4-L2-train-and-submit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'sm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from fastai.data_block import get_files\n",
    "import pickle\n",
    "import torch\n",
    "import numpy as np, pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class defaultlist(list):\n",
    "    def __init__(self, fx):\n",
    "        self._fx = fx\n",
    "    def _fill(self, index):\n",
    "        while len(self) <= index:\n",
    "            self.append(self._fx())\n",
    "    def __setitem__(self, index, value):\n",
    "        self._fill(index)\n",
    "        list.__setitem__(self, index, value)\n",
    "    def __getitem__(self, index):\n",
    "        self._fill(index)\n",
    "        return list.__getitem__(self, index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path('data/predictions')\n",
    "base_classes = [ 'any', 'epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', 'subdural' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stage = \"stage_2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_to_study_ix = pickle.load(open(f'data/{stage}_fn_to_study_ix.pickle', 'rb'))\n",
    "study_ix_to_fn = pickle.load(open(f'data/{stage}_study_ix_to_fn.pickle', 'rb'))\n",
    "study_to_data = pickle.load(open(f'data/{stage}_study_to_data.pickle', 'rb'))\n",
    "df = pd.read_csv(f\"data/{stage}_train_dicom_diags_norm.csv\")\n",
    "df = df.set_index(['fid'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_df = df.loc[:,'any':'subdural']\n",
    "labels_df.index.name = 'fn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0403_subdural_loss_fold3_of_5',\n",
       "  'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0399_subdural_loss_fold4_of_5',\n",
       "  'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0386_subdural_loss_fold5_of_5',\n",
       "  'data/predictions/densenet121_sz512_cv0.0749_weighted_loss_fold1_of_5',\n",
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       "  'data/predictions/densenet121_sz512_cv0.0739_weighted_loss_fold5_of_5',\n",
       "  'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0375_subdural_loss_fold1_of_5',\n",
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       "  'data/predictions/resnet101_sz512_cv0.0749_weighted_loss_fold5_of_5',\n",
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       "  'data/predictions/resnet34_sz512_cv0.0759_weighted_loss_fold5_of_5',\n",
       "  'data/predictions/resnet50_sz512_cv0.0761_weighted_loss_fold1_of_5',\n",
       "  'data/predictions/resnet50_sz512_cv0.0754_weighted_loss_fold2_of_5',\n",
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       "  'data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold5_of_5',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0387_subdural_loss_fold1_of_5',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0378_subdural_loss_fold2_of_5',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0381_subdural_loss_fold3_of_5',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0376_subdural_loss_fold4_of_5',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0372_subdural_loss_fold5_of_5'],\n",
       " ['data/predictions/densenet121',\n",
       "  'data/predictions/densenet121-53_train_cpu_window_uint8',\n",
       "  'data/predictions/resnet101',\n",
       "  'data/predictions/resnet101-53_train_cpu_window_uint8',\n",
       "  'data/predictions/resnet18',\n",
       "  'data/predictions/resnet18-53_train_cpu_window_uint8',\n",
       "  'data/predictions/resnet34',\n",
       "  'data/predictions/resnet34-53_train_cpu_window_uint8',\n",
       "  'data/predictions/resnet50',\n",
       "  'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csvs = sorted([str(fn) for fn in get_files(data_dir, extensions='.csv')])\n",
    "csv = csvs[0]\n",
    "def where_test(csv):\n",
    "    res = csv.find('_test') \n",
    "    return res if res!= -1 else csv.find('_valid') \n",
    "where_fold = lambda csv: csv.index('fold')\n",
    "where_stem = lambda csv: len(csv)-csv[::-1].index('/')\n",
    "where_sz   = lambda csv: csv.index('_sz')\n",
    "model_fns = sorted(np.unique([csv[:where_test(csv)] for csv in csvs]),\n",
    "                   key=lambda x:x[where_stem(x):where_sz(x)]+x[where_fold(x):where_test(x)])\n",
    "archs     = np.unique([model_fn[:where_sz(model_fn)] for model_fn in model_fns]).tolist()\n",
    "n_folds = len(model_fns)//len(archs)\n",
    "model_fns, archs, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 data/predictions/densenet121_sz512_cv0.0749_weighted_loss_fold1_of_5\n",
      "1 data/predictions/densenet121_sz512_cv0.0743_weighted_loss_fold2_of_5\n",
      "2 data/predictions/densenet121_sz512_cv0.0748_weighted_loss_fold3_of_5\n",
      "3 data/predictions/densenet121_sz512_cv0.0738_weighted_loss_fold4_of_5\n",
      "4 data/predictions/densenet121_sz512_cv0.0739_weighted_loss_fold5_of_5\n",
      "0 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0413_subdural_loss_fold1_of_5\n",
      "1 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0406_subdural_loss_fold2_of_5\n",
      "2 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0403_subdural_loss_fold3_of_5\n",
      "3 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0399_subdural_loss_fold4_of_5\n",
      "4 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0386_subdural_loss_fold5_of_5\n",
      "0 data/predictions/resnet101_sz512_cv0.0758_weighted_loss_fold1_of_5\n",
      "1 data/predictions/resnet101_sz512_cv0.0754_weighted_loss_fold2_of_5\n",
      "2 data/predictions/resnet101_sz512_cv0.0761_weighted_loss_fold3_of_5\n",
      "3 data/predictions/resnet101_sz512_cv0.0751_weighted_loss_fold4_of_5\n",
      "4 data/predictions/resnet101_sz512_cv0.0749_weighted_loss_fold5_of_5\n",
      "0 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0375_subdural_loss_fold1_of_5\n",
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      "4 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0365_subdural_loss_fold5_of_5\n",
      "0 data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold1_of_5\n",
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      "2 data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold3_of_5\n",
      "3 data/predictions/resnet18_sz512_cv0.0762_weighted_loss_fold4_of_5\n",
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      "0 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0556_subdural_loss_fold1_of_5\n",
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      "2 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0528_subdural_loss_fold3_of_5\n",
      "3 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0530_subdural_loss_fold4_of_5\n",
      "4 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0522_subdural_loss_fold5_of_5\n",
      "0 data/predictions/resnet34_sz512_cv0.0765_weighted_loss_fold1_of_5\n",
      "1 data/predictions/resnet34_sz512_cv0.0763_weighted_loss_fold2_of_5\n",
      "2 data/predictions/resnet34_sz512_cv0.0782_weighted_loss_fold3_of_5\n",
      "3 data/predictions/resnet34_sz512_cv0.0750_weighted_loss_fold4_of_5\n",
      "4 data/predictions/resnet34_sz512_cv0.0759_weighted_loss_fold5_of_5\n",
      "0 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0496_subdural_loss_fold1_of_5\n",
      "1 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0484_subdural_loss_fold2_of_5\n",
      "2 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0474_subdural_loss_fold3_of_5\n",
      "3 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0466_subdural_loss_fold4_of_5\n",
      "4 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0453_subdural_loss_fold5_of_5\n",
      "0 data/predictions/resnet50_sz512_cv0.0761_weighted_loss_fold1_of_5\n",
      "1 data/predictions/resnet50_sz512_cv0.0754_weighted_loss_fold2_of_5\n",
      "2 data/predictions/resnet50_sz512_cv0.0764_weighted_loss_fold3_of_5\n",
      "3 data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold4_of_5\n",
      "4 data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold5_of_5\n",
      "0 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0387_subdural_loss_fold1_of_5\n",
      "1 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0378_subdural_loss_fold2_of_5\n",
      "2 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0381_subdural_loss_fold3_of_5\n",
      "3 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0376_subdural_loss_fold4_of_5\n",
      "4 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0372_subdural_loss_fold5_of_5\n"
     ]
    }
   ],
   "source": [
    "arch_names = [arch[where_stem(arch):] for arch in archs]\n",
    "test_folds_dfs, train_arch_dfs = defaultlist(list),[]\n",
    "for arch in archs:\n",
    "    train_model_dfs = []\n",
    "    _arch = arch[where_stem(arch):]\n",
    "    for i,model_fn in enumerate([model for model in model_fns if model.find(arch+\"_\")!=-1]):\n",
    "        print(i,model_fn)\n",
    "        t_df = pd.read_csv(model_fn + '_valid_fns.csv')\n",
    "        t = torch.load(model_fn + '_valid.pth')\n",
    "        t_mean, t_std = t.mean(dim=0), t.std(dim=0)\n",
    "        train_model_dfs.append(pd.DataFrame({**{'fn' : t_df.values.squeeze(-1),},\n",
    "                      **{f'X{_arch}_{c}_mean' : t_mean[:,6+i].numpy() for i,c in enumerate(base_classes)},\n",
    "                      **{f'X{_arch}_{c}_std'  :  t_std[:,6+i].numpy() for i,c in enumerate(base_classes)}}))\n",
    "        \n",
    "        t_df = pd.read_csv(model_fn + '_test_fns.csv')\n",
    "        t = torch.load(model_fn + '_test.pth')\n",
    "        t_mean, t_std = t.mean(dim=0), t.std(dim=0)\n",
    "        test_folds_dfs[i].append(pd.DataFrame({**{'fn' : t_df.values.squeeze(-1),},\n",
    "                      **{f'X{_arch}_{c}_mean' : t_mean[:,6+i].numpy() for i,c in enumerate(base_classes)},\n",
    "                      **{f'X{_arch}_{c}_std'  :  t_std[:,6+i].numpy() for i,c in enumerate(base_classes)}}))\n",
    "\n",
    "    train_arch_dfs.append(pd.concat(train_model_dfs, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tta_df = train_arch_dfs[0]\n",
    "for arch_df in train_arch_dfs[1:]:\n",
    "    tta_df = tta_df.merge(arch_df,on='fn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tta_df = tta_df.merge(labels_df,on='fn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_std</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_std</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0000950d7</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.602864e-06</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>2.093691e-06</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000002</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_0000aee4b</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.363618e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.035392e-07</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000225</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_0001de0e8</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>8.438517e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1.205952e-05</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>0.000090</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0002003a8</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>8.040458e-07</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>3.861410e-04</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000947</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000229f2a</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>1.584314e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001312</td>\n",
       "      <td>1.682060e-06</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000120</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0000950d7               0.000216                    0.000009   \n",
       "1  ID_0000aee4b               0.000652                    0.000132   \n",
       "2  ID_0001de0e8               0.000875                    0.000079   \n",
       "3  ID_0002003a8               0.001583                    0.000008   \n",
       "4  ID_000229f2a               0.000251                    0.000041   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.000040                            0.000017   \n",
       "1                            0.000049                            0.000030   \n",
       "2                            0.000283                            0.000013   \n",
       "3                            0.000745                            0.000176   \n",
       "4                            0.000084                            0.000039   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.000131                    0.000070   \n",
       "1                        0.000237                    0.000415   \n",
       "2                        0.000218                    0.000636   \n",
       "3                        0.000233                    0.000478   \n",
       "4                        0.000162                    0.000084   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.000077               2.602864e-06   \n",
       "1              0.000478               7.363618e-05   \n",
       "2              0.001155               8.438517e-05   \n",
       "3              0.000657               8.040458e-07   \n",
       "4              0.000072               1.584314e-05   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  \\\n",
       "0                           0.000013  ...   \n",
       "1                           0.000018  ...   \n",
       "2                           0.000151  ...   \n",
       "3                           0.000355  ...   \n",
       "4                           0.000014  ...   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_std  \\\n",
       "0                                           0.000009                 \n",
       "1                                           0.000003                 \n",
       "2                                           0.000247                 \n",
       "3                                           0.000542                 \n",
       "4                                           0.001312                 \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_std  \\\n",
       "0                                       2.093691e-06                 \n",
       "1                                       4.035392e-07                 \n",
       "2                                       1.205952e-05                 \n",
       "3                                       3.861410e-04                 \n",
       "4                                       1.682060e-06                 \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std  \\\n",
       "0                                           0.000017             \n",
       "1                                           0.000253             \n",
       "2                                           0.001305             \n",
       "3                                           0.000488             \n",
       "4                                           0.000491             \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std  any  epidural  \\\n",
       "0                                           0.000002          0         0   \n",
       "1                                           0.000225          0         0   \n",
       "2                                           0.000090          0         0   \n",
       "3                                           0.000947          0         0   \n",
       "4                                           0.000120          0         0   \n",
       "\n",
       "   intraparenchymal  intraventricular  subarachnoid  subdural  \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",
       "[5 rows x 127 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(752802, 752802)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tta_df), len(labels_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Begin boosting data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std</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>study</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0000950d7</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.602864e-06</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000002</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>ID_84296c3845</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_0000aee4b</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.363618e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000225</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>ID_1e59488a44</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_0001de0e8</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>8.438517e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>0.000090</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>ID_9d97cf289b</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0002003a8</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>8.040458e-07</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000947</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>ID_805824f65e</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000229f2a</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>1.584314e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000120</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>ID_f54eba3225</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0000950d7               0.000216                    0.000009   \n",
       "1  ID_0000aee4b               0.000652                    0.000132   \n",
       "2  ID_0001de0e8               0.000875                    0.000079   \n",
       "3  ID_0002003a8               0.001583                    0.000008   \n",
       "4  ID_000229f2a               0.000251                    0.000041   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.000040                            0.000017   \n",
       "1                            0.000049                            0.000030   \n",
       "2                            0.000283                            0.000013   \n",
       "3                            0.000745                            0.000176   \n",
       "4                            0.000084                            0.000039   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.000131                    0.000070   \n",
       "1                        0.000237                    0.000415   \n",
       "2                        0.000218                    0.000636   \n",
       "3                        0.000233                    0.000478   \n",
       "4                        0.000162                    0.000084   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.000077               2.602864e-06   \n",
       "1              0.000478               7.363618e-05   \n",
       "2              0.001155               8.438517e-05   \n",
       "3              0.000657               8.040458e-07   \n",
       "4              0.000072               1.584314e-05   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  \\\n",
       "0                           0.000013  ...   \n",
       "1                           0.000018  ...   \n",
       "2                           0.000151  ...   \n",
       "3                           0.000355  ...   \n",
       "4                           0.000014  ...   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std  \\\n",
       "0                                           0.000017             \n",
       "1                                           0.000253             \n",
       "2                                           0.001305             \n",
       "3                                           0.000488             \n",
       "4                                           0.000491             \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std  any  epidural  \\\n",
       "0                                           0.000002          0         0   \n",
       "1                                           0.000225          0         0   \n",
       "2                                           0.000090          0         0   \n",
       "3                                           0.000947          0         0   \n",
       "4                                           0.000120          0         0   \n",
       "\n",
       "   intraparenchymal  intraventricular  subarachnoid  subdural          study  \\\n",
       "0                 0                 0             0         0  ID_84296c3845   \n",
       "1                 0                 0             0         0  ID_1e59488a44   \n",
       "2                 0                 0             0         0  ID_9d97cf289b   \n",
       "3                 0                 0             0         0  ID_805824f65e   \n",
       "4                 0                 0             0         0  ID_f54eba3225   \n",
       "\n",
       "    z  \n",
       "0  32  \n",
       "1   7  \n",
       "2  22  \n",
       "3  17  \n",
       "4  30  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add study and z position to dataframe to join\n",
    "tta_df['study'] = tta_df.fn.apply(lambda fn: fn_to_study_ix[fn][0] )\n",
    "tta_df['z'] = tta_df.fn.apply(lambda fn: int(fn_to_study_ix[fn][1]) )\n",
    "tta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnet50_intraparenchymal_mean-1</th>\n",
       "      <th>Xresnet50_intraventricular_mean-1</th>\n",
       "      <th>Xresnet50_subarachnoid_mean-1</th>\n",
       "      <th>Xresnet50_subdural_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean-1</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0000950d7</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.602864e-06</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.410099e-07</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_0000aee4b</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.363618e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>2.871577e-05</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_0001de0e8</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>8.438517e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>4.940505e-05</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.002983</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0002003a8</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>8.040458e-07</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>2.354449e-05</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000229f2a</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>1.584314e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>1.064072e-05</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0000950d7               0.000216                    0.000009   \n",
       "1  ID_0000aee4b               0.000652                    0.000132   \n",
       "2  ID_0001de0e8               0.000875                    0.000079   \n",
       "3  ID_0002003a8               0.001583                    0.000008   \n",
       "4  ID_000229f2a               0.000251                    0.000041   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.000040                            0.000017   \n",
       "1                            0.000049                            0.000030   \n",
       "2                            0.000283                            0.000013   \n",
       "3                            0.000745                            0.000176   \n",
       "4                            0.000084                            0.000039   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.000131                    0.000070   \n",
       "1                        0.000237                    0.000415   \n",
       "2                        0.000218                    0.000636   \n",
       "3                        0.000233                    0.000478   \n",
       "4                        0.000162                    0.000084   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.000077               2.602864e-06   \n",
       "1              0.000478               7.363618e-05   \n",
       "2              0.001155               8.438517e-05   \n",
       "3              0.000657               8.040458e-07   \n",
       "4              0.000072               1.584314e-05   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  Xresnet50_intraparenchymal_mean-1  \\\n",
       "0                           0.000013  ...                           0.000003   \n",
       "1                           0.000018  ...                           0.000032   \n",
       "2                           0.000151  ...                           0.000386   \n",
       "3                           0.000355  ...                           0.000253   \n",
       "4                           0.000014  ...                           0.000087   \n",
       "\n",
       "   Xresnet50_intraventricular_mean-1  Xresnet50_subarachnoid_mean-1  \\\n",
       "0                       2.410099e-07                       0.000009   \n",
       "1                       2.871577e-05                       0.000192   \n",
       "2                       4.940505e-05                       0.001212   \n",
       "3                       2.354449e-05                       0.000079   \n",
       "4                       1.064072e-05                       0.000406   \n",
       "\n",
       "   Xresnet50_subdural_mean-1  \\\n",
       "0                   0.000010   \n",
       "1                   0.000307   \n",
       "2                   0.001620   \n",
       "3                   0.000145   \n",
       "4                   0.000361   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean-1  \\\n",
       "0                                           0.000023       \n",
       "1                                           0.000320       \n",
       "2                                           0.002983       \n",
       "3                                           0.000816       \n",
       "4                                           0.000489       \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean-1  \\\n",
       "0                                           0.000012            \n",
       "1                                           0.000031            \n",
       "2                                           0.000075            \n",
       "3                                           0.000072            \n",
       "4                                           0.000057            \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean-1  \\\n",
       "0                                           0.000041                    \n",
       "1                                           0.000005                    \n",
       "2                                           0.000413                    \n",
       "3                                           0.000268                    \n",
       "4                                           0.000044                    \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean-1  \\\n",
       "0                                           0.000015                    \n",
       "1                                           0.000004                    \n",
       "2                                           0.000028                    \n",
       "3                                           0.000263                    \n",
       "4                                           0.000004                    \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean-1  \\\n",
       "0                                           0.000040                \n",
       "1                                           0.000129                \n",
       "2                                           0.001168                \n",
       "3                                           0.000419                \n",
       "4                                           0.000202                \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean-1  \n",
       "0                                           0.000004           \n",
       "1                                           0.000094           \n",
       "2                                           0.000841           \n",
       "3                                           0.000390           \n",
       "4                                           0.000139           \n",
       "\n",
       "[5 rows x 251 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now join top and bottom\n",
    "tta_df['z+'] = tta_df.z+1\n",
    "tta_df['z-'] = tta_df.z-1\n",
    "#only merge means to avoid too many columns\n",
    "merge_cols = [c for c in tta_df.columns if 'mean' in c] \n",
    "merged_df = pd.merge(tta_df, tta_df[merge_cols+ ['study','z+']], how='left', left_on=['study', 'z'], right_on=['study','z+'], suffixes=('','+1'))\n",
    "merged_df = pd.merge(merged_df, tta_df[merge_cols+ ['study','z-']], how='left', left_on=['study', 'z'], right_on=['study','z-'], suffixes=('','-1'))\n",
    "merged_df.drop(['z++1','z--1'], axis=1, inplace=True) #after merge these columns are not needed\n",
    "len(merged_df)\n",
    "merged_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnet50_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnet50_intraventricular_mean_max</th>\n",
       "      <th>Xresnet50_subarachnoid_mean_max</th>\n",
       "      <th>Xresnet50_subdural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0000950d7</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.602864e-06</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486492</td>\n",
       "      <td>0.905775</td>\n",
       "      <td>0.150154</td>\n",
       "      <td>0.099021</td>\n",
       "      <td>0.857974</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>0.361130</td>\n",
       "      <td>0.789858</td>\n",
       "      <td>0.168123</td>\n",
       "      <td>0.073204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_0000aee4b</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.363618e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>0.006184</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>0.027332</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.009463</td>\n",
       "      <td>0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_0001de0e8</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>8.438517e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.003913</td>\n",
       "      <td>0.004683</td>\n",
       "      <td>0.005613</td>\n",
       "      <td>0.018091</td>\n",
       "      <td>0.002842</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>0.009165</td>\n",
       "      <td>0.005672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0002003a8</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>8.040458e-07</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.002354</td>\n",
       "      <td>0.021156</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>0.028012</td>\n",
       "      <td>0.004112</td>\n",
       "      <td>0.020228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000229f2a</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>1.584314e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.059925</td>\n",
       "      <td>0.010892</td>\n",
       "      <td>0.015900</td>\n",
       "      <td>0.042269</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.012257</td>\n",
       "      <td>0.029033</td>\n",
       "      <td>0.014108</td>\n",
       "      <td>0.011063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 311 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0000950d7               0.000216                    0.000009   \n",
       "1  ID_0000aee4b               0.000652                    0.000132   \n",
       "2  ID_0001de0e8               0.000875                    0.000079   \n",
       "3  ID_0002003a8               0.001583                    0.000008   \n",
       "4  ID_000229f2a               0.000251                    0.000041   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.000040                            0.000017   \n",
       "1                            0.000049                            0.000030   \n",
       "2                            0.000283                            0.000013   \n",
       "3                            0.000745                            0.000176   \n",
       "4                            0.000084                            0.000039   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.000131                    0.000070   \n",
       "1                        0.000237                    0.000415   \n",
       "2                        0.000218                    0.000636   \n",
       "3                        0.000233                    0.000478   \n",
       "4                        0.000162                    0.000084   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.000077               2.602864e-06   \n",
       "1              0.000478               7.363618e-05   \n",
       "2              0.001155               8.438517e-05   \n",
       "3              0.000657               8.040458e-07   \n",
       "4              0.000072               1.584314e-05   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  \\\n",
       "0                           0.000013  ...   \n",
       "1                           0.000018  ...   \n",
       "2                           0.000151  ...   \n",
       "3                           0.000355  ...   \n",
       "4                           0.000014  ...   \n",
       "\n",
       "   Xresnet50_intraparenchymal_mean_max  Xresnet50_intraventricular_mean_max  \\\n",
       "0                             0.486492                             0.905775   \n",
       "1                             0.002535                             0.002878   \n",
       "2                             0.002166                             0.003913   \n",
       "3                             0.001896                             0.001544   \n",
       "4                             0.011392                             0.059925   \n",
       "\n",
       "   Xresnet50_subarachnoid_mean_max  Xresnet50_subdural_mean_max  \\\n",
       "0                         0.150154                     0.099021   \n",
       "1                         0.006184                     0.003424   \n",
       "2                         0.004683                     0.005613   \n",
       "3                         0.004229                     0.002354   \n",
       "4                         0.010892                     0.015900   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max  \\\n",
       "0                                           0.857974         \n",
       "1                                           0.027332         \n",
       "2                                           0.018091         \n",
       "3                                           0.021156         \n",
       "4                                           0.042269         \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max  \\\n",
       "0                                           0.001780              \n",
       "1                                           0.000609              \n",
       "2                                           0.002842              \n",
       "3                                           0.000476              \n",
       "4                                           0.001678              \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max  \\\n",
       "0                                           0.361130                      \n",
       "1                                           0.003878                      \n",
       "2                                           0.012398                      \n",
       "3                                           0.005249                      \n",
       "4                                           0.012257                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max  \\\n",
       "0                                           0.789858                      \n",
       "1                                           0.008006                      \n",
       "2                                           0.009452                      \n",
       "3                                           0.028012                      \n",
       "4                                           0.029033                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max  \\\n",
       "0                                           0.168123                  \n",
       "1                                           0.009463                  \n",
       "2                                           0.009165                  \n",
       "3                                           0.004112                  \n",
       "4                                           0.014108                  \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max  \n",
       "0                                           0.073204             \n",
       "1                                           0.006090             \n",
       "2                                           0.005672             \n",
       "3                                           0.020228             \n",
       "4                                           0.011063             \n",
       "\n",
       "[5 rows x 311 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now add maximum value for each merge_col by study\n",
    "group_df = merged_df[merge_cols+['study']].groupby('study').agg('max')\n",
    "merged_df = pd.merge(merged_df, group_df, how='left', on='study', suffixes=('','_max'))\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnet50_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnet50_intraventricular_mean_max</th>\n",
       "      <th>Xresnet50_subarachnoid_mean_max</th>\n",
       "      <th>Xresnet50_subdural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0000950d7</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.602864e-06</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486492</td>\n",
       "      <td>0.905775</td>\n",
       "      <td>0.150154</td>\n",
       "      <td>0.099021</td>\n",
       "      <td>0.857974</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>0.361130</td>\n",
       "      <td>0.789858</td>\n",
       "      <td>0.168123</td>\n",
       "      <td>0.073204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_0000aee4b</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.363618e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>0.006184</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>0.027332</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.009463</td>\n",
       "      <td>0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_0001de0e8</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>8.438517e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.003913</td>\n",
       "      <td>0.004683</td>\n",
       "      <td>0.005613</td>\n",
       "      <td>0.018091</td>\n",
       "      <td>0.002842</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>0.009165</td>\n",
       "      <td>0.005672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0002003a8</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>8.040458e-07</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.002354</td>\n",
       "      <td>0.021156</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>0.028012</td>\n",
       "      <td>0.004112</td>\n",
       "      <td>0.020228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000229f2a</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>1.584314e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.059925</td>\n",
       "      <td>0.010892</td>\n",
       "      <td>0.015900</td>\n",
       "      <td>0.042269</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.012257</td>\n",
       "      <td>0.029033</td>\n",
       "      <td>0.014108</td>\n",
       "      <td>0.011063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 311 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0000950d7               0.000216                    0.000009   \n",
       "1  ID_0000aee4b               0.000652                    0.000132   \n",
       "2  ID_0001de0e8               0.000875                    0.000079   \n",
       "3  ID_0002003a8               0.001583                    0.000008   \n",
       "4  ID_000229f2a               0.000251                    0.000041   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.000040                            0.000017   \n",
       "1                            0.000049                            0.000030   \n",
       "2                            0.000283                            0.000013   \n",
       "3                            0.000745                            0.000176   \n",
       "4                            0.000084                            0.000039   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.000131                    0.000070   \n",
       "1                        0.000237                    0.000415   \n",
       "2                        0.000218                    0.000636   \n",
       "3                        0.000233                    0.000478   \n",
       "4                        0.000162                    0.000084   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.000077               2.602864e-06   \n",
       "1              0.000478               7.363618e-05   \n",
       "2              0.001155               8.438517e-05   \n",
       "3              0.000657               8.040458e-07   \n",
       "4              0.000072               1.584314e-05   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  \\\n",
       "0                           0.000013  ...   \n",
       "1                           0.000018  ...   \n",
       "2                           0.000151  ...   \n",
       "3                           0.000355  ...   \n",
       "4                           0.000014  ...   \n",
       "\n",
       "   Xresnet50_intraparenchymal_mean_max  Xresnet50_intraventricular_mean_max  \\\n",
       "0                             0.486492                             0.905775   \n",
       "1                             0.002535                             0.002878   \n",
       "2                             0.002166                             0.003913   \n",
       "3                             0.001896                             0.001544   \n",
       "4                             0.011392                             0.059925   \n",
       "\n",
       "   Xresnet50_subarachnoid_mean_max  Xresnet50_subdural_mean_max  \\\n",
       "0                         0.150154                     0.099021   \n",
       "1                         0.006184                     0.003424   \n",
       "2                         0.004683                     0.005613   \n",
       "3                         0.004229                     0.002354   \n",
       "4                         0.010892                     0.015900   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max  \\\n",
       "0                                           0.857974         \n",
       "1                                           0.027332         \n",
       "2                                           0.018091         \n",
       "3                                           0.021156         \n",
       "4                                           0.042269         \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max  \\\n",
       "0                                           0.001780              \n",
       "1                                           0.000609              \n",
       "2                                           0.002842              \n",
       "3                                           0.000476              \n",
       "4                                           0.001678              \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max  \\\n",
       "0                                           0.361130                      \n",
       "1                                           0.003878                      \n",
       "2                                           0.012398                      \n",
       "3                                           0.005249                      \n",
       "4                                           0.012257                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max  \\\n",
       "0                                           0.789858                      \n",
       "1                                           0.008006                      \n",
       "2                                           0.009452                      \n",
       "3                                           0.028012                      \n",
       "4                                           0.029033                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max  \\\n",
       "0                                           0.168123                  \n",
       "1                                           0.009463                  \n",
       "2                                           0.009165                  \n",
       "3                                           0.004112                  \n",
       "4                                           0.014108                  \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max  \n",
       "0                                           0.073204             \n",
       "1                                           0.006090             \n",
       "2                                           0.005672             \n",
       "3                                           0.020228             \n",
       "4                                           0.011063             \n",
       "\n",
       "[5 rows x 311 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#manage NANs created by merging\n",
    "merged_cols = [c for c in merged_df.columns if c.endswith('1')]\n",
    "\n",
    "for c in merged_cols:\n",
    "    merged_df[c].fillna(merged_df[c.replace('-1', '').replace('+1', '')], inplace=True)\n",
    "    \n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     40785\n",
       "0.0     40527\n",
       "3.0     40439\n",
       "1.0     40068\n",
       "5.0     39983\n",
       "4.0     39953\n",
       "11.0    39882\n",
       "6.0     39779\n",
       "9.0     39644\n",
       "7.0     39625\n",
       "10.0    39378\n",
       "8.0     39360\n",
       "16.0    39355\n",
       "13.0    39301\n",
       "12.0    39183\n",
       "17.0    39136\n",
       "15.0    38904\n",
       "14.0    38781\n",
       "18.0    38719\n",
       "Name: fold, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add fold for training\n",
    "merged_df['fold'] = merged_df.study.apply(lambda s: study_to_data[s]['fold'])\n",
    "merged_df.fold.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare test data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Xdensenet121_any_mean',\n",
       " 'Xdensenet121_epidural_mean',\n",
       " 'Xdensenet121_intraparenchymal_mean',\n",
       " 'Xdensenet121_intraventricular_mean',\n",
       " 'Xdensenet121_subarachnoid_mean',\n",
       " 'Xdensenet121_subdural_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_any_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_epidural_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_intraparenchymal_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_intraventricular_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_subarachnoid_mean',\n",
       " 'Xdensenet121-53_train_cpu_window_uint8_subdural_mean',\n",
       " 'Xresnet101_any_mean',\n",
       " 'Xresnet101_epidural_mean',\n",
       " 'Xresnet101_intraparenchymal_mean',\n",
       " 'Xresnet101_intraventricular_mean',\n",
       " 'Xresnet101_subarachnoid_mean',\n",
       " 'Xresnet101_subdural_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_any_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_epidural_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_intraparenchymal_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_intraventricular_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_subarachnoid_mean',\n",
       " 'Xresnet101-53_train_cpu_window_uint8_subdural_mean',\n",
       " 'Xresnet18_any_mean',\n",
       " 'Xresnet18_epidural_mean',\n",
       " 'Xresnet18_intraparenchymal_mean',\n",
       " 'Xresnet18_intraventricular_mean',\n",
       " 'Xresnet18_subarachnoid_mean',\n",
       " 'Xresnet18_subdural_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_any_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_epidural_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_intraparenchymal_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_intraventricular_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_subarachnoid_mean',\n",
       " 'Xresnet18-53_train_cpu_window_uint8_subdural_mean',\n",
       " 'Xresnet34_any_mean',\n",
       " 'Xresnet34_epidural_mean',\n",
       " 'Xresnet34_intraparenchymal_mean',\n",
       " 'Xresnet34_intraventricular_mean',\n",
       " 'Xresnet34_subarachnoid_mean',\n",
       " 'Xresnet34_subdural_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_any_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_epidural_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_intraparenchymal_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_intraventricular_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_subarachnoid_mean',\n",
       " 'Xresnet34-53_train_cpu_window_uint8_subdural_mean',\n",
       " 'Xresnet50_any_mean',\n",
       " 'Xresnet50_epidural_mean',\n",
       " 'Xresnet50_intraparenchymal_mean',\n",
       " 'Xresnet50_intraventricular_mean',\n",
       " 'Xresnet50_subarachnoid_mean',\n",
       " 'Xresnet50_subdural_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean',\n",
       " 'Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>fn</th>\n",
       "      <th>Xdensenet121_any_mean</th>\n",
       "      <th>Xdensenet121_epidural_mean</th>\n",
       "      <th>Xdensenet121_intraparenchymal_mean</th>\n",
       "      <th>Xdensenet121_intraventricular_mean</th>\n",
       "      <th>Xdensenet121_subarachnoid_mean</th>\n",
       "      <th>Xdensenet121_subdural_mean</th>\n",
       "      <th>Xdensenet121_any_std</th>\n",
       "      <th>Xdensenet121_epidural_std</th>\n",
       "      <th>Xdensenet121_intraparenchymal_std</th>\n",
       "      <th>...</th>\n",
       "      <th>Xresnet50_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnet50_intraventricular_mean_max</th>\n",
       "      <th>Xresnet50_subarachnoid_mean_max</th>\n",
       "      <th>Xresnet50_subdural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n",
       "      <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_0fbf6a978</td>\n",
       "      <td>0.652952</td>\n",
       "      <td>0.064774</td>\n",
       "      <td>0.077681</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.292178</td>\n",
       "      <td>0.338067</td>\n",
       "      <td>0.096585</td>\n",
       "      <td>3.203979e-02</td>\n",
       "      <td>0.054162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996345</td>\n",
       "      <td>0.973147</td>\n",
       "      <td>0.959216</td>\n",
       "      <td>0.376672</td>\n",
       "      <td>0.998088</td>\n",
       "      <td>0.032759</td>\n",
       "      <td>0.990556</td>\n",
       "      <td>0.993277</td>\n",
       "      <td>0.982379</td>\n",
       "      <td>0.296742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_d62ec3412</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>3.052288e-06</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005606</td>\n",
       "      <td>0.011685</td>\n",
       "      <td>0.056988</td>\n",
       "      <td>0.012356</td>\n",
       "      <td>0.180219</td>\n",
       "      <td>0.004658</td>\n",
       "      <td>0.034183</td>\n",
       "      <td>0.070213</td>\n",
       "      <td>0.048766</td>\n",
       "      <td>0.028650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_cb544194b</td>\n",
       "      <td>0.013207</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.008092</td>\n",
       "      <td>0.005803</td>\n",
       "      <td>3.079419e-05</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.001944</td>\n",
       "      <td>0.181010</td>\n",
       "      <td>0.168275</td>\n",
       "      <td>0.316041</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.028160</td>\n",
       "      <td>0.006408</td>\n",
       "      <td>0.157353</td>\n",
       "      <td>0.208944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_0d62513ec</td>\n",
       "      <td>0.004513</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.002964</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>5.030724e-06</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.014528</td>\n",
       "      <td>0.013856</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.003476</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>0.016037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_fc45b2151</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>3.315683e-07</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.004180</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.019404</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.010232</td>\n",
       "      <td>0.004891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 305 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             fn  Xdensenet121_any_mean  Xdensenet121_epidural_mean  \\\n",
       "0  ID_0fbf6a978               0.652952                    0.064774   \n",
       "1  ID_d62ec3412               0.000902                    0.000007   \n",
       "2  ID_cb544194b               0.013207                    0.000106   \n",
       "3  ID_0d62513ec               0.004513                    0.000018   \n",
       "4  ID_fc45b2151               0.000032                    0.000001   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_mean  Xdensenet121_intraventricular_mean  \\\n",
       "0                            0.077681                            0.005225   \n",
       "1                            0.000197                            0.000157   \n",
       "2                            0.000226                            0.000060   \n",
       "3                            0.000599                            0.000379   \n",
       "4                            0.000014                            0.000009   \n",
       "\n",
       "   Xdensenet121_subarachnoid_mean  Xdensenet121_subdural_mean  \\\n",
       "0                        0.292178                    0.338067   \n",
       "1                        0.000337                    0.000279   \n",
       "2                        0.003247                    0.008092   \n",
       "3                        0.001197                    0.002964   \n",
       "4                        0.000016                    0.000031   \n",
       "\n",
       "   Xdensenet121_any_std  Xdensenet121_epidural_std  \\\n",
       "0              0.096585               3.203979e-02   \n",
       "1              0.000446               3.052288e-06   \n",
       "2              0.005803               3.079419e-05   \n",
       "3              0.001034               5.030724e-06   \n",
       "4              0.000014               3.315683e-07   \n",
       "\n",
       "   Xdensenet121_intraparenchymal_std  ...  \\\n",
       "0                           0.054162  ...   \n",
       "1                           0.000050  ...   \n",
       "2                           0.000056  ...   \n",
       "3                           0.000253  ...   \n",
       "4                           0.000004  ...   \n",
       "\n",
       "   Xresnet50_intraparenchymal_mean_max  Xresnet50_intraventricular_mean_max  \\\n",
       "0                             0.996345                             0.973147   \n",
       "1                             0.005606                             0.011685   \n",
       "2                             0.003033                             0.001944   \n",
       "3                             0.001040                             0.001171   \n",
       "4                             0.001387                             0.001326   \n",
       "\n",
       "   Xresnet50_subarachnoid_mean_max  Xresnet50_subdural_mean_max  \\\n",
       "0                         0.959216                     0.376672   \n",
       "1                         0.056988                     0.012356   \n",
       "2                         0.181010                     0.168275   \n",
       "3                         0.003586                     0.014528   \n",
       "4                         0.004180                     0.003761   \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max  \\\n",
       "0                                           0.998088         \n",
       "1                                           0.180219         \n",
       "2                                           0.316041         \n",
       "3                                           0.013856         \n",
       "4                                           0.019404         \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max  \\\n",
       "0                                           0.032759              \n",
       "1                                           0.004658              \n",
       "2                                           0.002888              \n",
       "3                                           0.000814              \n",
       "4                                           0.000497              \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max  \\\n",
       "0                                           0.990556                      \n",
       "1                                           0.034183                      \n",
       "2                                           0.028160                      \n",
       "3                                           0.003383                      \n",
       "4                                           0.003189                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max  \\\n",
       "0                                           0.993277                      \n",
       "1                                           0.070213                      \n",
       "2                                           0.006408                      \n",
       "3                                           0.003476                      \n",
       "4                                           0.000700                      \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max  \\\n",
       "0                                           0.982379                  \n",
       "1                                           0.048766                  \n",
       "2                                           0.157353                  \n",
       "3                                           0.002704                  \n",
       "4                                           0.010232                  \n",
       "\n",
       "   Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max  \n",
       "0                                           0.296742             \n",
       "1                                           0.028650             \n",
       "2                                           0.208944             \n",
       "3                                           0.016037             \n",
       "4                                           0.004891             \n",
       "\n",
       "[5 rows x 305 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill and create test dataframe\n",
    "test_dfs = []\n",
    "\n",
    "for i in range(len(test_folds_dfs)):\n",
    "    current_test_fold = test_folds_dfs[i]\n",
    "    test_df = current_test_fold[0]\n",
    "    for arch_df in current_test_fold[1:]:\n",
    "        test_df = test_df.merge(arch_df,on='fn')\n",
    "    test_dfs.append(test_df)\n",
    "\n",
    "for i in range(len(test_dfs)):\n",
    "    #add study and z position to dataframe to join\n",
    "    test_dfs[i]['study'] = test_dfs[i].fn.apply(lambda fn: fn_to_study_ix[fn][0] )\n",
    "    test_dfs[i]['z'] = test_dfs[i].fn.apply(lambda fn: int(fn_to_study_ix[fn][1]) )\n",
    "    \n",
    "    #now join top and bottom\n",
    "    test_dfs[i]['z+'] = test_dfs[i].z+1\n",
    "    test_dfs[i]['z-'] = test_dfs[i].z-1\n",
    "    \n",
    "    #only merge means to avoid too many columns\n",
    "    merge_cols = [c for c in tta_df.columns if 'mean' in c] \n",
    "    test_dfs[i] = pd.merge(test_dfs[i], test_dfs[i][merge_cols+ ['study','z+']], how='left', left_on=['study', 'z'], right_on=['study','z+'], suffixes=('','+1'))\n",
    "    test_dfs[i] = pd.merge(test_dfs[i], test_dfs[i][merge_cols+ ['study','z-']], how='left', left_on=['study', 'z'], right_on=['study','z-'], suffixes=('','-1'))\n",
    "    test_dfs[i].drop(['z++1','z--1'], axis=1, inplace=True) #after merge these columns are not needed\n",
    "    \n",
    "    #now add maximum value for each merge_col by study\n",
    "    group_df = test_dfs[i][merge_cols+['study']].groupby('study').agg('max')\n",
    "    test_dfs[i] = pd.merge(test_dfs[i], group_df, how='left', on='study', suffixes=('','_max'))\n",
    "    \n",
    "    #manage NANs created by merging\n",
    "    merged_cols = [c for c in test_dfs[i].columns if c.endswith('1')]\n",
    "\n",
    "    for c in merged_cols:\n",
    "        test_dfs[i][c].fillna(test_dfs[i][c.replace('-1', '').replace('+1', '')], inplace=True)\n",
    "\n",
    "test_dfs[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(633568, 119234)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folds = [1,5,12] #three random folds\n",
    "\n",
    "train_df = merged_df.query('fold not in @folds')\n",
    "val_df = merged_df.query('fold in @folds')\n",
    "\n",
    "len(train_df),len(val_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any Xresnet34_any_mean 0.10142146307769834\n",
      "epidural Xresnet34_epidural_mean 0.012571424002310823\n",
      "intraparenchymal Xresnet34_intraparenchymal_mean 0.04222755602983461\n",
      "intraventricular Xresnet34_intraventricular_mean 0.025140569354671405\n",
      "subarachnoid Xresnet34_subarachnoid_mean 0.06708897203821126\n",
      "subdural Xresnet34_subdural_mean 0.08416194895275723\n"
     ]
    }
   ],
   "source": [
    "#first check log loss for current model predictions\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "resnet34_vars = [c for c in merged_df.columns if c.startswith('Xresnet34') and c.endswith('mean')]\n",
    "y_true = val_df[base_classes]\n",
    "y_pred = val_df[resnet34_vars]\n",
    "\n",
    "for tc,pc in zip(y_true.columns,y_pred.columns):\n",
    "    loss = log_loss( y_true[tc], y_pred[pc] )\n",
    "    print(tc,pc,loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import os\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "#select only variables from deep learning model\n",
    "model_vars = [c  for c in merged_df.columns if c.startswith('X')]\n",
    "model_vars.append('z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_df[model_vars]\n",
    "y_train = train_df[base_classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training xgboost\n",
      "Done training\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = MultiOutputRegressor(xgb.XGBRegressor(objective='binary:logistic',tree_method='gpu_hist',\n",
    "                                            n_estimators=60, reg_lambda=14.0005, \n",
    "                                            max_depth=5,learning_rate=0.1188,gamma=0.0, reg_alpha=0.2043,\n",
    "                                            min_child_weight=0.0,max_delta_step=0.0,subsample=1.0,colsample_bytree=1.0,\n",
    "                                            colsample_bylevel=1.0,silent=0,nthread=-1,\n",
    "                                            scale_pos_weight=1.0,base_score=0.05,seed=1337,missing=None,))\n",
    "print('Training xgboost')\n",
    "xgb_clf.fit(x_train, y_train) \n",
    "\n",
    "#save trained xgb model\n",
    "pickle.dump(xgb_clf, open('data/xgb_%s.pickle'%experiment_name, 'wb'))\n",
    "\n",
    "print('Done training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6300112\ttotal: 14.2ms\tremaining: 7.09s\n",
      "1:\tlearn: 0.5741290\ttotal: 26.4ms\tremaining: 6.57s\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "MultiOutputRegressor(estimator=<catboost.core.CatBoostClassifier object at 0x7f8db10a8c18>,\n",
       "                     n_jobs=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_clf = MultiOutputRegressor(CatBoostClassifier(task_type='GPU',depth=5,l2_leaf_reg=30,iterations=500))\n",
    "cb_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run predictions on trained XGB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 any 0.09340231172025013\n",
      "1 epidural 0.012141253590899853\n",
      "2 intraparenchymal 0.039596951835288635\n",
      "3 intraventricular 0.02329420861999448\n",
      "4 subarachnoid 0.062225838595083294\n",
      "5 subdural 0.07753075725012087\n",
      "0.05737051904741248\n"
     ]
    }
   ],
   "source": [
    "#predictions from model and evaluation\n",
    "x_test = val_df[model_vars]\n",
    "y_pred = xgb_clf.predict(x_test)\n",
    "y_true = val_df[base_classes]\n",
    "\n",
    "l = 0\n",
    "for i,c in enumerate(y_true.columns):\n",
    "    loss = log_loss( y_true[c], y_pred[:,i] )\n",
    "    l += loss if i != 0 else loss*2\n",
    "    print(i,c,loss)\n",
    "print(l/7.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 any 0.0936741594705755\n",
      "1 epidural 0.012621494943967658\n",
      "2 intraparenchymal 0.039732457939622046\n",
      "3 intraventricular 0.023184423126184128\n",
      "4 subarachnoid 0.06195888386520253\n",
      "5 subdural 0.0781302903061038\n",
      "0.057567981303175884\n"
     ]
    }
   ],
   "source": [
    "#predictions from model and evaluation\n",
    "x_test = val_df[model_vars]\n",
    "_y_pred = [c.predict_proba(x_test)[:,1:] for c in cb_clf.estimators_]\n",
    "y_pred = np.concatenate(_y_pred,axis=-1)\n",
    "y_true = val_df[base_classes]\n",
    "\n",
    "l = 0\n",
    "for i,c in enumerate(y_true.columns):\n",
    "    loss = log_loss( y_true[c], y_pred[:,i] )\n",
    "    l += loss if i != 0 else loss*2\n",
    "    print(i,c,loss)\n",
    "print(l/7.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare submission with trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish\n"
     ]
    }
   ],
   "source": [
    "sub_path = 'data/submission_L2.csv'\n",
    "\n",
    "data = {}\n",
    "for test_df in test_dfs:\n",
    "    x_test = test_df[model_vars]\n",
    "    xgb_probas = xgb_clf.predict(x_test)\n",
    "    cb_probas  = np.concatenate([c.predict_proba(x_test)[:,1:] for c in cb_clf.estimators_],axis=-1)\n",
    "    \n",
    "    probas = (xgb_probas + cb_probas) / 2.\n",
    "    \n",
    "    for idx, row in test_df.iterrows():\n",
    "        file_id = row.fn\n",
    "        for klass_index, klass in enumerate(base_classes):\n",
    "            key =  file_id + '_' + klass\n",
    "            if key not in data:\n",
    "                data[key] = probas[idx, klass_index]\n",
    "            else:\n",
    "                data[key] += probas[idx, klass_index]\n",
    "\n",
    "print('finish')                \n",
    "                \n",
    "final_prediction = []\n",
    "for key in data:\n",
    "    final_prediction.append([key, data[key] / 5])\n",
    "\n",
    "df = pd.DataFrame(final_prediction, columns=['ID','Label'])\n",
    "df.to_csv(sub_path, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"L2 Stacking of densenet121 densenet121-53_train_cpu_window_uint8 resnet101 resnet101-53_train_cpu_window_uint8 resnet18 resnet18-53_train_cpu_window_uint8 resnet34 resnet34-53_train_cpu_window_uint8 resnet50 resnext50_32x4d-53_train_cpu_window_uint8 ensembled with xgboost + catboost\"'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg = '\"L2 Stacking of ' + \" \".join(arch_names) + ' ensembled with xgboost + catboost\"'\n",
    "msg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kaggle competitions submit -c rsna-intracranial-hemorrhage-detection -f {sub_path} -m {msg}"
   ]
  }
 ],
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