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       "      <th>0.1</th>\n",
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       "      <th>0.2</th>\n",
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       "      <th>2.2</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.715031</td>\n",
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       "      <td>0.570617</td>\n",
       "      <td>0.584465</td>\n",
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       "      <td>0.575538</td>\n",
       "      <td>0.649530</td>\n",
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       "      <td>0.031898</td>\n",
       "      <td>hupu</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.472092</td>\n",
       "      <td>0.547407</td>\n",
       "      <td>0.598427</td>\n",
       "      <td>0.375792</td>\n",
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      "text/plain": [
       "             0         1         2         3         4       0.1  1.1  2.1       3.1       4.1       0.2  1.2       2.2  3.2       4.2  label\n",
       "0     0.715031  0.608764  0.631124  0.650236  0.512070  0.277071  0.0  0.0  0.000000  0.000000  0.125949  0.0  0.003074  0.0  0.000000   hupu\n",
       "1     0.662252  0.541335  0.570617  0.584465  0.433609  0.451821  0.0  0.0  0.000000  0.000000  0.106180  0.0  0.000000  0.0  0.000000   hupu\n",
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       "3     0.743064  0.612346  0.575538  0.649530  0.692186  0.158237  0.0  0.0  0.000000  0.000000  0.032681  0.0  0.000000  0.0  0.031898   hupu\n",
       "4     0.667459  0.508679  0.472092  0.547407  0.598427  0.375792  0.0  0.0  0.184405  0.000000  0.112612  0.0  0.000000  0.0  0.027564   hupu\n",
       "...        ...       ...       ...       ...       ...       ...  ...  ...       ...       ...       ...  ...       ...  ...       ...    ...\n",
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       "1597  0.911794  0.816672  0.847797  0.946110  0.986384  0.000000  0.0  0.0  0.000000  0.057710  0.000000  0.0  0.000000  0.0  0.029776  zhihu\n",
       "1598  0.965672  0.957016  0.934561  0.991138  0.995043  0.000000  0.0  0.0  0.000000  0.097930  0.000000  0.0  0.000000  0.0  0.045403  zhihu\n",
       "1599  0.961447  0.945493  0.927631  0.987624  0.994379  0.000000  0.0  0.0  0.000000  0.084861  0.000000  0.0  0.000000  0.0  0.066429  zhihu\n",
       "1600  0.969485  0.967490  0.940832  0.994306  0.995641  0.000000  0.0  0.0  0.000000  0.000000  0.000000  0.0  0.000000  0.0  0.117410  zhihu\n",
       "\n",
       "[4670 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "train_path = '../datasets/2019-12-21/train.csv'\n",
    "test_path = '../datasets/2019-12-21/test.csv'\n",
    "train_df = pd.read_csv(train_path,index_col=0)\n",
    "test_df = pd.read_csv(test_path,index_col=0)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score,recall_score,precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:430: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  warnings.warn(\"Default solver will be changed to 'lbfgs' in 0.22. \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:467: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  warnings.warn(\"Default multi_class will be changed to 'auto' in\"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\svm\\base.py:189: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  warnings.warn(\"The default value of gamma will change \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\ensemble\\forest.py:244: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  warn(\"The default value of n_estimators will change from \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision    0.801927\n",
      "recall       0.801927\n",
      "f1           0.801927\n",
      "Name: RandomForest, dtype: float64\n"
     ]
    },
    {
     "data": {
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WbEzO+8ILFbddUx07dqRr1660b9++1b6HrZ8q+RvaBZhbsl0H7NNE+28Af1qXoizf6urq6NSpEz169EBSi59/ad07FbfduV3l339Fhw6Vn7fzzhW3XRMRwYIFC6irq6Nnz56t8j1s/VVJH3pDf+OjwYbSF0gC/bxGjg+TNFnS5Hnz5lVepeXKhx9+yJZbbtkqYZ53kthyyy1b7dONrd8qCfQ6oFvJdlfgjfJGknYHrgUGRsSChk4UEaMjok9E9KmtrV2bei0nHOZrz++dNaaSQJ8E9JLUU1IHYDAwvrSBpO7AHcDXIuIfLV+mmZk1p9k+9IhYJul04H6gBrguIqZLGp4eHwX8CNgSuDq9elgWEX1ar2zLkx4j723R840/ff8WPd+aOO7w47h5ws2NHj/88MMZO3YsW2yxRRtWZUVR0W37iJgATCjbN6rk9cnAyS1bmlm2li9fTk1NzRr9mabCHGDChAlNHjdbF35S1Arp9bmvMXBAP0749o/Y/T+P5qunnMMHixfTY5//x09/OZrPHXkSt93zF16ZPZdDjxvB3ocey9AvDWXWy7MAmP/WfM444QwGDRjEoAGDeObvzwDQd9u+ALz55pv079+f3r17s9tuu/Hoo48C0KNHD+bPnw/AZZddxm677cZuu+3G5ZdfDsDs2bPZeeedOeWUU9h111354he/yOLFi9v67bH1lAPdCmv2Ky8z7PhBTPvLrWzWaROuvuE2ADpu2IHH7rqOwQMPYdi5P+NXF5zHlPvGcvZPzuZn5/4MgIu+dxF99+vLHRPv4LYHb2OHnXZY5dxjx47lkEMOYerUqTz77LP07t17leNTpkxhzJgxPPXUUzz55JNcc801PPNM8kvh5ZdfZsSIEUyfPp0tttiC22+/vQ3eDcuDyp+UMMuZT2/Thf37JkF7/KDD+b/rxgFwzBFfBGDhog94fMo0/uub5wLwocSSJUsA+Ptjf+eiqy4CoKamhk6bdVrl3H379uWkk05i6dKlHHnkkasF+mOPPcZRRx3FJptsAsCgQYN49NFHOeKII+jZs+fH7ffee29mz57dCj+95ZED3QqrfPhf/fYmG28EwIoVK9his05M/XMS9NPX4MGi/v3788gjj3Dvvffyta99jXPOOYehQ4d+fDyiwUc5ANhwww0/fl1TU+MuF6uYu1yssN58vY4nJj8LwC1/vJ/P9V31KnqzTpvSs9s23Hb3n4EkhF98/kUA9jlgH34/5vdAcvN04fsLV/mzc+bMYauttuKUU07hG9/4Bk8//fQqx/v3789dd93FBx98wKJFi7jzzjs54IADWuXntOLwFbplbvbF/69Fzzetwkf/t+u1Izfcdg/fHHkhvXp259QTvsqvxoxbpc3NV17Iqef/Dz+74loWLl/OYUcdxk677cTIC0fyk+/+hDvG3kG7du344aU/pHfJL4SJEydy6aWX0r59ezbddFNuvPHGVc6711578fWvf51+/foBcPLJJ7Pnnnu6e8XWiQPdCksSoy75/ir7Zj+16pj4nt27cN/NVwGrdrl03qozv7rpV6udc9KcSQCccMIJnHDCCasdLw3ss846i7POOmuV4z169OD555//ePvss8+u8Kcxc5eLmVluONCtkLp0684dDz6RdRlmLcqBbmaWEw50M7OccKCbmeWEA93MLCc8bNGy9+PNW/Z8J89p2fNV6PXXXmfEcSO469G7mDhxIj//+c+55557MqnFislX6FZ4EcGKFSuyLsNsnfkK3Qrp9bmvMWLof3Hof+zBE1OmcebJxzLqpj/w0ZKlbL9tV8b88sdsusnGTJo6nW//6FIWfbCYFR035Ld3/JZ33n6H8087n8UfJHOsfO/i77Fnvz0z/onMHOhWYLNfeZmhl32Pn54znEEnn81ffj+KTTbeiEuuup7LRv+OkSNO5JhTR/L7X19M39678tRHS9iw44Z8svMnueYP17Bhxw2Z88oczvnmOdz6l1uz/nHMKgt0SYcCV5AsQXdtRFxcdnwnYAywF/D9iPh5Sxdq1tK27tqNfffenXv+/Agz/vEq+w88EYAlS5ey396789Irs9l6q8707b0rAJt22hSAxR8s5sKRF/LS8y/Rrl075szKps/erFyzgS6pBrgKOBioAyZJGh8RM0qavQ2cARzZKlWatYKNNtoYSPrQD+6/D7dcfdEqx6fN+MdqU+wC3DjqRras3ZLbJ97OihUr2Lvr3m1Sr1lzKrkp2g+YGRGzImIJMA4YWNogIt6KiEnA0lao0axV7bv37vxt0rPMfPU1AD5YvJh/vDKHnXboyRv/msekqdMBWLRwEcuWLWPhewup/VQt7dq14+5b72b58uVZlm/2sUq6XLoAc0u264B91uabSRoGDAPo3r372pzC8ujH77bs+SqcPrde7Zaf4Ppf/pghI77HR+mKRD87dwSf2X5bfv/ri/nWDy5h8YcfwcYbce0frmXwiYM586QzeWD8A/Tdvy8bpQtimGWtkkBf/TMnNL7cShMiYjQwGqBPnz5rdQ6zlrBycq5XATjwc/2YNOF3q7Xr23tXnrwnmcu8fvrcbTfdljsfvvPjNt/54XeSc3bvwl2P3gXAgAEDGDBgQCv+BGarq6TLpQ7oVrLdFXijdcoxM7O1VUmgTwJ6SeopqQMwGBjfumWZmdmaarbLJSKWSToduJ9k2OJ1ETFd0vD0+ChJnwYmA5sBKySdCewSEe+1Yu1mZlaionHoETEBmFC2b1TJ63+SdMWYmVlGPJeLmVlOONDNzHLCc7lY5j57w2db9Hw3H/Ro822u+w233XQde36mK2/8cx5PP/8iF543grOHD23RWszakgPdCunWG3/LVTfeRp9N5zOn7k3uuu+hrEsyW2fucrHCueD871D32my+fdIQbr5jAn1770r79r62sfWf/xZb4fzwol/y+MQHufbWu/l8Z4+stfzwFbqZWU440M3McsKBbmaWE+5Dt8w9d8JzLXq+aWswfe4/35pPn8OO572Fi2jXTlx+zVhmTPwDm6WrE5mtTxzoVkh/emIaAJ9u9x51U+7LuBqzluEuFzOznHCgm5nlhAPdMhHhBavWlt87a4wD3dpcx44dWbBggYNpLUQECxYsoGPHjlmXYlXIN0WtzXXt2pW6ujrmzZvXKuf/178XV9z2BVVewz83qPyfS7t5rXet1LFjR7p29fIDtjoHurW59u3b07Nnz1Y7/2Ej76247eyOx1bc9uie3Stu29JDMc0qUdFlhKRDJb0kaaakkQ0cl6T/S49Pk7RXy5dqZmZNaTbQJdUAVwGHAbsAQyTtUtbsMKBX+jUM+HUL12lmZs2o5Aq9HzAzImZFxBJgHDCwrM1A4MZIPAlsIWnrFq7VzMyaUEkfehdgbsl2HbBPBW26AG+WNpI0jOQKHmChpJfWqNp1oMYPdQbmr7rr+crP+/Umzlyl/F6s5PdiJb8XK1X5e7FtYwcqCfSGKigfb1ZJGyJiNDC6gu/ZZiRNjog+WddRDfxerOT3YiW/FytV+3tRSZdLHdCtZLsr8MZatDEzs1ZUSaBPAnpJ6impAzAYGF/WZjwwNB3tsi/wbkS8WX4iMzNrPc12uUTEMkmnA/cDNcB1ETFd0vD0+ChgAnA4MBP4ADix9UpucVXVBZQxvxcr+b1Yye/FSlX9XsiPX5uZ5YPncjEzywkHuplZTjjQzcxywoFuZtYESftXsq8aFPKmqKTPAOeQPHH18UifiDgws6LakKRpjR0CIiJ2b8t6siSpI3AM8G/gbuBc4ADgFeCCiJjfxB/PJUkbA98FukfEKZJ6ATtGxD0Zl5YJSU9HxF7N7asGRZ0+9zZgFHANsDzjWrKwguRJ3rEkIVb5BOL5cyOwFNiEJMSeB64EPgdcD3wps8qyMwaYAuyXbteR/JspVKBL2g/4D6BW0lklhzYjGcJddYoa6MsiorAzQkZEb0k7AUNIQn1G+t8HImJZpsW1vV0iYjdJGwB1EfH5dP99kp7NsrAMbR8Rx0gaAhARiyWtfxOyrLsOwKYkOdmpZP97wFczqagZRQ30uyWdBtwJfFS/MyLezq6kthURLwL/Dfy3pGNIrlQvAS7NtLC2twQ+foCufLqKIn56A1giaSPS+ZgkbU/Jv5OiiIiHgYclXR8RcwAktQM2jYj3sq2uYUXtQ3+1gd0REdu1eTEZkdSFZBqHo0j6j28F7oyIhZkW1sYkvUUyJbRI+tLH1R8Cjo6IT2VVW1YkHQz8gGT9gweA/YGvR8TELOvKiqSxwHCSX/BTgM2ByyKi6i5+ChnoRSfpYZKPkLcCfwBW+WRSpE8qkk5o6nhE3NBWtVQTSVsC+5L8YnuyiDeH60mamnZTHgfsDZwHTKnGwQOFDHRJ7YFTgf7pronAbyJiaWZFtSFJs1k5vXHpX4D6US6F+aRiq0v7y48DtouIn0rqDnw6Iv6ecWmZkDQd6E1yn+nKiHhY0rMRsUfGpa2mqH3ovwbaA1en219L952cWUVtKCJ6ZF1DtZA0hgbm7k9FRHyjLeupEleTjIQ6EPgp8D5wO9A3y6Iy9BtgNvAs8IikbUlujFadol6hr/bbtVp/47YGSTOA3wHjImJW1vVkSdJXGtjdHTgTqImIrm1cUubqx1hLeiYi9kz3FebfRyUkbVCNI8KK+qTo8vTOPQCStqNYIxqGkPSh/1nSU5LOlLRN1kVlISJur/8CniFZ8PxU4GKgqF1PS9PF4etHudSSXLEXkqRPSfqtpD+l27sATd57yUpRr9APInl4YhZJv/G2wIkR8VCmhWUgXZDkGOArJPPZ3xIR12RbVduStDPwfWBPkmGbv6vGq6+2kt78OwbYC7iBZMz1DyLitkwLy0ga5GOA70fEHukzC89ExGczLm01hQx0AEkbAjuSBPqLEVG4cbalJA0AfknyoM2GGZfTZiTdBvQBfk4y6meVT2pFGvFTKn3w7CCSfx8PRsQLGZeUGUmTIqJvWRfU1IjonXVt5Qp1U1TSgRHxV0mDyg5tL4mIuCOTwjIiqS9J98tXSG76jCZ5xLtI+pJ0LZxN8uh/6RORQcG6XdIHZ6ZFxG7Ai1nXUyUWpcM467ug9gXezbakhhUq0IHPA38FvtzAsQAKEeiS/gc4GniH5EGa/SOiLtuqsuERP6uKiBWSnpXUPSJey7qeKnEWybrJ20v6G1BLlT76X9gulyKTNAG4OCIeSbeHklylzwF+XOBuhi6sPgPnI9lVlA1JfyX55PJ3YFH9/og4IrOiMpJ+YtmX5L2o76J9qVqfWSnaFToAkr5NcpPjfZIZF/cCRkbEA5kW1nY+TTKrIJL6k4zo+BbJwxOjqdKrj9Yk6RKSG4EzWNmPHkDhAp1kQqrSWSZFMs9P4aSfWH4REfsB07OupzmFDHTgpIi4QtIhwFbAiSQBX5RAb1dyFX4MMDodtne7pKkZ1pWlI0nm/C70zfHUBunEVB9LJ+sqqgfS5xXuiCrv0ihqoNff+DocGBMRzxZsetANSh6MOAgYVnoso5qyNovk6eHCBrqkU4HTgO3KFkHpBPwtm6qqwlkk8+Uvl7SYlVNkbJZtWasr6j/eKZIeAHoC50vqRLEenLiFZFrQ+SSLWzwKIGkHqvTufRv4AJgq6UFWnVL5jOxKanNjgT8BFwEjS/a/X9T7KgAR0an5VtWhkDdF0xsdvYFZEfGOpE8CXSOisaXZcicderU1yaIWi9J9nyGZ6/npTIvLQGOzLhZ1tkVblaQjKJnMr1qX4ytqoO8PTI2IRZKOJ7kpekX9JPZmZvUkXUwy6ufmdNcQkulzRzb+p7JR1ECfBuwB7A7cBPwWGFSy/JgVTLoQ8kUkizp0rN/vqYQtzYveEbEi3a4hefS/6uZDL+rkXMvSu9UDSa7Mr2DVNQOteMaQTKG8DPgCyZJ8N2VakVWTLUpeb55ZFc0o6k3R9yWdTzIP+gHpb9z2Gddk2dooIh6UpLTr7ceSHiVZd9WK7SLgGUkPkYxw6Q+cn21JDStqoB8DHEsyHv2f6YosVbc+oLWpD9Ob5S9LOh14neQZBSu4iLhF0kSSfnQB50XEP7OtqmGF7EMHSFcd6RURf5G0McliBu9nXZdlI52o7AWSj9YXkHys/t+IeDLTwiwzkk6PiCvT17tGRO3Q8RIAAAkUSURBVNU/KVrIQJd0CsnDNJ+MiO3TG2KjIuKgjEszsypRv3JT+etqVtQulxFAP+ApgIh4WZI/XheQpMsj4kxJd9PA2qJFnJDKGrRePEle1ED/KCKW1D/tn65AUryPKgYrR7L8PNMqrBptIekoktGAm5Wvo1CN6ycUtcvlf0nmAh9KMsvgacCMiPh+poVZVZD0CaBbkZ4cttVJGtPE4YiIk9qsmAoVNdDbAd8AvkjyUep+4Npqn0nNWk86iuEIkk+tU4F5wMMRcVaWdZmticIFejrm/IaIOD7rWqx61K8XKelkkqvz/5Y0rRqfBrS2JWkLkk/zPVh18ZOqm7itcH3oEbFcUq2kDhGxJOt6rGpsIGlrkqX53PVmpSYATwLPUeWzshYu0FOzgb9JGs+qS2xdlllFlrWfknS9PRYRkyRtB7yccU1WHTquL11vhetyAZDU4OPcEfGTtq7FzKqbpO8AC4F7WHWu/KqbI76QgW5WLh359DOSBT/uI5mN88yI+F2mhVnmJI0ALiQZGVcfmFGNM3EWMtAbeYjkXWAy8JuI+LDtq7IsSZoaEb3TccdHAt8BHoqIPTIuzTIm6RVgn4iYn3UtzSnq9LmzSD5CXZN+vQf8C/hMum3FUz/b5uHALdX4cdoyM51kicKqV9SbontGRP+S7bslPRIR/SVV/QQ81irulvQiSZfLaZJqAX9SM4DlJOvNPkSVrzdb1ECvldQ9Il4DSKfP7Zwe81DGAoqIkZIuAd5Lh7YuIlkAxeyu9KvqFTXQvws8lvaNCehJclW2CeBFgQtI0tCS16WHbmz7aqyaRMQNkjqQdMkCvBQRS7OsqTGFvCkKIGlDYCeSQH/RN0KLTdKvSjY7AgcBT0fEVzMqyaqEpAEkF3qzSfKiG3BCRDySYVkNKmSgpwtanAVsGxGnpPOh7xgR92RcmlUJSZsDN3n6XJM0BTg2Il5Ktz9DcuN872wrW11RR7mMIekr3y/driMZg2xW7wOgV9ZFWFVoXx/mABHxD6p0DeKi9qFvHxHHSBoCEBGLVdZxasVS9mxCO2AX4NbsKrIqMlnSb1k5d/5xwJQM62lUUQN9iaSNSP8BS9qekuFIVkilC1wsA+ZERF1WxVhVOZVklbMzSPrQHwGuzrSiRhS1D/1g4AckV2EPAPsDX4+IiVnWZWa2LgoZ6ACStgT2JfmN++T68FivtR5J+wK/AnYGOgA1wKKI2CzTwiwzkp6jiaUpq3Gu/KJ2uRARC4B7ASTtKOmiiDgl47IsO1cCg4HbgD4kCxrskGlFlrUvpf8dkf63tA+9KqcCKNQoF0m7S3pA0vOSfibpU5JuBx4EZmRdn2UrImYCNRGxPCLGAF/IuibLTkTMiYg5wP4RcW5EPJd+jQQOybq+hhQq0Ekm3hoLfIVkzcinSSbq2iEifpllYZa5D9KnAZ+V9L/pHNibZF2UVYVNJH2ufkPSf1ClfzcK1YdeP0VqyfZcoEdELM+wLKsCkrYlmXGzA8nUuZsBv06v2q3AJO0NXAdsnu56BzgpIp7OrqqGFa0PvaOkPUluhEIyhe7u9WPQq/F/kLUuSQOBrhFxVbr9MLAVyc2wJwAHesFFxBRgD0mbkVwEv5t1TY0p2hX6Q00cjog4sM2Ksaog6W/A4IiYm25PBQ4ENgXGRMRBWdZn2UvnffoK0IOSi+CI+GlWNTWmUFfoEeGbXFauQ32Ypx5LF7d4O5190+yPJCuaTaHKH0As1BV6vXSNwJsj4p10+xPAkIioyqe/rPVImhkRDQ5PlPRKRGzf1jVZdZH0fETslnUdlSjaKJd6p9SHOUBE/BvwGPRiekrSav/vJX0T+HsG9Vj1eVzSZ7MuohJFvUKfBuwR6Q8vqQaYFhG7ZluZtTVJW5GsRvMRyTBWgL2BDYEjI+JfWdVm1UHSDJKHzF4l+XsikntuVfekaFED/VKSGxyjSEYzDAfmRsR3s6zLsiPpQKD+F/r0iPhrlvVY9UiHtK4mfeioqhQ10NsB3yRZlUYkE3Rd6/HoZtaY9NNcx/rt+jWJq0khA93MrFKSjgB+AWwDvAVsC7xQjV20hRq2KOnWiDi6sVnUqrFPzMwydwHJzKx/iYg9JX0BGJJxTQ0qVKAD307/+6UmW5mZrbQ0IhZIaiepXUQ8JOmSrItqSKGGLUbEm+nL0+pnUiuZUe20LGszs6r1jqRNSVYqulnSFSSrWlWdQvahS3o6IvYq2zfNXS5mVi59YngxyQXwcSSTdN2crqlQVQoV6JJOJbkS355VJ13qBPwtIo7PpDAzW2+kz60Mjoibs66lXNECfXPgE8BFwMiSQ++n83eYmQGQzq44AugCjAf+nG6fA0yNiIEZltegQgV6PUnbA3UR8ZGkAcDuwI2l0wGYWbFJ+iPwb5JplA8iuRjsAHw7IqZmWVtjihroU0nWjewB3E/y23fHiDg8y7rMrHpIei4iPpu+rgHmA90j4v1sK2tcoUa5lFgREcuAQcDlEfEdYOuMazKz6rK0/kX6FPmr1RzmULxx6PWWShpCsrL7l9N97TOsx8yqzx6S3ktfC9go3a6fnGuz7EprWFED/USSCbkujIhXJfUEfpdxTWZWRSKiJusa1lQh+9DNzPKoUFfonsvFzPKsUFfokraOiDfXp/mNzcwqVahANzPLs0J1udST9D6rd7m8C0wGvhsRs9q+KjOzdVPIQAcuA94AxpIMQRoMfBp4CbgOGJBZZWZma6mQXS6SnoqIfcr2PRkR+0p6NiL2yKo2M7O1VdgnRSUdXT9hvaSjS44V7zecmeVCUa/QtwOuAPZLdz0BfAd4Hdg7Ih7LqjYzs7VVyEA3M8ujQna5SOoq6U5Jb0n6l6TbJXXNui4zs3VRyEAHxpBMmbsNyeT1d6f7zMzWW4XscpE0NSJ6N7fPzGx9UtQr9PmSjpdUk34dD1Tdgq9mZmuiqFfo3YErSUa5BPA4cEZEvJZpYWZm66CQgd4QSWdGxOVZ12FmtrYc6ClJr0VE96zrMDNbW0XtQ2+Isi7AzGxdONBX8kcVM1uvFWq2xUamzYV0Adg2LsfMrEW5D93MLCfc5WJmlhMOdDOznHCgm5nlhAPdzCwnHOhmZjnx/wEl5RQS+jjrtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#type markov\n",
    "x_train = train_df.iloc[:,0:5].values.copy()\n",
    "y_train = train_df['label'].values.copy()\n",
    "x_test = test_df.iloc[:,0:5].values.copy()\n",
    "y_test = test_df['label'].values.copy()\n",
    "lr_classifer = LogisticRegression()\n",
    "lr_classifer.fit(x_train, y_train)\n",
    "y_pred = lr_classifer.predict(x_test)\n",
    "lr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "lr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "svm_classifer = SVC()\n",
    "svm_classifer.fit(x_train, y_train)\n",
    "y_pred = svm_classifer.predict(x_test)\n",
    "svm_precision = precision_score(y_test, y_pred, average='micro')\n",
    "svm_recall = recall_score(y_test, y_pred, average='micro')\n",
    "svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "gn_classifer = GaussianNB()\n",
    "gn_classifer.fit(x_train, y_train)\n",
    "y_pred = gn_classifer.predict(x_test)\n",
    "gn_precision = precision_score(y_test, y_pred, average='micro')\n",
    "gn_recall = recall_score(y_test, y_pred, average='micro')\n",
    "gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "tr_classifer = tree.DecisionTreeClassifier()\n",
    "tr_classifer.fit(x_train, y_train)\n",
    "y_pred = tr_classifer.predict(x_test)\n",
    "tr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "tr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "tr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "rf_classifer = RandomForestClassifier()\n",
    "rf_classifer.fit(x_train, y_train)\n",
    "y_pred = rf_classifer.predict(x_test)\n",
    "rf_precision = precision_score(y_test, y_pred, average='micro')\n",
    "rf_recall = recall_score(y_test, y_pred, average='micro')\n",
    "rf_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
    "                        columns = ['precision', 'recall', 'f1'])\n",
    "score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n",
    "score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
    "score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
    "score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
    "score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
    "ax = score_df.plot.bar(title='type markov')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('../figure/type.svg')\n",
    "print(score_df.loc['RandomForest'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:430: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  warnings.warn(\"Default solver will be changed to 'lbfgs' in 0.22. \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:467: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  warnings.warn(\"Default multi_class will be changed to 'auto' in\"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\svm\\base.py:189: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  warnings.warn(\"The default value of gamma will change \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\ensemble\\forest.py:244: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  warn(\"The default value of n_estimators will change from \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision    0.993148\n",
      "recall       0.993148\n",
      "f1           0.993148\n",
      "Name: RandomForest, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#type+length markov\n",
    "x_train = train_df.iloc[:,0:10].values.copy()\n",
    "y_train = train_df['label'].values.copy()\n",
    "x_test = test_df.iloc[:,0:10].values.copy()\n",
    "y_test = test_df['label'].values.copy()\n",
    "lr_classifer = LogisticRegression()\n",
    "lr_classifer.fit(x_train, y_train)\n",
    "y_pred = lr_classifer.predict(x_test)\n",
    "lr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "lr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "svm_classifer = SVC()\n",
    "svm_classifer.fit(x_train, y_train)\n",
    "y_pred = svm_classifer.predict(x_test)\n",
    "svm_precision = precision_score(y_test, y_pred, average='micro')\n",
    "svm_recall = recall_score(y_test, y_pred, average='micro')\n",
    "svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "gn_classifer = GaussianNB()\n",
    "gn_classifer.fit(x_train, y_train)\n",
    "y_pred = gn_classifer.predict(x_test)\n",
    "gn_precision = precision_score(y_test, y_pred, average='micro')\n",
    "gn_recall = recall_score(y_test, y_pred, average='micro')\n",
    "gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "tr_classifer = tree.DecisionTreeClassifier()\n",
    "tr_classifer.fit(x_train, y_train)\n",
    "y_pred = tr_classifer.predict(x_test)\n",
    "tr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "tr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "tr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "rf_classifer = RandomForestClassifier()\n",
    "rf_classifer.fit(x_train, y_train)\n",
    "y_pred = rf_classifer.predict(x_test)\n",
    "rf_precision = precision_score(y_test, y_pred, average='micro')\n",
    "rf_recall = recall_score(y_test, y_pred, average='micro')\n",
    "rf_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
    "                        columns = ['precision', 'recall', 'f1'])\n",
    "score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n",
    "score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
    "score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
    "score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
    "score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
    "ax = score_df.plot.bar(title='type+length markov')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('../figure/type_length.svg')\n",
    "print(score_df.loc['RandomForest'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:430: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  warnings.warn(\"Default solver will be changed to 'lbfgs' in 0.22. \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:467: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  warnings.warn(\"Default multi_class will be changed to 'auto' in\"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\svm\\base.py:189: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  warnings.warn(\"The default value of gamma will change \"\n",
      "c:\\python38\\lib\\site-packages\\sklearn\\ensemble\\forest.py:244: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  warn(\"The default value of n_estimators will change from \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision    0.99743\n",
      "recall       0.99743\n",
      "f1           0.99743\n",
      "Name: RandomForest, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#type+length+burst markov\n",
    "x_train = train_df.iloc[:,0:15].values.copy()\n",
    "y_train = train_df['label'].values.copy()\n",
    "x_test = test_df.iloc[:,0:15].values.copy()\n",
    "y_test = test_df['label'].values.copy()\n",
    "lr_classifer = LogisticRegression()\n",
    "lr_classifer.fit(x_train, y_train)\n",
    "y_pred = lr_classifer.predict(x_test)\n",
    "lr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "lr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "svm_classifer = SVC()\n",
    "svm_classifer.fit(x_train, y_train)\n",
    "y_pred = svm_classifer.predict(x_test)\n",
    "svm_precision = precision_score(y_test, y_pred, average='micro')\n",
    "svm_recall = recall_score(y_test, y_pred, average='micro')\n",
    "svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "gn_classifer = GaussianNB()\n",
    "gn_classifer.fit(x_train, y_train)\n",
    "y_pred = gn_classifer.predict(x_test)\n",
    "gn_precision = precision_score(y_test, y_pred, average='micro')\n",
    "gn_recall = recall_score(y_test, y_pred, average='micro')\n",
    "gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "tr_classifer = tree.DecisionTreeClassifier()\n",
    "tr_classifer.fit(x_train, y_train)\n",
    "y_pred = tr_classifer.predict(x_test)\n",
    "tr_precision = precision_score(y_test, y_pred, average='micro')\n",
    "tr_recall = recall_score(y_test, y_pred, average='micro')\n",
    "tr_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "rf_classifer = RandomForestClassifier()\n",
    "rf_classifer.fit(x_train, y_train)\n",
    "y_pred = rf_classifer.predict(x_test)\n",
    "rf_precision = precision_score(y_test, y_pred, average='micro')\n",
    "rf_recall = recall_score(y_test, y_pred, average='micro')\n",
    "rf_f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
    "                        columns = ['precision', 'recall', 'f1'])\n",
    "score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n",
    "score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
    "score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
    "score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
    "score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
    "ax = score_df.plot.bar(title='type+length+burst markov')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('../figure/type_length_burst.svg')\n",
    "print(score_df.loc['RandomForest'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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