Newer
Older
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
" SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 SBS7a SBS7b SBS7c SBS7d ... \\\n",
"mut_tri ... \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n",
"\n",
" SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 SBS60 \\\n",
"mut_tri \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"30 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" tumor_types \n",
"mut_tri \n",
"30 30 \n",
"30 30 \n",
"30 30 \n",
"30 30 \n",
"30 30 \n",
"\n",
"[5 rows x 66 columns]"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Some content from the full act set:\")\n",
"act_mut_all.head(5)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
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kmJgYLVu27Jw406ehnOkF5M+fc9lBixYtPB8/+OCDBicxJzAwUF999ZXpMYxq0aKFMjMztXLlSs/j5ZAhQxQbG2u7Dc1mz5590pNA0tLStGfPHlttnCtJ27dv18svv6xdu3bVWG/8/fffNziVGT9/gyY4ONjQJGYtWbJE6enpnr+Zl156qfbv3294Kut16dJFjzzyiG699dYaa0z74jKKFNO1cOTIEfXr10+S+7LL6nKlb9++mjdvnsnRfILdNuhZvXq11q9fr4yMDC1ZskT9+vXTkCFDbHWWlyStXLnypOvo3nPPPbrzzjtPuNTO33399deS3KXj3XffrXfffdfwRGacajOvgIAA2z1WnIrdcigpKTnl5qCHDx+2eBrz+vXrpx49eng2fvzHP/6hb7/9Vtdff71mzJjh95ens0+D19atW7Vv3z716tWrRhm9du1aRUdH2+55RcuWLfWnP/3J9BhGfffdd6ZH8Dnbt2/XwoULtXPnTluXTr/+9a/1xhtvKC4ursabunYpZQ8fPqyDBw+eUMbu2rVL559/vm32MJFOfRJIYmKiBg8ebLtiurp4GzJkiK3PFm/evLn279/veZ2xfv16z0kQdtOoUaMaGypLsuXvRvVziuPXnnc4HBTT56rjz+a5/vrraxyrqqqyehwjxowZc8qzQX/66ScDE5nVo0cP9ejRQ0ePHtUHH3yg4cOH6+GHH1ZCQoLp0Sx1sndhj383zk6Of5EQGBhoyzNBJTbzqjZ06NBTPmYWFRUZmMic6OjoU24O2rp1a4unMW/Dhg16/PHHPZ/fdNNNeu211/TII4/o2LFjBiczo6ysTFu3blWbNm1st/bfnDlzTlrEhoWFafbs2bZaF3Ljxo3asmWLfve730lyP+88ePCgJOmhhx46YY16f2Wn/Qdqa+zYsRo8eLDuuOMOWxYL1arXUJ41a5YcDoftrsibOXOm+vTpozZt2tS4fePGjfriiy80bdo0Q5NZ71QngQQGBtru5AfJ3cf8v//3/0yPYdyECRM0cuRI7d69W8OHD9eOHTv08ssvmx7LiPDwcOXn53v+PSxfvtyWrznOpc1QKaZroXqH0/POO09PPfWU5/Y9e/bY5l3q3/zmN6c81qlTJwsn8R15eXnKyMjQxx9/rD59+qhHjx6mR7JUWVmZKisrT1gDs6Ki4rQ7Z9uB3ZZqON6AAQM0ceLEk27mdeuttxqezjrjx49XeXn5CW9QHD161HaX1Z1LT4qsUFVVpa+//lpXX321JOk///mP5zHTDktDff7555o2bZrCwsL06KOPaty4caqsrNTRo0f17LPPqn///qZHtMzu3btP+hyqS5cu+t///mdgInPmz5+v++67z/P5tm3b9Nhjj+no0aNatGiRbYrpU11dUs0Xz3I624KCgnT//febHsO43Nxc0yMY9eWXX560fB44cKCt3sSTOAnk57p27arc3FzbdhLVunTpoiVLlniu4u3WrZtCQ0MNT2XG5MmTNX78eOXn56tv375q0qSJ7R4nJOmLL75Q586d1bx5c6WlpWnTpk0aOXKkT244TjFdC/Pnzz/p7aGhoVqwYIHF05hxxx131Ph87969yszMVHp6ulwul63WfPvLX/6i5cuXKzg4WHfccYcyMzNt8wbF8fr06aNnn31WkyZN8rxrX1VVpVmzZql3796GpzPLjmcrVBs9erSSkpLUu3fvEzbzevjhh80OZ6G1a9eechPIPXv2qFevXoYms96Z3qiy2+NncnKyxo0bpyZNmsjhcKi0tFRz5szRkSNHNGLECNPjnXUzZ87UxIkTVVJSoj/+8Y+aP3++rr32Wn3//fd67LHHbFVMV1RU1OmYPyoqKlLPnj09nwcHB+vGG2+UdOZ1l/3Jqa4ukXz38tuzrXfv3vr000/Vp08f06PAoNO9cWu3592cBFLTt99+q2XLlumSSy6pceWu3Zb72bJli9q0aeP5O3H06FFt3bpVHTp0MDyZ9QoKCvT666/rwIEDcrlcioiI0Pfff6+LLrrI9GiWmjZtmlasWKGtW7dq8eLFGjRokB5//HEtWbLE9GgnoJiuh2bNmtnqcv3KykplZ2crPT1dGzduVGVlpd544w2/36jp56ZNm6bOnTurVatWWrNmzQlnt8ydO9fQZNYaN26c/vjHP+rmm29W586dJUk5OTlq3bq1ZwNEO+nZs6fnifGhQ4d03XXXSfJufrhu3TqT41mGzbzcWP/Pq1u3bp5/Gz+/msBOlyFX6969uz7++GPl5+fL5XLp0ksv9ZxF//M3gf1RVVWV50XTvHnzPGfCduzY0eRYRkRERCgnJ8fzN7RaTk6O7ZY1+fkyNsdfaXHgwAGrxzGGK0xOdN111+nBBx9UQECAgoODbfe8qlpubq6Sk5OVm5tb46xYO/0NLS4uVkRExAm32e1KRU4CqWny5MmmR/AJSUlJ+utf/+r5PCgoSBMnTtSyZcsMTmXGzJkzlZGR4Xm8qKqq8txmJ0FBQXI4HPr000919913a/jw4fr73/9ueqyTophGrTzzzDP64IMPdPnll+uOO+7Q3LlzNWDAANuV0tLp1/8rKCiwcBKzmjdvrrffflv//ve/PQVkYmKip5C1m5NtBGlnF198se3K6OOx/p/Xr371Kx07dkxxcXGKjY1VWFiY6ZGMczqdCg4OltPp1M6dOyXJNhvdHf/7//ONaeywlMnxHnzwQT3wwAN66KGH9H//93+SpE2bNmnBggV68sknDU9nrcDAQO3bt09RUVGS5Nmwae/evbb7vZBOvaSHHc+Ynjp1qp555hldccUVtvxdqJaSkqI//elPeuaZZ/T6669r6dKlJzyG+rM777xTY8aM0YwZMzzPL3/44QdNmTLlhKvT/N3JTgL53//+p969e5+wxKIdVL/BffToUUmy1YmDx3M6nWrUqJHn8+rnmXZU/QZmtYCAAFtmUVlZqa+++kqrVq3SjBkzJMlnc7DfIxfq5J133lG3bt00atQoz6WWditXqrGsSU29evXStdde69lg4GTrTtvBhRdeaHoE+BDW//N66623tHv3bmVkZOiuu+7S5ZdfriFDhqh37962LBmWLl2q2bNnKzw83PN31OFwaPXq1YYns8bu3bs1duzYEz6ufmFtJ71799aMGTO0YMECzZgxQw6HQ507d9a0adNstyTW7373O40bN65G6bRz5049/vjjtiudpJpLepSXl2vz5s3q3LmzLYvpsLAwWy5P8HPl5eW67rrr5HK51LJlS40bN07Dhw/XqFGjTI9mid///vcqLi7WoEGDPMs1lJWVacSIEYqNjTU8nbUmTJig+++/X506dVJYWJgGDRqkkJAQLV68WOPGjbPdY+auXbs0fvx4bd682fN3dNasWT65ju7ZFBQUpF27dnnu986dO227YWzz5s31zTff6KqrrpIkffPNN7Z8w2Ls2LGaNm2aevbsqQ4dOig/P99nTxxzuOx27Qvq5NChQ1q5cqXS09P1008/KS4uTunp6frkk09Mj2YEy5p4bdq0SWPGjPFcXllZWakXX3xRV1xxhenRLDVmzJhTvlnjcDj0wgsvWDsQjJo7d67y8vJOuv7fRRddpHHjxhme0Jx//OMfmjJliu6///4am53ZRb9+/bRkyRLbvpl1psso7bCcSbUvvvjC83H10/Hj/4786le/snwmk55//nktXrxYTZo0keQtne6//37PGdR2tW3bNi1evNhzxpOdLFy4UGFhYbrttttqrB9rt/0J7rzzTqWlpWnYsGF64okn1KpVKw0dOlTZ2dmmR7PMvn379MMPP3iWv2rRooX+/Oc/a9myZTUeT/3dgAED9OGHH0qS/vznP2vNmjVatGiR9uzZoz/+8Y9avny54Qmtde+99+r222/X0KFDJUnLli1TVlaWFi9ebHgya/3zn//UlClTPG9grlmzRk899ZRnvwY7+c9//qOHH37YczXitm3b9NJLL9myqzlXUEzjF8vNzdX777+vrKwstW/fXgMHDtRdd91leizL/HxZk5tvvlkDBgyw1RPD4911110aO3asZwmPzz//XC+88ILeffddw5NZ62Rly6FDh7RkyRIdOHDAs0My7KGyslJJSUlavXr1Cev/paam2u6qApfLpbVr1yojI0O5ubm67bbbFB8fr1atWpkezXJ33XWX7R4fj5eXl6ft27fr5ptvliQ9/fTTKikpkSTdc889iomJMTmepapfREvS9u3b1b59+xoFtd02bpLcl2Jv27ZNknt5m2bNmunGG2+07YkQx/vtb39ry9+JTp06nXCbHfcnWLx4seLi4rRp0yaNHTtWVVVVGjNmjG3e4E1LS9OTTz6p8PBwnX/++Ro/frwmTJigG264QY888oitNjW74447PK87xowZo549eyohIUGSFBcXp8zMTIPTWW/w4MEnlPEnu80O8vPz9e9//1uSdMMNN/js2bFW+Omnn7Rx40a5XC5169bNtksJfvbZZ9q8ebPKyso8tz300EMGJzo5e70yRoPo1KmTnnjiCU2cOFEff/yx5/Jsu2BZk5pKS0trrCvds2dPlZaWGpzIjOPP8isvL9eSJUv05ptvqn///rZb3gVsAnm8WbNm6R//+Ie6deumu+66Sz169DA9klG9evXSzJkzdfvtt9c4+88ua0zPmzdPAwYM8Hy+Zs0a3XPPPTp69KheffVVPf/88wans9bxexPExcXZsnT8uWbNmqlLly41brPjOTTHrzFdVVWlTZs2qaqqyuBE5uTm5poewSfce++9kqQ+ffpow4YNKisr81yRZQdvvvmmMjIy1KFDB3311Vf6/e9/r9mzZ9t2mZfCwkKFhYVpw4YNGjNmjOf248snuwgICND27dt16aWXSnKXs3ZdwuKSSy7RJZdcYnoMnxAWFmbL5a+ON3v2bG3atEnbtm1Tv379tHr1ap/dD4xiGnXWqFEjDRgwoMYLTDv47LPPtHLlSs2cOdOzrImvLiJvhaZNm+rzzz/3lPQbNmyw3eWV1aqqqpSWlqaXX35Z1157rd599121adPG9FgwyO6bQErSG2+8oUsuuURbt27VrFmzTjhutzKu+kym43fFttMa0zt37lT//v09nzdt2lSJiYmS5Pm/dmTnN7jPxI7ZHL/GdFBQkNq2bau5c+canMisAwcO6JtvvpHD4dBVV12l8PBw0yNZpvoKglOxy5uaQUFB6tChgyTpmmuuUZs2bWxbSo8aNUpxcXFq1KiRrrnmGs/vwMaNG3XBBRcYns5648aNU2JioueKq9zcXM2cOdPwVNb7+uuvNWvWLO3atUtOp9OzAeC6detMjwZD1qxZo4yMDA0ZMkTTpk3T6NGjfXZzbYpp4BcKDQ1VYmKiEhMTPcuaHDt2TImJibZb1kSSJk+erLFjx3rWe6uoqNC8efMMT2W9v/3tb5o7d64uueQSvfbaa54nz4DdLVq0SOXl5SdsOnL06FHP44ad2HXZp2qVlZU1Pp8zZ47n40OHDlk9DnzE6cq3n//O2MFbb71legSfsXbtWj366KOe0un777/XrFmzdP311xuezBqn29zQTm9qVlRUKC8vz3MFRUBAQI3P7VLQS9Jtt92m7t27a//+/TWWuomOjtb06dMNTmZGnz599MEHH+ibb76Ry+VS165dFRERYXosyz3++ON68MEH1bVrV1tuLo4TBQcHKygoSA6HQxUVFWrVqpX27NljeqyTopgG6sHuy5pIUpcuXfTRRx8pPz9fLpdLl156qRo1amR6LMuNGzdOF1xwgRo1aqSXXnrphON2PtMJ9rZ27VpdeumlJ+wSv2TJEu3Zs0e9evUyNJm1ysvLFRwcfMqljuxypUlFRYUOHz7suQS9ffv2kqTDhw+rvLzc5GiWO76MLSsrq1GySPYqWk5Xvh2/5I2/W7p06WmP2/Gqgueff15Lly71PFbk5eXp0UcftU0xbfc3M6sdO3ZMI0eOrHFb9ed2KuirRUVFKSoqqsZtdty34+fsXMg2adJEAwcOND0GfEjz5s1VWlqqbt26KSkpSVFRUT67zA2bHwKot9LSUu3Zs6fGkiZ2ekEtuXeAPt3lxsevQQ3Yye23364VK1ac8ETI6XRq8ODBysrKMjSZtao3K+rUqZMcDkeNAtJOG3m9+OKL2rp1q55++mlPOX348GE98cQTuuSSSzR27FjDE1qnb9++pzxmx6IF0qRJkyS5l67YsGGDZy3IdevW6YYbbrDlFWmDBg3SihUrznibvzrTvi12eVMTOJWPPvpIU6ZM0ZVXXqmqqirl5uZq+vTpuummm0yPZqnnn39eV199te3XVYbX/v37FRoaKqfTqcWLF6ukpETDhw/3ySV/KKYB1MvSpUs1e/ZshYeHe4pZO76gdjqd+uijjxQWFqZevXrpz3/+s9atW6d27dpp9OjRCgkJMT0iYMTAgQO1cuXKX3wM/qmyslJJSUlavXq12rVrJ0nasWOH+vXrp2effVZBQVzMB4wePVpJSUlq27atJGnXrl167rnnbLU5aLURI0Zo0KBBGjJkiCQpIyNDy5cv15tvvml2MIuc7M3ManZ6UxM4ldtuu00LFizwbPq3Y8cOPfDAA/rb3/5meDJr9ezZUwcPHlTz5s0VHBzMGtM4p/DsH0C9LFq0SFlZWbrwwgtNj2LUk08+qS1btqi8vFxt2rRRWVmZbrzxRn3xxReaOnWqLV9MApJ7CYvS0tITzuo6cuSI7ZZukKS0tDRdd911tt0YNSgoSLNnz9YPP/ygnJwcSVLnzp1tv0kocLz//e9/nlJaktq2bav8/HyDE5kzbdo0TZgwQcnJyXI4HIqJiTnpRrr+Kjc31/QIgE8LCwvzlNKS1K5dO1ttkFotPT3d9AjwEWPGjDntldy+uMQoxTSAeomKirJ9KS1JX375pT744AOVlpbqhhtu0Oeff67g4GDFx8dr0KBBpscDjBkwYIAmTpxYY+mGkpISTZ06Vbfeeqvh6ayXk5OjN954QxUVFerZs6euu+469ezZUy1atDA9mqUuvvhiymjgFMLDwzV//nzP2vzp6em2LFok6aKLLtJ7772nI0eOyOVyef6OALC36mVubrjhBr388sv67W9/K5fLpWXLlunmm282PJ31eD2Oar/5zW9Mj/CLsZQHgHqZN2+ejh07pttvv73GBkV2W2M6Li5OmZmZJ3wsedeWBezoVEs39O3bV6mpqbZdumHPnj365JNP9Oqrr6qgoIDLsQF4FBYWasaMGVq/fr0k9yXakydPtu3mZjt37tTOnTtr7GVil3VUf//73+vPf/6zevbsWeMMOC7Th92xzE1NBQUFmjVrlnJzc1VWVua53W7La+LcZM9XgwAaTHUB+/e//91zmx3XmC4vL1deXp5cLleNjyXVeHIA2M3Pl25wuVy64oorbHu27Hfffad169Zp3bp12rdvn2644QbPBmcAIEmtWrWy5UaHJzNnzhylpaWpffv2CggIkOR+nmmXYrp62RIu0wdqYpmbmiZPnqwBAwZo8+bNmj17tt555x1ddNFFpseCQXv37tVTTz2l9evXy+FwqEePHnr88cfVsmVL06OdgDOmAaAB9O3b95TH7FjUAzi5Tp06qVu3bho/fry6d+9uehwAPqi0tFSvvPKKdu3apTlz5igvL0/5+fm66aabTI9muZtvvlkZGRks4SGpoqJC+fn5cjgcuuSSS2x7xRGAE1VfsVu9sXhVVZVGjBihJUuWmB4NhowYMULdu3evsSzYhg0bfHLzYP6aAai3devWKS8vT8OGDVNRUZEOHTpUYxMKO8jOzjY9AoBzwHvvvafPP/9c8+fPV1FRka6++mr16tVLt9xyi+nRAPiIlJQURUVFec4IbN26tcaPH2/LYjoqKopSWu69TMaPH68mTZp4rs577rnndPXVV5seDYAPaNSokSSpWbNm+vHHH9WiRQv9+OOPhqeCSQcOHNBDDz3k+fzBBx/UqlWrDE50ahTTAOrl1Vdf1Zo1a7Rv3z4NGzZMFRUVmjx5st555x3TowGAz+nSpYu6dOmiwYMH65NPPtErr7yiv/71r7ZbCxHAqW3ZskWpqan67LPPJEnNmzdXVVWV4anM6Nq1qx555BHdeuutNfYysctSHtWmTZum2bNn61e/+pUkd1GdkpKiFStWGJ4MgC/o3r27Dh48qLvvvltDhgxRcHCwLTcZh9fFF1+sH374wbN84s6dO9WhQwfDU50cxTSAesnKylJ6errnEpHWrVvr8OHDhqcCAN80bdo0rVu3TmVlZerZs6f+9Kc/qWfPnqbHAuBDqs98q1ZWVnbSDb7sYNOmTZKkt956y3ObndaYrta4cWNPKS25S6gmTZoYnAiAL+ndu7cCAwMVFxena6+9Vj/++KMqKipMjwWDDh8+rMGDB+uaa66RJH399df61a9+pbFjx0qS5s6da3K8GiimAdRLkyZNTngBdfyu4QAA6ccff9QFF1ygjh076t5771Xbtm09x/773//65EYkAMzo3r27Fi5cqPLycq1fv16LFy8+7V4W/uz4QtrOunfvrhUrVmjQoEGSpJUrV6pPnz6GpwLgK2bOnKmMjAxJ0gUXXKDWrVtr6NChnttgPwMHDtTAgQM9n8fGxhqc5vQopgHUS+vWrfXll1/K4XCoqqpKCxcu9NlLRADAlNGjRysjI0Px8fH67W9/q/fff99z7IknnuCFAwCPcePG6fXXX1fz5s01e/Zs9e3bV6NGjTI9ljElJSXKz89XWVmZ57bjzx62g4yMDC1evFhPPPGEJKm8vFzh4eF6++235XA4tG7dOsMTAjDJ5XLVODksICBATqfT4EQw7Y477jA9Qq1RTAOolylTpmjixInaunWrrrrqKnXv3l2zZ882PRYA+JTjL8OvrKw85TEA9rV06VLPx6GhoYqLi/N8/u677yoxMdHAVGZ9+OGHSk1N1aFDh9SyZUvt3LlTnTp1st2beenp6aZHAODDmjdvrm+++UZXXXWVJOmbb75Rs2bNDE8Fk4qLizV9+nStW7dODodDvXr10uOPP66IiAjTo52AYhpAvURFRWnRokUqLS1VVVWVmjdvbnokAPA5x5/F8vPljlj+CIAkTZ8+XVdeeSVXnh1n4cKFWrZsme677z5lZmbqX//6lz766CPTY1nuwgsvND0CAB/26KOPavTo0brsssskSdu2bdNLL71keCqYlJycrMsuu0xJSUlyuVx67733NHXqVJ/8vaCYBlAna9asOe1xu21KAwCnU1ZWpry8PLlcrhofVx8DgBkzZigzM1Pbtm1TXFycYmNjFRYWZnoso4KCghQZGem5JP3666/Xiy++aHgq6xUUFGjWrFnKzc2t8Tdj9erVBqcC4Cu6deumDz74QBs3bpTL5VK3bt1s//fD7nbu3Fnj7+WYMWM0ePBggxOdGsU0gDp5/fXXJbnXuNu0aZMuv/xySdKWLVvUtWtXimkAOM6xY8c0cuRIz+fHf8wZ0wAkaejQoRo6dKh2796tjIwM3X333br88sv1wAMPqGPHjqbHMyI4OFgul0sXX3yx3nrrLV144YU6cOCA6bEsN3nyZA0YMECbN2/W7Nmz9c477+iiiy4yPRYAHxIWFsZrcHhUVVWpqKhIkZGRkqSioiJVVVUZnurkKKYB1En1LumPPvqoJk+e7FnP6ttvv62xqRcAQMrOzjY9AoBzRJs2bTRixAi1aNFC8+bN0/XXX2/bYnrs2LE6fPiwJkyYoJSUFJWUlCg5Odn0WJY7cOCA7rzzTi1ZskTdunXTVVddpREjRpgeCwDgo+677z7FxcXpxhtvlMPh0Jo1a/TII4+YHuukKKYB1EteXp6nlJakLl26KCUlxdxAAAAA5yCXy6W1a9dq2bJl2rJli2677Ta99957atu2renRjLnuuuskSSEhIXrzzTfNDmNQo0aNJEnNmjXTjz/+qBYtWujHH380PBUAwFfFxcWpc+fO2rBhg1wul+655x7PGuS+hmIaQL0EBQVp+fLlnvWKVqxYoaAgHlo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"text/plain": [
"<Figure size 1800x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25, 5))\n",
"sns.set_theme()\n",
"act_mut_all[\"tumor_types\"].value_counts().sort_index().plot(kind=\"bar\")\n",
"#sns.countplot(x=profile_mut_all[\"tumor_types\"], palette=sns.hls_palette(2))\n",
"plt.xticks(rotation=90);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multinomial Naive Bayes classifier"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"AdenoCA 7712\n",
"SCC 2188\n",
"cancer 1639\n",
"HCC 1318\n",
"Melanoma 1231\n",
"BNHL 822\n",
"RCC 775\n",
"GBM 605\n",
"Medullo 557\n",
"CA 462\n",
"cell 389\n",
"CMDI 357\n",
"LGG 326\n",
"CLL 302\n",
"Papillary 297\n",
"neoplasm 247\n",
"ALL 240\n",
"Ewings 231\n",
"AML 230\n",
"30 212\n",
"bone 203\n",
"Name: tumor_types, dtype: int64\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 48\n",
" 1 0.00 0.00 0.00 41\n",
" 2 0.00 0.00 0.00 59\n",
" 3 0.38 1.00 0.55 1532\n",
" 4 0.00 0.00 0.00 149\n",
" 5 0.00 0.00 0.00 91\n",
" 6 0.00 0.00 0.00 69\n",
" 7 0.00 0.00 0.00 75\n",
" 8 0.00 0.00 0.00 45\n",
" 9 0.00 0.00 0.00 114\n",
" 10 0.00 0.00 0.00 272\n",
" 11 0.00 0.00 0.00 68\n",
" 12 0.00 0.00 0.00 111\n",
" 13 0.83 0.02 0.04 242\n",
" 14 0.00 0.00 0.00 61\n",
" 15 0.00 0.00 0.00 149\n",
" 16 0.00 0.00 0.00 460\n",
" 17 0.00 0.00 0.00 39\n",
" 18 0.00 0.00 0.00 315\n",
" 19 0.00 0.00 0.00 76\n",
" 20 0.00 0.00 0.00 53\n",
"\n",
" accuracy 0.38 4069\n",
" macro avg 0.06 0.05 0.03 4069\n",
"weighted avg 0.19 0.38 0.21 4069\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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" All Train Test Pred\n",
"0 212 164 48 NaN\n",
"1 240 199 41 NaN\n",
"2 230 171 59 NaN\n",
"3 7712 6180 1532 4063.0\n",
"4 822 673 149 NaN\n",
"5 462 371 91 NaN\n",
"6 302 233 69 NaN\n",
"7 357 282 75 NaN\n",
"8 231 186 45 NaN\n",
"9 605 491 114 NaN\n",
"10 1318 1046 272 NaN\n",
"11 326 258 68 NaN\n",
"12 557 446 111 NaN\n",
"13 1231 989 242 6.0\n",
"14 297 236 61 NaN\n",
"15 775 626 149 NaN\n",
"16 2188 1728 460 NaN\n",
"17 203 164 39 NaN\n",
"18 1639 1324 315 NaN\n",
"19 389 313 76 NaN\n",
"20 247 194 53 NaN\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 2160x2160 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"X_prf = profile_mut_all.copy()\n",
"#idx = X_prf[X_prf[\"tumor_types\"] == \"AdenoCA\"].index.values\n",
"#down_fix_list = [\"Breast-cancer\", \"Liver-HCC\", \"ColoRect-AdenoCA\", \"Prost-AdenoCA\"]\n",
"#down_fix_list = [\"AdenoCA\"]\n",
"#for name in down_fix_list:\n",
"# idx = X_prf[X_prf[\"tumor_types\"] == name].index.values\n",
"# n_idx = len(idx)\n",
"# idx = np.random.choice(idx, n_idx-1000, replace= False)\n",
"# X_prf = X_prf.loc[X_prf.index.difference(idx)]\n",
"\n",
"print(X_prf[\"tumor_types\"].value_counts())\n",
"label_encoder = LabelEncoder()\n",
"\n",
"y_prf = X_prf[\"tumor_types\"].values\n",
"y_prfm = label_encoder.fit_transform(y_prf)\n",
"\n",
"X_prf.drop(\"tumor_types\", axis=1, inplace=True)\n",
"\n",
"integer_mapping = {l: i for i, l in enumerate(label_encoder.classes_)}\n",
"labs = list(integer_mapping.keys())\n",
"\n",
"X_prfm_train, X_prfm_test, y_prfm_train, y_prfm_test = train_test_split(X_prf, y_prfm, test_size = 0.2)\n",
"\n",
"model_nbm = MultinomialNB()\n",
"model_nbm.fit(X_prfm_train, y_prfm_train)\n",
"\n",
"y_prfm_pred = model_nbm.predict(X_prfm_test)\n",
"\n",
"print(classification_report(y_prfm_test, y_prfm_pred))\n",
"\n",
"plot_trn_tst_dist(y_prfm, y_prfm_train, y_prfm_test, y_prfm_pred, in_cols=False)\n",
"plot_confusion_mat(y_prfm_test, y_prfm_pred, labs=labs, size=(30, 30))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That didn't go very well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### XGBoost"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: xgboost in /home/jr/miniconda3/lib/python3.9/site-packages (1.5.2)\n",
"Requirement already satisfied: scipy in /home/jr/miniconda3/lib/python3.9/site-packages (from xgboost) (1.6.2)\n",
"Requirement already satisfied: numpy in /home/jr/miniconda3/lib/python3.9/site-packages (from xgboost) (1.19.2)\n"
]
}
],
"source": [
"# You need to install imbalanced-learn for the following cells to work.\n",
"# Install a pip package in the current Jupyter kernel\n",
"import sys\n",
"!{sys.executable} -m pip install xgboost"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[19:00:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
" precision recall f1-score support\n",
"\n",
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" 0 0.53 0.38 0.44 48\n",
" 1 0.10 0.05 0.07 41\n",
" 2 0.64 0.27 0.38 59\n",
" 3 0.61 0.85 0.71 1532\n",
" 4 0.36 0.33 0.35 149\n",
" 5 0.32 0.18 0.23 91\n",
" 6 0.87 0.57 0.68 69\n",
" 7 0.53 0.53 0.53 75\n",
" 8 0.00 0.00 0.00 45\n",
" 9 0.57 0.27 0.37 114\n",
" 10 0.75 0.68 0.71 272\n",
" 11 0.13 0.10 0.11 68\n",
" 12 0.33 0.26 0.29 111\n",
" 13 0.85 0.79 0.82 242\n",
" 14 0.48 0.25 0.33 61\n",
" 15 0.55 0.52 0.54 149\n",
" 16 0.59 0.46 0.52 460\n",
" 17 0.00 0.00 0.00 39\n",
" 18 0.54 0.38 0.45 315\n",
" 19 0.56 0.47 0.51 76\n",
" 20 0.23 0.06 0.09 53\n",
" accuracy 0.59 4069\n",
" macro avg 0.45 0.35 0.39 4069\n",
"weighted avg 0.57 0.59 0.56 4069\n",
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" All Train Test Pred\n",
"0 212 48 48 34\n",
"1 240 41 41 20\n",
"2 230 59 59 25\n",
"3 7712 1532 1532 2143\n",
"4 822 149 149 135\n",
"5 462 91 91 50\n",
"6 302 69 69 45\n",
"7 357 75 75 76\n",
"8 231 45 45 17\n",
"9 605 114 114 54\n",
"10 1318 272 272 249\n",
"11 326 68 68 54\n",
"12 557 111 111 89\n",
"13 1231 242 242 224\n",
"14 297 61 61 31\n",
"15 775 149 149 141\n",
"16 2188 460 460 360\n",
"17 203 39 39 19\n",
"18 1639 315 315 226\n",
"19 389 76 76 64\n",
"20 247 53 53 13\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2160x2160 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from xgboost import XGBClassifier\n",
"\n",
"model_mxgb = XGBClassifier(n_estimators=200, max_depth=32, use_label_encoder=False)\n",
"model_mxgb.fit(X_prfm_train, y_prfm_train)\n",
"y_prfm_pred = model_mxgb.predict(X_prfm_test)\n",
"\n",
"#model_nbm.fit(X_prfm_train, y_prfm_train)\n",
"#y_prfm_pred = model_nbm.predict(X_prfm_test)\n",
"\n",
"print(classification_report(y_prfm_test, y_prfm_pred))\n",
"\n",
"plot_trn_tst_dist(y_prfm, y_prfm_test, y_prfm_test, y_prfm_pred, in_cols=False)\n",
"plot_confusion_mat(y_prfm_test, y_prfm_pred, labs=labs, size=(30, 30))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That went better."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.5"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}