True\n",
    "                                ,rounded=True\n",
    "                                )\n",
    "#dot_data.replace('helvetica','\"Microsoft YaHei\"'), encoding='utf-8'\n",
    "graph = graphviz.Source(dot_data.replace('helvetica','\"Microsoft YaHei\"'), encoding='utf-8')\n",
    "graph.view()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ada839b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = []\n",
    "for i in range(30):\n",
    "    clf = tree.DecisionTreeClassifier(max_depth=i+1\n",
    "                                    ,criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    )\n",
    "\n",
    "    clf = clf.fit(X_train, y_train)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    test.append(score)\n",
    "plt.plot(range(1,31),test,color=\"red\",label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38474f6e",
   "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
}