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4-L2-train-and-submit.ipynb
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{
"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": {
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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",
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" '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": [
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"0 data/predictions/resnet101_sz512_cv0.0758_weighted_loss_fold1_of_5\n",
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"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/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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" <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",
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" <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",
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" <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": {
"text/html": [
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" 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>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",
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" <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",
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" <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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" <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": [
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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>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",
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"</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",
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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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},
{
"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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]
},
{
"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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},
{
"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}"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}