{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [],

   "source": [

    "CLOUD = False\n",

    "\n",

    "import sys\n",

    "\n",

    "from pathlib import Path\n",

    "from PIL import ImageDraw, ImageFont, Image\n",

    "from matplotlib import patches, patheffects\n",

    "import time\n",

    "from random import randint\n",

    "import numpy as np\n",

    "import pandas as pd\n",

    "import pickle\n",

    "\n",

    "from sklearn.model_selection import KFold, StratifiedKFold, GroupKFold\n",

    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, LabelBinarizer\n",

    "from sklearn.preprocessing import StandardScaler\n",

    "from sklearn.metrics import mean_squared_error,log_loss\n",

    "from scipy.stats import ks_2samp\n",

    "\n",

    "import pdb\n",

    "\n",

    "import scipy as sp\n",

    "from tqdm import tqdm, tqdm_notebook\n",

    "\n",

    "import os\n",

    "import glob\n",

    "\n",

    "import torch\n",

    "if not CLOUD: torch.cuda.current_device()\n",

    "\n",

    "import torch.nn as nn\n",

    "import torch.utils.data as D\n",

    "import torch.nn.functional as F\n",

    "import torch.utils as U\n",

    "\n",

    "import torchvision\n",

    "from torchvision import transforms as T\n",

    "from torchvision import models as M\n",

    "\n",

    "import matplotlib.pyplot as plt\n",

    "\n",

    "if CLOUD:\n",

    "    PATH = Path('/home/zahar_chikishev')\n",

    "    PATH_WORK = Path('/home/zahar_chikishev/running')\n",

    "else:\n",

    "    PATH = Path('C:/StudioProjects/Hemorrhage')\n",

    "    PATH_WORK = Path('C:/StudioProjects/Hemorrhage/running')\n",

    "\n",

    "if not CLOUD:\n",

    "    import lightgbm as lgb\n",

    "    from eli5.permutation_importance import get_score_importances\n",

    "\n",

    "from collections import defaultdict, Counter\n",

    "import random\n",

    "import seaborn as sn\n",

    "\n",

    "pd.set_option(\"display.max_columns\", 100)\n",

    "\n",

    "all_ich = ['any','epidural','intraparenchymal','intraventricular','subarachnoid','subdural']"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "sys.path.insert(0, \"C:\\\\fastai\")\n",

    "from fastai import *\n",

    "from fastai.vision import *\n",

    "from fastai.tabular import *\n",

    "from fastprogress import *"

   ]

  },

  {

   "cell_type": "raw",

   "metadata": {},

   "source": [

    "Duplicated patient IDs, all labels zeros:\n",

    "['ID_a64d5deed','ID_921490062','ID_489ae4179','ID_854fba667']"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_df = pd.read_csv(PATH_WORK/'train_df.csv')\n",

    "train_md = pd.read_csv(PATH_WORK/'train_md.csv')\n",

    "test_md = pd.read_csv(PATH_WORK/'test_md.csv')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# Prepare meta data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_csv = pd.read_csv(PATH/'stage_1_train.csv')\n",

    "\n",

    "train_csv = train_csv.loc[~train_csv.ID.duplicated()].sort_values('ID').reset_index(drop=True)\n",

    "all_sop_ids = train_csv.ID.str.split('_').apply(lambda x: x[0]+'_'+x[1]).unique()\n",

    "train_df = pd.DataFrame(train_csv.Label.values.reshape((-1,6)), columns = all_ich)\n",

    "train_df['sop_id'] = all_sop_ids\n",

    "\n",

    "train_df.to_csv(PATH_WORK/'train_df.csv', index=False)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_md = pd.read_csv(PATH/'train_metadata.csv')\n",

    "test_md = pd.read_csv(PATH/'test_metadata.csv')\n",

    "test_md = test_md.sort_values('SOPInstanceUID')\n",

    "data_md = pd.concat([train_md, test_md], axis=0, sort=False).reset_index(drop=True)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "metadata": {},

   "outputs": [],

   "source": [

    "del data_md['Unnamed: 0']\n",

    "\n",

    "for i in range(6):\n",

    "    data_md['ImageOrientationPatient_{}'.format(i)] \\\n",

    "        = data_md['ImageOrientationPatient'].str.split('\\'').apply(lambda x: x[1+2*i])\n",

    "\n",

    "for i in range(3):\n",

    "    data_md['ImagePositionPatient_{}'.format(i)] \\\n",

    "        = data_md['ImagePositionPatient'].str.split('\\'').apply(lambda x: x[1+2*i])\n",

    "\n",

    "for i in range(2):\n",

    "    data_md['PixelSpacing_{}'.format(i)] \\\n",

    "        = data_md['PixelSpacing'].str.split('\\'').apply(lambda x: x[1+2*i])\n",

    "\n",

    "data_md['WindowCenter_0'] \\\n",

    "    = data_md['WindowCenter'].str.split('\\'').apply(lambda x: float(x[1]) if len(x) > 1 else float(x[0]))\n",

    "\n",

    "data_md['WindowCenter_1'] \\\n",

    "    = data_md['WindowCenter'].str.split('\\'').apply(lambda x: float(x[3]) if len(x) > 1 else np.nan)\n",

    "\n",

    "data_md['WindowCenter_1_NAN'] = data_md.WindowCenter_1.isnull()\n",

    "\n",

    "data_md.loc[data_md.WindowCenter_1.isnull(), 'WindowCenter_1'] \\\n",

    "    = data_md.loc[~data_md.WindowCenter_1.isnull(), 'WindowCenter_1'].mean()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 30,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_md['ImagePositionPatient_2'] = data_md['ImagePositionPatient_2'].astype(float)\n",

    "\n",

    "data_md = data_md.sort_values(['SeriesInstanceUID','ImagePositionPatient_2']).reset_index(drop=True)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 31,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_md['pos_max'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: max(x))\n",

    "data_md['pos_min'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: min(x))\n",

    "data_md['pos_size'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: len(x))\n",

    "\n",

    "data_md['pos_idx'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: np.arange(len(x)))\n",

    "\n",

    "data_md['pos_inc'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: np.concatenate([[0],np.diff(x.values)]))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 32,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_md['pos_range'] = (data_md['pos_max'] - data_md['pos_min'])\n",

    "data_md['pos_rel'] = (data_md['ImagePositionPatient_2'] - data_md['pos_min'])/data_md['pos_range']\n",

    "\n",

    "data_md['pos_zeros'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: (np.diff(x.values) <= 0.001).sum())\n",

    "\n",

    "data_md['pos_inc_rng'] = data_md[['SeriesInstanceUID','ImagePositionPatient_2']]\\\n",

    "    .groupby('SeriesInstanceUID').transform(lambda x: np.diff(x.values)[np.diff(x.values) > 0.1].max() /\n",

    "                                                      np.diff(x.values)[np.diff(x.values) > 0.1].min())\n",

    "data_md['pos_inc_rng'] = np.clip(data_md['pos_inc_rng'],0,6)\n",

    "\n",

    "data_md = data_md.sort_values('SOPInstanceUID').reset_index(drop=True)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 36,

   "metadata": {},

   "outputs": [],

   "source": [

    "cols_cat = ['BitsStored','PixelRepresentation','RescaleIntercept','WindowCenter_1_NAN']\n",

    "cols_float = ['ImageOrientationPatient_0', 'ImageOrientationPatient_1',\n",

    "       'ImageOrientationPatient_2', 'ImageOrientationPatient_3',\n",

    "       'ImageOrientationPatient_4', 'ImageOrientationPatient_5',\n",

    "       'ImagePositionPatient_0', 'ImagePositionPatient_1','ImagePositionPatient_2',\n",

    "       'PixelSpacing_0', 'PixelSpacing_1', 'WindowCenter_0', 'WindowCenter_1',\n",

    "       'pos_max', 'pos_min', 'pos_size', 'pos_idx', 'pos_inc',\n",

    "       'pos_range', 'pos_rel', 'pos_zeros', 'pos_inc_rng']\n",

    "pickle.dump((cols_cat,cols_float), open(PATH_WORK/'covs','wb'))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 37,

   "metadata": {},

   "outputs": [],

   "source": [

    "for col in cols_float:\n",

    "    data_md[col] = data_md[col].astype(float)\n",

    "\n",

    "for col in cols_cat:\n",

    "    data_md[col] = pd.factorize(data_md[col])[0]\n",

    "    data_md[col] = data_md[col].astype('category')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 41,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_md = data_md.join(train_df.set_index('sop_id'), on = 'SOPInstanceUID')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 42,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "any          False  True \n",

      "BitsStored               \n",

      "0           341679  75369\n",

      "1           332579   3176\n",

      "any                   False  True \n",

      "PixelRepresentation               \n",

      "0                    343931  75429\n",

      "1                    330327   3116\n",

      "any                False  True \n",

      "RescaleIntercept               \n",

      "0                 662279  78457\n",

      "1                   6653     28\n",

      "2                   5276     60\n",

      "3                     50      0\n",

      "any                  False  True \n",

      "WindowCenter_1_NAN               \n",

      "0                   341679  75369\n",

      "1                   332579   3176\n"

     ]

    }

   ],

   "source": [

    "for col in cols_cat:\n",

    "    print(pd.crosstab(data_md[col], data_md['any'].isnull()))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 43,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_md = data_md.loc[~data_md['any'].isnull()].copy().reset_index(drop=True)\n",

    "for col in all_ich:\n",

    "    train_md[col] = train_md[col].astype(int)\n",

    "train_md.to_csv(PATH_WORK/'train_md.csv', index=False)\n",

    "\n",

    "test_md = data_md.loc[data_md['any'].isnull()].copy().reset_index(drop=True)\n",

    "test_md.to_csv(PATH_WORK/'test_md.csv', index=False)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 47,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 1728x2160 with 21 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "fig, axes = plt.subplots(7, 3, figsize=(24, 30))\n",

    "\n",

    "for i, ax in enumerate(axes.flatten()):\n",

    "    if i == len(cols_float): continue\n",

    "    col = cols_float[i]\n",

    "    a = ax.hist(train_md[col], bins=100, density=True, alpha=0.5)\n",

    "    a = ax.hist(test_md[col], bins=100, density=True, alpha=0.5)\n",

    "    ks = ks_2samp(train_md[col].values, test_md[col].values)\n",

    "    ax.set_title(col + (' IMPORTANT' if col in significant_cols else '') + ' ks {:.4f}'.format(ks.statistic))"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# Scans"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 103,

   "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>BitsAllocated</th>\n",

       "      <th>BitsStored</th>\n",

       "      <th>HighBit</th>\n",

       "      <th>ImageOrientationPatient</th>\n",

       "      <th>ImagePositionPatient</th>\n",

       "      <th>Modality</th>\n",

       "      <th>PatientID</th>\n",

       "      <th>PhotometricInterpretation</th>\n",

       "      <th>PixelRepresentation</th>\n",

       "      <th>PixelSpacing</th>\n",

       "      <th>RescaleIntercept</th>\n",

       "      <th>RescaleSlope</th>\n",

       "      <th>SOPInstanceUID</th>\n",

       "      <th>SamplesPerPixel</th>\n",

       "      <th>SeriesInstanceUID</th>\n",

       "      <th>StudyID</th>\n",

       "      <th>StudyInstanceUID</th>\n",

       "      <th>WindowCenter</th>\n",

       "      <th>WindowWidth</th>\n",

       "      <th>ImageOrientationPatient_0</th>\n",

       "      <th>ImageOrientationPatient_1</th>\n",

       "      <th>ImageOrientationPatient_2</th>\n",

       "      <th>ImageOrientationPatient_3</th>\n",

       "      <th>ImageOrientationPatient_4</th>\n",

       "      <th>ImageOrientationPatient_5</th>\n",

       "      <th>ImagePositionPatient_0</th>\n",

       "      <th>ImagePositionPatient_1</th>\n",

       "      <th>ImagePositionPatient_2</th>\n",

       "      <th>PixelSpacing_0</th>\n",

       "      <th>PixelSpacing_1</th>\n",

       "      <th>WindowCenter_0</th>\n",

       "      <th>WindowCenter_1</th>\n",

       "      <th>WindowCenter_1_NAN</th>\n",

       "      <th>any</th>\n",

       "      <th>epidural</th>\n",

       "      <th>intraparenchymal</th>\n",

       "      <th>intraventricular</th>\n",

       "      <th>subarachnoid</th>\n",

       "      <th>subdural</th>\n",

       "      <th>weight</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000', '-123.101', '104.307']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_b81a287f</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_231d901c1</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_15dcd6057a</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_dd37ba3adb</td>\n",

       "      <td>40</td>\n",

       "      <td>100</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.984808</td>\n",

       "      <td>-0.173648</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-123.101000</td>\n",

       "      <td>104.307000</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>40.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>1</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>1</td>\n",

       "      <td>0</td>\n",

       "      <td>0.015365</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>16</td>\n",

       "      <td>1</td>\n",

       "      <td>11</td>\n",

       "      <td>['1', '0', '0', '0', '0.933580426', '-0.358367...</td>\n",

       "      <td>['-125', '53.6282216', '223.572015']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_400facde</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>1</td>\n",

       "      <td>['0.48828125', '0.48828125']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_994bc0470</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_4ba12c2161</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_c5277f0c63</td>\n",

       "      <td>['00047', '00047']</td>\n",

       "      <td>['00080', '00080']</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.933580</td>\n",

       "      <td>-0.358368</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>53.628222</td>\n",

       "      <td>223.572015</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>47.0</td>\n",

       "      <td>47.000000</td>\n",

       "      <td>1</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.001997</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-123.646240', '124.321068']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_42910d3d</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_127689cce</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_c4b4931314</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_db93ade25b</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.972370</td>\n",

       "      <td>-0.233445</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-123.646240</td>\n",

       "      <td>124.321068</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.269672</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>16</td>\n",

       "      <td>1</td>\n",

       "      <td>11</td>\n",

       "      <td>['1', '0', '0', '0', '1', '0']</td>\n",

       "      <td>['-114', '-6', '171.999939']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_329aafa7</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>1</td>\n",

       "      <td>['0.4453125', '0.4453125']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_25457734a</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_116558f409</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_8dd6d32f3b</td>\n",

       "      <td>['00036', '00036']</td>\n",

       "      <td>['00080', '00080']</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1.000000</td>\n",

       "      <td>0.000000</td>\n",

       "      <td>-114.0</td>\n",

       "      <td>-6.000000</td>\n",

       "      <td>171.999939</td>\n",

       "      <td>0.445312</td>\n",

       "      <td>0.445312</td>\n",

       "      <td>36.0</td>\n",

       "      <td>36.000000</td>\n",

       "      <td>1</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.003259</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>16</td>\n",

       "      <td>1</td>\n",

       "      <td>11</td>\n",

       "      <td>['1', '0', '0', '0', '1', '0']</td>\n",

       "      <td>['-115', '-1', '230.5']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6b544c3c</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>1</td>\n",

       "      <td>['0.44921875', '0.44921875']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_81c9aa125</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_f56d7bd0f9</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_2685c5d5c0</td>\n",

       "      <td>['00036', '00036']</td>\n",

       "      <td>['00080', '00080']</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1.000000</td>\n",

       "      <td>0.000000</td>\n",

       "      <td>-115.0</td>\n",

       "      <td>-1.000000</td>\n",

       "      <td>230.500000</td>\n",

       "      <td>0.449219</td>\n",

       "      <td>0.449219</td>\n",

       "      <td>36.0</td>\n",

       "      <td>36.000000</td>\n",

       "      <td>1</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.005538</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "   BitsAllocated BitsStored  HighBit  \\\n",

       "0             16          0       15   \n",

       "1             16          1       11   \n",

       "2             16          0       15   \n",

       "3             16          1       11   \n",

       "4             16          1       11   \n",

       "\n",

       "                             ImageOrientationPatient  \\\n",

       "0  ['1.000000', '0.000000', '0.000000', '0.000000...   \n",

       "1  ['1', '0', '0', '0', '0.933580426', '-0.358367...   \n",

       "2  ['1.000000', '0.000000', '0.000000', '0.000000...   \n",

       "3                     ['1', '0', '0', '0', '1', '0']   \n",

       "4                     ['1', '0', '0', '0', '1', '0']   \n",

       "\n",

       "                           ImagePositionPatient Modality    PatientID  \\\n",

       "0           ['-125.000', '-123.101', '104.307']       CT  ID_b81a287f   \n",

       "1          ['-125', '53.6282216', '223.572015']       CT  ID_400facde   \n",

       "2  ['-125.000000', '-123.646240', '124.321068']       CT  ID_42910d3d   \n",

       "3                  ['-114', '-6', '171.999939']       CT  ID_329aafa7   \n",

       "4                       ['-115', '-1', '230.5']       CT  ID_6b544c3c   \n",

       "\n",

       "  PhotometricInterpretation PixelRepresentation                  PixelSpacing  \\\n",

       "0               MONOCHROME2                   0      ['0.488281', '0.488281']   \n",

       "1               MONOCHROME2                   1  ['0.48828125', '0.48828125']   \n",

       "2               MONOCHROME2                   0      ['0.488281', '0.488281']   \n",

       "3               MONOCHROME2                   1    ['0.4453125', '0.4453125']   \n",

       "4               MONOCHROME2                   1  ['0.44921875', '0.44921875']   \n",

       "\n",

       "  RescaleIntercept  RescaleSlope SOPInstanceUID  SamplesPerPixel  \\\n",

       "0                0           1.0   ID_231d901c1                1   \n",

       "1                0           1.0   ID_994bc0470                1   \n",

       "2                0           1.0   ID_127689cce                1   \n",

       "3                0           1.0   ID_25457734a                1   \n",

       "4                0           1.0   ID_81c9aa125                1   \n",

       "\n",

       "  SeriesInstanceUID  StudyID StudyInstanceUID        WindowCenter  \\\n",

       "0     ID_15dcd6057a      NaN    ID_dd37ba3adb                  40   \n",

       "1     ID_4ba12c2161      NaN    ID_c5277f0c63  ['00047', '00047']   \n",

       "2     ID_c4b4931314      NaN    ID_db93ade25b                  30   \n",

       "3     ID_116558f409      NaN    ID_8dd6d32f3b  ['00036', '00036']   \n",

       "4     ID_f56d7bd0f9      NaN    ID_2685c5d5c0  ['00036', '00036']   \n",

       "\n",

       "          WindowWidth  ImageOrientationPatient_0  ImageOrientationPatient_1  \\\n",

       "0                 100                        1.0                        0.0   \n",

       "1  ['00080', '00080']                        1.0                        0.0   \n",

       "2                  80                        1.0                        0.0   \n",

       "3  ['00080', '00080']                        1.0                        0.0   \n",

       "4  ['00080', '00080']                        1.0                        0.0   \n",

       "\n",

       "   ImageOrientationPatient_2  ImageOrientationPatient_3  \\\n",

       "0                        0.0                        0.0   \n",

       "1                        0.0                        0.0   \n",

       "2                        0.0                        0.0   \n",

       "3                        0.0                        0.0   \n",

       "4                        0.0                        0.0   \n",

       "\n",

       "   ImageOrientationPatient_4  ImageOrientationPatient_5  \\\n",

       "0                   0.984808                  -0.173648   \n",

       "1                   0.933580                  -0.358368   \n",

       "2                   0.972370                  -0.233445   \n",

       "3                   1.000000                   0.000000   \n",

       "4                   1.000000                   0.000000   \n",

       "\n",

       "   ImagePositionPatient_0  ImagePositionPatient_1  ImagePositionPatient_2  \\\n",

       "0                  -125.0             -123.101000              104.307000   \n",

       "1                  -125.0               53.628222              223.572015   \n",

       "2                  -125.0             -123.646240              124.321068   \n",

       "3                  -114.0               -6.000000              171.999939   \n",

       "4                  -115.0               -1.000000              230.500000   \n",

       "\n",

       "   PixelSpacing_0  PixelSpacing_1  WindowCenter_0  WindowCenter_1  \\\n",

       "0        0.488281        0.488281            40.0       38.015255   \n",

       "1        0.488281        0.488281            47.0       47.000000   \n",

       "2        0.488281        0.488281            30.0       38.015255   \n",

       "3        0.445312        0.445312            36.0       36.000000   \n",

       "4        0.449219        0.449219            36.0       36.000000   \n",

       "\n",

       "  WindowCenter_1_NAN  any  epidural  intraparenchymal  intraventricular  \\\n",

       "0                  0    1         0                 0                 0   \n",

       "1                  1    0         0                 0                 0   \n",

       "2                  0    0         0                 0                 0   \n",

       "3                  1    0         0                 0                 0   \n",

       "4                  1    0         0                 0                 0   \n",

       "\n",

       "   subarachnoid  subdural    weight  \n",

       "0             1         0  0.015365  \n",

       "1             0         0  0.001997  \n",

       "2             0         0  0.269672  \n",

       "3             0         0  0.003259  \n",

       "4             0         0  0.005538  "

      ]

     },

     "execution_count": 103,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_md.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 151,

   "metadata": {},

   "outputs": [

    {

     "data": {

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       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

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       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>BitsAllocated</th>\n",

       "      <th>BitsStored</th>\n",

       "      <th>HighBit</th>\n",

       "      <th>ImageOrientationPatient</th>\n",

       "      <th>ImagePositionPatient</th>\n",

       "      <th>Modality</th>\n",

       "      <th>PatientID</th>\n",

       "      <th>PhotometricInterpretation</th>\n",

       "      <th>PixelRepresentation</th>\n",

       "      <th>PixelSpacing</th>\n",

       "      <th>RescaleIntercept</th>\n",

       "      <th>RescaleSlope</th>\n",

       "      <th>SOPInstanceUID</th>\n",

       "      <th>SamplesPerPixel</th>\n",

       "      <th>SeriesInstanceUID</th>\n",

       "      <th>StudyID</th>\n",

       "      <th>StudyInstanceUID</th>\n",

       "      <th>WindowCenter</th>\n",

       "      <th>WindowWidth</th>\n",

       "      <th>ImageOrientationPatient_0</th>\n",

       "      <th>ImageOrientationPatient_1</th>\n",

       "      <th>ImageOrientationPatient_2</th>\n",

       "      <th>ImageOrientationPatient_3</th>\n",

       "      <th>ImageOrientationPatient_4</th>\n",

       "      <th>ImageOrientationPatient_5</th>\n",

       "      <th>ImagePositionPatient_0</th>\n",

       "      <th>ImagePositionPatient_1</th>\n",

       "      <th>ImagePositionPatient_2</th>\n",

       "      <th>PixelSpacing_0</th>\n",

       "      <th>PixelSpacing_1</th>\n",

       "      <th>WindowCenter_0</th>\n",

       "      <th>WindowCenter_1</th>\n",

       "      <th>WindowCenter_1_NAN</th>\n",

       "      <th>any</th>\n",

       "      <th>epidural</th>\n",

       "      <th>intraparenchymal</th>\n",

       "      <th>intraventricular</th>\n",

       "      <th>subarachnoid</th>\n",

       "      <th>subdural</th>\n",

       "      <th>weight</th>\n",

       "      <th>pos_max</th>\n",

       "      <th>pos_min</th>\n",

       "      <th>pos_size</th>\n",

       "      <th>pos_idx</th>\n",

       "      <th>pos_inc</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>6173</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '18.730394']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_fca334534</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.210224</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>0</td>\n",

       "      <td>0.000000</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6174</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '24.123068']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_bb3ca1577</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>24.123068</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

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       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.210186</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>1</td>\n",

       "      <td>5.392674</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6175</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '29.515741']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_78ce791af</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>29.515741</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.212440</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>2</td>\n",

       "      <td>5.392673</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6176</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '34.908417']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_3c9c2db71</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>34.908417</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.201545</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>3</td>\n",

       "      <td>5.392676</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6177</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '40.300396']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_069d12e61</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>40.300396</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.206333</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>4</td>\n",

       "      <td>5.391979</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6178</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '45.693069']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_7bdb10aaa</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",

       "      <td>-0.374607</td>\n",

       "      <td>-125.0</td>\n",

       "      <td>-107.097977</td>\n",

       "      <td>45.693069</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>0.488281</td>\n",

       "      <td>30.0</td>\n",

       "      <td>38.015255</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0.199955</td>\n",

       "      <td>185.898392</td>\n",

       "      <td>18.730394</td>\n",

       "      <td>36</td>\n",

       "      <td>5</td>\n",

       "      <td>5.392673</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6179</th>\n",

       "      <td>16</td>\n",

       "      <td>0</td>\n",

       "      <td>15</td>\n",

       "      <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",

       "      <td>['-125.000000', '-107.097977', '51.085743']</td>\n",

       "      <td>CT</td>\n",

       "      <td>ID_6a88f066</td>\n",

       "      <td>MONOCHROME2</td>\n",

       "      <td>0</td>\n",

       "      <td>['0.488281', '0.488281']</td>\n",

       "      <td>0</td>\n",

       "      <td>1.0</td>\n",

       "      <td>ID_be063f884</td>\n",

       "      <td>1</td>\n",

       "      <td>ID_025d684f04</td>\n",

       "      <td>NaN</td>\n",

       "      <td>ID_75eaa2e6de</td>\n",

       "      <td>30</td>\n",

       "      <td>80</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.927184</td>\n",