Datasets
Models
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"VERSION = 33\n",
"\n",
"FOCAL_LOSS = 0\n",
"CLOUD_SINGLE = True\n",
"MIXUP = False\n",
"NO_BLACK_LOSS = True\n",
"DATA_SMALL = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if VERSION in [31,32]:\n",
" TRAIN_ON_STAGE_1 = False\n",
"else:\n",
" TRAIN_ON_STAGE_1 = True\n",
"\n",
"if VERSION in [32,34,36]:\n",
" WEIGHTED = True\n",
"else:\n",
" WEIGHTED = False"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%run ./Code.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"if VERSION in [31,32]:\n",
" # old features, no stage2 training\n",
" train_md, test_md = loadMetadata()\n",
"elif VERSION in [33,34]:\n",
" # old features, with stage2 training\n",
" train_md, test_md = loadMetadata3()\n",
"elif VERSION in [35,36]:\n",
" # new features\n",
" train_md, test_md = loadMetadata2()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OOF"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 14\n",
"DataSet 7 valid size 7232 fold 0\n",
"dataset valid: 7232 loader valid: 226\n",
"loading model model.b3.f0.d7.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.039 time per batch: 0.221\n",
"Batch 100 device: cuda time passed: 19.674 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.256 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.735 time per batch: 0.184\n",
"ver 34, iter 0, fold 0, val ll: 0.0629, cor: 0.8425, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.369 time per batch: 0.227\n",
"Batch 100 device: cuda time passed: 19.874 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.322 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 36.820 time per batch: 0.184\n",
"ver 34, iter 1, fold 0, val ll: 0.0633, cor: 0.8416, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.085 time per batch: 0.222\n",
"Batch 100 device: cuda time passed: 19.790 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.354 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 36.403 time per batch: 0.182\n",
"ver 34, iter 2, fold 0, val ll: 0.0630, cor: 0.8423, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.630 time per batch: 0.233\n",
"Batch 100 device: cuda time passed: 20.129 time per batch: 0.201\n",
"Batch 150 device: cuda time passed: 28.720 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 36.561 time per batch: 0.183\n",
"ver 34, iter 3, fold 0, val ll: 0.0631, cor: 0.8421, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.798 time per batch: 0.216\n",
"Batch 100 device: cuda time passed: 20.275 time per batch: 0.203\n",
"Batch 150 device: cuda time passed: 28.541 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 36.693 time per batch: 0.183\n",
"ver 34, iter 4, fold 0, val ll: 0.0630, cor: 0.8427, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.537 time per batch: 0.231\n",
"Batch 100 device: cuda time passed: 20.034 time per batch: 0.200\n",
"Batch 150 device: cuda time passed: 29.120 time per batch: 0.194\n",
"Batch 200 device: cuda time passed: 37.470 time per batch: 0.187\n",
"ver 34, iter 5, fold 0, val ll: 0.0631, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.616 time per batch: 0.232\n",
"Batch 100 device: cuda time passed: 20.549 time per batch: 0.205\n",
"Batch 150 device: cuda time passed: 28.847 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 37.005 time per batch: 0.185\n",
"ver 34, iter 6, fold 0, val ll: 0.0632, cor: 0.8420, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.390 time per batch: 0.228\n",
"Batch 100 device: cuda time passed: 19.692 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.482 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 37.211 time per batch: 0.186\n",
"ver 34, iter 7, fold 0, val ll: 0.0632, cor: 0.8418, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.109 time per batch: 0.222\n",
"Batch 100 device: cuda time passed: 19.665 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 27.973 time per batch: 0.186\n",
"Batch 200 device: cuda time passed: 36.279 time per batch: 0.181\n",
"ver 34, iter 8, fold 0, val ll: 0.0631, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.182 time per batch: 0.224\n",
"Batch 100 device: cuda time passed: 20.280 time per batch: 0.203\n",
"Batch 150 device: cuda time passed: 28.800 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 37.683 time per batch: 0.188\n",
"ver 34, iter 9, fold 0, val ll: 0.0632, cor: 0.8418, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.546 time per batch: 0.231\n",
"Batch 100 device: cuda time passed: 19.935 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.393 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 37.353 time per batch: 0.187\n",
"ver 34, iter 10, fold 0, val ll: 0.0631, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.706 time per batch: 0.234\n",
"Batch 100 device: cuda time passed: 20.097 time per batch: 0.201\n",
"Batch 150 device: cuda time passed: 28.629 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 36.971 time per batch: 0.185\n",
"ver 34, iter 11, fold 0, val ll: 0.0632, cor: 0.8416, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.344 time per batch: 0.227\n",
"Batch 100 device: cuda time passed: 20.333 time per batch: 0.203\n",
"Batch 150 device: cuda time passed: 28.633 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 36.853 time per batch: 0.184\n",
"ver 34, iter 12, fold 0, val ll: 0.0632, cor: 0.8418, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.884 time per batch: 0.218\n",
"Batch 100 device: cuda time passed: 19.948 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.653 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 37.139 time per batch: 0.186\n",
"ver 34, iter 13, fold 0, val ll: 0.0632, cor: 0.8422, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.232 time per batch: 0.225\n",
"Batch 100 device: cuda time passed: 19.680 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.180 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.829 time per batch: 0.184\n",
"ver 34, iter 14, fold 0, val ll: 0.0631, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.134 time per batch: 0.223\n",
"Batch 100 device: cuda time passed: 19.750 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.254 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 37.052 time per batch: 0.185\n",
"ver 34, iter 15, fold 0, val ll: 0.0632, cor: 0.8419, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.076 time per batch: 0.222\n",
"Batch 100 device: cuda time passed: 19.921 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.521 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 36.744 time per batch: 0.184\n",
"ver 34, iter 16, fold 0, val ll: 0.0633, cor: 0.8417, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.846 time per batch: 0.217\n",
"Batch 100 device: cuda time passed: 20.198 time per batch: 0.202\n",
"Batch 150 device: cuda time passed: 28.638 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 36.762 time per batch: 0.184\n",
"ver 34, iter 17, fold 0, val ll: 0.0631, cor: 0.8423, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.145 time per batch: 0.223\n",
"Batch 100 device: cuda time passed: 19.770 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.000 time per batch: 0.187\n",
"Batch 200 device: cuda time passed: 36.639 time per batch: 0.183\n",
"ver 34, iter 18, fold 0, val ll: 0.0631, cor: 0.8418, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.940 time per batch: 0.219\n",
"Batch 100 device: cuda time passed: 19.199 time per batch: 0.192\n",
"Batch 150 device: cuda time passed: 27.382 time per batch: 0.183\n",
"Batch 200 device: cuda time passed: 35.991 time per batch: 0.180\n",
"ver 34, iter 19, fold 0, val ll: 0.0633, cor: 0.8418, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.303 time per batch: 0.226\n",
"Batch 100 device: cuda time passed: 20.052 time per batch: 0.201\n",
"Batch 150 device: cuda time passed: 28.800 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 37.209 time per batch: 0.186\n",
"ver 34, iter 20, fold 0, val ll: 0.0631, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.012 time per batch: 0.220\n",
"Batch 100 device: cuda time passed: 19.801 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.285 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 36.655 time per batch: 0.183\n",
"ver 34, iter 21, fold 0, val ll: 0.0631, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.588 time per batch: 0.232\n",
"Batch 100 device: cuda time passed: 19.972 time per batch: 0.200\n",
"Batch 150 device: cuda time passed: 28.349 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 36.708 time per batch: 0.184\n",
"ver 34, iter 22, fold 0, val ll: 0.0632, cor: 0.8421, auc: 0.9880\n",
"setFeats, augmentation -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 50 device: cuda time passed: 11.373 time per batch: 0.227\n",
"Batch 100 device: cuda time passed: 19.848 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.645 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 37.166 time per batch: 0.186\n",
"ver 34, iter 23, fold 0, val ll: 0.0631, cor: 0.8424, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.732 time per batch: 0.235\n",
"Batch 100 device: cuda time passed: 20.379 time per batch: 0.204\n",
"Batch 150 device: cuda time passed: 28.964 time per batch: 0.193\n",
"Batch 200 device: cuda time passed: 37.185 time per batch: 0.186\n",
"ver 34, iter 24, fold 0, val ll: 0.0632, cor: 0.8421, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.942 time per batch: 0.219\n",
"Batch 100 device: cuda time passed: 19.584 time per batch: 0.196\n",
"Batch 150 device: cuda time passed: 27.962 time per batch: 0.186\n",
"Batch 200 device: cuda time passed: 36.295 time per batch: 0.181\n",
"ver 34, iter 25, fold 0, val ll: 0.0631, cor: 0.8421, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.547 time per batch: 0.231\n",
"Batch 100 device: cuda time passed: 19.793 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.204 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.624 time per batch: 0.183\n",
"ver 34, iter 26, fold 0, val ll: 0.0630, cor: 0.8426, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.957 time per batch: 0.219\n",
"Batch 100 device: cuda time passed: 18.892 time per batch: 0.189\n",
"Batch 150 device: cuda time passed: 27.679 time per batch: 0.185\n",
"Batch 200 device: cuda time passed: 36.440 time per batch: 0.182\n",
"ver 34, iter 27, fold 0, val ll: 0.0630, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.715 time per batch: 0.214\n",
"Batch 100 device: cuda time passed: 19.980 time per batch: 0.200\n",
"Batch 150 device: cuda time passed: 28.835 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 37.150 time per batch: 0.186\n",
"ver 34, iter 28, fold 0, val ll: 0.0633, cor: 0.8417, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.384 time per batch: 0.228\n",
"Batch 100 device: cuda time passed: 19.805 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.613 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 37.061 time per batch: 0.185\n",
"ver 34, iter 29, fold 0, val ll: 0.0631, cor: 0.8421, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.388 time per batch: 0.228\n",
"Batch 100 device: cuda time passed: 19.790 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.816 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 36.974 time per batch: 0.185\n",
"ver 34, iter 30, fold 0, val ll: 0.0633, cor: 0.8417, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.891 time per batch: 0.218\n",
"Batch 100 device: cuda time passed: 19.773 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 28.208 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.513 time per batch: 0.183\n",
"ver 34, iter 31, fold 0, val ll: 0.0632, cor: 0.8419, auc: 0.9880\n",
"total running time 1743.487956047058\n",
"total time 1744.0221991539001\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 30\n",
"DataSet 7 valid size 7328 fold 1\n",
"dataset valid: 7328 loader valid: 229\n",
"loading model model.b3.f1.d7.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.726 time per batch: 0.235\n",
"Batch 100 device: cuda time passed: 20.041 time per batch: 0.200\n",
"Batch 150 device: cuda time passed: 28.446 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 37.606 time per batch: 0.188\n",
"ver 34, iter 0, fold 1, val ll: 0.0641, cor: 0.8352, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.401 time per batch: 0.228\n",
"Batch 100 device: cuda time passed: 19.701 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.377 time per batch: 0.189\n",
"Batch 200 device: cuda time passed: 37.516 time per batch: 0.188\n",
"ver 34, iter 1, fold 1, val ll: 0.0644, cor: 0.8346, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.045 time per batch: 0.221\n",
"Batch 100 device: cuda time passed: 19.526 time per batch: 0.195\n",
"Batch 150 device: cuda time passed: 28.070 time per batch: 0.187\n",
"Batch 200 device: cuda time passed: 36.553 time per batch: 0.183\n",
"ver 34, iter 2, fold 1, val ll: 0.0645, cor: 0.8347, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.522 time per batch: 0.230\n",
"Batch 100 device: cuda time passed: 20.039 time per batch: 0.200\n",
"Batch 150 device: cuda time passed: 28.182 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.515 time per batch: 0.183\n",
"ver 34, iter 3, fold 1, val ll: 0.0643, cor: 0.8352, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.412 time per batch: 0.228\n",
"Batch 100 device: cuda time passed: 19.794 time per batch: 0.198\n",
"Batch 150 device: cuda time passed: 27.859 time per batch: 0.186\n",
"Batch 200 device: cuda time passed: 35.826 time per batch: 0.179\n",
"ver 34, iter 4, fold 1, val ll: 0.0644, cor: 0.8346, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.184 time per batch: 0.224\n",
"Batch 100 device: cuda time passed: 19.415 time per batch: 0.194\n",
"Batch 150 device: cuda time passed: 27.791 time per batch: 0.185\n",
"Batch 200 device: cuda time passed: 36.132 time per batch: 0.181\n",
"ver 34, iter 5, fold 1, val ll: 0.0642, cor: 0.8355, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.913 time per batch: 0.218\n",
"Batch 100 device: cuda time passed: 20.135 time per batch: 0.201\n",
"Batch 150 device: cuda time passed: 28.498 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 36.584 time per batch: 0.183\n",
"ver 34, iter 6, fold 1, val ll: 0.0644, cor: 0.8350, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.169 time per batch: 0.223\n",
"Batch 100 device: cuda time passed: 19.419 time per batch: 0.194\n",
"Batch 150 device: cuda time passed: 27.835 time per batch: 0.186\n",
"Batch 200 device: cuda time passed: 36.749 time per batch: 0.184\n",
"ver 34, iter 7, fold 1, val ll: 0.0644, cor: 0.8346, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.093 time per batch: 0.222\n",
"Batch 100 device: cuda time passed: 19.735 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 27.976 time per batch: 0.187\n",
"Batch 200 device: cuda time passed: 36.522 time per batch: 0.183\n",
"ver 34, iter 8, fold 1, val ll: 0.0643, cor: 0.8351, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.886 time per batch: 0.218\n",
"Batch 100 device: cuda time passed: 19.310 time per batch: 0.193\n",
"Batch 150 device: cuda time passed: 27.555 time per batch: 0.184\n",
"Batch 200 device: cuda time passed: 35.897 time per batch: 0.179\n",
"ver 34, iter 9, fold 1, val ll: 0.0640, cor: 0.8360, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.190 time per batch: 0.224\n",
"Batch 100 device: cuda time passed: 19.659 time per batch: 0.197\n",
"Batch 150 device: cuda time passed: 28.059 time per batch: 0.187\n",
"Batch 200 device: cuda time passed: 36.597 time per batch: 0.183\n",
"ver 34, iter 10, fold 1, val ll: 0.0645, cor: 0.8345, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.462 time per batch: 0.229\n",
"Batch 100 device: cuda time passed: 19.851 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.426 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 37.003 time per batch: 0.185\n",
"ver 34, iter 11, fold 1, val ll: 0.0643, cor: 0.8353, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.183 time per batch: 0.224\n",
"Batch 100 device: cuda time passed: 19.891 time per batch: 0.199\n",
"Batch 150 device: cuda time passed: 28.179 time per batch: 0.188\n",
"Batch 200 device: cuda time passed: 36.768 time per batch: 0.184\n",
"ver 34, iter 12, fold 1, val ll: 0.0644, cor: 0.8347, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.197 time per batch: 0.224\n",
"Batch 100 device: cuda time passed: 19.529 time per batch: 0.195\n",
"Batch 150 device: cuda time passed: 27.817 time per batch: 0.185\n",
"Batch 200 device: cuda time passed: 36.686 time per batch: 0.183\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ver 34, iter 13, fold 1, val ll: 0.0642, cor: 0.8353, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.934 time per batch: 0.219\n",
"Batch 100 device: cuda time passed: 19.565 time per batch: 0.196\n",
"Batch 150 device: cuda time passed: 28.726 time per batch: 0.192\n",
"Batch 200 device: cuda time passed: 37.472 time per batch: 0.187\n",
"ver 34, iter 14, fold 1, val ll: 0.0644, cor: 0.8351, auc: 0.9875\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.664 time per batch: 0.233\n",
"Batch 100 device: cuda time passed: 20.181 time per batch: 0.202\n",
"Batch 150 device: cuda time passed: 28.592 time per batch: 0.191\n",
"Batch 200 device: cuda time passed: 37.140 time per batch: 0.186\n",
"ver 34, iter 15, fold 1, val ll: 0.0642, cor: 0.8355, auc: 0.9876\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.248 time per batch: 0.225\n",
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"ver 34, iter 31, fold 1, val ll: 0.0645, cor: 0.8347, auc: 0.9875\n",
"total running time 1752.2469551563263\n",
"total time 3496.7637753486633\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 4\n",
"DataSet 7 valid size 7232 fold 2\n",
"dataset valid: 7232 loader valid: 226\n",
"loading model model.b3.f2.d7.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.025 time per batch: 0.221\n",
"Batch 100 device: cuda time passed: 20.043 time per batch: 0.200\n",
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"ver 34, iter 0, fold 2, val ll: 0.0603, cor: 0.8423, auc: 0.9893\n",
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"ver 34, iter 1, fold 2, val ll: 0.0601, cor: 0.8425, auc: 0.9894\n",
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"ver 34, iter 2, fold 2, val ll: 0.0603, cor: 0.8422, auc: 0.9893\n",
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"ver 34, iter 3, fold 2, val ll: 0.0602, cor: 0.8424, auc: 0.9893\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 200 device: cuda time passed: 35.943 time per batch: 0.180\n",
"ver 34, iter 4, fold 2, val ll: 0.0602, cor: 0.8425, auc: 0.9893\n",
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"setFeats, augmentation -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 50 device: cuda time passed: 10.998 time per batch: 0.220\n",
"Batch 100 device: cuda time passed: 19.483 time per batch: 0.195\n",
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"ver 34, iter 30, fold 2, val ll: 0.0602, cor: 0.8424, auc: 0.9894\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 19.957 time per batch: 0.200\n",
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"ver 34, iter 31, fold 2, val ll: 0.0600, cor: 0.8430, auc: 0.9894\n",
"total running time 1731.3292746543884\n",
"total time 5228.603754520416\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 14\n",
"DataSet 9 valid size 7232 fold 0\n",
"dataset valid: 7232 loader valid: 226\n",
"loading model model.b3.f0.d9.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.536 time per batch: 0.231\n",
"Batch 100 device: cuda time passed: 19.442 time per batch: 0.194\n",
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"ver 34, iter 0, fold 0, val ll: 0.0632, cor: 0.8416, auc: 0.9881\n",
"setFeats, augmentation -1\n",
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"ver 34, iter 2, fold 0, val ll: 0.0633, cor: 0.8414, auc: 0.9880\n",
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"ver 34, iter 3, fold 0, val ll: 0.0631, cor: 0.8417, auc: 0.9881\n",
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]
},
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"output_type": "stream",
"text": [
"ver 34, iter 18, fold 0, val ll: 0.0632, cor: 0.8414, auc: 0.9882\n",
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"ver 34, iter 31, fold 0, val ll: 0.0631, cor: 0.8417, auc: 0.9881\n",
"total running time 1487.4923713207245\n",
"total time 6716.580273866653\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 30\n",
"DataSet 9 valid size 7328 fold 1\n",
"dataset valid: 7328 loader valid: 229\n",
"loading model model.b3.f1.d9.v34\n",
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]
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"text": [
"Batch 200 device: cuda time passed: 36.950 time per batch: 0.185\n",
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"ver 34, iter 25, fold 1, val ll: 0.0632, cor: 0.8391, auc: 0.9879\n",
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"ver 34, iter 27, fold 1, val ll: 0.0633, cor: 0.8389, auc: 0.9879\n",
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"ver 34, iter 28, fold 1, val ll: 0.0632, cor: 0.8392, auc: 0.9879\n",
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"ver 34, iter 29, fold 1, val ll: 0.0632, cor: 0.8390, auc: 0.9879\n",
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"ver 34, iter 30, fold 1, val ll: 0.0633, cor: 0.8389, auc: 0.9879\n",
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"Batch 100 device: cuda time passed: 19.595 time per batch: 0.196\n",
"Batch 150 device: cuda time passed: 27.997 time per batch: 0.187\n",
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"ver 34, iter 31, fold 1, val ll: 0.0633, cor: 0.8388, auc: 0.9879\n",
"total running time 1502.9731421470642\n",
"total time 8220.030895471573\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 4\n",
"DataSet 9 valid size 7232 fold 2\n",
"dataset valid: 7232 loader valid: 226\n",
"loading model model.b3.f2.d9.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 11.523 time per batch: 0.230\n",
"Batch 100 device: cuda time passed: 19.846 time per batch: 0.198\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 150 device: cuda time passed: 28.448 time per batch: 0.190\n",
"Batch 200 device: cuda time passed: 36.151 time per batch: 0.181\n",
"ver 34, iter 0, fold 2, val ll: 0.0604, cor: 0.8411, auc: 0.9892\n",
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"ver 34, iter 1, fold 2, val ll: 0.0603, cor: 0.8412, auc: 0.9893\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 10.997 time per batch: 0.220\n",
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"ver 34, iter 3, fold 2, val ll: 0.0605, cor: 0.8407, auc: 0.9892\n",
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"ver 34, iter 4, fold 2, val ll: 0.0604, cor: 0.8411, auc: 0.9891\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ver 34, iter 23, fold 2, val ll: 0.0603, cor: 0.8414, auc: 0.9892\n",
"setFeats, augmentation -1\n",
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"ver 34, iter 31, fold 2, val ll: 0.0603, cor: 0.8414, auc: 0.9893\n",
"total running time 1486.592945575714\n",
"total time 9707.101170063019\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 9\n",
"DataSet 11 valid size 4384 fold 0\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f0.d11.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.940 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.756 time per batch: 0.148\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 100 device: cuda time passed: 14.021 time per batch: 0.140\n",
"ver 34, iter 23, fold 0, val ll: 0.0608, cor: 0.8453, auc: 0.9889\n",
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"Batch 100 device: cuda time passed: 14.106 time per batch: 0.141\n",
"ver 34, iter 31, fold 0, val ll: 0.0609, cor: 0.8450, auc: 0.9888\n",
"total running time 741.6375591754913\n",
"total time 10448.977521657944\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 12\n",
"DataSet 11 valid size 4288 fold 1\n",
"dataset valid: 4288 loader valid: 134\n",
"loading model model.b3.f1.d11.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.855 time per batch: 0.157\n",
"Batch 100 device: cuda time passed: 13.890 time per batch: 0.139\n",
"ver 34, iter 0, fold 1, val ll: 0.0597, cor: 0.8468, auc: 0.9897\n",
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"ver 34, iter 1, fold 1, val ll: 0.0598, cor: 0.8465, auc: 0.9896\n",
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"ver 34, iter 2, fold 1, val ll: 0.0597, cor: 0.8467, auc: 0.9898\n",
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"ver 34, iter 3, fold 1, val ll: 0.0596, cor: 0.8468, auc: 0.9899\n",
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"Batch 100 device: cuda time passed: 14.249 time per batch: 0.142\n"
]
},
{
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"output_type": "stream",
"text": [
"ver 34, iter 27, fold 1, val ll: 0.0599, cor: 0.8462, auc: 0.9897\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.806 time per batch: 0.156\n",
"Batch 100 device: cuda time passed: 14.135 time per batch: 0.141\n",
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"Batch 100 device: cuda time passed: 14.820 time per batch: 0.148\n",
"ver 34, iter 30, fold 1, val ll: 0.0594, cor: 0.8474, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 15.174 time per batch: 0.152\n",
"ver 34, iter 31, fold 1, val ll: 0.0595, cor: 0.8472, auc: 0.9898\n",
"total running time 721.0183691978455\n",
"total time 11170.231662034988\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 27\n",
"DataSet 11 valid size 4416 fold 2\n",
"dataset valid: 4416 loader valid: 138\n",
"loading model model.b3.f2.d11.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.321 time per batch: 0.166\n",
"Batch 100 device: cuda time passed: 14.535 time per batch: 0.145\n",
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"total running time 752.1568894386292\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"total time 11922.615085601807\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 11 valid size 4352 fold 3\n",
"dataset valid: 4352 loader valid: 136\n",
"loading model model.b3.f3.d11.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.425 time per batch: 0.168\n",
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"Batch 100 device: cuda time passed: 13.975 time per batch: 0.140\n",
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"Batch 100 device: cuda time passed: 14.795 time per batch: 0.148\n",
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"Batch 100 device: cuda time passed: 13.972 time per batch: 0.140\n",
"ver 34, iter 30, fold 3, val ll: 0.0632, cor: 0.8405, auc: 0.9886\n",
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"Batch 100 device: cuda time passed: 14.658 time per batch: 0.147\n",
"ver 34, iter 31, fold 3, val ll: 0.0629, cor: 0.8412, auc: 0.9887\n",
"total running time 734.1475803852081\n",
"total time 12656.995476007462\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 11 valid size 4384 fold 4\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f4.d11.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.537 time per batch: 0.151\n",
"Batch 100 device: cuda time passed: 14.791 time per batch: 0.148\n",
"ver 34, iter 0, fold 4, val ll: 0.0621, cor: 0.8422, auc: 0.9883\n",
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"Batch 100 device: cuda time passed: 14.894 time per batch: 0.149\n",
"ver 34, iter 1, fold 4, val ll: 0.0621, cor: 0.8425, auc: 0.9881\n",
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"ver 34, iter 2, fold 4, val ll: 0.0621, cor: 0.8424, auc: 0.9882\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 100 device: cuda time passed: 14.614 time per batch: 0.146\n",
"ver 34, iter 3, fold 4, val ll: 0.0621, cor: 0.8423, auc: 0.9882\n",
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"Batch 100 device: cuda time passed: 14.991 time per batch: 0.150\n",
"ver 34, iter 29, fold 4, val ll: 0.0620, cor: 0.8427, auc: 0.9882\n",
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"Batch 100 device: cuda time passed: 14.845 time per batch: 0.148\n",
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"ver 34, iter 31, fold 4, val ll: 0.0621, cor: 0.8423, auc: 0.9882\n",
"total running time 742.8772552013397\n",
"total time 13400.111248254776\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 9\n",
"DataSet 12 valid size 4384 fold 0\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f0.d12.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.200 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 14.845 time per batch: 0.148\n",
"ver 34, iter 0, fold 0, val ll: 0.0608, cor: 0.8451, auc: 0.9887\n",
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"Batch 100 device: cuda time passed: 14.944 time per batch: 0.149\n"
]
},
{
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"output_type": "stream",
"text": [
"ver 34, iter 7, fold 0, val ll: 0.0609, cor: 0.8444, auc: 0.9889\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.524 time per batch: 0.145\n",
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"ver 34, iter 25, fold 0, val ll: 0.0608, cor: 0.8451, auc: 0.9888\n",
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"ver 34, iter 26, fold 0, val ll: 0.0608, cor: 0.8449, auc: 0.9888\n",
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"Batch 100 device: cuda time passed: 14.240 time per batch: 0.142\n",
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"Batch 100 device: cuda time passed: 14.701 time per batch: 0.147\n",
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"Batch 100 device: cuda time passed: 13.886 time per batch: 0.139\n",
"ver 34, iter 29, fold 0, val ll: 0.0609, cor: 0.8444, auc: 0.9888\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.618 time per batch: 0.146\n",
"ver 34, iter 30, fold 0, val ll: 0.0609, cor: 0.8448, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.908 time per batch: 0.158\n",
"Batch 100 device: cuda time passed: 14.168 time per batch: 0.142\n",
"ver 34, iter 31, fold 0, val ll: 0.0609, cor: 0.8445, auc: 0.9888\n",
"total running time 743.3103971481323\n",
"total time 14143.665662765503\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 12\n",
"DataSet 12 valid size 4288 fold 1\n",
"dataset valid: 4288 loader valid: 134\n",
"loading model model.b3.f1.d12.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.167 time per batch: 0.163\n",
"Batch 100 device: cuda time passed: 14.378 time per batch: 0.144\n",
"ver 34, iter 0, fold 1, val ll: 0.0597, cor: 0.8453, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.542 time per batch: 0.145\n",
"ver 34, iter 1, fold 1, val ll: 0.0594, cor: 0.8461, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 13.462 time per batch: 0.135\n",
"ver 34, iter 2, fold 1, val ll: 0.0597, cor: 0.8455, auc: 0.9897\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.099 time per batch: 0.162\n",
"Batch 100 device: cuda time passed: 14.128 time per batch: 0.141\n",
"ver 34, iter 3, fold 1, val ll: 0.0595, cor: 0.8458, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.410 time per batch: 0.144\n",
"ver 34, iter 4, fold 1, val ll: 0.0597, cor: 0.8456, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.514 time per batch: 0.145\n",
"ver 34, iter 5, fold 1, val ll: 0.0597, cor: 0.8454, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 14.776 time per batch: 0.148\n",
"ver 34, iter 6, fold 1, val ll: 0.0596, cor: 0.8457, auc: 0.9897\n",
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"Batch 100 device: cuda time passed: 14.817 time per batch: 0.148\n",
"ver 34, iter 7, fold 1, val ll: 0.0597, cor: 0.8454, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 13.820 time per batch: 0.138\n",
"ver 34, iter 8, fold 1, val ll: 0.0597, cor: 0.8454, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.223 time per batch: 0.142\n",
"ver 34, iter 9, fold 1, val ll: 0.0598, cor: 0.8453, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.392 time per batch: 0.144\n",
"ver 34, iter 10, fold 1, val ll: 0.0596, cor: 0.8456, auc: 0.9897\n",
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"Batch 100 device: cuda time passed: 14.334 time per batch: 0.143\n",
"ver 34, iter 11, fold 1, val ll: 0.0595, cor: 0.8461, auc: 0.9898\n",
"setFeats, augmentation -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 50 device: cuda time passed: 8.393 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 14.742 time per batch: 0.147\n",
"ver 34, iter 12, fold 1, val ll: 0.0595, cor: 0.8458, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 14.623 time per batch: 0.146\n",
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"Batch 100 device: cuda time passed: 14.130 time per batch: 0.141\n",
"ver 34, iter 18, fold 1, val ll: 0.0594, cor: 0.8461, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 14.405 time per batch: 0.144\n",
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"Batch 100 device: cuda time passed: 14.408 time per batch: 0.144\n",
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"ver 34, iter 24, fold 1, val ll: 0.0596, cor: 0.8460, auc: 0.9897\n",
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"Batch 100 device: cuda time passed: 14.345 time per batch: 0.143\n",
"ver 34, iter 25, fold 1, val ll: 0.0593, cor: 0.8464, auc: 0.9898\n",
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"ver 34, iter 26, fold 1, val ll: 0.0597, cor: 0.8456, auc: 0.9897\n",
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"Batch 100 device: cuda time passed: 14.919 time per batch: 0.149\n",
"ver 34, iter 27, fold 1, val ll: 0.0592, cor: 0.8466, auc: 0.9899\n",
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"Batch 100 device: cuda time passed: 14.347 time per batch: 0.143\n",
"ver 34, iter 28, fold 1, val ll: 0.0595, cor: 0.8456, auc: 0.9898\n",
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"Batch 100 device: cuda time passed: 14.347 time per batch: 0.143\n",
"ver 34, iter 29, fold 1, val ll: 0.0595, cor: 0.8462, auc: 0.9898\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.563 time per batch: 0.146\n",
"ver 34, iter 30, fold 1, val ll: 0.0597, cor: 0.8455, auc: 0.9897\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.506 time per batch: 0.145\n",
"ver 34, iter 31, fold 1, val ll: 0.0595, cor: 0.8458, auc: 0.9898\n",
"total running time 723.6225900650024\n",
"total time 14867.529915571213\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 27\n",
"DataSet 12 valid size 4416 fold 2\n",
"dataset valid: 4416 loader valid: 138\n",
"loading model model.b3.f2.d12.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.349 time per batch: 0.167\n",
"Batch 100 device: cuda time passed: 14.921 time per batch: 0.149\n",
"ver 34, iter 0, fold 2, val ll: 0.0605, cor: 0.8428, auc: 0.9889\n",
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"ver 34, iter 1, fold 2, val ll: 0.0600, cor: 0.8438, auc: 0.9891\n",
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"ver 34, iter 2, fold 2, val ll: 0.0603, cor: 0.8434, auc: 0.9890\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 100 device: cuda time passed: 15.089 time per batch: 0.151\n",
"ver 34, iter 16, fold 2, val ll: 0.0605, cor: 0.8425, auc: 0.9890\n",
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"Batch 100 device: cuda time passed: 14.609 time per batch: 0.146\n",
"ver 34, iter 29, fold 2, val ll: 0.0602, cor: 0.8436, auc: 0.9890\n",
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"Batch 100 device: cuda time passed: 15.589 time per batch: 0.156\n",
"ver 34, iter 30, fold 2, val ll: 0.0600, cor: 0.8438, auc: 0.9891\n",
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"Batch 100 device: cuda time passed: 14.568 time per batch: 0.146\n",
"ver 34, iter 31, fold 2, val ll: 0.0603, cor: 0.8431, auc: 0.9889\n",
"total running time 756.8606667518616\n",
"total time 15624.633174657822\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 12 valid size 4352 fold 3\n",
"dataset valid: 4352 loader valid: 136\n",
"loading model model.b3.f3.d12.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.148 time per batch: 0.163\n",
"Batch 100 device: cuda time passed: 14.303 time per batch: 0.143\n",
"ver 34, iter 0, fold 3, val ll: 0.0624, cor: 0.8412, auc: 0.9887\n",
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"ver 34, iter 1, fold 3, val ll: 0.0628, cor: 0.8402, auc: 0.9886\n",
"setFeats, augmentation -1\n",
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"ver 34, iter 2, fold 3, val ll: 0.0627, cor: 0.8401, auc: 0.9887\n",
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"Batch 100 device: cuda time passed: 13.991 time per batch: 0.140\n",
"ver 34, iter 3, fold 3, val ll: 0.0629, cor: 0.8401, auc: 0.9885\n",
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"ver 34, iter 8, fold 3, val ll: 0.0627, cor: 0.8405, auc: 0.9887\n",
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"ver 34, iter 14, fold 3, val ll: 0.0626, cor: 0.8405, auc: 0.9887\n",
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"Batch 100 device: cuda time passed: 14.642 time per batch: 0.146\n",
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"Batch 100 device: cuda time passed: 14.557 time per batch: 0.146\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ver 34, iter 20, fold 3, val ll: 0.0627, cor: 0.8404, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.006 time per batch: 0.160\n",
"Batch 100 device: cuda time passed: 14.026 time per batch: 0.140\n",
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"Batch 100 device: cuda time passed: 14.435 time per batch: 0.144\n",
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"Batch 100 device: cuda time passed: 14.495 time per batch: 0.145\n",
"ver 34, iter 23, fold 3, val ll: 0.0627, cor: 0.8403, auc: 0.9888\n",
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"Batch 100 device: cuda time passed: 14.538 time per batch: 0.145\n",
"ver 34, iter 24, fold 3, val ll: 0.0623, cor: 0.8408, auc: 0.9888\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.826 time per batch: 0.157\n",
"Batch 100 device: cuda time passed: 15.779 time per batch: 0.158\n",
"ver 34, iter 25, fold 3, val ll: 0.0627, cor: 0.8404, auc: 0.9886\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.512 time per batch: 0.145\n",
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"Batch 100 device: cuda time passed: 14.392 time per batch: 0.144\n",
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"Batch 100 device: cuda time passed: 14.356 time per batch: 0.144\n",
"ver 34, iter 28, fold 3, val ll: 0.0627, cor: 0.8403, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.696 time per batch: 0.154\n",
"Batch 100 device: cuda time passed: 14.316 time per batch: 0.143\n",
"ver 34, iter 29, fold 3, val ll: 0.0627, cor: 0.8403, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.732 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 14.896 time per batch: 0.149\n",
"ver 34, iter 30, fold 3, val ll: 0.0626, cor: 0.8407, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.028 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.561 time per batch: 0.146\n",
"ver 34, iter 31, fold 3, val ll: 0.0625, cor: 0.8406, auc: 0.9888\n",
"total running time 748.939469575882\n",
"total time 16373.82175731659\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 12 valid size 4384 fold 4\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f4.d12.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.040 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 15.289 time per batch: 0.153\n",
"ver 34, iter 0, fold 4, val ll: 0.0611, cor: 0.8442, auc: 0.9884\n",
"setFeats, augmentation -1\n",
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"ver 34, iter 1, fold 4, val ll: 0.0613, cor: 0.8441, auc: 0.9885\n",
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"Batch 100 device: cuda time passed: 14.522 time per batch: 0.145\n",
"ver 34, iter 2, fold 4, val ll: 0.0614, cor: 0.8440, auc: 0.9883\n",
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"ver 34, iter 3, fold 4, val ll: 0.0613, cor: 0.8439, auc: 0.9884\n",
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"ver 34, iter 4, fold 4, val ll: 0.0613, cor: 0.8437, auc: 0.9883\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 50 device: cuda time passed: 8.386 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 14.843 time per batch: 0.148\n",
"ver 34, iter 25, fold 4, val ll: 0.0610, cor: 0.8443, auc: 0.9885\n",
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"ver 34, iter 29, fold 4, val ll: 0.0613, cor: 0.8439, auc: 0.9884\n",
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"ver 34, iter 30, fold 4, val ll: 0.0612, cor: 0.8438, auc: 0.9885\n",
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"ver 34, iter 31, fold 4, val ll: 0.0612, cor: 0.8444, auc: 0.9884\n",
"total running time 753.5520458221436\n",
"total time 17127.620171546936\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 9\n",
"DataSet 13 valid size 4384 fold 0\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f0.d13.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.140 time per batch: 0.163\n",
"Batch 100 device: cuda time passed: 14.948 time per batch: 0.149\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 100 device: cuda time passed: 14.007 time per batch: 0.140\n",
"ver 34, iter 29, fold 0, val ll: 0.0608, cor: 0.8444, auc: 0.9889\n",
"setFeats, augmentation -1\n",
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"Batch 100 device: cuda time passed: 14.598 time per batch: 0.146\n",
"ver 34, iter 30, fold 0, val ll: 0.0610, cor: 0.8436, auc: 0.9889\n",
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"Batch 100 device: cuda time passed: 14.582 time per batch: 0.146\n",
"ver 34, iter 31, fold 0, val ll: 0.0610, cor: 0.8437, auc: 0.9889\n",
"total running time 753.0968706607819\n",
"total time 17880.959055662155\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 12\n",
"DataSet 13 valid size 4288 fold 1\n",
"dataset valid: 4288 loader valid: 134\n",
"loading model model.b3.f1.d13.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.968 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.655 time per batch: 0.147\n",
"ver 34, iter 0, fold 1, val ll: 0.0600, cor: 0.8442, auc: 0.9897\n",
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"ver 34, iter 1, fold 1, val ll: 0.0597, cor: 0.8452, auc: 0.9897\n",
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"ver 34, iter 2, fold 1, val ll: 0.0599, cor: 0.8446, auc: 0.9896\n",
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"ver 34, iter 3, fold 1, val ll: 0.0598, cor: 0.8447, auc: 0.9897\n",
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"ver 34, iter 30, fold 1, val ll: 0.0600, cor: 0.8442, auc: 0.9897\n",
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"ver 34, iter 31, fold 1, val ll: 0.0598, cor: 0.8446, auc: 0.9897\n",
"total running time 734.7177088260651\n",
"total time 18615.920258760452\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 27\n",
"DataSet 13 valid size 4416 fold 2\n",
"dataset valid: 4416 loader valid: 138\n",
"loading model model.b3.f2.d13.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.169 time per batch: 0.163\n",
"Batch 100 device: cuda time passed: 14.845 time per batch: 0.148\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ver 34, iter 0, fold 2, val ll: 0.0602, cor: 0.8434, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.255 time per batch: 0.165\n",
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"Batch 50 device: cuda time passed: 8.230 time per batch: 0.165\n",
"Batch 100 device: cuda time passed: 14.510 time per batch: 0.145\n",
"ver 34, iter 24, fold 2, val ll: 0.0602, cor: 0.8431, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.489 time per batch: 0.170\n",
"Batch 100 device: cuda time passed: 14.700 time per batch: 0.147\n",
"ver 34, iter 25, fold 2, val ll: 0.0601, cor: 0.8434, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.810 time per batch: 0.156\n",
"Batch 100 device: cuda time passed: 14.881 time per batch: 0.149\n",
"ver 34, iter 26, fold 2, val ll: 0.0601, cor: 0.8438, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.647 time per batch: 0.173\n",
"Batch 100 device: cuda time passed: 14.903 time per batch: 0.149\n",
"ver 34, iter 27, fold 2, val ll: 0.0602, cor: 0.8434, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.024 time per batch: 0.160\n",
"Batch 100 device: cuda time passed: 15.108 time per batch: 0.151\n",
"ver 34, iter 28, fold 2, val ll: 0.0601, cor: 0.8436, auc: 0.9891\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.179 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 15.084 time per batch: 0.151\n",
"ver 34, iter 29, fold 2, val ll: 0.0603, cor: 0.8433, auc: 0.9889\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.489 time per batch: 0.170\n",
"Batch 100 device: cuda time passed: 15.248 time per batch: 0.152\n",
"ver 34, iter 30, fold 2, val ll: 0.0602, cor: 0.8435, auc: 0.9890\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.780 time per batch: 0.156\n",
"Batch 100 device: cuda time passed: 14.653 time per batch: 0.147\n",
"ver 34, iter 31, fold 2, val ll: 0.0602, cor: 0.8432, auc: 0.9890\n",
"total running time 759.7050180435181\n",
"total time 19375.86375927925\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 13 valid size 4352 fold 3\n",
"dataset valid: 4352 loader valid: 136\n",
"loading model model.b3.f3.d13.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.209 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 14.432 time per batch: 0.144\n",
"ver 34, iter 0, fold 3, val ll: 0.0629, cor: 0.8398, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.060 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.405 time per batch: 0.144\n",
"ver 34, iter 1, fold 3, val ll: 0.0631, cor: 0.8396, auc: 0.9885\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.998 time per batch: 0.160\n",
"Batch 100 device: cuda time passed: 14.574 time per batch: 0.146\n",
"ver 34, iter 2, fold 3, val ll: 0.0632, cor: 0.8388, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.088 time per batch: 0.162\n",
"Batch 100 device: cuda time passed: 14.750 time per batch: 0.148\n",
"ver 34, iter 3, fold 3, val ll: 0.0630, cor: 0.8399, auc: 0.9885\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.872 time per batch: 0.157\n",
"Batch 100 device: cuda time passed: 15.131 time per batch: 0.151\n",
"ver 34, iter 4, fold 3, val ll: 0.0629, cor: 0.8396, auc: 0.9887\n",
"setFeats, augmentation -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 50 device: cuda time passed: 7.764 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 14.443 time per batch: 0.144\n",
"ver 34, iter 5, fold 3, val ll: 0.0630, cor: 0.8392, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.046 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.542 time per batch: 0.145\n",
"ver 34, iter 6, fold 3, val ll: 0.0630, cor: 0.8400, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.369 time per batch: 0.167\n",
"Batch 100 device: cuda time passed: 14.797 time per batch: 0.148\n",
"ver 34, iter 7, fold 3, val ll: 0.0629, cor: 0.8397, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.385 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 15.082 time per batch: 0.151\n",
"ver 34, iter 8, fold 3, val ll: 0.0630, cor: 0.8396, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.233 time per batch: 0.165\n",
"Batch 100 device: cuda time passed: 14.812 time per batch: 0.148\n",
"ver 34, iter 9, fold 3, val ll: 0.0630, cor: 0.8392, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.719 time per batch: 0.174\n",
"Batch 100 device: cuda time passed: 15.227 time per batch: 0.152\n",
"ver 34, iter 10, fold 3, val ll: 0.0631, cor: 0.8393, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.403 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 14.953 time per batch: 0.150\n",
"ver 34, iter 11, fold 3, val ll: 0.0630, cor: 0.8396, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.628 time per batch: 0.153\n",
"Batch 100 device: cuda time passed: 15.699 time per batch: 0.157\n",
"ver 34, iter 12, fold 3, val ll: 0.0632, cor: 0.8390, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.949 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.760 time per batch: 0.148\n",
"ver 34, iter 13, fold 3, val ll: 0.0630, cor: 0.8398, auc: 0.9885\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.102 time per batch: 0.162\n",
"Batch 100 device: cuda time passed: 14.558 time per batch: 0.146\n",
"ver 34, iter 14, fold 3, val ll: 0.0631, cor: 0.8389, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.056 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.553 time per batch: 0.146\n",
"ver 34, iter 15, fold 3, val ll: 0.0633, cor: 0.8389, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.476 time per batch: 0.170\n",
"Batch 100 device: cuda time passed: 14.739 time per batch: 0.147\n",
"ver 34, iter 16, fold 3, val ll: 0.0630, cor: 0.8395, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.207 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 14.548 time per batch: 0.145\n",
"ver 34, iter 17, fold 3, val ll: 0.0632, cor: 0.8392, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.305 time per batch: 0.166\n",
"Batch 100 device: cuda time passed: 14.417 time per batch: 0.144\n",
"ver 34, iter 18, fold 3, val ll: 0.0630, cor: 0.8397, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.726 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 15.397 time per batch: 0.154\n",
"ver 34, iter 19, fold 3, val ll: 0.0630, cor: 0.8398, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.974 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.515 time per batch: 0.145\n",
"ver 34, iter 20, fold 3, val ll: 0.0634, cor: 0.8385, auc: 0.9885\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.898 time per batch: 0.158\n",
"Batch 100 device: cuda time passed: 14.685 time per batch: 0.147\n",
"ver 34, iter 21, fold 3, val ll: 0.0629, cor: 0.8399, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.814 time per batch: 0.156\n",
"Batch 100 device: cuda time passed: 15.322 time per batch: 0.153\n",
"ver 34, iter 22, fold 3, val ll: 0.0631, cor: 0.8392, auc: 0.9885\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.499 time per batch: 0.170\n",
"Batch 100 device: cuda time passed: 15.189 time per batch: 0.152\n",
"ver 34, iter 23, fold 3, val ll: 0.0630, cor: 0.8397, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.627 time per batch: 0.153\n",
"Batch 100 device: cuda time passed: 14.563 time per batch: 0.146\n",
"ver 34, iter 24, fold 3, val ll: 0.0630, cor: 0.8396, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.223 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 14.639 time per batch: 0.146\n",
"ver 34, iter 25, fold 3, val ll: 0.0630, cor: 0.8394, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.255 time per batch: 0.165\n",
"Batch 100 device: cuda time passed: 14.365 time per batch: 0.144\n",
"ver 34, iter 26, fold 3, val ll: 0.0629, cor: 0.8395, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.287 time per batch: 0.166\n",
"Batch 100 device: cuda time passed: 14.326 time per batch: 0.143\n",
"ver 34, iter 27, fold 3, val ll: 0.0631, cor: 0.8390, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.750 time per batch: 0.175\n",
"Batch 100 device: cuda time passed: 14.716 time per batch: 0.147\n",
"ver 34, iter 28, fold 3, val ll: 0.0630, cor: 0.8395, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.832 time per batch: 0.157\n",
"Batch 100 device: cuda time passed: 14.454 time per batch: 0.145\n",
"ver 34, iter 29, fold 3, val ll: 0.0629, cor: 0.8397, auc: 0.9887\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.247 time per batch: 0.165\n",
"Batch 100 device: cuda time passed: 14.407 time per batch: 0.144\n",
"ver 34, iter 30, fold 3, val ll: 0.0630, cor: 0.8393, auc: 0.9886\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.728 time per batch: 0.175\n",
"Batch 100 device: cuda time passed: 14.980 time per batch: 0.150\n",
"ver 34, iter 31, fold 3, val ll: 0.0634, cor: 0.8388, auc: 0.9884\n",
"total running time 748.6304786205292\n",
"total time 20124.740348815918\n",
"completed epochs: 3 iters starting now: 32\n",
"adding dummy serieses 16\n",
"DataSet 13 valid size 4384 fold 4\n",
"dataset valid: 4384 loader valid: 137\n",
"loading model model.b3.f4.d13.v34\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.680 time per batch: 0.154\n",
"Batch 100 device: cuda time passed: 14.974 time per batch: 0.150\n",
"ver 34, iter 0, fold 4, val ll: 0.0616, cor: 0.8422, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.849 time per batch: 0.177\n",
"Batch 100 device: cuda time passed: 15.070 time per batch: 0.151\n",
"ver 34, iter 1, fold 4, val ll: 0.0618, cor: 0.8420, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.719 time per batch: 0.154\n",
"Batch 100 device: cuda time passed: 14.696 time per batch: 0.147\n",
"ver 34, iter 2, fold 4, val ll: 0.0618, cor: 0.8423, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.469 time per batch: 0.169\n",
"Batch 100 device: cuda time passed: 14.909 time per batch: 0.149\n",
"ver 34, iter 3, fold 4, val ll: 0.0618, cor: 0.8418, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.626 time per batch: 0.153\n",
"Batch 100 device: cuda time passed: 14.660 time per batch: 0.147\n",
"ver 34, iter 4, fold 4, val ll: 0.0618, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.404 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 14.750 time per batch: 0.147\n",
"ver 34, iter 5, fold 4, val ll: 0.0619, cor: 0.8417, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.219 time per batch: 0.164\n",
"Batch 100 device: cuda time passed: 14.164 time per batch: 0.142\n",
"ver 34, iter 6, fold 4, val ll: 0.0616, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.061 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.689 time per batch: 0.147\n",
"ver 34, iter 7, fold 4, val ll: 0.0617, cor: 0.8423, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.938 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.192 time per batch: 0.142\n",
"ver 34, iter 8, fold 4, val ll: 0.0617, cor: 0.8421, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.663 time per batch: 0.173\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch 100 device: cuda time passed: 14.850 time per batch: 0.149\n",
"ver 34, iter 9, fold 4, val ll: 0.0618, cor: 0.8418, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.651 time per batch: 0.173\n",
"Batch 100 device: cuda time passed: 14.415 time per batch: 0.144\n",
"ver 34, iter 10, fold 4, val ll: 0.0617, cor: 0.8423, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.635 time per batch: 0.153\n",
"Batch 100 device: cuda time passed: 15.063 time per batch: 0.151\n",
"ver 34, iter 11, fold 4, val ll: 0.0617, cor: 0.8423, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.388 time per batch: 0.168\n",
"Batch 100 device: cuda time passed: 14.779 time per batch: 0.148\n",
"ver 34, iter 12, fold 4, val ll: 0.0618, cor: 0.8418, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.871 time per batch: 0.157\n",
"Batch 100 device: cuda time passed: 14.803 time per batch: 0.148\n",
"ver 34, iter 13, fold 4, val ll: 0.0617, cor: 0.8418, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.072 time per batch: 0.161\n",
"Batch 100 device: cuda time passed: 14.433 time per batch: 0.144\n",
"ver 34, iter 14, fold 4, val ll: 0.0618, cor: 0.8419, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.584 time per batch: 0.172\n",
"Batch 100 device: cuda time passed: 15.084 time per batch: 0.151\n",
"ver 34, iter 15, fold 4, val ll: 0.0618, cor: 0.8421, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.715 time per batch: 0.154\n",
"Batch 100 device: cuda time passed: 15.169 time per batch: 0.152\n",
"ver 34, iter 16, fold 4, val ll: 0.0618, cor: 0.8420, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.982 time per batch: 0.160\n",
"Batch 100 device: cuda time passed: 15.258 time per batch: 0.153\n",
"ver 34, iter 17, fold 4, val ll: 0.0614, cor: 0.8429, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.944 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.644 time per batch: 0.146\n",
"ver 34, iter 18, fold 4, val ll: 0.0620, cor: 0.8415, auc: 0.9879\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.880 time per batch: 0.158\n",
"Batch 100 device: cuda time passed: 14.599 time per batch: 0.146\n",
"ver 34, iter 19, fold 4, val ll: 0.0618, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.238 time per batch: 0.165\n",
"Batch 100 device: cuda time passed: 14.637 time per batch: 0.146\n",
"ver 34, iter 20, fold 4, val ll: 0.0617, cor: 0.8419, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.506 time per batch: 0.170\n",
"Batch 100 device: cuda time passed: 14.624 time per batch: 0.146\n",
"ver 34, iter 21, fold 4, val ll: 0.0617, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.764 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 14.605 time per batch: 0.146\n",
"ver 34, iter 22, fold 4, val ll: 0.0617, cor: 0.8422, auc: 0.9882\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.726 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 14.446 time per batch: 0.144\n",
"ver 34, iter 23, fold 4, val ll: 0.0617, cor: 0.8422, auc: 0.9881\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.077 time per batch: 0.162\n",
"Batch 100 device: cuda time passed: 14.366 time per batch: 0.144\n",
"ver 34, iter 24, fold 4, val ll: 0.0621, cor: 0.8415, auc: 0.9879\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.973 time per batch: 0.159\n",
"Batch 100 device: cuda time passed: 14.194 time per batch: 0.142\n",
"ver 34, iter 25, fold 4, val ll: 0.0617, cor: 0.8420, auc: 0.9883\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.393 time per batch: 0.148\n",
"Batch 100 device: cuda time passed: 15.218 time per batch: 0.152\n",
"ver 34, iter 26, fold 4, val ll: 0.0615, cor: 0.8427, auc: 0.9883\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.723 time per batch: 0.174\n",
"Batch 100 device: cuda time passed: 15.348 time per batch: 0.153\n",
"ver 34, iter 27, fold 4, val ll: 0.0617, cor: 0.8418, auc: 0.9883\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 7.751 time per batch: 0.155\n",
"Batch 100 device: cuda time passed: 14.291 time per batch: 0.143\n",
"ver 34, iter 28, fold 4, val ll: 0.0619, cor: 0.8419, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.019 time per batch: 0.160\n",
"Batch 100 device: cuda time passed: 15.011 time per batch: 0.150\n",
"ver 34, iter 29, fold 4, val ll: 0.0620, cor: 0.8414, auc: 0.9879\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.106 time per batch: 0.162\n",
"Batch 100 device: cuda time passed: 15.439 time per batch: 0.154\n",
"ver 34, iter 30, fold 4, val ll: 0.0618, cor: 0.8419, auc: 0.9880\n",
"setFeats, augmentation -1\n",
"Batch 50 device: cuda time passed: 8.527 time per batch: 0.171\n",
"Batch 100 device: cuda time passed: 15.187 time per batch: 0.152\n",
"ver 34, iter 31, fold 4, val ll: 0.0619, cor: 0.8414, auc: 0.9881\n",
"total running time 749.8348104953766\n",
"total time 20874.81984090805\n"
]
}
],
"source": [
"stg = time.time()\n",
"for ds in (my_datasets3 + my_datasets5):\n",
" folds = getNFolds(ds)\n",
" for fold in range(folds):\n",
" #pp = pickle.load(open(PATH_DISK/'ensemble/oof_d{}_f{}_v{}'.format(ds, fold, VERSION),'rb'))\n",
" predictions = oof_one(num_iter=32, bs=32, fold=fold, dataset=ds)\n",
" #predictions = np.concatenate([pp,predictions],axis=0)\n",
" pickle.dump(predictions, open(PATH_DISK/'ensemble/oof_d{}_f{}_v{}'.format(ds, fold, VERSION),'wb'))\n",
" print('total time', time.time() - stg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#range(6,13) x8\n",
"#5113.189187049866\n",
"#20878.715314388275"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.231111111111111"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#total running time 1201.68962931633\n",
"#total time 15020.348212480545"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"preds_all = getPredsOOF(aug=32,datasets=my_datasets3,datasets5=my_datasets5,ver=33)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"preds_all = getPredsOOF(aug=32,datasets=my_datasets3,datasets5=my_datasets5,ver=34)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5, 32, 752797, 6)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_all.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#preds_all2 = getPredsOOF(aug=32,datasets=[],datasets5=[14],ver=35)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#preds_all2.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#preds_all = np.concatenate([preds_all, preds_all2], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.14302406, 0.00424933, 0.04813841, 0.03484004, 0.04746119,\n",
" 0.06259691])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# weighted \n",
"# [0.15059251, 0.00462303, 0.05034504, 0.03602126, 0.04910235, 0.06661193]\n",
"\n",
"# non-weighted\n",
"# [0.14268919, 0.00409448, 0.04815497, 0.03553187, 0.04749233, 0.06196157]\n",
"\n",
"# non-weighted stage2\n",
"# [0.14302406, 0.00424933, 0.04813841, 0.03484004, 0.04746119, 0.06259691]\n",
"\n",
"# weighted stage2\n",
"# [0.14172827, 0.00397889, 0.04794982, 0.0350942 , 0.04717257, 0.06180147]\n",
"\n",
"preds_all.mean((0,1,2))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"names_y = [\n",
" #'model_Densenet201_3_version_classifier_splits_fullhead_resmodel_pool2_3_type_OOF_pred_split_{}.pkl',\n",
" #'model_Densenet161_3_version_classifier_splits_fullhead_resmodel_pool2_3_type_OOF_pred_split_{}.pkl',\n",
" 'model_Densenet169_3_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl',\n",
" 'model_se_resnext101_32x4d_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl',\n",
" 'model_se_resnet101_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"names_y5 = [\n",
" 'model_se_resnext101_32x4d_version_new_splits_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl',\n",
" 'model_se_resnet101_version_new_splits_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl',\n",
" 'model_se_resnet101_version_new_splits_focal_fullhead_resmodel_pool2_stage2_3_type_OOF_pred_split_{}.pkl',\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adding yuval_idx\n",
"adding yuval_idx\n"
]
}
],
"source": [
"preds_y = getYuvalOOF(train_md=train_md, names=names_y, names5=names_y5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.14321291, 0.00391866, 0.04807696, 0.03472973, 0.04762993,\n",
" 0.06291145])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_y.mean((0,1))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6, 752797, 6)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_y.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"preds_all = np.concatenate([preds_all.mean(1), preds_y], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"del preds_y"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11, 752797, 6)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_all.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Elimination"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def getMaskedLoss(preds_all, mask, weighted):\n",
" \n",
" loss = ((- train_md[all_ich].values * np.log(preds_all[mask].mean(0)) \\\n",
" - (1 - train_md[all_ich].values) * np.log(1 - preds_all[mask].mean(0)))*class_weights)\n",
" \n",
" if weighted:\n",
" loss = (loss * np.expand_dims(train_md['weights'].values,axis=1)).mean()\n",
" else:\n",
" loss = loss.mean()\n",
" return loss\n",
"\n",
"def produceDSMask(weighted):\n",
" \n",
" N = len(preds_all)\n",
" ds_mask = np.ones(N, dtype=bool)\n",
" best_loss = getMaskedLoss(preds_all, ds_mask, weighted)\n",
"\n",
" for i in range(N):\n",
" worst_k = -1\n",
" worst_loss = best_loss\n",
" print('starting iter',i,'loss',best_loss,'eliminated',(~ds_mask).sum())\n",
" for k in range(N):\n",
" mask2 = ds_mask.copy()\n",
" mask2[k] = False\n",
" loss = getMaskedLoss(preds_all, mask2, weighted)\n",
" if loss < worst_loss:\n",
" worst_loss = loss\n",
" worst_k = k\n",
" if worst_k >= 0:\n",
" print('eliminating',worst_k,'new loss',worst_loss)\n",
" ds_mask[worst_k] = False\n",
" best_loss = worst_loss\n",
" else:\n",
" break\n",
" \n",
" print('removed', np.where(~ds_mask)[0])\n",
" \n",
" return ds_mask"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"starting iter 0 loss 0.057515051687738426 eliminated 0\n",
"eliminating 1 new loss 0.057490836031309125\n",
"starting iter 1 loss 0.057490836031309125 eliminated 1\n",
"removed [1]\n",
"\n",
"starting iter 0 loss 0.05434281856430104 eliminated 0\n",
"eliminating 1 new loss 0.05431221808734583\n",
"starting iter 1 loss 0.05431221808734583 eliminated 1\n",
"eliminating 4 new loss 0.054308387316921135\n",
"starting iter 2 loss 0.054308387316921135 eliminated 2\n",
"eliminating 5 new loss 0.054305186238067175\n",
"starting iter 3 loss 0.054305186238067175 eliminated 3\n",
"removed [1 4 5]\n"
]
}
],
"source": [
"ds_mask1 = produceDSMask(False)\n",
"print('')\n",
"ds_mask2 = produceDSMask(True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"ds_mask = ds_mask1 | ds_mask2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, True, True, True, True, True, True, True,\n",
" True, True])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_mask"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"preds_all = preds_all[ds_mask]\n",
"my_len = ds_mask[:my_len].sum()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OOF analysis"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"any [0.099 0.096 0.096 0.096 0.1 0.099 0.098 0.097 0.097 0.097]\n",
"epidural [0.017 0.015 0.016 0.016 0.016 0.015 0.015 0.015 0.015 0.015]\n",
"intraparenchymal [0.043 0.041 0.042 0.042 0.043 0.041 0.042 0.041 0.041 0.041]\n",
"intraventricular [0.026 0.025 0.025 0.025 0.026 0.025 0.025 0.025 0.025 0.025]\n",
"subarachnoid [0.066 0.064 0.064 0.064 0.066 0.064 0.064 0.064 0.063 0.063]\n",
"subdural [0.081 0.079 0.079 0.079 0.08 0.079 0.079 0.079 0.078 0.078]\n"
]
}
],
"source": [
"np.set_printoptions(precision=3)\n",
"\n",
"loss = (- train_md[all_ich].values * np.log(preds_all) \\\n",
" - (1 - train_md[all_ich].values) * np.log(1 - preds_all)).mean(1)\n",
"for k in range(6):\n",
" print('{:20s} {}'.format(all_ich[k],loss[:,k]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.142 0.143 0.143 0.143 0.143 0.145 0.144 0.142 0.142 0.143]\n",
" [0.004 0.005 0.004 0.004 0.003 0.004 0.004 0.004 0.004 0.004]\n",
" [0.048 0.048 0.048 0.048 0.048 0.048 0.049 0.048 0.048 0.048]\n",
" [0.035 0.035 0.035 0.035 0.035 0.034 0.035 0.035 0.035 0.035]\n",
" [0.047 0.047 0.047 0.048 0.048 0.049 0.047 0.047 0.047 0.048]\n",
" [0.062 0.062 0.063 0.063 0.063 0.065 0.064 0.062 0.062 0.063]]\n"
]
}
],
"source": [
"print(preds_all.mean(1).transpose())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f82b89bfc10>]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"zz = preds_all.mean(0)[:,0]\n",
"\n",
"train_md['prob'] = zz\n",
"\n",
"plt.plot(train_md[['prob','pos_idx']].groupby('pos_idx').mean())\n",
"plt.plot([0,60],[0,0])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0934667744853716"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_loss(train_md['any'],train_md['prob'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 5)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#zz = preds_all.mean(1)[0,:,0]\n",
"k=0\n",
"zz = preds_all.mean(0)[:,k]\n",
"#zz = preds_all[0,0,:,k]\n",
"#zz = scalePreds(zz,power=1.3)\n",
"\n",
"\n",
"plt.figure(figsize=(16, 7))\n",
"a = plt.hist(zz - train_md[all_ich[k]],bins=100,alpha=0.5,density=True)\n",
"b = 0.5*(a[1][1:] + a[1][:-1])\n",
"plt.plot(b,-7*np.log(1-abs(b))*a[0])\n",
"plt.ylim([0,5])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 [3.57e-05 6.13e-05 9.31e-05 1.79e-03 9.95e-01 9.98e-01 9.99e-01]\n",
"1 [7.12e-06 9.84e-06 1.44e-05 9.54e-05 8.47e-02 4.76e-01 8.64e-01]\n",
"2 [1.66e-05 2.25e-05 3.18e-05 3.06e-04 9.82e-01 9.94e-01 9.97e-01]\n",
"3 [8.39e-06 1.13e-05 1.72e-05 1.02e-04 9.76e-01 9.92e-01 9.95e-01]\n",
"4 [2.32e-05 3.56e-05 5.27e-05 4.59e-04 9.57e-01 9.92e-01 9.96e-01]\n",
"5 [2.51e-05 4.16e-05 6.20e-05 9.25e-04 9.68e-01 9.93e-01 9.96e-01]\n"
]
}
],
"source": [
"np.set_printoptions(precision=2)\n",
"zz = preds_all.mean(0)\n",
"for k in range(6):\n",
" print(k,np.quantile(zz[:,k],[0.0001,0.001,0.01,0.5,0.99,0.999,0.9999]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bounding"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 752797, 6)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_all.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.057490836031309125\n"
]
}
],
"source": [
"loss = ((- train_md[all_ich].values * np.log(preds_all.mean(0)) \\\n",
" - (1 - train_md[all_ich].values) * np.log(1 - preds_all.mean(0)))*class_weights).mean()\n",
"print(loss)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"initial score 0.057490836031309125\n",
"any too low inconsistencies\n",
"1 class: 0.004179878506423379\n",
"2 class: 0.025429033325053103\n",
"3 class: 0.012410782720972586\n",
"4 class: 0.033147714456885455\n",
"5 class: 0.09211925658577279\n",
"total 0.14343999776832267\n",
"any too low corrected score 0.05748886375218989\n",
"any too high inconsistencies\n",
"total 0.24964844440134593\n",
"any too high corrected score 0.0574848864789516\n"
]
}
],
"source": [
"preds_all = predBounding(preds_all, target=train_md[all_ich].values)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0574848864789516\n"
]
}
],
"source": [
"loss = ((- train_md[all_ich].values * np.log(preds_all.mean(0)) \\\n",
" - (1 - train_md[all_ich].values) * np.log(1 - preds_all.mean(0)))*class_weights).mean()\n",
"print(loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Models behavior per groups"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0: 452096 84109 [0.0744 0.0723 0.0724 0.0725 0.0742 0.0729 0.073 0.072 0.0718 0.0718]\n",
" 1: 300701 37123 [0.0418 0.0406 0.0404 0.0408 0.0425 0.0415 0.0417 0.041 0.0405 0.0406]\n"
]
}
],
"source": [
"np.set_printoptions(precision=4)\n",
"for col in ['PxlMin_zero']:\n",
" for i in train_md[col].unique():\n",
" res = ((- train_md[all_ich].values * np.log(preds_all) - (1 - train_md[all_ich].values) \\\n",
" * np.log(1 - preds_all)) * class_weights)[:,(train_md[col] == i)].mean((1,2))\n",
" sz = (train_md[col] == i).sum()\n",
" sz_test = (test_md[col] == i).sum()\n",
" print('{:2d}: {:6d} {:6d} {}'.format(i,sz,sz_test,res))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inference"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"completed epochs: 13\n",
"loading model model.b13.f0.d14.v35\n",
"adding dummy serieses 2\n",
"DataSet 14 test size 3520 fold 0\n",
"dataset test: 3520 loader test: 110 anum: 0\n",
"setFeats, augmentation -1\n"
]
},
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/mnt/edisk/running/features/se_resnet101_5n/test2/test.f0.a0'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-84-de854db3530e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mpreds2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0manum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_one\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mpreds2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-82-5f73bc7ddaa9>\u001b[0m in \u001b[0;36minference_one\u001b[0;34m(dataset, bs, add_seed, fold, anum)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dataset test:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtst_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'loader test:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader_tst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'anum:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mtst_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetFeats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manum\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0mloc_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtst_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-82-44e0138eb17e>\u001b[0m in \u001b[0;36msetFeats\u001b[0;34m(self, anum, epoch)\u001b[0m\n\u001b[1;32m 81\u001b[0m getAPathFeats('test', self.metadata.test.sum(), self.test_mask)] ,axis=0)\n\u001b[1;32m 82\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mfeats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetAPathFeats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'valid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mTRAIN_ON_STAGE_1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-82-44e0138eb17e>\u001b[0m in \u001b[0;36mgetAPathFeats\u001b[0;34m(mode, sz, mask)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetAPathFeats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mmax_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'test'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mfeats2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetAPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0man\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0man\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_a\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfeats2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeats2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-82-44e0138eb17e>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetAPathFeats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mmax_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'test'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mfeats2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetAPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0man\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0man\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_a\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfeats2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeats2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/edisk/running/features/se_resnet101_5n/test2/test.f0.a0'"
]
}
],
"source": [
"stg = time.time()\n",
"\n",
"#for ds in (my_datasets3 + my_datasets5):\n",
"for ds in [14]:\n",
" folds = getNFolds(ds)\n",
" preds = []\n",
" for fold in range(folds):\n",
" preds2 = []\n",
" for anum in range(32):\n",
" predictions = inference_one(fold = fold, anum = anum, bs=bs, dataset=ds)\n",
" preds2.append(predictions)\n",
" preds.append(np.stack(preds2))\n",
" preds = np.stack(preds)\n",
" print('total time', time.time() - stg)\n",
" \n",
" pickle.dump(preds, open(PATH_DISK/'preds_d{}_v{}'.format(ds, VERSION),'wb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#11221.892060995102"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#total time 1466.092379808426 5x8\n",
"#total time 5399.404406309128 5x32"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Files transfer"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copying file:///home/zahar_chikishev/running/oof_d6_f0_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d6_f1_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d6_f2_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d7_f0_v20 [Content-Type=application/octet-stream]...\n",
"- [4 files][164.5 MiB/164.5 MiB] \n",
"==> NOTE: You are performing a sequence of gsutil operations that may\n",
"run significantly faster if you instead use gsutil -m cp ... Please\n",
"see the -m section under \"gsutil help options\" for further information\n",
"about when gsutil -m can be advantageous.\n",
"\n",
"Copying file:///home/zahar_chikishev/running/oof_d7_f1_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d7_f2_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d8_f0_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d8_f1_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d8_f2_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d9_f0_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d9_f1_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/oof_d9_f2_v20 [Content-Type=application/octet-stream]...\n",
"| [12 files][493.8 MiB/493.8 MiB] 35.9 MiB/s \n",
"Operation completed over 12 objects/493.8 MiB. \n"
]
}
],
"source": [
"!gsutil cp /home/zahar_chikishev/running/oof* gs://rsna-hemorrhage/results"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copying file:///home/zahar_chikishev/running/preds_d6_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/preds_d7_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/preds_d8_v20 [Content-Type=application/octet-stream]...\n",
"Copying file:///home/zahar_chikishev/running/preds_d9_v20 [Content-Type=application/octet-stream]...\n",
"\\ [4 files][172.6 MiB/172.6 MiB] \n",
"Operation completed over 4 objects/172.6 MiB. \n"
]
}
],
"source": [
"!gsutil cp /home/zahar_chikishev/running/preds* gs://rsna-hemorrhage/results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil -m cp gs://rsna-hemorrhage/results/* C:\\StudioProjects\\Hemorrhage\\running\\ensemble"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil -m cp gs://rsna-hemorrhage/yuvals/model_Densenet161_3_version_classifier_splits_fullhead_resmodel_type_OOF_pred_split_* ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil -m cp gs://rsna-hemorrhage/yuvals/model_*_version_classifier_splits_fullhead_resmodel_type_OOF_pred_split_* ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil -m cp gs://rsna-hemorrhage/yuvals/model_Densenet161_3_version_classifier_splits_fullhead_resmodel_type_test_pred_ensamble_split_* ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cp gs://rsna-hemorrhage/yuvals/OOF_validation_image_ids.pkl .\n",
"!gsutil cp gs://rsna-hemorrhage/yuvals/ensemble_test_image_ids.pkl ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"!rm /home/zahar_chikishev/running/*v53"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/zahar_chikishev/running/preds_se_resnext101_32x4d_v53\r\n",
"/home/zahar_chikishev/running/stats.f0.v53\r\n",
"/home/zahar_chikishev/running/stats.f1.v53\r\n",
"/home/zahar_chikishev/running/stats.f2.v53\r\n"
]
}
],
"source": [
"!ls /home/zahar_chikishev/running/*v53"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/zahar_chikishev/running/oof_Densenet161_f0_v72\r\n",
"/home/zahar_chikishev/running/oof_Densenet161_f1_v72\r\n",
"/home/zahar_chikishev/running/oof_Densenet161_f2_v72\r\n",
"/home/zahar_chikishev/running/oof_Densenet169_f0_v73\r\n",
"/home/zahar_chikishev/running/oof_Densenet169_f1_v73\r\n",
"/home/zahar_chikishev/running/oof_Densenet169_f2_v73\r\n",
"/home/zahar_chikishev/running/oof_Densenet201_f0_v74\r\n",
"/home/zahar_chikishev/running/oof_Densenet201_f1_v74\r\n",
"/home/zahar_chikishev/running/oof_Densenet201_f2_v74\r\n",
"/home/zahar_chikishev/running/oof_se_resnext101_32x4d_f0_v75\r\n",
"/home/zahar_chikishev/running/oof_se_resnext101_32x4d_f1_v75\r\n",
"/home/zahar_chikishev/running/oof_se_resnext101_32x4d_f2_v75\r\n"
]
}
],
"source": [
"!ls /home/zahar_chikishev/running/oof*"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/zahar_chikishev/running/preds_Densenet161_v72\r\n",
"/home/zahar_chikishev/running/preds_Densenet169_v73\r\n",
"/home/zahar_chikishev/running/preds_Densenet201_v74\r\n",
"/home/zahar_chikishev/running/preds_se_resnext101_32x4d_v75\r\n"
]
}
],
"source": [
"!ls /home/zahar_chikishev/running/preds*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ensembling"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 752797, 6)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds_all.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8298f99450>]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#dd = pd.DataFrame(preds_all.mean(1)[4], columns=all_ich)\n",
"dd = pd.DataFrame(preds_all.mean(0), columns=all_ich)\n",
"\n",
"k=5\n",
"plt.plot([0,100],[0,1])\n",
"plt.plot(train_md[[all_ich[k]]].groupby(pd.cut(dd[all_ich[k]],np.arange(101)/100)).mean().values)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8298d8b790>]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"k = 0\n",
"dd = pd.DataFrame((preds_all.mean(0)), columns=all_ich)\n",
"vals = (train_md[all_ich[k]]*train_md['weights']).groupby(pd.cut(dd[all_ich[k]],np.arange(101)/100)).mean()/ \\\n",
" train_md['weights'].groupby(pd.cut(dd[all_ich[k]],np.arange(101)/100)).mean()\n",
"\n",
"#dd = pd.DataFrame(preds_all.mean(1)[4], columns=all_ich)\n",
"\n",
"plt.plot([0,100],[0,1])\n",
"plt.plot(vals.values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.05430755335203294\n"
]
}
],
"source": [
"res = np.zeros(6)\n",
"for k in range(6):\n",
" res[k] = log_loss(train_md[all_ich[k]], preds_all.mean(0)[:,k], eps=1e-7, labels=[0,1], \\\n",
" sample_weight=train_md.weights)\n",
"print((res*class_weights).mean())"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.05507244939620463\n"
]
}
],
"source": [
"res = np.zeros(6)\n",
"for k in range(6):\n",
" res[k] = log_loss(train_md[all_ich[k]], preds_all.mean(0)[:,k]**(0.9), eps=1e-7, labels=[0,1], \\\n",
" sample_weight=train_md.weights)\n",
"print((res*class_weights).mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"%run ./Code.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"stats_fn = PATH_DISK/'ensemble'/'stats.v{}'.format(VERSION)\n",
"if stats_fn.is_file():\n",
" stats_fn.unlink()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"starting fold 0 target 0\n",
"my_len 4\n",
"obj 0.08727681341480276\n",
"obj 0.08727680961247067\n",
"obj 0.08727681635930583\n",
"obj 0.08727683790936251\n",
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"obj 0.08727680763252753\n",
"obj 0.08727680110920166\n",
"obj 0.0872767999865726\n",
"model [0.5022 0.4978] sum 0.9999676813453886\n",
"my_len 4\n",
"v34 f0 t0: original ll 0.0935/0.0886, ensemble ll 0.0935/0.0886\n",
"running time 3.408698320388794\n",
"starting fold 0 target 1\n",
"my_len 4\n",
"obj 0.012081623796648948\n",
"obj 0.012064844467400441\n",
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"model [0.2831 0.6969] sum 0.9800013450345408\n",
"my_len 4\n",
"v34 f0 t1: original ll 0.0150/0.0139, ensemble ll 0.0149/0.0138\n",
"running time 2.795881509780884\n",
"starting fold 0 target 2\n",
"my_len 4\n",
"obj 0.03643239100707699\n",
"obj 0.03645343904689349\n",
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"model [0.2802 0.7198] sum 0.9999991225720998\n",
"my_len 4\n",
"v34 f0 t2: original ll 0.0415/0.0392, ensemble ll 0.0415/0.0392\n",
"running time 2.8015294075012207\n",
"starting fold 0 target 3\n",
"my_len 4\n",
"obj 0.022513923350337257\n",
"obj 0.022514823788354697\n",
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"obj 0.02246025802669623\n",
"model [0.2562 0.7438] sum 0.9999997939535652\n",
"my_len 4\n",
"v34 f0 t3: original ll 0.0243/0.0239, ensemble ll 0.0243/0.0239\n",
"running time 2.816469669342041\n",
"starting fold 0 target 4\n",
"my_len 4\n",
"obj 0.05866908744304866\n",
"obj 0.058655421224092855\n",
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"obj 0.0585477836014417\n",
"obj 0.05854778093821152\n",
"obj 0.05854778093568046\n",
"model [0.1849 0.8112] sum 0.9961132357622775\n",
"my_len 4\n",
"v34 f0 t4: original ll 0.0617/0.0596, ensemble ll 0.0617/0.0596\n",
"running time 2.985300064086914\n",
"starting fold 0 target 5\n",
"my_len 4\n",
"obj 0.07111888085217989\n",
"obj 0.07109943900079979\n",
"obj 0.07109626046895302\n",
"obj 0.07109808791436022\n",
"obj 0.07111092588847316\n",
"obj 0.0710957678702807\n",
"obj 0.07109475918862561\n",
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"obj 0.07107889783936418\n",
"obj 0.07107915998501899\n",
"obj 0.07106262999212018\n",
"obj 0.07106191775845142\n",
"obj 0.07106191688882099\n",
"model [0.3466 0.6453] sum 0.9918766105998069\n",
"my_len 4\n",
"v34 f0 t5: original ll 0.0789/0.0757, ensemble ll 0.0789/0.0757\n",
"running time 2.694082736968994\n",
"starting fold 1 target 0\n",
"my_len 4\n",
"obj 0.086462634155086\n",
"obj 0.08646150298320077\n",
"obj 0.0864614802906338\n",
"obj 0.08646149569706231\n",
"obj 0.08646172873837056\n",
"obj 0.08646289351196788\n",
"obj 0.08646556301640597\n",
"obj 0.08648759494216639\n",
"obj 0.0866088306497439\n",
"obj 0.08657377479849461\n",
"obj 0.08646196862069534\n",
"obj 0.08644958080609667\n",
"obj 0.08644661615687646\n",
"obj 0.0864458232439799\n",
"obj 0.08644566400280582\n",
"obj 0.08644565114567859\n",
"model [0.5941 0.4059] sum 0.9999997875126851\n",
"my_len 4\n",
"v34 f1 t0: original ll 0.0964/0.0902, ensemble ll 0.0965/0.0902\n",
"running time 3.123203992843628\n",
"starting fold 1 target 1\n",
"my_len 4\n",
"obj 0.013554873239082691\n",
"obj 0.013536021601660043\n",
"obj 0.013540780340295681\n",
"obj 0.013566508884523805\n",
"obj 0.013564699748967186\n",
"obj 0.013552392374760967\n",
"obj 0.013547296797121735\n",
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"obj 0.013410353069281782\n",
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"model [0.1071 0.8929] sum 0.9999981188620239\n",
"my_len 4\n",
"v34 f1 t1: original ll 0.0128/0.0110, ensemble ll 0.0129/0.0111\n",
"running time 3.057985782623291\n",
"starting fold 1 target 2\n",
"my_len 4\n",
"obj 0.03700983338270924\n",
"obj 0.03700942511496985\n",
"obj 0.03700941857757188\n",
"obj 0.037009493333549054\n",
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"obj 0.036983398096645255\n",
"obj 0.036983397111147245\n",
"model [0.351 0.649] sum 0.9999845409128088\n",
"my_len 4\n",
"v34 f1 t2: original ll 0.0401/0.0381, ensemble ll 0.0400/0.0380\n",
"running time 3.355295419692993\n",
"starting fold 1 target 3\n",
"my_len 4\n",
"obj 0.023432825312353096\n",
"obj 0.023430311446247156\n",
"obj 0.023430223179945912\n",
"obj 0.023430262634468815\n",
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"obj 0.023438794235891954\n",
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"obj 0.02348204017957184\n",
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"obj 0.023476355780291332\n",
"obj 0.02340827851846919\n",
"obj 0.023406000871457035\n",
"obj 0.023405403449075084\n",
"obj 0.023405297323883776\n",
"obj 0.0234052886876152\n",
"model [0.3264 0.6736] sum 0.9999998723930892\n",
"my_len 4\n",
"v34 f1 t3: original ll 0.0233/0.0221, ensemble ll 0.0233/0.0220\n",
"running time 2.9708051681518555\n",
"starting fold 1 target 4\n",
"my_len 4\n",
"obj 0.05876162822408629\n",
"obj 0.05877796616366613\n",
"obj 0.058777955484026846\n",
"obj 0.058778224218930426\n",
"obj 0.0587782146454934\n",
"obj 0.05877773096065451\n",
"obj 0.05877722216040082\n",
"obj 0.05877439407851651\n",
"obj 0.058760743102489674\n",
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"obj 0.05865935503477886\n",
"obj 0.05865909561692464\n",
"obj 0.05865909552127853\n",
"obj 0.05865908163335363\n",
"model [0.2072 0.7928] sum 0.9999997236086989\n",
"my_len 4\n",
"v34 f1 t4: original ll 0.0632/0.0594, ensemble ll 0.0632/0.0594\n",
"running time 2.8280203342437744\n",
"starting fold 1 target 5\n",
"my_len 4\n",
"obj 0.07183112972231193\n",
"obj 0.07182105040716405\n",
"obj 0.07182017839075075\n",
"obj 0.07182183010763957\n",
"obj 0.07183346670238054\n",
"obj 0.07181975923420639\n",
"obj 0.07182105338358573\n",
"obj 0.07182045801712832\n",
"obj 0.07180832229135492\n",
"obj 0.07180906614397237\n",
"obj 0.07179208329104021\n",
"obj 0.07178991670375105\n",
"obj 0.07178988693665318\n",
"obj 0.07178988689714585\n",
"model [0.3481 0.6455] sum 0.9935939570813068\n",
"my_len 4\n",
"v34 f1 t5: original ll 0.0780/0.0742, ensemble ll 0.0780/0.0742\n",
"running time 2.8228495121002197\n",
"starting fold 2 target 0\n",
"my_len 4\n",
"obj 0.08937950117764316\n",
"obj 0.08937672934263124\n",
"obj 0.08937644247528813\n",
"obj 0.08937645539574389\n",
"obj 0.08937661990672387\n",
"obj 0.08937751986380715\n",
"obj 0.08938010041832908\n",
"obj 0.0894013641490466\n",
"obj 0.08953362229177689\n",
"obj 0.08950694179309915\n",
"obj 0.08939116686116341\n",
"obj 0.08937686219260021\n",
"obj 0.08937344427477412\n",
"obj 0.08937270519391056\n",
"obj 0.08937262922565642\n",
"model [0.5579 0.4421] sum 0.9999993467855719\n",
"my_len 4\n",
"v34 f2 t0: original ll 0.0905/0.0844, ensemble ll 0.0905/0.0844\n",
"running time 2.990800380706787\n",
"starting fold 2 target 1\n",
"my_len 4\n",
"obj 0.012436140247391692\n",
"obj 0.012426658871830904\n",
"obj 0.012428926899146497\n",
"obj 0.012424188907848488\n",
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"obj 0.01242496008015588\n",
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"obj 0.012348143529084353\n",
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"model [0.1901 0.7899] sum 0.9800025514053641\n",
"my_len 4\n",
"v34 f2 t1: original ll 0.0150/0.0132, ensemble ll 0.0149/0.0131\n",
"running time 2.79952335357666\n",
"starting fold 2 target 2\n",
"my_len 4\n",
"obj 0.03864261774481127\n",
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"obj 0.038642060704163234\n",
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"obj 0.03864537483997842\n",
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"obj 0.03866640020838346\n",
"obj 0.0386494241163526\n",
"obj 0.03865924189909652\n",
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"obj 0.03861377744256826\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"obj 0.03861288644457599\n",
"obj 0.03861282478178117\n",
"obj 0.03861282377857218\n",
"obj 0.03861282377493841\n",
"model [0.3423 0.6557] sum 0.998008898781132\n",
"my_len 4\n",
"v34 f2 t2: original ll 0.0370/0.0348, ensemble ll 0.0369/0.0348\n",
"running time 3.07651686668396\n",
"starting fold 2 target 3\n",
"my_len 4\n",
"obj 0.022995224518522383\n",
"obj 0.02299340932717575\n",
"obj 0.022993385766960923\n",
"obj 0.0229934166686739\n",
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"obj 0.0230039489232303\n",
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"obj 0.022955983453470243\n",
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"model [0.2945 0.7055] sum 0.9999997552090265\n",
"my_len 4\n",
"v34 f2 t3: original ll 0.0236/0.0230, ensemble ll 0.0235/0.0229\n",
"running time 3.2023861408233643\n",
"starting fold 2 target 4\n",
"my_len 4\n",
"obj 0.05949662701641982\n",
"obj 0.059496427471729306\n",
"obj 0.059496342460381116\n",
"obj 0.059496656778369565\n",
"obj 0.05949867889401739\n",
"obj 0.05949875155855878\n",
"obj 0.05950472150471753\n",
"obj 0.059513989228462204\n",
"obj 0.05949473181554839\n",
"obj 0.05949902808253696\n",
"obj 0.05946635705740825\n",
"obj 0.05945508305105207\n",
"obj 0.05945429089413502\n",
"obj 0.05945422305596472\n",
"obj 0.059454221242697204\n",
"obj 0.059454221233066123\n",
"model [0.3094 0.6888] sum 0.9982120655965214\n",
"my_len 4\n",
"v34 f2 t4: original ll 0.0608/0.0579, ensemble ll 0.0606/0.0578\n",
"running time 3.11377215385437\n",
"starting fold 2 target 5\n",
"my_len 4\n",
"obj 0.07494273297957547\n",
"obj 0.07494193934979902\n",
"obj 0.0749418088452629\n",
"obj 0.07494215623220447\n",
"obj 0.07494534611048678\n",
"obj 0.07494407120248021\n",
"obj 0.07494607787971268\n",
"obj 0.07495872278249605\n",
"obj 0.07493779709805286\n",
"obj 0.0749435731885866\n",
"obj 0.0749166852814284\n",
"obj 0.07490477996495228\n",
"obj 0.0749038705129651\n",
"obj 0.07490382029340723\n",
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"obj 0.07490381954523084\n",
"model [0.3463 0.6515] sum 0.9977752843293807\n",
"my_len 4\n",
"v34 f2 t5: original ll 0.0719/0.0680, ensemble ll 0.0718/0.0680\n",
"running time 3.083256244659424\n",
"total running time 54.07902908325195\n"
]
}
],
"source": [
"stg = time.time()\n",
"for fold in range(3):\n",
" for target in range(6):\n",
" train_ensemble(train_md, preds_all, fold=fold, target=target, weighted=True)\n",
"print('total running time', time.time() - stg)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"starting fold 0 target 0\n",
"my_len 4\n",
"obj 0.0933852150101332\n",
"obj 0.09338046248442476\n",
"obj 0.09338029486041102\n",
"obj 0.09338035972731744\n",
"obj 0.0933811143108087\n",
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"model [0.5182 0.4808] sum 0.9989492472281913\n",
"my_len 4\n",
"v33 f0 t0: original ll 0.0935/0.0887, ensemble ll 0.0935/0.0887\n",
"running time 3.2507238388061523\n",
"starting fold 0 target 1\n",
"my_len 4\n",
"obj 0.013897395265353997\n",
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"model [0.2427 0.7373] sum 0.9800015590878185\n",
"my_len 4\n",
"v33 f0 t1: original ll 0.0149/0.0138, ensemble ll 0.0148/0.0137\n",
"running time 3.0066232681274414\n",
"starting fold 0 target 2\n",
"my_len 4\n",
"obj 0.03855627126313789\n",
"obj 0.03856925253658012\n",
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"my_len 4\n",
"v33 f0 t2: original ll 0.0415/0.0392, ensemble ll 0.0415/0.0393\n",
"running time 2.8931853771209717\n",
"starting fold 0 target 3\n",
"my_len 4\n",
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"my_len 4\n",
"v33 f0 t3: original ll 0.0243/0.0239, ensemble ll 0.0243/0.0239\n",
"running time 2.9580657482147217\n",
"starting fold 0 target 4\n",
"my_len 4\n",
"obj 0.06201436184726342\n",
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"my_len 4\n",
"v33 f0 t4: original ll 0.0617/0.0596, ensemble ll 0.0617/0.0596\n",
"running time 3.1102256774902344\n",
"starting fold 0 target 5\n",
"my_len 4\n",
"obj 0.07485970317009938\n",
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"v33 f0 t5: original ll 0.0789/0.0757, ensemble ll 0.0789/0.0757\n",
"running time 3.0676307678222656\n",
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"my_len 4\n",
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"my_len 4\n",
"v33 f1 t0: original ll 0.0963/0.0902, ensemble ll 0.0963/0.0902\n",
"running time 3.3472983837127686\n",
"starting fold 1 target 1\n",
"my_len 4\n",
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"my_len 4\n",
"v33 f1 t1: original ll 0.0128/0.0110, ensemble ll 0.0128/0.0110\n",
"running time 2.9967260360717773\n",
"starting fold 1 target 2\n",
"my_len 4\n",
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"my_len 4\n",
"v33 f1 t2: original ll 0.0401/0.0381, ensemble ll 0.0401/0.0381\n",
"running time 3.1578216552734375\n",
"starting fold 1 target 3\n",
"my_len 4\n",
"obj 0.02395696038549021\n",
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"my_len 4\n",
"v33 f1 t3: original ll 0.0233/0.0221, ensemble ll 0.0233/0.0220\n",
"running time 3.0442991256713867\n",
"starting fold 1 target 4\n",
"my_len 4\n",
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"my_len 4\n",
"v33 f1 t4: original ll 0.0632/0.0594, ensemble ll 0.0632/0.0594\n",
"running time 2.8806824684143066\n",
"starting fold 1 target 5\n",
"my_len 4\n",
"obj 0.07535926071665933\n",
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"my_len 4\n",
"v33 f1 t5: original ll 0.0779/0.0742, ensemble ll 0.0779/0.0742\n",
"running time 2.8882410526275635\n",
"starting fold 2 target 0\n",
"my_len 4\n",
"obj 0.09489490438158145\n",
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"my_len 4\n",
"v33 f2 t0: original ll 0.0905/0.0845, ensemble ll 0.0905/0.0845\n",
"running time 3.214533567428589\n",
"starting fold 2 target 1\n",
"my_len 4\n",
"obj 0.013868197433815987\n",
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"my_len 4\n",
"v33 f2 t1: original ll 0.0150/0.0133, ensemble ll 0.0149/0.0131\n",
"running time 2.7753772735595703\n",
"starting fold 2 target 2\n",
"my_len 4\n",
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{
"name": "stdout",
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"text": [
"obj 0.040770119816992766\n",
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"my_len 4\n",
"v33 f2 t2: original ll 0.0370/0.0348, ensemble ll 0.0370/0.0348\n",
"running time 3.0440049171447754\n",
"starting fold 2 target 3\n",
"my_len 4\n",
"obj 0.023823516135291243\n",
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"model [0.3532 0.6468] sum 0.9999989006282439\n",
"my_len 4\n",
"v33 f2 t3: original ll 0.0236/0.0230, ensemble ll 0.0236/0.0229\n",
"running time 3.017551898956299\n",
"starting fold 2 target 4\n",
"my_len 4\n",
"obj 0.06243179531025402\n",
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"model [0.3265 0.6706] sum 0.9971639400865144\n",
"my_len 4\n",
"v33 f2 t4: original ll 0.0608/0.0580, ensemble ll 0.0607/0.0578\n",
"running time 3.0167412757873535\n",
"starting fold 2 target 5\n",
"my_len 4\n",
"obj 0.07838134560788608\n",
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"model [0.366 0.6339] sum 0.999906731906863\n",
"my_len 4\n",
"v33 f2 t5: original ll 0.0718/0.0680, ensemble ll 0.0718/0.0680\n",
"running time 3.3957345485687256\n",
"total running time 55.21563124656677\n"
]
}
],
"source": [
"stg = time.time()\n",
"for fold in range(3):\n",
" for target in range(6):\n",
" train_ensemble(train_md, preds_all, fold=fold, target=target, weighted=False)\n",
"print('total running time', time.time() - stg)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" <th></th>\n",
" <th></th>\n",
" <th>valid_loss</th>\n",
" <th>valid_loss_ens</th>\n",
" <th>valid_w_loss</th>\n",
" <th>valid_w_loss_ens</th>\n",
" </tr>\n",
" <tr>\n",
" <th>weighted</th>\n",
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" <td>0.093428</td>\n",
" <td>0.087781</td>\n",
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" <td>1</td>\n",
" <td>0.014243</td>\n",
" <td>0.014183</td>\n",
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" <td>0.012633</td>\n",
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" <tr>\n",
" <td>2</td>\n",
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" <td>0.039512</td>\n",
" <td>0.037398</td>\n",
" <td>0.037367</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.023751</td>\n",
" <td>0.023728</td>\n",
" <td>0.022988</td>\n",
" <td>0.022953</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.061900</td>\n",
" <td>0.061866</td>\n",
" <td>0.058991</td>\n",
" <td>0.058947</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.076200</td>\n",
" <td>0.076194</td>\n",
" <td>0.072653</td>\n",
" <td>0.072625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" valid_loss valid_loss_ens valid_w_loss valid_w_loss_ens\n",
"weighted target \n",
"False 0 0.093420 0.093428 0.087781 0.087797\n",
" 1 0.014243 0.014183 0.012694 0.012633\n",
" 2 0.039539 0.039512 0.037398 0.037367\n",
" 3 0.023751 0.023728 0.022988 0.022953\n",
" 4 0.061900 0.061866 0.058991 0.058947\n",
" 5 0.076200 0.076194 0.072653 0.072625"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats = pd.read_csv(PATH_DISK/'ensemble'/'stats.v{}'.format(VERSION))\n",
"stats.groupby(['weighted','target'])[['valid_loss','valid_loss_ens','valid_w_loss','valid_w_loss_ens']].mean()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>not weighted</th>\n",
" <th>weighted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>valid_loss</td>\n",
" <td>0.057518</td>\n",
" <td>0.057518</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss</td>\n",
" <td>0.054293</td>\n",
" <td>0.054293</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_loss_ens</td>\n",
" <td>0.057500</td>\n",
" <td>0.057498</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss_ens</td>\n",
" <td>0.054274</td>\n",
" <td>0.054269</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" not weighted weighted\n",
"valid_loss 0.057518 0.057518\n",
"valid_w_loss 0.054293 0.054293\n",
"valid_loss_ens 0.057500 0.057498\n",
"valid_w_loss_ens 0.054274 0.054269"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# STAGE2 weighted models\n",
"tt = pd.concat([\n",
"stats.loc[stats.weighted == False].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean(),\n",
"stats.loc[stats.weighted == True].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean()\n",
"],axis=1)\n",
"tt.columns = ['not weighted','weighted']\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>not weighted</th>\n",
" <th>weighted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>valid_loss</td>\n",
" <td>0.057496</td>\n",
" <td>0.057496</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss</td>\n",
" <td>0.054326</td>\n",
" <td>0.054326</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_loss_ens</td>\n",
" <td>0.057477</td>\n",
" <td>0.057475</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss_ens</td>\n",
" <td>0.054303</td>\n",
" <td>0.054299</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" not weighted weighted\n",
"valid_loss 0.057496 0.057496\n",
"valid_w_loss 0.054326 0.054326\n",
"valid_loss_ens 0.057477 0.057475\n",
"valid_w_loss_ens 0.054303 0.054299"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# STAGE2 non-wegihted models\n",
"tt = pd.concat([\n",
"stats.loc[stats.weighted == False].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean(),\n",
"stats.loc[stats.weighted == True].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean()\n",
"],axis=1)\n",
"tt.columns = ['not weighted','weighted']\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>not weighted</th>\n",
" <th>weighted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>valid_loss</td>\n",
" <td>0.057750</td>\n",
" <td>0.057750</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss</td>\n",
" <td>0.061831</td>\n",
" <td>0.061831</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_loss_ens</td>\n",
" <td>0.057658</td>\n",
" <td>0.058210</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss_ens</td>\n",
" <td>0.062176</td>\n",
" <td>0.061514</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" not weighted weighted\n",
"valid_loss 0.057750 0.057750\n",
"valid_w_loss 0.061831 0.061831\n",
"valid_loss_ens 0.057658 0.058210\n",
"valid_w_loss_ens 0.062176 0.061514"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wegihted models\n",
"tt = pd.concat([\n",
"stats.loc[stats.weighted == False].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean(),\n",
"stats.loc[stats.weighted == True].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean()\n",
"],axis=1)\n",
"tt.columns = ['not weighted','weighted']\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>not weighted</th>\n",
" <th>weighted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>valid_loss</td>\n",
" <td>0.057661</td>\n",
" <td>0.057661</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss</td>\n",
" <td>0.062715</td>\n",
" <td>0.062715</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_loss_ens</td>\n",
" <td>0.057638</td>\n",
" <td>0.057799</td>\n",
" </tr>\n",
" <tr>\n",
" <td>valid_w_loss_ens</td>\n",
" <td>0.062705</td>\n",
" <td>0.062648</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" not weighted weighted\n",
"valid_loss 0.057661 0.057661\n",
"valid_w_loss 0.062715 0.062715\n",
"valid_loss_ens 0.057638 0.057799\n",
"valid_w_loss_ens 0.062705 0.062648"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# non-weighted models\n",
"tt = pd.concat([\n",
"stats.loc[stats.weighted == False].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean(),\n",
"stats.loc[stats.weighted == True].groupby('target')[['valid_loss','valid_w_loss',\n",
" 'valid_loss_ens','valid_w_loss_ens']].mean()\\\n",
" .apply(lambda x: x*class_weights).mean()\n",
"],axis=1)\n",
"tt.columns = ['not weighted','weighted']\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 [0.5599 0.4398] 0.9996370455376221\n",
"1 [0.2003 0.7864] 0.9866685528969734\n",
"2 [0.3142 0.6845] 0.99873854778024\n",
"3 [0.3394 0.6606] 0.9999990672969397\n",
"4 [0.2394 0.7571] 0.9964796234369401\n",
"5 [0.3639 0.6317] 0.9955373459864658\n",
"total [0.3362 0.66 ] 0.9961766971558637\n"
]
}
],
"source": [
"np.set_printoptions(precision=4)\n",
"res2_all = []\n",
"for target in range(6):\n",
" res2 = np.zeros((3, 2))\n",
" for fold in range(3):\n",
" model = pickle.load(open(PATH_DISK/'ensemble'/'model.f{}.t{}.v{}'\n",
" .format(fold,target,VERSION),'rb'))\n",
" res2[fold] = model.x\n",
" #print(fold,target,model.x)\n",
" print(target, res2.mean(0), res2.mean(0).sum())\n",
" res2_all.append(res2)\n",
"print('total', np.stack(res2_all).mean((0,1)), np.stack(res2_all).mean((0,1)).sum())"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# STAGE2 weighted\n",
"0 [0.5514 0.4486] 0.999988938547882\n",
"1 [0.1934 0.7932] 0.9866673384339762\n",
"2 [0.3245 0.6748] 0.99933085408868\n",
"3 [0.2924 0.7076] 0.999999807185227\n",
"4 [0.2338 0.7643] 0.9981083416558326\n",
"5 [0.347 0.6474] 0.9944152840034981\n",
"total [0.3237 0.6727] 0.9964184273191827\n",
"\n",
"\n",
"# STAGE2 non-weighted\n",
"0 [0.5599 0.4398] 0.9996370455376221\n",
"1 [0.2003 0.7864] 0.9866685528969734\n",
"2 [0.3142 0.6845] 0.99873854778024\n",
"3 [0.3394 0.6606] 0.9999990672969397\n",
"4 [0.2394 0.7571] 0.9964796234369401\n",
"5 [0.3639 0.6317] 0.9955373459864658\n",
"total [0.3362 0.66 ] 0.9961766971558637\n",
"\n",
"# weighted + focal both\n",
"0 [9.8936e-01 4.6289e-06] 0.9893636389192056\n",
"1 [0.1052 0.8948] 0.9999984169006635\n",
"2 [0.4988 0.49 ] 0.9887665083664696\n",
"3 [0.3443 0.6427] 0.9869733391806492\n",
"4 [0.4923 0.4959] 0.9882284866416893\n",
"5 [0.7736 0.217 ] 0.9906108307629572\n",
"total [0.5339 0.4567] 0.9906568701286057\n",
"\n",
"# weighted + focal\n",
"0 [0.9758 0.0133] 0.9890208236733224\n",
"1 [0.0755 0.9245] 0.99999750757956\n",
"2 [0.5121 0.4765] 0.9885906315242545\n",
"3 [0.3465 0.6394] 0.9858337972624043\n",
"4 [0.4733 0.5144] 0.987734118671811\n",
"5 [0.6697 0.3208] 0.9904312808036815\n",
"total [0.5088 0.4815] 0.990268026585839\n",
"\n",
"# weighted\n",
"0 [0.9856 0.0034] 0.9890322827488863\n",
"1 [0.1394 0.8606] 0.9999977090921796\n",
"2 [0.5308 0.4582] 0.9890278807370222\n",
"3 [0.3542 0.6317] 0.9858961392052805\n",
"4 [0.4851 0.5028] 0.9879196279125524\n",
"5 [0.7677 0.2223] 0.9900299923215763\n",
"total [0.5438 0.4465] 0.9903172720029163\n",
"\n",
"# non-weighted\n",
"0 [0.3504 0.6483] 0.9987279422120475\n",
"1 [0.16 0.8267] 0.9866673030259768\n",
"2 [0.2285 0.7706] 0.9990219638127198\n",
"3 [0.2355 0.7636] 0.9991180197159109\n",
"4 [0.1587 0.8378] 0.9965445852859403\n",
"5 [0.2654 0.7281] 0.9934664823990461\n",
"total [0.2331 0.7625] 0.995591049408607"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"preds3 = np.stack([pickle.load(open(PATH_DISK/'preds_d{}_v{}'.format(ds, VERSION),'rb')) for ds in my_datasets3])\n",
"preds5 = np.stack([pickle.load(open(PATH_DISK/'preds_d{}_v{}'.format(ds, VERSION),'rb')) for ds in my_datasets5])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"preds = np.concatenate([preds3.mean((1,2)), preds5.mean((1,2))],axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#del test_md['yuval_idx']"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"yuval_test = pickle.load(open(PATH_DISK/'yuval/ensemble_test_image_ids_stage2.pkl','rb'))\n",
"assert len(yuval_test) == len(test_md)\n",
"\n",
"df = pd.DataFrame(np.arange(len(yuval_test)), columns=['yuval_idx'])\n",
"df.index = yuval_test\n",
"test_md = test_md.join(df, on = 'img_id')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"names_y3 = [\n",
" #'model_Densenet201_3_version_classifier_splits_fullhead_resmodel_pool2_3_type_OOF_pred_split_{}.pkl',\n",
" #'model_Densenet161_3_version_classifier_splits_fullhead_resmodel_pool2_3_type_OOF_pred_split_{}.pkl',\n",
"'model_Densenet169_3_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl',\n",
"'model_se_resnext101_32x4d_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl',\n",
"'model_se_resnet101_version_classifier_splits_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"names_y5 = [\n",
"'model_se_resnext101_32x4d_version_new_splits_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl',\n",
"'model_se_resnet101_version_new_splits_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl',\n",
"'model_se_resnet101_version_new_splits_focal_fullhead_resmodel_pool2_stage2_3_type_test_pred_ensemble_split_{}.pkl',\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"preds_y3 = np.stack([torch.sigmoid(torch.stack([torch.stack(pickle.load(\n",
" open(PATH_DISK/'yuval/OOF_stage2'/name.format(fold),'rb'))) for fold in range(3)])).numpy() for name in names_y3])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"preds_y5 = np.stack([torch.sigmoid(torch.stack([torch.stack(pickle.load(\n",
" open(PATH_DISK/'yuval/OOF_stage2'/name.format(fold),'rb'))) for fold in range(5)])).numpy() for name in names_y5])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"preds_y = np.concatenate([preds_y3.mean((1,2)), preds_y5.mean((1,2))],axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"preds_y = preds_y[:,test_md.yuval_idx]\n",
"preds_y = preds_y[:,:,np.array([5,0,1,2,3,4])]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"preds = np.concatenate([preds, preds_y], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"preds = preds[ds_mask]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 121232, 6)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"any too low inconsistencies\n",
"1 class: 0.000837237693018345\n",
"2 class: 0.016652369011482118\n",
"3 class: 0.008991850336544807\n",
"4 class: 0.016291903127887027\n",
"5 class: 0.05360135937706216\n",
"total 0.08837930579384981\n",
"any too high inconsistencies\n",
"total 0.21430810347103074\n"
]
}
],
"source": [
"preds = predBounding(preds)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"#predictions = preds.mean((0,1))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"version 33 my_len 4\n",
"total running time 0.07065796852111816\n"
]
}
],
"source": [
"stg = time.time()\n",
"\n",
"test_preds_trgt = []\n",
"print('version', VERSION, 'my_len', my_len)\n",
"for target in range(6):\n",
" \n",
" test_preds_fold = []\n",
" for fold in range(3):\n",
" X = np.stack([preds[:my_len,:,target].mean(0), \n",
" preds[my_len:,:,target].mean(0)], axis=0)\n",
" \n",
" model = pickle.load(open(PATH_DISK/'ensemble'/'model.f{}.t{}.v{}'.format(fold,target,VERSION),'rb'))\n",
" test_preds_fold.append((X*np.expand_dims(model.x, axis=1)).sum(0))\n",
" \n",
" test_preds_trgt.append(np.stack(test_preds_fold).mean(0))\n",
"\n",
"predictions = np.stack(test_preds_trgt,axis=1)\n",
"\n",
"print('total running time', time.time() - stg)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1. , 0.9956, 0.9954, 0.9953, 0.9958, 0.9937, 0.9942, 0.9947,\n",
" 0.9946, 0.9943],\n",
" [0.9956, 1. , 0.9978, 0.9977, 0.9951, 0.997 , 0.9964, 0.9986,\n",
" 0.997 , 0.9967],\n",
" [0.9954, 0.9978, 1. , 0.9992, 0.9951, 0.9956, 0.9972, 0.9966,\n",
" 0.9984, 0.9978],\n",
" [0.9953, 0.9977, 0.9992, 1. , 0.995 , 0.9957, 0.9973, 0.9967,\n",
" 0.9982, 0.9985],\n",
" [0.9958, 0.9951, 0.9951, 0.995 , 1. , 0.9944, 0.9949, 0.9954,\n",
" 0.9955, 0.9951],\n",
" [0.9937, 0.997 , 0.9956, 0.9957, 0.9944, 1. , 0.9965, 0.998 ,\n",
" 0.9968, 0.9967],\n",
" [0.9942, 0.9964, 0.9972, 0.9973, 0.9949, 0.9965, 1. , 0.9971,\n",
" 0.9983, 0.9982],\n",
" [0.9947, 0.9986, 0.9966, 0.9967, 0.9954, 0.998 , 0.9971, 1. ,\n",
" 0.9979, 0.9977],\n",
" [0.9946, 0.997 , 0.9984, 0.9982, 0.9955, 0.9968, 0.9983, 0.9979,\n",
" 1. , 0.9993],\n",
" [0.9943, 0.9967, 0.9978, 0.9985, 0.9951, 0.9967, 0.9982, 0.9977,\n",
" 0.9993, 1. ]])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.corrcoef(preds[:,:,0])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(121232, 6)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submitting"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"id_column = np.array([a + '_' + b for a in test_md.SOPInstanceUID for b in all_ich])\n",
"sub = pd.DataFrame({'ID': id_column, 'Label': predictions.reshape(-1)})\n",
"sub.to_csv(PATH/'sub.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sanity checks"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"test_md['pred_any'] = predictions[:,0]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>img_id</th>\n",
" <th>SOPInstanceUID</th>\n",
" <th>Modality</th>\n",
" <th>PatientID</th>\n",
" <th>StudyInstanceUID</th>\n",
" <th>SeriesInstanceUID</th>\n",
" <th>StudyID</th>\n",
" <th>ImagePositionPatient</th>\n",
" <th>ImageOrientationPatient</th>\n",
" <th>SamplesPerPixel</th>\n",
" <th>PhotometricInterpretation</th>\n",
" <th>Rows</th>\n",
" <th>Columns</th>\n",
" <th>PixelSpacing</th>\n",
" <th>BitsAllocated</th>\n",
" <th>BitsStored</th>\n",
" <th>HighBit</th>\n",
" <th>PixelRepresentation</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>RescaleIntercept</th>\n",
" <th>RescaleSlope</th>\n",
" <th>PxlMin</th>\n",
" <th>PxlMax</th>\n",
" <th>PxlStd</th>\n",
" <th>PxlMean</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" <th>ImageOrientationPatient_0</th>\n",
" <th>ImageOrientationPatient_1</th>\n",
" <th>ImageOrientationPatient_2</th>\n",
" <th>ImageOrientationPatient_3</th>\n",
" <th>ImageOrientationPatient_4</th>\n",
" <th>ImageOrientationPatient_5</th>\n",
" <th>ImagePositionPatient_0</th>\n",
" <th>ImagePositionPatient_1</th>\n",
" <th>ImagePositionPatient_2</th>\n",
" <th>PixelSpacing_0</th>\n",
" <th>PixelSpacing_1</th>\n",
" <th>WindowCenter_0</th>\n",
" <th>WindowCenter_1</th>\n",
" <th>WindowCenter_1_NAN</th>\n",
" <th>WindowWidth_0</th>\n",
" <th>WindowWidth_1</th>\n",
" <th>WindowWidth_0_le</th>\n",
" <th>WindowWidth_1_le</th>\n",
" <th>WindowCenter_1_le</th>\n",
" <th>BitType_le</th>\n",
" <th>ImageOrientationPatient_4_f</th>\n",
" <th>ImageOrientationPatient_4_enc_0</th>\n",
" <th>...</th>\n",
" <th>ImageOrientationPatient_5_f</th>\n",
" <th>ImageOrientationPatient_5_enc_0</th>\n",
" <th>ImageOrientationPatient_5_enc_1</th>\n",
" <th>ImagePositionPatient_0_f</th>\n",
" <th>ImagePositionPatient_0_enc_0</th>\n",
" <th>ImagePositionPatient_0_enc_1</th>\n",
" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
" <th>ImagePositionPatient_2_f</th>\n",
" <th>ImagePositionPatient_2_f_r05</th>\n",
" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
" <th>PixelSpacing_1_enc_1</th>\n",
" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
" <th>pos_idx2</th>\n",
" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>12436</td>\n",
" <td>68c2b8b03</td>\n",
" <td>ID_68c2b8b03</td>\n",
" <td>CT</td>\n",
" <td>ID_db5b61c1</td>\n",
" <td>ID_451abcb4a1</td>\n",
" <td>ID_36778f2a4a</td>\n",
" <td>NaN</td>\n",
" <td>['-125.000', '-148.300', '135.250']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.488281', '0.488281']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>150</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.064000</td>\n",
" <td>-1.548000</td>\n",
" <td>-1.402099</td>\n",
" <td>-1.620352</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-125.0</td>\n",
" <td>-148.3</td>\n",
" <td>135.250000</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-1.110667</td>\n",
" <td>0.0</td>\n",
" <td>-0.045487</td>\n",
" <td>0.0</td>\n",
" <td>-0.480</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.5810</td>\n",
" <td>-0.1190</td>\n",
" <td>0.5</td>\n",
" <td>1.355932</td>\n",
" <td>37</td>\n",
" <td>-1.016949</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.266667</td>\n",
" <td>1.771429</td>\n",
" <td>1.6</td>\n",
" <td>-0.600000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>80726</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82308</td>\n",
" <td>7f95e978e</td>\n",
" <td>ID_7f95e978e</td>\n",
" <td>CT</td>\n",
" <td>ID_ae6fa62a</td>\n",
" <td>ID_3a1815c27a</td>\n",
" <td>ID_64db061397</td>\n",
" <td>NaN</td>\n",
" <td>['-108.000', '-116.300', '114.000']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.421875', '0.421875']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>100</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.064000</td>\n",
" <td>-1.572000</td>\n",
" <td>-1.392679</td>\n",
" <td>-1.599246</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-108.0</td>\n",
" <td>-116.3</td>\n",
" <td>114.000000</td>\n",
" <td>0.421875</td>\n",
" <td>0.421875</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>1.733333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.684000</td>\n",
" <td>0.0</td>\n",
" <td>-0.075931</td>\n",
" <td>1.0</td>\n",
" <td>1.295</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.4760</td>\n",
" <td>-0.1840</td>\n",
" <td>-0.1</td>\n",
" <td>1.016949</td>\n",
" <td>32</td>\n",
" <td>-1.084746</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>1.878788</td>\n",
" <td>0.0</td>\n",
" <td>-0.600000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>68171</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60800</td>\n",
" <td>84735b84a</td>\n",
" <td>ID_84735b84a</td>\n",
" <td>CT</td>\n",
" <td>ID_ddcad7d4</td>\n",
" <td>ID_d7e80c40be</td>\n",
" <td>ID_11c94b7b33</td>\n",
" <td>NaN</td>\n",
" <td>['-155', '23', '138.699997']</td>\n",
" <td>['1', '0', '0', '0', '1', '0']</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00036', '00036']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.314667</td>\n",
" <td>-1.914667</td>\n",
" <td>-2.872322</td>\n",
" <td>-0.693297</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-155.0</td>\n",
" <td>23.0</td>\n",
" <td>138.699997</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>36.0</td>\n",
" <td>36.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>0.480000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.173333</td>\n",
" <td>1.0</td>\n",
" <td>-0.040544</td>\n",
" <td>0.0</td>\n",
" <td>-0.480</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>0.5748</td>\n",
" <td>-0.1252</td>\n",
" <td>0.1</td>\n",
" <td>1.152542</td>\n",
" <td>34</td>\n",
" <td>-1.084746</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>0.266666</td>\n",
" <td>1.885714</td>\n",
" <td>0.0</td>\n",
" <td>-0.599994</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>27981</td>\n",
" <td>0.000083</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102453</td>\n",
" <td>d6a5e0432</td>\n",
" <td>ID_d6a5e0432</td>\n",
" <td>CT</td>\n",
" <td>ID_73887cfd</td>\n",
" <td>ID_4cc0b3574d</td>\n",
" <td>ID_bd88957d37</td>\n",
" <td>NaN</td>\n",
" <td>['-125.000', '-131.700', '105.000']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.488281', '0.488281']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>150</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.064000</td>\n",
" <td>-1.558667</td>\n",
" <td>-1.401150</td>\n",
" <td>-1.616006</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-125.0</td>\n",
" <td>-131.7</td>\n",
" <td>105.000000</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.889333</td>\n",
" <td>0.0</td>\n",
" <td>-0.088825</td>\n",
" <td>1.0</td>\n",
" <td>-0.480</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.4400</td>\n",
" <td>-0.1800</td>\n",
" <td>-0.3</td>\n",
" <td>0.881356</td>\n",
" <td>30</td>\n",
" <td>-1.084746</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-0.266667</td>\n",
" <td>1.870968</td>\n",
" <td>0.0</td>\n",
" <td>-0.600000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>90163</td>\n",
" <td>0.000085</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10097</td>\n",
" <td>6df94672e</td>\n",
" <td>ID_6df94672e</td>\n",
" <td>CT</td>\n",
" <td>ID_39c82642</td>\n",
" <td>ID_9f4b3b7a4d</td>\n",
" <td>ID_81c1365f46</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '18', '-120.099976']</td>\n",
" <td>['1', '0', '0', '0', '1', '0']</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00036', '00036']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.108000</td>\n",
" <td>-2.544276</td>\n",
" <td>-0.594726</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-125.0</td>\n",
" <td>18.0</td>\n",
" <td>-120.099976</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>36.0</td>\n",
" <td>36.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.106667</td>\n",
" <td>1.0</td>\n",
" <td>-0.411318</td>\n",
" <td>0.0</td>\n",
" <td>-0.480</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>-0.4404</td>\n",
" <td>-1.4004</td>\n",
" <td>1.4</td>\n",
" <td>1.966102</td>\n",
" <td>46</td>\n",
" <td>-1.016949</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>10</td>\n",
" <td>2.000000</td>\n",
" <td>1.833333</td>\n",
" <td>0.0</td>\n",
" <td>-0.600000</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>14678</td>\n",
" <td>0.000086</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"12436 68c2b8b03 ID_68c2b8b03 CT ID_db5b61c1 ID_451abcb4a1 \n",
"82308 7f95e978e ID_7f95e978e CT ID_ae6fa62a ID_3a1815c27a \n",
"60800 84735b84a ID_84735b84a CT ID_ddcad7d4 ID_d7e80c40be \n",
"102453 d6a5e0432 ID_d6a5e0432 CT ID_73887cfd ID_4cc0b3574d \n",
"10097 6df94672e ID_6df94672e CT ID_39c82642 ID_9f4b3b7a4d \n",
"\n",
" SeriesInstanceUID StudyID ImagePositionPatient \\\n",
"12436 ID_36778f2a4a NaN ['-125.000', '-148.300', '135.250'] \n",
"82308 ID_64db061397 NaN ['-108.000', '-116.300', '114.000'] \n",
"60800 ID_11c94b7b33 NaN ['-155', '23', '138.699997'] \n",
"102453 ID_bd88957d37 NaN ['-125.000', '-131.700', '105.000'] \n",
"10097 ID_81c1365f46 NaN ['-125', '18', '-120.099976'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"12436 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"82308 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"60800 ['1', '0', '0', '0', '1', '0'] 1 \n",
"102453 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"10097 ['1', '0', '0', '0', '1', '0'] 1 \n",
"\n",
" PhotometricInterpretation Rows Columns PixelSpacing \\\n",
"12436 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"82308 MONOCHROME2 512 512 ['0.421875', '0.421875'] \n",
"60800 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"102453 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"10097 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"\n",
" BitsAllocated BitsStored HighBit PixelRepresentation \\\n",
"12436 16 16 15 1 \n",
"82308 16 16 15 1 \n",
"60800 16 12 11 0 \n",
"102453 16 16 15 1 \n",
"10097 16 12 11 0 \n",
"\n",
" WindowCenter WindowWidth RescaleIntercept \\\n",
"12436 40 150 -1024.0 \n",
"82308 40 100 -1024.0 \n",
"60800 ['00036', '00036'] ['00080', '00080'] -1024.0 \n",
"102453 40 150 -1024.0 \n",
"10097 ['00036', '00036'] ['00080', '00080'] -1024.0 \n",
"\n",
" RescaleSlope PxlMin PxlMax PxlStd PxlMean test test2 \\\n",
"12436 1.0 -0.064000 -1.548000 -1.402099 -1.620352 False True \n",
"82308 1.0 -0.064000 -1.572000 -1.392679 -1.599246 False True \n",
"60800 1.0 1.314667 -1.914667 -2.872322 -0.693297 False True \n",
"102453 1.0 -0.064000 -1.558667 -1.401150 -1.616006 False True \n",
"10097 1.0 1.301333 0.108000 -2.544276 -0.594726 False True \n",
"\n",
" ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n",
"12436 1.0 0.0 \n",
"82308 1.0 0.0 \n",
"60800 1.0 0.0 \n",
"102453 1.0 0.0 \n",
"10097 1.0 0.0 \n",
"\n",
" ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n",
"12436 0.0 0.0 \n",
"82308 0.0 0.0 \n",
"60800 0.0 0.0 \n",
"102453 0.0 0.0 \n",
"10097 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_4 ImageOrientationPatient_5 \\\n",
"12436 1.0 0.0 \n",
"82308 1.0 0.0 \n",
"60800 1.0 0.0 \n",
"102453 1.0 0.0 \n",
"10097 1.0 0.0 \n",
"\n",
" ImagePositionPatient_0 ImagePositionPatient_1 \\\n",
"12436 -125.0 -148.3 \n",
"82308 -108.0 -116.3 \n",
"60800 -155.0 23.0 \n",
"102453 -125.0 -131.7 \n",
"10097 -125.0 18.0 \n",
"\n",
" ImagePositionPatient_2 PixelSpacing_0 PixelSpacing_1 \\\n",
"12436 135.250000 0.488281 0.488281 \n",
"82308 114.000000 0.421875 0.421875 \n",
"60800 138.699997 0.488281 0.488281 \n",
"102453 105.000000 0.488281 0.488281 \n",
"10097 -120.099976 0.488281 0.488281 \n",
"\n",
" WindowCenter_0 WindowCenter_1 WindowCenter_1_NAN WindowWidth_0 \\\n",
"12436 40.0 NaN True 150.0 \n",
"82308 40.0 NaN True 100.0 \n",
"60800 36.0 36.0 False 80.0 \n",
"102453 40.0 NaN True 150.0 \n",
"10097 36.0 36.0 False 80.0 \n",
"\n",
" WindowWidth_1 WindowWidth_0_le WindowWidth_1_le WindowCenter_1_le \\\n",
"12436 NaN 1 1 3 \n",
"82308 NaN 2 1 3 \n",
"60800 80.0 0 0 0 \n",
"102453 NaN 1 1 3 \n",
"10097 80.0 0 0 0 \n",
"\n",
" BitType_le ImageOrientationPatient_4_f \\\n",
"12436 0 -1.333333 \n",
"82308 0 -1.333333 \n",
"60800 1 -1.333333 \n",
"102453 0 -1.333333 \n",
"10097 1 -1.333333 \n",
"\n",
" ImageOrientationPatient_4_enc_0 ... ImageOrientationPatient_5_f \\\n",
"12436 1.0 ... -0.666667 \n",
"82308 1.0 ... -0.666667 \n",
"60800 1.0 ... -0.666667 \n",
"102453 1.0 ... -0.666667 \n",
"10097 1.0 ... -0.666667 \n",
"\n",
" ImageOrientationPatient_5_enc_0 ImageOrientationPatient_5_enc_1 \\\n",
"12436 1.0 False \n",
"82308 1.0 False \n",
"60800 1.0 False \n",
"102453 1.0 False \n",
"10097 1.0 False \n",
"\n",
" ImagePositionPatient_0_f ImagePositionPatient_0_enc_0 \\\n",
"12436 -0.720000 1.0 \n",
"82308 1.733333 0.0 \n",
"60800 0.480000 0.0 \n",
"102453 -0.720000 1.0 \n",
"10097 -0.720000 1.0 \n",
"\n",
" ImagePositionPatient_0_enc_1 ImagePositionPatient_0_f_r1 \\\n",
"12436 0.0 1.0 \n",
"82308 0.0 1.0 \n",
"60800 0.0 1.0 \n",
"102453 0.0 1.0 \n",
"10097 0.0 1.0 \n",
"\n",
" ImagePositionPatient_0_f_r05 ImagePositionPatient_1_f \\\n",
"12436 1.0 -1.110667 \n",
"82308 1.0 -0.684000 \n",
"60800 1.0 1.173333 \n",
"102453 1.0 -0.889333 \n",
"10097 1.0 1.106667 \n",
"\n",
" ImagePositionPatient_1_enc_0 ImagePositionPatient_2_f \\\n",
"12436 0.0 -0.045487 \n",
"82308 0.0 -0.075931 \n",
"60800 1.0 -0.040544 \n",
"102453 0.0 -0.088825 \n",
"10097 1.0 -0.411318 \n",
"\n",
" ImagePositionPatient_2_f_r05 PixelSpacing_1_f PixelSpacing_1_enc_0 \\\n",
"12436 0.0 -0.480 1.0 \n",
"82308 1.0 1.295 0.0 \n",
"60800 0.0 -0.480 1.0 \n",
"102453 1.0 -0.480 1.0 \n",
"10097 0.0 -0.480 1.0 \n",
"\n",
" PixelSpacing_1_enc_1 WindowCenter_0_le pos_max pos_min pos_size \\\n",
"12436 False 2 0.5810 -0.1190 0.5 \n",
"82308 False 2 0.4760 -0.1840 -0.1 \n",
"60800 False 1 0.5748 -0.1252 0.1 \n",
"102453 False 2 0.4400 -0.1800 -0.3 \n",
"10097 False 1 -0.4404 -1.4004 1.4 \n",
"\n",
" pos_idx1 pos_idx pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le \\\n",
"12436 1.355932 37 -1.016949 -1.5 -1.5 3 \n",
"82308 1.016949 32 -1.084746 -1.5 -1.5 3 \n",
"60800 1.152542 34 -1.084746 -1.5 -1.5 3 \n",
"102453 0.881356 30 -1.084746 -1.5 -1.5 3 \n",
"10097 1.966102 46 -1.016949 -1.5 -1.5 3 \n",
"\n",
" pos_inc2_grp_le pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 \\\n",
"12436 3 1.0 1.0 0.0 \n",
"82308 3 1.0 1.0 0.0 \n",
"60800 3 1.0 1.0 0.0 \n",
"102453 3 1.0 1.0 0.0 \n",
"10097 3 1.0 1.0 0.0 \n",
"\n",
" pos_inc2_enc_0 pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le \\\n",
"12436 0.0 1.0 1.0 1 \n",
"82308 0.0 1.0 1.0 4 \n",
"60800 0.0 1.0 1.0 2 \n",
"102453 0.0 1.0 1.0 0 \n",
"10097 0.0 1.0 1.0 10 \n",
"\n",
" pos_range pos_rel pos_zeros pos_inc_rng pos_zeros_le \\\n",
"12436 0.266667 1.771429 1.6 -0.600000 1 \n",
"82308 0.000000 1.878788 0.0 -0.600000 0 \n",
"60800 0.266666 1.885714 0.0 -0.599994 0 \n",
"102453 -0.266667 1.870968 0.0 -0.600000 0 \n",
"10097 2.000000 1.833333 0.0 -0.600000 0 \n",
"\n",
" PxlMin_grp_le PxlMin_zero any epidural intraparenchymal \\\n",
"12436 1 False NaN NaN NaN \n",
"82308 1 False NaN NaN NaN \n",
"60800 2 False NaN NaN NaN \n",
"102453 1 False NaN NaN NaN \n",
"10097 2 False NaN NaN NaN \n",
"\n",
" intraventricular subarachnoid subdural any_series SeriesPP \\\n",
"12436 NaN NaN NaN False -0.5 \n",
"82308 NaN NaN NaN False -0.5 \n",
"60800 NaN NaN NaN False -0.5 \n",
"102453 NaN NaN NaN False -0.5 \n",
"10097 NaN NaN NaN False -0.5 \n",
"\n",
" yuval_idx pred_any \n",
"12436 80726 0.000082 \n",
"82308 68171 0.000082 \n",
"60800 27981 0.000083 \n",
"102453 90163 0.000085 \n",
"10097 14678 0.000086 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md.sort_values('pred_any').head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>img_id</th>\n",
" <th>SOPInstanceUID</th>\n",
" <th>Modality</th>\n",
" <th>PatientID</th>\n",
" <th>StudyInstanceUID</th>\n",
" <th>SeriesInstanceUID</th>\n",
" <th>StudyID</th>\n",
" <th>ImagePositionPatient</th>\n",
" <th>ImageOrientationPatient</th>\n",
" <th>SamplesPerPixel</th>\n",
" <th>PhotometricInterpretation</th>\n",
" <th>Rows</th>\n",
" <th>Columns</th>\n",
" <th>PixelSpacing</th>\n",
" <th>BitsAllocated</th>\n",
" <th>BitsStored</th>\n",
" <th>HighBit</th>\n",
" <th>PixelRepresentation</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>RescaleIntercept</th>\n",
" <th>RescaleSlope</th>\n",
" <th>PxlMin</th>\n",
" <th>PxlMax</th>\n",
" <th>PxlStd</th>\n",
" <th>PxlMean</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" <th>ImageOrientationPatient_0</th>\n",
" <th>ImageOrientationPatient_1</th>\n",
" <th>ImageOrientationPatient_2</th>\n",
" <th>ImageOrientationPatient_3</th>\n",
" <th>ImageOrientationPatient_4</th>\n",
" <th>ImageOrientationPatient_5</th>\n",
" <th>ImagePositionPatient_0</th>\n",
" <th>ImagePositionPatient_1</th>\n",
" <th>ImagePositionPatient_2</th>\n",
" <th>PixelSpacing_0</th>\n",
" <th>PixelSpacing_1</th>\n",
" <th>WindowCenter_0</th>\n",
" <th>WindowCenter_1</th>\n",
" <th>WindowCenter_1_NAN</th>\n",
" <th>WindowWidth_0</th>\n",
" <th>WindowWidth_1</th>\n",
" <th>WindowWidth_0_le</th>\n",
" <th>WindowWidth_1_le</th>\n",
" <th>WindowCenter_1_le</th>\n",
" <th>BitType_le</th>\n",
" <th>ImageOrientationPatient_4_f</th>\n",
" <th>ImageOrientationPatient_4_enc_0</th>\n",
" <th>...</th>\n",
" <th>ImageOrientationPatient_5_f</th>\n",
" <th>ImageOrientationPatient_5_enc_0</th>\n",
" <th>ImageOrientationPatient_5_enc_1</th>\n",
" <th>ImagePositionPatient_0_f</th>\n",
" <th>ImagePositionPatient_0_enc_0</th>\n",
" <th>ImagePositionPatient_0_enc_1</th>\n",
" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
" <th>ImagePositionPatient_2_f</th>\n",
" <th>ImagePositionPatient_2_f_r05</th>\n",
" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
" <th>PixelSpacing_1_enc_1</th>\n",
" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
" <th>pos_idx2</th>\n",
" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>100029</td>\n",
" <td>d3bd67ff1</td>\n",
" <td>ID_d3bd67ff1</td>\n",
" <td>CT</td>\n",
" <td>ID_07aa4e90</td>\n",
" <td>ID_19039aeb7f</td>\n",
" <td>ID_83a456ed02</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '-5.28788193', '235.817384']</td>\n",
" <td>['1', '0', '0', '0', '0.927183855', '-0.374606...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.093333</td>\n",
" <td>-0.618874</td>\n",
" <td>1.229975</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.927184</td>\n",
" <td>-0.374607</td>\n",
" <td>-125.000000</td>\n",
" <td>-5.287882</td>\n",
" <td>235.817384</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.695785</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.835956</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.796162</td>\n",
" <td>1.0</td>\n",
" <td>0.098592</td>\n",
" <td>0.0</td>\n",
" <td>-0.480000</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.202425</td>\n",
" <td>0.502892</td>\n",
" <td>-0.7</td>\n",
" <td>0.000000</td>\n",
" <td>17</td>\n",
" <td>-0.474576</td>\n",
" <td>2.247192</td>\n",
" <td>2.247192</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>0.263550</td>\n",
" <td>0.518123</td>\n",
" <td>0.0</td>\n",
" <td>-0.575802</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>52190</td>\n",
" <td>0.998915</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101618</td>\n",
" <td>b5c2fbbe1</td>\n",
" <td>ID_b5c2fbbe1</td>\n",
" <td>CT</td>\n",
" <td>ID_877a2214</td>\n",
" <td>ID_f5d8b2ad40</td>\n",
" <td>ID_c37347c9a3</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '92.449158']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.148000</td>\n",
" <td>-0.975081</td>\n",
" <td>1.044669</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>92.449158</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>-0.106806</td>\n",
" <td>0.0</td>\n",
" <td>-0.480000</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.609797</td>\n",
" <td>-0.010203</td>\n",
" <td>-0.3</td>\n",
" <td>0.135593</td>\n",
" <td>19</td>\n",
" <td>-0.338983</td>\n",
" <td>-1.500000</td>\n",
" <td>-1.500000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-0.266667</td>\n",
" <td>0.451613</td>\n",
" <td>0.0</td>\n",
" <td>-0.600000</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>93649</td>\n",
" <td>0.998922</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54912</td>\n",
" <td>5519471d4</td>\n",
" <td>ID_5519471d4</td>\n",
" <td>CT</td>\n",
" <td>ID_35384be6</td>\n",
" <td>ID_cc5b6c0a29</td>\n",
" <td>ID_5d7a4ca229</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '72.8792912', '193.380843']</td>\n",
" <td>['1', '0', '0', '0', '0.920504853', '-0.390731...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>1.525333</td>\n",
" <td>-0.941557</td>\n",
" <td>1.166305</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.920505</td>\n",
" <td>-0.390731</td>\n",
" <td>-125.000000</td>\n",
" <td>72.879291</td>\n",
" <td>193.380843</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.606731</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.728459</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.838391</td>\n",
" <td>1.0</td>\n",
" <td>0.037795</td>\n",
" <td>0.0</td>\n",
" <td>-0.480000</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.078723</td>\n",
" <td>0.490115</td>\n",
" <td>-0.7</td>\n",
" <td>-0.271186</td>\n",
" <td>13</td>\n",
" <td>-0.203390</td>\n",
" <td>1.726074</td>\n",
" <td>1.723938</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>-0.475944</td>\n",
" <td>-0.074042</td>\n",
" <td>0.0</td>\n",
" <td>-0.598386</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>115357</td>\n",
" <td>0.998966</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29363</td>\n",
" <td>dfc1d30ba</td>\n",
" <td>ID_dfc1d30ba</td>\n",
" <td>CT</td>\n",
" <td>ID_7ed798ca</td>\n",
" <td>ID_bca01d4025</td>\n",
" <td>ID_bf75646cb6</td>\n",
" <td>NaN</td>\n",
" <td>['-132.5', '13.0711274', '189.612208']</td>\n",
" <td>['1', '0', '0', '0', '0.965925826', '-0.258819...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.517578125', '0.517578125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.333333</td>\n",
" <td>-0.217333</td>\n",
" <td>-0.920688</td>\n",
" <td>0.910508</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.965926</td>\n",
" <td>-0.258819</td>\n",
" <td>-132.500000</td>\n",
" <td>13.071127</td>\n",
" <td>189.612208</td>\n",
" <td>0.517578</td>\n",
" <td>0.517578</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2.212344</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.607873</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>1.08</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.040948</td>\n",
" <td>1.0</td>\n",
" <td>0.032396</td>\n",
" <td>0.0</td>\n",
" <td>2.060625</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.026354</td>\n",
" <td>0.428849</td>\n",
" <td>-0.5</td>\n",
" <td>-0.067797</td>\n",
" <td>16</td>\n",
" <td>-0.271186</td>\n",
" <td>1.561829</td>\n",
" <td>1.588135</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>-0.416634</td>\n",
" <td>0.206507</td>\n",
" <td>0.0</td>\n",
" <td>-0.579463</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>111444</td>\n",
" <td>0.999020</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92120</td>\n",
" <td>e33160522</td>\n",
" <td>ID_e33160522</td>\n",
" <td>CT</td>\n",
" <td>ID_7ed798ca</td>\n",
" <td>ID_bca01d4025</td>\n",
" <td>ID_bf75646cb6</td>\n",
" <td>NaN</td>\n",
" <td>['-132.5', '13.0711274', '184.488551']</td>\n",
" <td>['1', '0', '0', '0', '0.965925826', '-0.258819...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.517578125', '0.517578125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.333333</td>\n",
" <td>-0.197333</td>\n",
" <td>-0.930595</td>\n",
" <td>0.931032</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.965926</td>\n",
" <td>-0.258819</td>\n",
" <td>-132.500000</td>\n",
" <td>13.071127</td>\n",
" <td>184.488551</td>\n",
" <td>0.517578</td>\n",
" <td>0.517578</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2.212344</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.607873</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>1.08</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.040948</td>\n",
" <td>1.0</td>\n",
" <td>0.025055</td>\n",
" <td>0.0</td>\n",
" <td>2.060625</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.026354</td>\n",
" <td>0.428849</td>\n",
" <td>-0.5</td>\n",
" <td>-0.135593</td>\n",
" <td>15</td>\n",
" <td>-0.203390</td>\n",
" <td>1.588134</td>\n",
" <td>1.561829</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>-0.416634</td>\n",
" <td>0.069305</td>\n",
" <td>0.0</td>\n",
" <td>-0.579463</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>111443</td>\n",
" <td>0.999043</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"100029 d3bd67ff1 ID_d3bd67ff1 CT ID_07aa4e90 ID_19039aeb7f \n",
"101618 b5c2fbbe1 ID_b5c2fbbe1 CT ID_877a2214 ID_f5d8b2ad40 \n",
"54912 5519471d4 ID_5519471d4 CT ID_35384be6 ID_cc5b6c0a29 \n",
"29363 dfc1d30ba ID_dfc1d30ba CT ID_7ed798ca ID_bca01d4025 \n",
"92120 e33160522 ID_e33160522 CT ID_7ed798ca ID_bca01d4025 \n",
"\n",
" SeriesInstanceUID StudyID \\\n",
"100029 ID_83a456ed02 NaN \n",
"101618 ID_c37347c9a3 NaN \n",
"54912 ID_5d7a4ca229 NaN \n",
"29363 ID_bf75646cb6 NaN \n",
"92120 ID_bf75646cb6 NaN \n",
"\n",
" ImagePositionPatient \\\n",
"100029 ['-125', '-5.28788193', '235.817384'] \n",
"101618 ['-126.408875', '-126.408875', '92.449158'] \n",
"54912 ['-125', '72.8792912', '193.380843'] \n",
"29363 ['-132.5', '13.0711274', '189.612208'] \n",
"92120 ['-132.5', '13.0711274', '184.488551'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"100029 ['1', '0', '0', '0', '0.927183855', '-0.374606... 1 \n",
"101618 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"54912 ['1', '0', '0', '0', '0.920504853', '-0.390731... 1 \n",
"29363 ['1', '0', '0', '0', '0.965925826', '-0.258819... 1 \n",
"92120 ['1', '0', '0', '0', '0.965925826', '-0.258819... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns \\\n",
"100029 MONOCHROME2 512 512 \n",
"101618 MONOCHROME2 512 512 \n",
"54912 MONOCHROME2 512 512 \n",
"29363 MONOCHROME2 512 512 \n",
"92120 MONOCHROME2 512 512 \n",
"\n",
" PixelSpacing BitsAllocated BitsStored \\\n",
"100029 ['0.48828125', '0.48828125'] 16 12 \n",
"101618 ['0.494750976563', '0.494750976563'] 16 16 \n",
"54912 ['0.48828125', '0.48828125'] 16 12 \n",
"29363 ['0.517578125', '0.517578125'] 16 12 \n",
"92120 ['0.517578125', '0.517578125'] 16 12 \n",
"\n",
" HighBit PixelRepresentation WindowCenter WindowWidth \\\n",
"100029 11 0 ['00040', '00040'] ['00080', '00080'] \n",
"101618 15 1 35.000000 135.000000 \n",
"54912 11 0 ['00040', '00040'] ['00080', '00080'] \n",
"29363 11 1 ['00040', '00040'] ['00080', '00080'] \n",
"92120 11 1 ['00040', '00040'] ['00080', '00080'] \n",
"\n",
" RescaleIntercept RescaleSlope PxlMin PxlMax PxlStd \\\n",
"100029 -1024.0 1.0 1.301333 0.093333 -0.618874 \n",
"101618 -1024.0 1.0 1.301333 0.148000 -0.975081 \n",
"54912 -1024.0 1.0 1.301333 1.525333 -0.941557 \n",
"29363 0.0 1.0 1.333333 -0.217333 -0.920688 \n",
"92120 0.0 1.0 1.333333 -0.197333 -0.930595 \n",
"\n",
" PxlMean test test2 ImageOrientationPatient_0 \\\n",
"100029 1.229975 False True 1.0 \n",
"101618 1.044669 False True 1.0 \n",
"54912 1.166305 False True 1.0 \n",
"29363 0.910508 False True 1.0 \n",
"92120 0.931032 False True 1.0 \n",
"\n",
" ImageOrientationPatient_1 ImageOrientationPatient_2 \\\n",
"100029 0.0 0.0 \n",
"101618 0.0 0.0 \n",
"54912 0.0 0.0 \n",
"29363 0.0 0.0 \n",
"92120 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_3 ImageOrientationPatient_4 \\\n",
"100029 0.0 0.927184 \n",
"101618 0.0 1.000000 \n",
"54912 0.0 0.920505 \n",
"29363 0.0 0.965926 \n",
"92120 0.0 0.965926 \n",
"\n",
" ImageOrientationPatient_5 ImagePositionPatient_0 \\\n",
"100029 -0.374607 -125.000000 \n",
"101618 0.000000 -126.408875 \n",
"54912 -0.390731 -125.000000 \n",
"29363 -0.258819 -132.500000 \n",
"92120 -0.258819 -132.500000 \n",
"\n",
" ImagePositionPatient_1 ImagePositionPatient_2 PixelSpacing_0 \\\n",
"100029 -5.287882 235.817384 0.488281 \n",
"101618 -126.408875 92.449158 0.494751 \n",
"54912 72.879291 193.380843 0.488281 \n",
"29363 13.071127 189.612208 0.517578 \n",
"92120 13.071127 184.488551 0.517578 \n",
"\n",
" PixelSpacing_1 WindowCenter_0 WindowCenter_1 WindowCenter_1_NAN \\\n",
"100029 0.488281 40.0 40.0 False \n",
"101618 0.494751 35.0 NaN True \n",
"54912 0.488281 40.0 40.0 False \n",
"29363 0.517578 40.0 40.0 False \n",
"92120 0.517578 40.0 40.0 False \n",
"\n",
" WindowWidth_0 WindowWidth_1 WindowWidth_0_le WindowWidth_1_le \\\n",
"100029 80.0 80.0 0 0 \n",
"101618 135.0 NaN 3 1 \n",
"54912 80.0 80.0 0 0 \n",
"29363 80.0 80.0 0 0 \n",
"92120 80.0 80.0 0 0 \n",
"\n",
" WindowCenter_1_le BitType_le ImageOrientationPatient_4_f \\\n",
"100029 1 1 1.695785 \n",
"101618 3 0 -1.333333 \n",
"54912 1 1 1.606731 \n",
"29363 1 2 2.212344 \n",
"92120 1 2 2.212344 \n",
"\n",
" ImageOrientationPatient_4_enc_0 ... ImageOrientationPatient_5_f \\\n",
"100029 0.0 ... 0.835956 \n",
"101618 1.0 ... -0.666667 \n",
"54912 0.0 ... 0.728459 \n",
"29363 0.0 ... 1.607873 \n",
"92120 0.0 ... 1.607873 \n",
"\n",
" ImageOrientationPatient_5_enc_0 ImageOrientationPatient_5_enc_1 \\\n",
"100029 0.0 False \n",
"101618 1.0 False \n",
"54912 0.0 False \n",
"29363 0.0 False \n",
"92120 0.0 False \n",
"\n",
" ImagePositionPatient_0_f ImagePositionPatient_0_enc_0 \\\n",
"100029 -0.72 1.0 \n",
"101618 -0.72 0.0 \n",
"54912 -0.72 1.0 \n",
"29363 1.08 0.0 \n",
"92120 1.08 0.0 \n",
"\n",
" ImagePositionPatient_0_enc_1 ImagePositionPatient_0_f_r1 \\\n",
"100029 0.0 1.0 \n",
"101618 1.0 1.0 \n",
"54912 0.0 1.0 \n",
"29363 0.0 0.0 \n",
"92120 0.0 0.0 \n",
"\n",
" ImagePositionPatient_0_f_r05 ImagePositionPatient_1_f \\\n",
"100029 1.0 0.796162 \n",
"101618 1.0 -0.818785 \n",
"54912 1.0 1.838391 \n",
"29363 1.0 1.040948 \n",
"92120 1.0 1.040948 \n",
"\n",
" ImagePositionPatient_1_enc_0 ImagePositionPatient_2_f \\\n",
"100029 1.0 0.098592 \n",
"101618 0.0 -0.106806 \n",
"54912 1.0 0.037795 \n",
"29363 1.0 0.032396 \n",
"92120 1.0 0.025055 \n",
"\n",
" ImagePositionPatient_2_f_r05 PixelSpacing_1_f PixelSpacing_1_enc_0 \\\n",
"100029 0.0 -0.480000 1.0 \n",
"101618 0.0 -0.480000 0.0 \n",
"54912 0.0 -0.480000 1.0 \n",
"29363 0.0 2.060625 0.0 \n",
"92120 0.0 2.060625 0.0 \n",
"\n",
" PixelSpacing_1_enc_1 WindowCenter_0_le pos_max pos_min pos_size \\\n",
"100029 False 2 1.202425 0.502892 -0.7 \n",
"101618 True 3 0.609797 -0.010203 -0.3 \n",
"54912 False 2 1.078723 0.490115 -0.7 \n",
"29363 False 2 1.026354 0.428849 -0.5 \n",
"92120 False 2 1.026354 0.428849 -0.5 \n",
"\n",
" pos_idx1 pos_idx pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le \\\n",
"100029 0.000000 17 -0.474576 2.247192 2.247192 3 \n",
"101618 0.135593 19 -0.338983 -1.500000 -1.500000 3 \n",
"54912 -0.271186 13 -0.203390 1.726074 1.723938 3 \n",
"29363 -0.067797 16 -0.271186 1.561829 1.588135 3 \n",
"92120 -0.135593 15 -0.203390 1.588134 1.561829 3 \n",
"\n",
" pos_inc2_grp_le pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 \\\n",
"100029 3 0.0 0.0 0.0 \n",
"101618 3 1.0 1.0 0.0 \n",
"54912 3 0.0 0.0 0.0 \n",
"29363 3 0.0 0.0 0.0 \n",
"92120 3 0.0 0.0 0.0 \n",
"\n",
" pos_inc2_enc_0 pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le \\\n",
"100029 0.0 0.0 0.0 3 \n",
"101618 0.0 1.0 1.0 0 \n",
"54912 0.0 0.0 0.0 3 \n",
"29363 0.0 0.0 0.0 5 \n",
"92120 0.0 0.0 0.0 5 \n",
"\n",
" pos_range pos_rel pos_zeros pos_inc_rng pos_zeros_le \\\n",
"100029 0.263550 0.518123 0.0 -0.575802 0 \n",
"101618 -0.266667 0.451613 0.0 -0.600000 0 \n",
"54912 -0.475944 -0.074042 0.0 -0.598386 0 \n",
"29363 -0.416634 0.206507 0.0 -0.579463 0 \n",
"92120 -0.416634 0.069305 0.0 -0.579463 0 \n",
"\n",
" PxlMin_grp_le PxlMin_zero any epidural intraparenchymal \\\n",
"100029 2 False NaN NaN NaN \n",
"101618 2 False NaN NaN NaN \n",
"54912 2 False NaN NaN NaN \n",
"29363 2 False NaN NaN NaN \n",
"92120 2 False NaN NaN NaN \n",
"\n",
" intraventricular subarachnoid subdural any_series SeriesPP \\\n",
"100029 NaN NaN NaN False -0.5 \n",
"101618 NaN NaN NaN False -0.5 \n",
"54912 NaN NaN NaN False -0.5 \n",
"29363 NaN NaN NaN False -0.5 \n",
"92120 NaN NaN NaN False -0.5 \n",
"\n",
" yuval_idx pred_any \n",
"100029 52190 0.998915 \n",
"101618 93649 0.998922 \n",
"54912 115357 0.998966 \n",
"29363 111444 0.999020 \n",
"92120 111443 0.999043 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
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" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
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" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
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" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>85421</td>\n",
" <td>ba1a7894c</td>\n",
" <td>ID_ba1a7894c</td>\n",
" <td>CT</td>\n",
" <td>ID_6f87831a</td>\n",
" <td>ID_a6ca244172</td>\n",
" <td>ID_d00cee7f0c</td>\n",
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" <td>1</td>\n",
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" <td>0.079290</td>\n",
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" <td>1.249860</td>\n",
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" <td>-0.193775</td>\n",
" <td>0.0</td>\n",
" <td>-0.584230</td>\n",
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" <td>2</td>\n",
" <td>False</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>35441</td>\n",
" <td>0.911084</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90394</td>\n",
" <td>5d403bd8a</td>\n",
" <td>ID_5d403bd8a</td>\n",
" <td>CT</td>\n",
" <td>ID_61101bd3</td>\n",
" <td>ID_6aca8f9834</td>\n",
" <td>ID_1cb45bbcea</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '42.4079503', '206.464926']</td>\n",
" <td>['1', '0', '0', '0', '0.939692621', '-0.342020...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
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" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.010667</td>\n",
" <td>-0.780873</td>\n",
" <td>1.496547</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>-0.342020</td>\n",
" <td>-125.0</td>\n",
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" <td>206.464926</td>\n",
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" <td>0.488281</td>\n",
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" <td>NaN</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <td>23519</td>\n",
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" <td>ID_61101bd3</td>\n",
" <td>ID_6aca8f9834</td>\n",
" <td>ID_1cb45bbcea</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '42.4079503', '211.7441']</td>\n",
" <td>['1', '0', '0', '0', '0.939692621', '-0.342020...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.037333</td>\n",
" <td>-0.754200</td>\n",
" <td>1.500107</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.939693</td>\n",
" <td>-0.342020</td>\n",
" <td>-125.0</td>\n",
" <td>42.407950</td>\n",
" <td>211.744100</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.862568</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.053199</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.432106</td>\n",
" <td>1.0</td>\n",
" <td>0.064103</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.249860</td>\n",
" <td>0.592577</td>\n",
" <td>-0.3</td>\n",
" <td>-0.338983</td>\n",
" <td>12</td>\n",
" <td>0.135593</td>\n",
" <td>1.639587</td>\n",
" <td>1.660400</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>-0.018112</td>\n",
" <td>-0.451810</td>\n",
" <td>0.0</td>\n",
" <td>-0.584230</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>35439</td>\n",
" <td>0.916977</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41043</td>\n",
" <td>0f43a379c</td>\n",
" <td>ID_0f43a379c</td>\n",
" <td>CT</td>\n",
" <td>ID_61101bd3</td>\n",
" <td>ID_6aca8f9834</td>\n",
" <td>ID_1cb45bbcea</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '42.4079503', '217.064901']</td>\n",
" <td>['1', '0', '0', '0', '0.939692621', '-0.342020...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.000000</td>\n",
" <td>-0.718398</td>\n",
" <td>1.505203</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.939693</td>\n",
" <td>-0.342020</td>\n",
" <td>-125.0</td>\n",
" <td>42.407950</td>\n",
" <td>217.064901</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.862568</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.053199</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.432106</td>\n",
" <td>1.0</td>\n",
" <td>0.071726</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.249860</td>\n",
" <td>0.592577</td>\n",
" <td>-0.3</td>\n",
" <td>-0.271186</td>\n",
" <td>13</td>\n",
" <td>0.067797</td>\n",
" <td>1.660400</td>\n",
" <td>1.639649</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>-0.018112</td>\n",
" <td>-0.322287</td>\n",
" <td>0.0</td>\n",
" <td>-0.584230</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>35440</td>\n",
" <td>0.917555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"85421 ba1a7894c ID_ba1a7894c CT ID_6f87831a ID_a6ca244172 \n",
"46168 7f9480ae5 ID_7f9480ae5 CT ID_61101bd3 ID_6aca8f9834 \n",
"90394 5d403bd8a ID_5d403bd8a CT ID_61101bd3 ID_6aca8f9834 \n",
"23519 645917b86 ID_645917b86 CT ID_61101bd3 ID_6aca8f9834 \n",
"41043 0f43a379c ID_0f43a379c CT ID_61101bd3 ID_6aca8f9834 \n",
"\n",
" SeriesInstanceUID StudyID \\\n",
"85421 ID_d00cee7f0c NaN \n",
"46168 ID_1cb45bbcea NaN \n",
"90394 ID_1cb45bbcea NaN \n",
"23519 ID_1cb45bbcea NaN \n",
"41043 ID_1cb45bbcea NaN \n",
"\n",
" ImagePositionPatient \\\n",
"85421 ['-125.000000', '-119.997978', '127.192337'] \n",
"46168 ['-125', '42.4079503', '222.344198'] \n",
"90394 ['-125', '42.4079503', '206.464926'] \n",
"23519 ['-125', '42.4079503', '211.7441'] \n",
"41043 ['-125', '42.4079503', '217.064901'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"85421 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"46168 ['1', '0', '0', '0', '0.939692621', '-0.342020... 1 \n",
"90394 ['1', '0', '0', '0', '0.939692621', '-0.342020... 1 \n",
"23519 ['1', '0', '0', '0', '0.939692621', '-0.342020... 1 \n",
"41043 ['1', '0', '0', '0', '0.939692621', '-0.342020... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns PixelSpacing \\\n",
"85421 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"46168 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"90394 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"23519 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"41043 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"\n",
" BitsAllocated BitsStored HighBit PixelRepresentation \\\n",
"85421 16 16 15 1 \n",
"46168 16 12 11 0 \n",
"90394 16 12 11 0 \n",
"23519 16 12 11 0 \n",
"41043 16 12 11 0 \n",
"\n",
" WindowCenter WindowWidth RescaleIntercept RescaleSlope \\\n",
"85421 30 80 -1024.0 1.0 \n",
"46168 ['00040', '00040'] ['00080', '00080'] -1024.0 1.0 \n",
"90394 ['00040', '00040'] ['00080', '00080'] -1024.0 1.0 \n",
"23519 ['00040', '00040'] ['00080', '00080'] -1024.0 1.0 \n",
"41043 ['00040', '00040'] ['00080', '00080'] -1024.0 1.0 \n",
"\n",
" PxlMin PxlMax PxlStd PxlMean test test2 \\\n",
"85421 -1.365333 0.310667 1.642553 -0.881730 False True \n",
"46168 1.301333 0.006667 -0.700807 1.484853 False True \n",
"90394 1.301333 0.010667 -0.780873 1.496547 False True \n",
"23519 1.301333 0.037333 -0.754200 1.500107 False True \n",
"41043 1.301333 0.000000 -0.718398 1.505203 False True \n",
"\n",
" ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n",
"85421 1.0 0.0 \n",
"46168 1.0 0.0 \n",
"90394 1.0 0.0 \n",
"23519 1.0 0.0 \n",
"41043 1.0 0.0 \n",
"\n",
" ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n",
"85421 0.0 0.0 \n",
"46168 0.0 0.0 \n",
"90394 0.0 0.0 \n",
"23519 0.0 0.0 \n",
"41043 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_4 ImageOrientationPatient_5 \\\n",
"85421 0.927184 -0.374607 \n",
"46168 0.939693 -0.342020 \n",
"90394 0.939693 -0.342020 \n",
"23519 0.939693 -0.342020 \n",
"41043 0.939693 -0.342020 \n",
"\n",
" ImagePositionPatient_0 ImagePositionPatient_1 ImagePositionPatient_2 \\\n",
"85421 -125.0 -119.997978 127.192337 \n",
"46168 -125.0 42.407950 222.344198 \n",
"90394 -125.0 42.407950 206.464926 \n",
"23519 -125.0 42.407950 211.744100 \n",
"41043 -125.0 42.407950 217.064901 \n",
"\n",
" PixelSpacing_0 PixelSpacing_1 WindowCenter_0 WindowCenter_1 \\\n",
"85421 0.488281 0.488281 30.0 NaN \n",
"46168 0.488281 0.488281 40.0 40.0 \n",
"90394 0.488281 0.488281 40.0 40.0 \n",
"23519 0.488281 0.488281 40.0 40.0 \n",
"41043 0.488281 0.488281 40.0 40.0 \n",
"\n",
" WindowCenter_1_NAN WindowWidth_0 WindowWidth_1 WindowWidth_0_le \\\n",
"85421 True 80.0 NaN 0 \n",
"46168 False 80.0 80.0 0 \n",
"90394 False 80.0 80.0 0 \n",
"23519 False 80.0 80.0 0 \n",
"41043 False 80.0 80.0 0 \n",
"\n",
" WindowWidth_1_le WindowCenter_1_le BitType_le \\\n",
"85421 1 3 0 \n",
"46168 0 1 1 \n",
"90394 0 1 1 \n",
"23519 0 1 1 \n",
"41043 0 1 1 \n",
"\n",
" ImageOrientationPatient_4_f ImageOrientationPatient_4_enc_0 ... \\\n",
"85421 -1.333333 0.0 ... \n",
"46168 1.862568 0.0 ... \n",
"90394 1.862568 0.0 ... \n",
"23519 1.862568 0.0 ... \n",
"41043 1.862568 0.0 ... \n",
"\n",
" ImageOrientationPatient_5_f ImageOrientationPatient_5_enc_0 \\\n",
"85421 -0.666667 0.0 \n",
"46168 1.053199 0.0 \n",
"90394 1.053199 0.0 \n",
"23519 1.053199 0.0 \n",
"41043 1.053199 0.0 \n",
"\n",
" ImageOrientationPatient_5_enc_1 ImagePositionPatient_0_f \\\n",
"85421 True -0.72 \n",
"46168 False -0.72 \n",
"90394 False -0.72 \n",
"23519 False -0.72 \n",
"41043 False -0.72 \n",
"\n",
" ImagePositionPatient_0_enc_0 ImagePositionPatient_0_enc_1 \\\n",
"85421 1.0 0.0 \n",
"46168 1.0 0.0 \n",
"90394 1.0 0.0 \n",
"23519 1.0 0.0 \n",
"41043 1.0 0.0 \n",
"\n",
" ImagePositionPatient_0_f_r1 ImagePositionPatient_0_f_r05 \\\n",
"85421 1.0 1.0 \n",
"46168 1.0 1.0 \n",
"90394 1.0 1.0 \n",
"23519 1.0 1.0 \n",
"41043 1.0 1.0 \n",
"\n",
" ImagePositionPatient_1_f ImagePositionPatient_1_enc_0 \\\n",
"85421 -0.733306 0.0 \n",
"46168 1.432106 1.0 \n",
"90394 1.432106 1.0 \n",
"23519 1.432106 1.0 \n",
"41043 1.432106 1.0 \n",
"\n",
" ImagePositionPatient_2_f ImagePositionPatient_2_f_r05 \\\n",
"85421 -0.057031 0.0 \n",
"46168 0.079290 0.0 \n",
"90394 0.056540 0.0 \n",
"23519 0.064103 0.0 \n",
"41043 0.071726 0.0 \n",
"\n",
" PixelSpacing_1_f PixelSpacing_1_enc_0 PixelSpacing_1_enc_1 \\\n",
"85421 -0.48 1.0 False \n",
"46168 -0.48 1.0 False \n",
"90394 -0.48 1.0 False \n",
"23519 -0.48 1.0 False \n",
"41043 -0.48 1.0 False \n",
"\n",
" WindowCenter_0_le pos_max pos_min pos_size pos_idx1 pos_idx \\\n",
"85421 0 0.918601 0.249929 -0.3 -0.338983 12 \n",
"46168 2 1.249860 0.592577 -0.3 -0.203390 14 \n",
"90394 2 1.249860 0.592577 -0.3 -0.406780 11 \n",
"23519 2 1.249860 0.592577 -0.3 -0.338983 12 \n",
"41043 2 1.249860 0.592577 -0.3 -0.271186 13 \n",
"\n",
" pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le pos_inc2_grp_le \\\n",
"85421 0.135593 1.695991 1.696335 3 3 \n",
"46168 0.000000 1.639649 1.660400 3 3 \n",
"90394 0.203390 1.660400 1.639587 3 3 \n",
"23519 0.135593 1.639587 1.660400 3 3 \n",
"41043 0.067797 1.660400 1.639649 3 3 \n",
"\n",
" pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 pos_inc2_enc_0 \\\n",
"85421 0.0 0.0 0.0 0.0 \n",
"46168 0.0 0.0 0.0 0.0 \n",
"90394 0.0 0.0 0.0 0.0 \n",
"23519 0.0 0.0 0.0 0.0 \n",
"41043 0.0 0.0 0.0 0.0 \n",
"\n",
" pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le pos_range pos_rel \\\n",
"85421 0.0 0.0 0 0.057814 -0.451618 \n",
"46168 0.0 0.0 0 -0.018112 -0.193775 \n",
"90394 0.0 0.0 0 -0.018112 -0.580318 \n",
"23519 0.0 0.0 0 -0.018112 -0.451810 \n",
"41043 0.0 0.0 0 -0.018112 -0.322287 \n",
"\n",
" pos_zeros pos_inc_rng pos_zeros_le PxlMin_grp_le PxlMin_zero any \\\n",
"85421 0.0 -0.599737 0 0 True NaN \n",
"46168 0.0 -0.584230 0 2 False NaN \n",
"90394 0.0 -0.584230 0 2 False NaN \n",
"23519 0.0 -0.584230 0 2 False NaN \n",
"41043 0.0 -0.584230 0 2 False NaN \n",
"\n",
" epidural intraparenchymal intraventricular subarachnoid subdural \\\n",
"85421 NaN NaN NaN NaN NaN \n",
"46168 NaN NaN NaN NaN NaN \n",
"90394 NaN NaN NaN NaN NaN \n",
"23519 NaN NaN NaN NaN NaN \n",
"41043 NaN NaN NaN NaN NaN \n",
"\n",
" any_series SeriesPP yuval_idx pred_any \n",
"85421 False -0.5 111470 0.902253 \n",
"46168 False -0.5 35441 0.911084 \n",
"90394 False -0.5 35438 0.911625 \n",
"23519 False -0.5 35439 0.916977 \n",
"41043 False -0.5 35440 0.917555 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md['pred_any'] = predictions[:,1]\n",
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: middle;\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>img_id</th>\n",
" <th>SOPInstanceUID</th>\n",
" <th>Modality</th>\n",
" <th>PatientID</th>\n",
" <th>StudyInstanceUID</th>\n",
" <th>SeriesInstanceUID</th>\n",
" <th>StudyID</th>\n",
" <th>ImagePositionPatient</th>\n",
" <th>ImageOrientationPatient</th>\n",
" <th>SamplesPerPixel</th>\n",
" <th>PhotometricInterpretation</th>\n",
" <th>Rows</th>\n",
" <th>Columns</th>\n",
" <th>PixelSpacing</th>\n",
" <th>BitsAllocated</th>\n",
" <th>BitsStored</th>\n",
" <th>HighBit</th>\n",
" <th>PixelRepresentation</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>RescaleIntercept</th>\n",
" <th>RescaleSlope</th>\n",
" <th>PxlMin</th>\n",
" <th>PxlMax</th>\n",
" <th>PxlStd</th>\n",
" <th>PxlMean</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" <th>ImageOrientationPatient_0</th>\n",
" <th>ImageOrientationPatient_1</th>\n",
" <th>ImageOrientationPatient_2</th>\n",
" <th>ImageOrientationPatient_3</th>\n",
" <th>ImageOrientationPatient_4</th>\n",
" <th>ImageOrientationPatient_5</th>\n",
" <th>ImagePositionPatient_0</th>\n",
" <th>ImagePositionPatient_1</th>\n",
" <th>ImagePositionPatient_2</th>\n",
" <th>PixelSpacing_0</th>\n",
" <th>PixelSpacing_1</th>\n",
" <th>WindowCenter_0</th>\n",
" <th>WindowCenter_1</th>\n",
" <th>WindowCenter_1_NAN</th>\n",
" <th>WindowWidth_0</th>\n",
" <th>WindowWidth_1</th>\n",
" <th>WindowWidth_0_le</th>\n",
" <th>WindowWidth_1_le</th>\n",
" <th>WindowCenter_1_le</th>\n",
" <th>BitType_le</th>\n",
" <th>ImageOrientationPatient_4_f</th>\n",
" <th>ImageOrientationPatient_4_enc_0</th>\n",
" <th>...</th>\n",
" <th>ImageOrientationPatient_5_f</th>\n",
" <th>ImageOrientationPatient_5_enc_0</th>\n",
" <th>ImageOrientationPatient_5_enc_1</th>\n",
" <th>ImagePositionPatient_0_f</th>\n",
" <th>ImagePositionPatient_0_enc_0</th>\n",
" <th>ImagePositionPatient_0_enc_1</th>\n",
" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
" <th>ImagePositionPatient_2_f</th>\n",
" <th>ImagePositionPatient_2_f_r05</th>\n",
" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
" <th>PixelSpacing_1_enc_1</th>\n",
" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
" <th>pos_idx2</th>\n",
" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>56318</td>\n",
" <td>aaea1517d</td>\n",
" <td>ID_aaea1517d</td>\n",
" <td>CT</td>\n",
" <td>ID_e875aaac</td>\n",
" <td>ID_ace87fc419</td>\n",
" <td>ID_c2050c1b62</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '157.507935']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.309333</td>\n",
" <td>-0.835506</td>\n",
" <td>1.294749</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>157.507935</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>-0.013599</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.850032</td>\n",
" <td>-0.009968</td>\n",
" <td>0.9</td>\n",
" <td>1.016949</td>\n",
" <td>32</td>\n",
" <td>-0.406780</td>\n",
" <td>-1.500000</td>\n",
" <td>-1.500000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>9</td>\n",
" <td>1.333334</td>\n",
" <td>0.976744</td>\n",
" <td>0.0</td>\n",
" <td>-0.599997</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>20605</td>\n",
" <td>0.995869</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11838</td>\n",
" <td>8a3a7113f</td>\n",
" <td>ID_8a3a7113f</td>\n",
" <td>CT</td>\n",
" <td>ID_1f7020f7</td>\n",
" <td>ID_ffd91b71d1</td>\n",
" <td>ID_a0997b616a</td>\n",
" <td>NaN</td>\n",
" <td>['-118', '20.437079', '167.587618']</td>\n",
" <td>['1', '0', '0', '0', '0.978147601', '-0.207911...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.126667</td>\n",
" <td>-0.975261</td>\n",
" <td>0.972069</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.978148</td>\n",
" <td>-0.207912</td>\n",
" <td>-118.000000</td>\n",
" <td>20.437079</td>\n",
" <td>167.587618</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.375301</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.947255</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>1.466667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.139161</td>\n",
" <td>1.0</td>\n",
" <td>0.000842</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.078350</td>\n",
" <td>0.445865</td>\n",
" <td>-0.3</td>\n",
" <td>-0.406780</td>\n",
" <td>11</td>\n",
" <td>0.203390</td>\n",
" <td>1.560669</td>\n",
" <td>1.539367</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>-0.183431</td>\n",
" <td>-0.580297</td>\n",
" <td>0.0</td>\n",
" <td>-0.583175</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>49264</td>\n",
" <td>0.995902</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60633</td>\n",
" <td>fd5080c37</td>\n",
" <td>ID_fd5080c37</td>\n",
" <td>CT</td>\n",
" <td>ID_16b922cc</td>\n",
" <td>ID_b48b0482e3</td>\n",
" <td>ID_653f493476</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '32.528565', '161.22819']</td>\n",
" <td>['1', '0', '0', '0', '0.939692621', '-0.342020...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>-0.090667</td>\n",
" <td>-0.796026</td>\n",
" <td>1.330736</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.939693</td>\n",
" <td>-0.342020</td>\n",
" <td>-125.000000</td>\n",
" <td>32.528565</td>\n",
" <td>161.228190</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.862568</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.053199</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.300381</td>\n",
" <td>1.0</td>\n",
" <td>-0.008269</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.132596</td>\n",
" <td>0.432912</td>\n",
" <td>-0.1</td>\n",
" <td>-0.474576</td>\n",
" <td>10</td>\n",
" <td>0.406780</td>\n",
" <td>1.639588</td>\n",
" <td>1.660400</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>0.264557</td>\n",
" <td>-0.788021</td>\n",
" <td>0.0</td>\n",
" <td>-0.584137</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>59637</td>\n",
" <td>0.995940</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30971</td>\n",
" <td>8dbff5245</td>\n",
" <td>ID_8dbff5245</td>\n",
" <td>CT</td>\n",
" <td>ID_f0ef989c</td>\n",
" <td>ID_fcdfd2db4e</td>\n",
" <td>ID_b9627ee31c</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '19.0514449', '123.026026']</td>\n",
" <td>['1', '0', '0', '0', '0.981627183', '-0.190808...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.164000</td>\n",
" <td>-0.675198</td>\n",
" <td>1.380203</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.981627</td>\n",
" <td>-0.190809</td>\n",
" <td>-125.000000</td>\n",
" <td>19.051445</td>\n",
" <td>123.026026</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.421696</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>2.061273</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.120686</td>\n",
" <td>1.0</td>\n",
" <td>-0.063000</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.940904</td>\n",
" <td>0.267694</td>\n",
" <td>-0.1</td>\n",
" <td>-0.406780</td>\n",
" <td>11</td>\n",
" <td>0.338983</td>\n",
" <td>1.551270</td>\n",
" <td>1.548767</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>0.088070</td>\n",
" <td>-0.666627</td>\n",
" <td>0.0</td>\n",
" <td>-0.597988</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>59366</td>\n",
" <td>0.995958</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23816</td>\n",
" <td>423cee314</td>\n",
" <td>ID_423cee314</td>\n",
" <td>CT</td>\n",
" <td>ID_e875aaac</td>\n",
" <td>ID_ace87fc419</td>\n",
" <td>ID_c2050c1b62</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '167.507935']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.265333</td>\n",
" <td>-0.826401</td>\n",
" <td>1.185491</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>167.507935</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>0.000728</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.850032</td>\n",
" <td>-0.009968</td>\n",
" <td>0.9</td>\n",
" <td>1.152542</td>\n",
" <td>34</td>\n",
" <td>-0.542373</td>\n",
" <td>-1.500000</td>\n",
" <td>-1.500000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>9</td>\n",
" <td>1.333334</td>\n",
" <td>1.162791</td>\n",
" <td>0.0</td>\n",
" <td>-0.599997</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>20607</td>\n",
" <td>0.996068</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"56318 aaea1517d ID_aaea1517d CT ID_e875aaac ID_ace87fc419 \n",
"11838 8a3a7113f ID_8a3a7113f CT ID_1f7020f7 ID_ffd91b71d1 \n",
"60633 fd5080c37 ID_fd5080c37 CT ID_16b922cc ID_b48b0482e3 \n",
"30971 8dbff5245 ID_8dbff5245 CT ID_f0ef989c ID_fcdfd2db4e \n",
"23816 423cee314 ID_423cee314 CT ID_e875aaac ID_ace87fc419 \n",
"\n",
" SeriesInstanceUID StudyID \\\n",
"56318 ID_c2050c1b62 NaN \n",
"11838 ID_a0997b616a NaN \n",
"60633 ID_653f493476 NaN \n",
"30971 ID_b9627ee31c NaN \n",
"23816 ID_c2050c1b62 NaN \n",
"\n",
" ImagePositionPatient \\\n",
"56318 ['-126.408875', '-126.408875', '157.507935'] \n",
"11838 ['-118', '20.437079', '167.587618'] \n",
"60633 ['-125', '32.528565', '161.22819'] \n",
"30971 ['-125', '19.0514449', '123.026026'] \n",
"23816 ['-126.408875', '-126.408875', '167.507935'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"56318 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"11838 ['1', '0', '0', '0', '0.978147601', '-0.207911... 1 \n",
"60633 ['1', '0', '0', '0', '0.939692621', '-0.342020... 1 \n",
"30971 ['1', '0', '0', '0', '0.981627183', '-0.190808... 1 \n",
"23816 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns \\\n",
"56318 MONOCHROME2 512 512 \n",
"11838 MONOCHROME2 512 512 \n",
"60633 MONOCHROME2 512 512 \n",
"30971 MONOCHROME2 512 512 \n",
"23816 MONOCHROME2 512 512 \n",
"\n",
" PixelSpacing BitsAllocated BitsStored \\\n",
"56318 ['0.494750976563', '0.494750976563'] 16 16 \n",
"11838 ['0.48828125', '0.48828125'] 16 12 \n",
"60633 ['0.48828125', '0.48828125'] 16 12 \n",
"30971 ['0.48828125', '0.48828125'] 16 12 \n",
"23816 ['0.494750976563', '0.494750976563'] 16 16 \n",
"\n",
" HighBit PixelRepresentation WindowCenter WindowWidth \\\n",
"56318 15 1 35.000000 135.000000 \n",
"11838 11 0 ['00040', '00040'] ['00080', '00080'] \n",
"60633 11 0 ['00040', '00040'] ['00080', '00080'] \n",
"30971 11 0 ['00040', '00040'] ['00080', '00080'] \n",
"23816 15 1 35.000000 135.000000 \n",
"\n",
" RescaleIntercept RescaleSlope PxlMin PxlMax PxlStd PxlMean \\\n",
"56318 -1024.0 1.0 1.301333 0.309333 -0.835506 1.294749 \n",
"11838 -1024.0 1.0 1.301333 0.126667 -0.975261 0.972069 \n",
"60633 -1024.0 1.0 1.301333 -0.090667 -0.796026 1.330736 \n",
"30971 -1024.0 1.0 1.301333 0.164000 -0.675198 1.380203 \n",
"23816 -1024.0 1.0 1.301333 0.265333 -0.826401 1.185491 \n",
"\n",
" test test2 ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n",
"56318 False True 1.0 0.0 \n",
"11838 False True 1.0 0.0 \n",
"60633 False True 1.0 0.0 \n",
"30971 False True 1.0 0.0 \n",
"23816 False True 1.0 0.0 \n",
"\n",
" ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n",
"56318 0.0 0.0 \n",
"11838 0.0 0.0 \n",
"60633 0.0 0.0 \n",
"30971 0.0 0.0 \n",
"23816 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_4 ImageOrientationPatient_5 \\\n",
"56318 1.000000 0.000000 \n",
"11838 0.978148 -0.207912 \n",
"60633 0.939693 -0.342020 \n",
"30971 0.981627 -0.190809 \n",
"23816 1.000000 0.000000 \n",
"\n",
" ImagePositionPatient_0 ImagePositionPatient_1 ImagePositionPatient_2 \\\n",
"56318 -126.408875 -126.408875 157.507935 \n",
"11838 -118.000000 20.437079 167.587618 \n",
"60633 -125.000000 32.528565 161.228190 \n",
"30971 -125.000000 19.051445 123.026026 \n",
"23816 -126.408875 -126.408875 167.507935 \n",
"\n",
" PixelSpacing_0 PixelSpacing_1 WindowCenter_0 WindowCenter_1 \\\n",
"56318 0.494751 0.494751 35.0 NaN \n",
"11838 0.488281 0.488281 40.0 40.0 \n",
"60633 0.488281 0.488281 40.0 40.0 \n",
"30971 0.488281 0.488281 40.0 40.0 \n",
"23816 0.494751 0.494751 35.0 NaN \n",
"\n",
" WindowCenter_1_NAN WindowWidth_0 WindowWidth_1 WindowWidth_0_le \\\n",
"56318 True 135.0 NaN 3 \n",
"11838 False 80.0 80.0 0 \n",
"60633 False 80.0 80.0 0 \n",
"30971 False 80.0 80.0 0 \n",
"23816 True 135.0 NaN 3 \n",
"\n",
" WindowWidth_1_le WindowCenter_1_le BitType_le \\\n",
"56318 1 3 0 \n",
"11838 0 1 1 \n",
"60633 0 1 1 \n",
"30971 0 1 1 \n",
"23816 1 3 0 \n",
"\n",
" ImageOrientationPatient_4_f ImageOrientationPatient_4_enc_0 ... \\\n",
"56318 -1.333333 1.0 ... \n",
"11838 2.375301 0.0 ... \n",
"60633 1.862568 0.0 ... \n",
"30971 2.421696 0.0 ... \n",
"23816 -1.333333 1.0 ... \n",
"\n",
" ImageOrientationPatient_5_f ImageOrientationPatient_5_enc_0 \\\n",
"56318 -0.666667 1.0 \n",
"11838 1.947255 0.0 \n",
"60633 1.053199 0.0 \n",
"30971 2.061273 0.0 \n",
"23816 -0.666667 1.0 \n",
"\n",
" ImageOrientationPatient_5_enc_1 ImagePositionPatient_0_f \\\n",
"56318 False -0.720000 \n",
"11838 False 1.466667 \n",
"60633 False -0.720000 \n",
"30971 False -0.720000 \n",
"23816 False -0.720000 \n",
"\n",
" ImagePositionPatient_0_enc_0 ImagePositionPatient_0_enc_1 \\\n",
"56318 0.0 1.0 \n",
"11838 0.0 0.0 \n",
"60633 1.0 0.0 \n",
"30971 1.0 0.0 \n",
"23816 0.0 1.0 \n",
"\n",
" ImagePositionPatient_0_f_r1 ImagePositionPatient_0_f_r05 \\\n",
"56318 1.0 1.0 \n",
"11838 1.0 1.0 \n",
"60633 1.0 1.0 \n",
"30971 1.0 1.0 \n",
"23816 1.0 1.0 \n",
"\n",
" ImagePositionPatient_1_f ImagePositionPatient_1_enc_0 \\\n",
"56318 -0.818785 0.0 \n",
"11838 1.139161 1.0 \n",
"60633 1.300381 1.0 \n",
"30971 1.120686 1.0 \n",
"23816 -0.818785 0.0 \n",
"\n",
" ImagePositionPatient_2_f ImagePositionPatient_2_f_r05 \\\n",
"56318 -0.013599 0.0 \n",
"11838 0.000842 0.0 \n",
"60633 -0.008269 0.0 \n",
"30971 -0.063000 0.0 \n",
"23816 0.000728 0.0 \n",
"\n",
" PixelSpacing_1_f PixelSpacing_1_enc_0 PixelSpacing_1_enc_1 \\\n",
"56318 -0.48 0.0 True \n",
"11838 -0.48 1.0 False \n",
"60633 -0.48 1.0 False \n",
"30971 -0.48 1.0 False \n",
"23816 -0.48 0.0 True \n",
"\n",
" WindowCenter_0_le pos_max pos_min pos_size pos_idx1 pos_idx \\\n",
"56318 3 0.850032 -0.009968 0.9 1.016949 32 \n",
"11838 2 1.078350 0.445865 -0.3 -0.406780 11 \n",
"60633 2 1.132596 0.432912 -0.1 -0.474576 10 \n",
"30971 2 0.940904 0.267694 -0.1 -0.406780 11 \n",
"23816 3 0.850032 -0.009968 0.9 1.152542 34 \n",
"\n",
" pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le pos_inc2_grp_le \\\n",
"56318 -0.406780 -1.500000 -1.500000 3 3 \n",
"11838 0.203390 1.560669 1.539367 3 3 \n",
"60633 0.406780 1.639588 1.660400 3 3 \n",
"30971 0.338983 1.551270 1.548767 3 3 \n",
"23816 -0.542373 -1.500000 -1.500000 3 3 \n",
"\n",
" pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 pos_inc2_enc_0 \\\n",
"56318 1.0 1.0 0.0 0.0 \n",
"11838 0.0 0.0 0.0 0.0 \n",
"60633 0.0 0.0 0.0 0.0 \n",
"30971 0.0 0.0 0.0 0.0 \n",
"23816 1.0 1.0 0.0 0.0 \n",
"\n",
" pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le pos_range pos_rel \\\n",
"56318 1.0 1.0 9 1.333334 0.976744 \n",
"11838 0.0 0.0 0 -0.183431 -0.580297 \n",
"60633 0.0 0.0 4 0.264557 -0.788021 \n",
"30971 0.0 0.0 4 0.088070 -0.666627 \n",
"23816 1.0 1.0 9 1.333334 1.162791 \n",
"\n",
" pos_zeros pos_inc_rng pos_zeros_le PxlMin_grp_le PxlMin_zero any \\\n",
"56318 0.0 -0.599997 0 2 False NaN \n",
"11838 0.0 -0.583175 0 2 False NaN \n",
"60633 0.0 -0.584137 0 2 False NaN \n",
"30971 0.0 -0.597988 0 2 False NaN \n",
"23816 0.0 -0.599997 0 2 False NaN \n",
"\n",
" epidural intraparenchymal intraventricular subarachnoid subdural \\\n",
"56318 NaN NaN NaN NaN NaN \n",
"11838 NaN NaN NaN NaN NaN \n",
"60633 NaN NaN NaN NaN NaN \n",
"30971 NaN NaN NaN NaN NaN \n",
"23816 NaN NaN NaN NaN NaN \n",
"\n",
" any_series SeriesPP yuval_idx pred_any \n",
"56318 False -0.5 20605 0.995869 \n",
"11838 False -0.5 49264 0.995902 \n",
"60633 False -0.5 59637 0.995940 \n",
"30971 False -0.5 59366 0.995958 \n",
"23816 False -0.5 20607 0.996068 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md['pred_any'] = predictions[:,2]\n",
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"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>img_id</th>\n",
" <th>SOPInstanceUID</th>\n",
" <th>Modality</th>\n",
" <th>PatientID</th>\n",
" <th>StudyInstanceUID</th>\n",
" <th>SeriesInstanceUID</th>\n",
" <th>StudyID</th>\n",
" <th>ImagePositionPatient</th>\n",
" <th>ImageOrientationPatient</th>\n",
" <th>SamplesPerPixel</th>\n",
" <th>PhotometricInterpretation</th>\n",
" <th>Rows</th>\n",
" <th>Columns</th>\n",
" <th>PixelSpacing</th>\n",
" <th>BitsAllocated</th>\n",
" <th>BitsStored</th>\n",
" <th>HighBit</th>\n",
" <th>PixelRepresentation</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>RescaleIntercept</th>\n",
" <th>RescaleSlope</th>\n",
" <th>PxlMin</th>\n",
" <th>PxlMax</th>\n",
" <th>PxlStd</th>\n",
" <th>PxlMean</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" <th>ImageOrientationPatient_0</th>\n",
" <th>ImageOrientationPatient_1</th>\n",
" <th>ImageOrientationPatient_2</th>\n",
" <th>ImageOrientationPatient_3</th>\n",
" <th>ImageOrientationPatient_4</th>\n",
" <th>ImageOrientationPatient_5</th>\n",
" <th>ImagePositionPatient_0</th>\n",
" <th>ImagePositionPatient_1</th>\n",
" <th>ImagePositionPatient_2</th>\n",
" <th>PixelSpacing_0</th>\n",
" <th>PixelSpacing_1</th>\n",
" <th>WindowCenter_0</th>\n",
" <th>WindowCenter_1</th>\n",
" <th>WindowCenter_1_NAN</th>\n",
" <th>WindowWidth_0</th>\n",
" <th>WindowWidth_1</th>\n",
" <th>WindowWidth_0_le</th>\n",
" <th>WindowWidth_1_le</th>\n",
" <th>WindowCenter_1_le</th>\n",
" <th>BitType_le</th>\n",
" <th>ImageOrientationPatient_4_f</th>\n",
" <th>ImageOrientationPatient_4_enc_0</th>\n",
" <th>...</th>\n",
" <th>ImageOrientationPatient_5_f</th>\n",
" <th>ImageOrientationPatient_5_enc_0</th>\n",
" <th>ImageOrientationPatient_5_enc_1</th>\n",
" <th>ImagePositionPatient_0_f</th>\n",
" <th>ImagePositionPatient_0_enc_0</th>\n",
" <th>ImagePositionPatient_0_enc_1</th>\n",
" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
" <th>ImagePositionPatient_2_f</th>\n",
" <th>ImagePositionPatient_2_f_r05</th>\n",
" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
" <th>PixelSpacing_1_enc_1</th>\n",
" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
" <th>pos_idx2</th>\n",
" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>8592</td>\n",
" <td>2b3878103</td>\n",
" <td>ID_2b3878103</td>\n",
" <td>CT</td>\n",
" <td>ID_b81caf1c</td>\n",
" <td>ID_3d31a06240</td>\n",
" <td>ID_25c620d29b</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '77.500000']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.394667</td>\n",
" <td>-0.776436</td>\n",
" <td>1.134899</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>77.5</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>-0.128223</td>\n",
" <td>1.0</td>\n",
" <td>-0.4800</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.61</td>\n",
" <td>-0.01</td>\n",
" <td>-0.3</td>\n",
" <td>-0.067797</td>\n",
" <td>16</td>\n",
" <td>-0.135593</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-0.266667</td>\n",
" <td>0.064516</td>\n",
" <td>0.0</td>\n",
" <td>-0.6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>95036</td>\n",
" <td>0.995983</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101233</td>\n",
" <td>734856256</td>\n",
" <td>ID_734856256</td>\n",
" <td>CT</td>\n",
" <td>ID_4482f018</td>\n",
" <td>ID_5ccd14e6b7</td>\n",
" <td>ID_b75da817b2</td>\n",
" <td>NaN</td>\n",
" <td>['-126.438', '-126.438', '97.500']</td>\n",
" <td>['1.0', '0.0', '0.0', '0.0', '1.0', '0.0']</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.4949', '0.4949']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>135.0</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.378667</td>\n",
" <td>-0.862722</td>\n",
" <td>1.089866</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-126.438000</td>\n",
" <td>-126.438000</td>\n",
" <td>97.5</td>\n",
" <td>0.494900</td>\n",
" <td>0.494900</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>1.241653</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.819173</td>\n",
" <td>0.0</td>\n",
" <td>-0.099570</td>\n",
" <td>1.0</td>\n",
" <td>1.8792</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" <td>0.67</td>\n",
" <td>0.01</td>\n",
" <td>-0.1</td>\n",
" <td>0.135593</td>\n",
" <td>19</td>\n",
" <td>-0.203390</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>0.303030</td>\n",
" <td>0.0</td>\n",
" <td>-0.6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>23495</td>\n",
" <td>0.996200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38580</td>\n",
" <td>1bb3fe555</td>\n",
" <td>ID_1bb3fe555</td>\n",
" <td>CT</td>\n",
" <td>ID_ca92b4e6</td>\n",
" <td>ID_e14681614d</td>\n",
" <td>ID_23f8022c7d</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '72.500000']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.472000</td>\n",
" <td>-0.733947</td>\n",
" <td>1.184966</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>72.5</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>-0.135387</td>\n",
" <td>1.0</td>\n",
" <td>-0.4800</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.61</td>\n",
" <td>-0.01</td>\n",
" <td>-0.3</td>\n",
" <td>-0.135593</td>\n",
" <td>15</td>\n",
" <td>-0.067797</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-0.266667</td>\n",
" <td>-0.064516</td>\n",
" <td>0.0</td>\n",
" <td>-0.6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>40054</td>\n",
" <td>0.996345</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29351</td>\n",
" <td>5bd2084d9</td>\n",
" <td>ID_5bd2084d9</td>\n",
" <td>CT</td>\n",
" <td>ID_4482f018</td>\n",
" <td>ID_5ccd14e6b7</td>\n",
" <td>ID_b75da817b2</td>\n",
" <td>NaN</td>\n",
" <td>['-126.438', '-126.438', '102.500']</td>\n",
" <td>['1.0', '0.0', '0.0', '0.0', '1.0', '0.0']</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.4949', '0.4949']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>135.0</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.442667</td>\n",
" <td>-0.849438</td>\n",
" <td>1.059636</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-126.438000</td>\n",
" <td>-126.438000</td>\n",
" <td>102.5</td>\n",
" <td>0.494900</td>\n",
" <td>0.494900</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>1.241653</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.819173</td>\n",
" <td>0.0</td>\n",
" <td>-0.092407</td>\n",
" <td>1.0</td>\n",
" <td>1.8792</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" <td>0.67</td>\n",
" <td>0.01</td>\n",
" <td>-0.1</td>\n",
" <td>0.203390</td>\n",
" <td>20</td>\n",
" <td>-0.271186</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>0.424242</td>\n",
" <td>0.0</td>\n",
" <td>-0.6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>23496</td>\n",
" <td>0.996412</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90564</td>\n",
" <td>2941d6eba</td>\n",
" <td>ID_2941d6eba</td>\n",
" <td>CT</td>\n",
" <td>ID_ca92b4e6</td>\n",
" <td>ID_e14681614d</td>\n",
" <td>ID_23f8022c7d</td>\n",
" <td>NaN</td>\n",
" <td>['-126.408875', '-126.408875', '77.500000']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.494750976563', '0.494750976563']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>135.000000</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.482667</td>\n",
" <td>-0.703788</td>\n",
" <td>1.179639</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>-126.408875</td>\n",
" <td>-126.408875</td>\n",
" <td>77.5</td>\n",
" <td>0.494751</td>\n",
" <td>0.494751</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>135.0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>-1.333333</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>-0.666667</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>-0.720000</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.818785</td>\n",
" <td>0.0</td>\n",
" <td>-0.128223</td>\n",
" <td>1.0</td>\n",
" <td>-0.4800</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>0.61</td>\n",
" <td>-0.01</td>\n",
" <td>-0.3</td>\n",
" <td>-0.067797</td>\n",
" <td>16</td>\n",
" <td>-0.135593</td>\n",
" <td>-1.5</td>\n",
" <td>-1.5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-0.266667</td>\n",
" <td>0.064516</td>\n",
" <td>0.0</td>\n",
" <td>-0.6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>40055</td>\n",
" <td>0.996628</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"8592 2b3878103 ID_2b3878103 CT ID_b81caf1c ID_3d31a06240 \n",
"101233 734856256 ID_734856256 CT ID_4482f018 ID_5ccd14e6b7 \n",
"38580 1bb3fe555 ID_1bb3fe555 CT ID_ca92b4e6 ID_e14681614d \n",
"29351 5bd2084d9 ID_5bd2084d9 CT ID_4482f018 ID_5ccd14e6b7 \n",
"90564 2941d6eba ID_2941d6eba CT ID_ca92b4e6 ID_e14681614d \n",
"\n",
" SeriesInstanceUID StudyID \\\n",
"8592 ID_25c620d29b NaN \n",
"101233 ID_b75da817b2 NaN \n",
"38580 ID_23f8022c7d NaN \n",
"29351 ID_b75da817b2 NaN \n",
"90564 ID_23f8022c7d NaN \n",
"\n",
" ImagePositionPatient \\\n",
"8592 ['-126.408875', '-126.408875', '77.500000'] \n",
"101233 ['-126.438', '-126.438', '97.500'] \n",
"38580 ['-126.408875', '-126.408875', '72.500000'] \n",
"29351 ['-126.438', '-126.438', '102.500'] \n",
"90564 ['-126.408875', '-126.408875', '77.500000'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"8592 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"101233 ['1.0', '0.0', '0.0', '0.0', '1.0', '0.0'] 1 \n",
"38580 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"29351 ['1.0', '0.0', '0.0', '0.0', '1.0', '0.0'] 1 \n",
"90564 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns \\\n",
"8592 MONOCHROME2 512 512 \n",
"101233 MONOCHROME2 512 512 \n",
"38580 MONOCHROME2 512 512 \n",
"29351 MONOCHROME2 512 512 \n",
"90564 MONOCHROME2 512 512 \n",
"\n",
" PixelSpacing BitsAllocated BitsStored \\\n",
"8592 ['0.494750976563', '0.494750976563'] 16 16 \n",
"101233 ['0.4949', '0.4949'] 16 16 \n",
"38580 ['0.494750976563', '0.494750976563'] 16 16 \n",
"29351 ['0.4949', '0.4949'] 16 16 \n",
"90564 ['0.494750976563', '0.494750976563'] 16 16 \n",
"\n",
" HighBit PixelRepresentation WindowCenter WindowWidth \\\n",
"8592 15 1 35.000000 135.000000 \n",
"101233 15 1 35.0 135.0 \n",
"38580 15 1 35.000000 135.000000 \n",
"29351 15 1 35.0 135.0 \n",
"90564 15 1 35.000000 135.000000 \n",
"\n",
" RescaleIntercept RescaleSlope PxlMin PxlMax PxlStd \\\n",
"8592 -1024.0 1.0 1.301333 0.394667 -0.776436 \n",
"101233 -1024.0 1.0 1.301333 0.378667 -0.862722 \n",
"38580 -1024.0 1.0 1.301333 0.472000 -0.733947 \n",
"29351 -1024.0 1.0 1.301333 0.442667 -0.849438 \n",
"90564 -1024.0 1.0 1.301333 0.482667 -0.703788 \n",
"\n",
" PxlMean test test2 ImageOrientationPatient_0 \\\n",
"8592 1.134899 False True 1.0 \n",
"101233 1.089866 False True 1.0 \n",
"38580 1.184966 False True 1.0 \n",
"29351 1.059636 False True 1.0 \n",
"90564 1.179639 False True 1.0 \n",
"\n",
" ImageOrientationPatient_1 ImageOrientationPatient_2 \\\n",
"8592 0.0 0.0 \n",
"101233 0.0 0.0 \n",
"38580 0.0 0.0 \n",
"29351 0.0 0.0 \n",
"90564 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_3 ImageOrientationPatient_4 \\\n",
"8592 0.0 1.0 \n",
"101233 0.0 1.0 \n",
"38580 0.0 1.0 \n",
"29351 0.0 1.0 \n",
"90564 0.0 1.0 \n",
"\n",
" ImageOrientationPatient_5 ImagePositionPatient_0 \\\n",
"8592 0.0 -126.408875 \n",
"101233 0.0 -126.438000 \n",
"38580 0.0 -126.408875 \n",
"29351 0.0 -126.438000 \n",
"90564 0.0 -126.408875 \n",
"\n",
" ImagePositionPatient_1 ImagePositionPatient_2 PixelSpacing_0 \\\n",
"8592 -126.408875 77.5 0.494751 \n",
"101233 -126.438000 97.5 0.494900 \n",
"38580 -126.408875 72.5 0.494751 \n",
"29351 -126.438000 102.5 0.494900 \n",
"90564 -126.408875 77.5 0.494751 \n",
"\n",
" PixelSpacing_1 WindowCenter_0 WindowCenter_1 WindowCenter_1_NAN \\\n",
"8592 0.494751 35.0 NaN True \n",
"101233 0.494900 35.0 NaN True \n",
"38580 0.494751 35.0 NaN True \n",
"29351 0.494900 35.0 NaN True \n",
"90564 0.494751 35.0 NaN True \n",
"\n",
" WindowWidth_0 WindowWidth_1 WindowWidth_0_le WindowWidth_1_le \\\n",
"8592 135.0 NaN 3 1 \n",
"101233 135.0 NaN 3 1 \n",
"38580 135.0 NaN 3 1 \n",
"29351 135.0 NaN 3 1 \n",
"90564 135.0 NaN 3 1 \n",
"\n",
" WindowCenter_1_le BitType_le ImageOrientationPatient_4_f \\\n",
"8592 3 0 -1.333333 \n",
"101233 3 0 -1.333333 \n",
"38580 3 0 -1.333333 \n",
"29351 3 0 -1.333333 \n",
"90564 3 0 -1.333333 \n",
"\n",
" ImageOrientationPatient_4_enc_0 ... ImageOrientationPatient_5_f \\\n",
"8592 1.0 ... -0.666667 \n",
"101233 1.0 ... -0.666667 \n",
"38580 1.0 ... -0.666667 \n",
"29351 1.0 ... -0.666667 \n",
"90564 1.0 ... -0.666667 \n",
"\n",
" ImageOrientationPatient_5_enc_0 ImageOrientationPatient_5_enc_1 \\\n",
"8592 1.0 False \n",
"101233 1.0 False \n",
"38580 1.0 False \n",
"29351 1.0 False \n",
"90564 1.0 False \n",
"\n",
" ImagePositionPatient_0_f ImagePositionPatient_0_enc_0 \\\n",
"8592 -0.720000 0.0 \n",
"101233 1.241653 0.0 \n",
"38580 -0.720000 0.0 \n",
"29351 1.241653 0.0 \n",
"90564 -0.720000 0.0 \n",
"\n",
" ImagePositionPatient_0_enc_1 ImagePositionPatient_0_f_r1 \\\n",
"8592 1.0 1.0 \n",
"101233 0.0 0.0 \n",
"38580 1.0 1.0 \n",
"29351 0.0 0.0 \n",
"90564 1.0 1.0 \n",
"\n",
" ImagePositionPatient_0_f_r05 ImagePositionPatient_1_f \\\n",
"8592 1.0 -0.818785 \n",
"101233 0.0 -0.819173 \n",
"38580 1.0 -0.818785 \n",
"29351 0.0 -0.819173 \n",
"90564 1.0 -0.818785 \n",
"\n",
" ImagePositionPatient_1_enc_0 ImagePositionPatient_2_f \\\n",
"8592 0.0 -0.128223 \n",
"101233 0.0 -0.099570 \n",
"38580 0.0 -0.135387 \n",
"29351 0.0 -0.092407 \n",
"90564 0.0 -0.128223 \n",
"\n",
" ImagePositionPatient_2_f_r05 PixelSpacing_1_f PixelSpacing_1_enc_0 \\\n",
"8592 1.0 -0.4800 0.0 \n",
"101233 1.0 1.8792 0.0 \n",
"38580 1.0 -0.4800 0.0 \n",
"29351 1.0 1.8792 0.0 \n",
"90564 1.0 -0.4800 0.0 \n",
"\n",
" PixelSpacing_1_enc_1 WindowCenter_0_le pos_max pos_min pos_size \\\n",
"8592 True 3 0.61 -0.01 -0.3 \n",
"101233 False 3 0.67 0.01 -0.1 \n",
"38580 True 3 0.61 -0.01 -0.3 \n",
"29351 False 3 0.67 0.01 -0.1 \n",
"90564 True 3 0.61 -0.01 -0.3 \n",
"\n",
" pos_idx1 pos_idx pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le \\\n",
"8592 -0.067797 16 -0.135593 -1.5 -1.5 3 \n",
"101233 0.135593 19 -0.203390 -1.5 -1.5 3 \n",
"38580 -0.135593 15 -0.067797 -1.5 -1.5 3 \n",
"29351 0.203390 20 -0.271186 -1.5 -1.5 3 \n",
"90564 -0.067797 16 -0.135593 -1.5 -1.5 3 \n",
"\n",
" pos_inc2_grp_le pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 \\\n",
"8592 3 1.0 1.0 0.0 \n",
"101233 3 1.0 1.0 0.0 \n",
"38580 3 1.0 1.0 0.0 \n",
"29351 3 1.0 1.0 0.0 \n",
"90564 3 1.0 1.0 0.0 \n",
"\n",
" pos_inc2_enc_0 pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le \\\n",
"8592 0.0 1.0 1.0 0 \n",
"101233 0.0 1.0 1.0 4 \n",
"38580 0.0 1.0 1.0 0 \n",
"29351 0.0 1.0 1.0 4 \n",
"90564 0.0 1.0 1.0 0 \n",
"\n",
" pos_range pos_rel pos_zeros pos_inc_rng pos_zeros_le \\\n",
"8592 -0.266667 0.064516 0.0 -0.6 0 \n",
"101233 0.000000 0.303030 0.0 -0.6 0 \n",
"38580 -0.266667 -0.064516 0.0 -0.6 0 \n",
"29351 0.000000 0.424242 0.0 -0.6 0 \n",
"90564 -0.266667 0.064516 0.0 -0.6 0 \n",
"\n",
" PxlMin_grp_le PxlMin_zero any epidural intraparenchymal \\\n",
"8592 2 False NaN NaN NaN \n",
"101233 2 False NaN NaN NaN \n",
"38580 2 False NaN NaN NaN \n",
"29351 2 False NaN NaN NaN \n",
"90564 2 False NaN NaN NaN \n",
"\n",
" intraventricular subarachnoid subdural any_series SeriesPP \\\n",
"8592 NaN NaN NaN False -0.5 \n",
"101233 NaN NaN NaN False -0.5 \n",
"38580 NaN NaN NaN False -0.5 \n",
"29351 NaN NaN NaN False -0.5 \n",
"90564 NaN NaN NaN False -0.5 \n",
"\n",
" yuval_idx pred_any \n",
"8592 95036 0.995983 \n",
"101233 23495 0.996200 \n",
"38580 40054 0.996345 \n",
"29351 23496 0.996412 \n",
"90564 40055 0.996628 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md['pred_any'] = predictions[:,3]\n",
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
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" <td>f3a75309f</td>\n",
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" <td>CT</td>\n",
" <td>ID_f6723c35</td>\n",
" <td>ID_fc07fac521</td>\n",
" <td>ID_cfd350c878</td>\n",
" <td>NaN</td>\n",
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" <td>2113</td>\n",
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" <td>ID_fc07fac521</td>\n",
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" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.68972</td>\n",
" <td>0.127984</td>\n",
" <td>-0.7</td>\n",
" <td>0.271186</td>\n",
" <td>21</td>\n",
" <td>-0.745763</td>\n",
" <td>1.6005</td>\n",
" <td>1.6010</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>-0.655093</td>\n",
" <td>1.111098</td>\n",
" <td>0.0</td>\n",
" <td>-0.599615</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>55352</td>\n",
" <td>0.993728</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61829</td>\n",
" <td>b2d64d052</td>\n",
" <td>ID_b2d64d052</td>\n",
" <td>CT</td>\n",
" <td>ID_f6723c35</td>\n",
" <td>ID_fc07fac521</td>\n",
" <td>ID_cfd350c878</td>\n",
" <td>NaN</td>\n",
" <td>['-125.000', '-118.558', '136.021']</td>\n",
" <td>['1.000000', '0.000000', '0.000000', '0.000000...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.488281', '0.488281']</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>150</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.064</td>\n",
" <td>0.148000</td>\n",
" <td>0.208909</td>\n",
" <td>-0.156506</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.961262</td>\n",
" <td>-0.275637</td>\n",
" <td>-125.0</td>\n",
" <td>-118.558</td>\n",
" <td>136.021</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2.15016</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.495753</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.714107</td>\n",
" <td>0.0</td>\n",
" <td>-0.044383</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>0.68972</td>\n",
" <td>0.127984</td>\n",
" <td>-0.7</td>\n",
" <td>0.203390</td>\n",
" <td>20</td>\n",
" <td>-0.677966</td>\n",
" <td>1.6005</td>\n",
" <td>1.6005</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>-0.655093</td>\n",
" <td>0.962958</td>\n",
" <td>0.0</td>\n",
" <td>-0.599615</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>55351</td>\n",
" <td>0.993775</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"46433 f3a75309f ID_f3a75309f CT ID_f6723c35 ID_fc07fac521 \n",
"59590 dc7e09cbd ID_dc7e09cbd CT ID_f6723c35 ID_fc07fac521 \n",
"33815 397e899f6 ID_397e899f6 CT ID_f6723c35 ID_fc07fac521 \n",
"2113 ed2a8477b ID_ed2a8477b CT ID_f6723c35 ID_fc07fac521 \n",
"61829 b2d64d052 ID_b2d64d052 CT ID_f6723c35 ID_fc07fac521 \n",
"\n",
" SeriesInstanceUID StudyID ImagePositionPatient \\\n",
"46433 ID_cfd350c878 NaN ['-125.000', '-118.558', '151.625'] \n",
"59590 ID_cfd350c878 NaN ['-125.000', '-118.558', '130.820'] \n",
"33815 ID_cfd350c878 NaN ['-125.000', '-118.558', '146.424'] \n",
"2113 ID_cfd350c878 NaN ['-125.000', '-118.558', '141.222'] \n",
"61829 ID_cfd350c878 NaN ['-125.000', '-118.558', '136.021'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"46433 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"59590 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"33815 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"2113 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"61829 ['1.000000', '0.000000', '0.000000', '0.000000... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns PixelSpacing \\\n",
"46433 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"59590 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"33815 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"2113 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"61829 MONOCHROME2 512 512 ['0.488281', '0.488281'] \n",
"\n",
" BitsAllocated BitsStored HighBit PixelRepresentation WindowCenter \\\n",
"46433 16 16 15 1 40 \n",
"59590 16 16 15 1 40 \n",
"33815 16 16 15 1 40 \n",
"2113 16 16 15 1 40 \n",
"61829 16 16 15 1 40 \n",
"\n",
" WindowWidth RescaleIntercept RescaleSlope PxlMin PxlMax PxlStd \\\n",
"46433 150 -1024.0 1.0 -0.064 0.292000 0.113617 \n",
"59590 150 -1024.0 1.0 -0.064 0.180000 0.216861 \n",
"33815 150 -1024.0 1.0 -0.064 0.218667 0.157382 \n",
"2113 150 -1024.0 1.0 -0.064 0.169333 0.193354 \n",
"61829 150 -1024.0 1.0 -0.064 0.148000 0.208909 \n",
"\n",
" PxlMean test test2 ImageOrientationPatient_0 \\\n",
"46433 -0.426832 False True 1.0 \n",
"59590 -0.102710 False True 1.0 \n",
"33815 -0.319805 False True 1.0 \n",
"2113 -0.226621 False True 1.0 \n",
"61829 -0.156506 False True 1.0 \n",
"\n",
" ImageOrientationPatient_1 ImageOrientationPatient_2 \\\n",
"46433 0.0 0.0 \n",
"59590 0.0 0.0 \n",
"33815 0.0 0.0 \n",
"2113 0.0 0.0 \n",
"61829 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_3 ImageOrientationPatient_4 \\\n",
"46433 0.0 0.961262 \n",
"59590 0.0 0.961262 \n",
"33815 0.0 0.961262 \n",
"2113 0.0 0.961262 \n",
"61829 0.0 0.961262 \n",
"\n",
" ImageOrientationPatient_5 ImagePositionPatient_0 \\\n",
"46433 -0.275637 -125.0 \n",
"59590 -0.275637 -125.0 \n",
"33815 -0.275637 -125.0 \n",
"2113 -0.275637 -125.0 \n",
"61829 -0.275637 -125.0 \n",
"\n",
" ImagePositionPatient_1 ImagePositionPatient_2 PixelSpacing_0 \\\n",
"46433 -118.558 151.625 0.488281 \n",
"59590 -118.558 130.820 0.488281 \n",
"33815 -118.558 146.424 0.488281 \n",
"2113 -118.558 141.222 0.488281 \n",
"61829 -118.558 136.021 0.488281 \n",
"\n",
" PixelSpacing_1 WindowCenter_0 WindowCenter_1 WindowCenter_1_NAN \\\n",
"46433 0.488281 40.0 NaN True \n",
"59590 0.488281 40.0 NaN True \n",
"33815 0.488281 40.0 NaN True \n",
"2113 0.488281 40.0 NaN True \n",
"61829 0.488281 40.0 NaN True \n",
"\n",
" WindowWidth_0 WindowWidth_1 WindowWidth_0_le WindowWidth_1_le \\\n",
"46433 150.0 NaN 1 1 \n",
"59590 150.0 NaN 1 1 \n",
"33815 150.0 NaN 1 1 \n",
"2113 150.0 NaN 1 1 \n",
"61829 150.0 NaN 1 1 \n",
"\n",
" WindowCenter_1_le BitType_le ImageOrientationPatient_4_f \\\n",
"46433 3 0 2.15016 \n",
"59590 3 0 2.15016 \n",
"33815 3 0 2.15016 \n",
"2113 3 0 2.15016 \n",
"61829 3 0 2.15016 \n",
"\n",
" ImageOrientationPatient_4_enc_0 ... ImageOrientationPatient_5_f \\\n",
"46433 0.0 ... 1.495753 \n",
"59590 0.0 ... 1.495753 \n",
"33815 0.0 ... 1.495753 \n",
"2113 0.0 ... 1.495753 \n",
"61829 0.0 ... 1.495753 \n",
"\n",
" ImageOrientationPatient_5_enc_0 ImageOrientationPatient_5_enc_1 \\\n",
"46433 0.0 False \n",
"59590 0.0 False \n",
"33815 0.0 False \n",
"2113 0.0 False \n",
"61829 0.0 False \n",
"\n",
" ImagePositionPatient_0_f ImagePositionPatient_0_enc_0 \\\n",
"46433 -0.72 1.0 \n",
"59590 -0.72 1.0 \n",
"33815 -0.72 1.0 \n",
"2113 -0.72 1.0 \n",
"61829 -0.72 1.0 \n",
"\n",
" ImagePositionPatient_0_enc_1 ImagePositionPatient_0_f_r1 \\\n",
"46433 0.0 1.0 \n",
"59590 0.0 1.0 \n",
"33815 0.0 1.0 \n",
"2113 0.0 1.0 \n",
"61829 0.0 1.0 \n",
"\n",
" ImagePositionPatient_0_f_r05 ImagePositionPatient_1_f \\\n",
"46433 1.0 -0.714107 \n",
"59590 1.0 -0.714107 \n",
"33815 1.0 -0.714107 \n",
"2113 1.0 -0.714107 \n",
"61829 1.0 -0.714107 \n",
"\n",
" ImagePositionPatient_1_enc_0 ImagePositionPatient_2_f \\\n",
"46433 0.0 -0.022027 \n",
"59590 0.0 -0.051834 \n",
"33815 0.0 -0.029479 \n",
"2113 0.0 -0.036931 \n",
"61829 0.0 -0.044383 \n",
"\n",
" ImagePositionPatient_2_f_r05 PixelSpacing_1_f PixelSpacing_1_enc_0 \\\n",
"46433 0.0 -0.48 1.0 \n",
"59590 0.0 -0.48 1.0 \n",
"33815 0.0 -0.48 1.0 \n",
"2113 0.0 -0.48 1.0 \n",
"61829 0.0 -0.48 1.0 \n",
"\n",
" PixelSpacing_1_enc_1 WindowCenter_0_le pos_max pos_min pos_size \\\n",
"46433 False 2 0.68972 0.127984 -0.7 \n",
"59590 False 2 0.68972 0.127984 -0.7 \n",
"33815 False 2 0.68972 0.127984 -0.7 \n",
"2113 False 2 0.68972 0.127984 -0.7 \n",
"61829 False 2 0.68972 0.127984 -0.7 \n",
"\n",
" pos_idx1 pos_idx pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le \\\n",
"46433 0.406780 23 -0.881356 1.6005 1.6005 3 \n",
"59590 0.135593 19 -0.610169 1.6005 1.6005 3 \n",
"33815 0.338983 22 -0.813559 1.6010 1.6005 3 \n",
"2113 0.271186 21 -0.745763 1.6005 1.6010 3 \n",
"61829 0.203390 20 -0.677966 1.6005 1.6005 3 \n",
"\n",
" pos_inc2_grp_le pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 \\\n",
"46433 3 0.0 1.0 0.0 \n",
"59590 3 0.0 1.0 0.0 \n",
"33815 3 0.0 1.0 0.0 \n",
"2113 3 0.0 1.0 0.0 \n",
"61829 3 0.0 1.0 0.0 \n",
"\n",
" pos_inc2_enc_0 pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le pos_range \\\n",
"46433 0.0 0.0 0.0 3 -0.655093 \n",
"59590 0.0 0.0 0.0 3 -0.655093 \n",
"33815 0.0 0.0 0.0 3 -0.655093 \n",
"2113 0.0 0.0 0.0 3 -0.655093 \n",
"61829 0.0 0.0 0.0 3 -0.655093 \n",
"\n",
" pos_rel pos_zeros pos_inc_rng pos_zeros_le PxlMin_grp_le \\\n",
"46433 1.407408 0.0 -0.599615 0 1 \n",
"59590 0.814817 0.0 -0.599615 0 1 \n",
"33815 1.259268 0.0 -0.599615 0 1 \n",
"2113 1.111098 0.0 -0.599615 0 1 \n",
"61829 0.962958 0.0 -0.599615 0 1 \n",
"\n",
" PxlMin_zero any epidural intraparenchymal intraventricular \\\n",
"46433 False NaN NaN NaN NaN \n",
"59590 False NaN NaN NaN NaN \n",
"33815 False NaN NaN NaN NaN \n",
"2113 False NaN NaN NaN NaN \n",
"61829 False NaN NaN NaN NaN \n",
"\n",
" subarachnoid subdural any_series SeriesPP yuval_idx pred_any \n",
"46433 NaN NaN False -0.5 55354 0.993246 \n",
"59590 NaN NaN False -0.5 55350 0.993314 \n",
"33815 NaN NaN False -0.5 55353 0.993623 \n",
"2113 NaN NaN False -0.5 55352 0.993728 \n",
"61829 NaN NaN False -0.5 55351 0.993775 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md['pred_any'] = predictions[:,4]\n",
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>img_id</th>\n",
" <th>SOPInstanceUID</th>\n",
" <th>Modality</th>\n",
" <th>PatientID</th>\n",
" <th>StudyInstanceUID</th>\n",
" <th>SeriesInstanceUID</th>\n",
" <th>StudyID</th>\n",
" <th>ImagePositionPatient</th>\n",
" <th>ImageOrientationPatient</th>\n",
" <th>SamplesPerPixel</th>\n",
" <th>PhotometricInterpretation</th>\n",
" <th>Rows</th>\n",
" <th>Columns</th>\n",
" <th>PixelSpacing</th>\n",
" <th>BitsAllocated</th>\n",
" <th>BitsStored</th>\n",
" <th>HighBit</th>\n",
" <th>PixelRepresentation</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>RescaleIntercept</th>\n",
" <th>RescaleSlope</th>\n",
" <th>PxlMin</th>\n",
" <th>PxlMax</th>\n",
" <th>PxlStd</th>\n",
" <th>PxlMean</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" <th>ImageOrientationPatient_0</th>\n",
" <th>ImageOrientationPatient_1</th>\n",
" <th>ImageOrientationPatient_2</th>\n",
" <th>ImageOrientationPatient_3</th>\n",
" <th>ImageOrientationPatient_4</th>\n",
" <th>ImageOrientationPatient_5</th>\n",
" <th>ImagePositionPatient_0</th>\n",
" <th>ImagePositionPatient_1</th>\n",
" <th>ImagePositionPatient_2</th>\n",
" <th>PixelSpacing_0</th>\n",
" <th>PixelSpacing_1</th>\n",
" <th>WindowCenter_0</th>\n",
" <th>WindowCenter_1</th>\n",
" <th>WindowCenter_1_NAN</th>\n",
" <th>WindowWidth_0</th>\n",
" <th>WindowWidth_1</th>\n",
" <th>WindowWidth_0_le</th>\n",
" <th>WindowWidth_1_le</th>\n",
" <th>WindowCenter_1_le</th>\n",
" <th>BitType_le</th>\n",
" <th>ImageOrientationPatient_4_f</th>\n",
" <th>ImageOrientationPatient_4_enc_0</th>\n",
" <th>...</th>\n",
" <th>ImageOrientationPatient_5_f</th>\n",
" <th>ImageOrientationPatient_5_enc_0</th>\n",
" <th>ImageOrientationPatient_5_enc_1</th>\n",
" <th>ImagePositionPatient_0_f</th>\n",
" <th>ImagePositionPatient_0_enc_0</th>\n",
" <th>ImagePositionPatient_0_enc_1</th>\n",
" <th>ImagePositionPatient_0_f_r1</th>\n",
" <th>ImagePositionPatient_0_f_r05</th>\n",
" <th>ImagePositionPatient_1_f</th>\n",
" <th>ImagePositionPatient_1_enc_0</th>\n",
" <th>ImagePositionPatient_2_f</th>\n",
" <th>ImagePositionPatient_2_f_r05</th>\n",
" <th>PixelSpacing_1_f</th>\n",
" <th>PixelSpacing_1_enc_0</th>\n",
" <th>PixelSpacing_1_enc_1</th>\n",
" <th>WindowCenter_0_le</th>\n",
" <th>pos_max</th>\n",
" <th>pos_min</th>\n",
" <th>pos_size</th>\n",
" <th>pos_idx1</th>\n",
" <th>pos_idx</th>\n",
" <th>pos_idx2</th>\n",
" <th>pos_inc1</th>\n",
" <th>pos_inc2</th>\n",
" <th>pos_inc1_grp_le</th>\n",
" <th>pos_inc2_grp_le</th>\n",
" <th>pos_inc1_r1</th>\n",
" <th>pos_inc1_r0001</th>\n",
" <th>pos_inc1_enc_0</th>\n",
" <th>pos_inc2_enc_0</th>\n",
" <th>pos_inc1_enc_1</th>\n",
" <th>pos_inc2_enc_1</th>\n",
" <th>pos_size_le</th>\n",
" <th>pos_range</th>\n",
" <th>pos_rel</th>\n",
" <th>pos_zeros</th>\n",
" <th>pos_inc_rng</th>\n",
" <th>pos_zeros_le</th>\n",
" <th>PxlMin_grp_le</th>\n",
" <th>PxlMin_zero</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>any_series</th>\n",
" <th>SeriesPP</th>\n",
" <th>yuval_idx</th>\n",
" <th>pred_any</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>9661</td>\n",
" <td>feb0a9076</td>\n",
" <td>ID_feb0a9076</td>\n",
" <td>CT</td>\n",
" <td>ID_252323d1</td>\n",
" <td>ID_0257ad04c2</td>\n",
" <td>ID_58b46714eb</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '24.4169536', '204.228683']</td>\n",
" <td>['1', '0', '0', '0', '0.990268069', '-0.139173...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.126667</td>\n",
" <td>-0.761993</td>\n",
" <td>1.166719</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.990268</td>\n",
" <td>-0.139173</td>\n",
" <td>-125.0</td>\n",
" <td>24.416954</td>\n",
" <td>204.228683</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.536908</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>2.405513</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.192226</td>\n",
" <td>1.0</td>\n",
" <td>0.053336</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.099715</td>\n",
" <td>0.473493</td>\n",
" <td>-0.3</td>\n",
" <td>0.000000</td>\n",
" <td>17</td>\n",
" <td>-0.203390</td>\n",
" <td>1.527710</td>\n",
" <td>1.522339</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>-0.225189</td>\n",
" <td>0.193609</td>\n",
" <td>0.0</td>\n",
" <td>-0.595692</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>49823</td>\n",
" <td>0.992338</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107285</td>\n",
" <td>cfbf38afe</td>\n",
" <td>ID_cfbf38afe</td>\n",
" <td>CT</td>\n",
" <td>ID_6e75b42a</td>\n",
" <td>ID_b94674c76f</td>\n",
" <td>ID_93d835d9f3</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '17.5586391', '177.279488']</td>\n",
" <td>['1', '0', '0', '0', '0.933580426', '-0.358367...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.098667</td>\n",
" <td>-0.807341</td>\n",
" <td>1.049002</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.933580</td>\n",
" <td>-0.358368</td>\n",
" <td>-125.0</td>\n",
" <td>17.558639</td>\n",
" <td>177.279488</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.781072</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.944214</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.100782</td>\n",
" <td>1.0</td>\n",
" <td>0.014727</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.051518</td>\n",
" <td>0.388022</td>\n",
" <td>-0.3</td>\n",
" <td>-0.135593</td>\n",
" <td>15</td>\n",
" <td>-0.067797</td>\n",
" <td>1.687011</td>\n",
" <td>1.663025</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.023307</td>\n",
" <td>-0.064219</td>\n",
" <td>0.0</td>\n",
" <td>-0.581939</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>74776</td>\n",
" <td>0.992465</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81787</td>\n",
" <td>95aace9ba</td>\n",
" <td>ID_95aace9ba</td>\n",
" <td>CT</td>\n",
" <td>ID_6e75b42a</td>\n",
" <td>ID_b94674c76f</td>\n",
" <td>ID_93d835d9f3</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '17.5586391', '193.305489']</td>\n",
" <td>['1', '0', '0', '0', '0.933580426', '-0.358367...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.086667</td>\n",
" <td>-0.789830</td>\n",
" <td>0.920645</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.933580</td>\n",
" <td>-0.358368</td>\n",
" <td>-125.0</td>\n",
" <td>17.558639</td>\n",
" <td>193.305489</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.781072</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.944214</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.100782</td>\n",
" <td>1.0</td>\n",
" <td>0.037687</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.051518</td>\n",
" <td>0.388022</td>\n",
" <td>-0.3</td>\n",
" <td>0.067797</td>\n",
" <td>18</td>\n",
" <td>-0.271186</td>\n",
" <td>1.662964</td>\n",
" <td>1.687012</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.023307</td>\n",
" <td>0.322242</td>\n",
" <td>0.0</td>\n",
" <td>-0.581939</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>74779</td>\n",
" <td>0.992568</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87951</td>\n",
" <td>5092c392f</td>\n",
" <td>ID_5092c392f</td>\n",
" <td>CT</td>\n",
" <td>ID_6e75b42a</td>\n",
" <td>ID_b94674c76f</td>\n",
" <td>ID_93d835d9f3</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '17.5586391', '187.979561']</td>\n",
" <td>['1', '0', '0', '0', '0.933580426', '-0.358367...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.130667</td>\n",
" <td>-0.782852</td>\n",
" <td>0.978311</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.933580</td>\n",
" <td>-0.358368</td>\n",
" <td>-125.0</td>\n",
" <td>17.558639</td>\n",
" <td>187.979561</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.781072</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.944214</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.100782</td>\n",
" <td>1.0</td>\n",
" <td>0.030057</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.051518</td>\n",
" <td>0.388022</td>\n",
" <td>-0.3</td>\n",
" <td>0.000000</td>\n",
" <td>17</td>\n",
" <td>-0.203390</td>\n",
" <td>1.687011</td>\n",
" <td>1.662964</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.023307</td>\n",
" <td>0.193809</td>\n",
" <td>0.0</td>\n",
" <td>-0.581939</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>74778</td>\n",
" <td>0.992820</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86533</td>\n",
" <td>3a20374d3</td>\n",
" <td>ID_3a20374d3</td>\n",
" <td>CT</td>\n",
" <td>ID_6e75b42a</td>\n",
" <td>ID_b94674c76f</td>\n",
" <td>ID_93d835d9f3</td>\n",
" <td>NaN</td>\n",
" <td>['-125', '17.5586391', '182.605538']</td>\n",
" <td>['1', '0', '0', '0', '0.933580426', '-0.358367...</td>\n",
" <td>1</td>\n",
" <td>MONOCHROME2</td>\n",
" <td>512</td>\n",
" <td>512</td>\n",
" <td>['0.48828125', '0.48828125']</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>['00040', '00040']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>-1024.0</td>\n",
" <td>1.0</td>\n",
" <td>1.301333</td>\n",
" <td>0.124000</td>\n",
" <td>-0.788199</td>\n",
" <td>1.016346</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.933580</td>\n",
" <td>-0.358368</td>\n",
" <td>-125.0</td>\n",
" <td>17.558639</td>\n",
" <td>182.605538</td>\n",
" <td>0.488281</td>\n",
" <td>0.488281</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>False</td>\n",
" <td>80.0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.781072</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.944214</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>-0.72</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.100782</td>\n",
" <td>1.0</td>\n",
" <td>0.022358</td>\n",
" <td>0.0</td>\n",
" <td>-0.48</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1.051518</td>\n",
" <td>0.388022</td>\n",
" <td>-0.3</td>\n",
" <td>-0.067797</td>\n",
" <td>16</td>\n",
" <td>-0.135593</td>\n",
" <td>1.663025</td>\n",
" <td>1.687011</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.023307</td>\n",
" <td>0.064217</td>\n",
" <td>0.0</td>\n",
" <td>-0.581939</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>-0.5</td>\n",
" <td>74777</td>\n",
" <td>0.992826</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" img_id SOPInstanceUID Modality PatientID StudyInstanceUID \\\n",
"9661 feb0a9076 ID_feb0a9076 CT ID_252323d1 ID_0257ad04c2 \n",
"107285 cfbf38afe ID_cfbf38afe CT ID_6e75b42a ID_b94674c76f \n",
"81787 95aace9ba ID_95aace9ba CT ID_6e75b42a ID_b94674c76f \n",
"87951 5092c392f ID_5092c392f CT ID_6e75b42a ID_b94674c76f \n",
"86533 3a20374d3 ID_3a20374d3 CT ID_6e75b42a ID_b94674c76f \n",
"\n",
" SeriesInstanceUID StudyID ImagePositionPatient \\\n",
"9661 ID_58b46714eb NaN ['-125', '24.4169536', '204.228683'] \n",
"107285 ID_93d835d9f3 NaN ['-125', '17.5586391', '177.279488'] \n",
"81787 ID_93d835d9f3 NaN ['-125', '17.5586391', '193.305489'] \n",
"87951 ID_93d835d9f3 NaN ['-125', '17.5586391', '187.979561'] \n",
"86533 ID_93d835d9f3 NaN ['-125', '17.5586391', '182.605538'] \n",
"\n",
" ImageOrientationPatient SamplesPerPixel \\\n",
"9661 ['1', '0', '0', '0', '0.990268069', '-0.139173... 1 \n",
"107285 ['1', '0', '0', '0', '0.933580426', '-0.358367... 1 \n",
"81787 ['1', '0', '0', '0', '0.933580426', '-0.358367... 1 \n",
"87951 ['1', '0', '0', '0', '0.933580426', '-0.358367... 1 \n",
"86533 ['1', '0', '0', '0', '0.933580426', '-0.358367... 1 \n",
"\n",
" PhotometricInterpretation Rows Columns PixelSpacing \\\n",
"9661 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"107285 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"81787 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"87951 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"86533 MONOCHROME2 512 512 ['0.48828125', '0.48828125'] \n",
"\n",
" BitsAllocated BitsStored HighBit PixelRepresentation \\\n",
"9661 16 12 11 0 \n",
"107285 16 12 11 0 \n",
"81787 16 12 11 0 \n",
"87951 16 12 11 0 \n",
"86533 16 12 11 0 \n",
"\n",
" WindowCenter WindowWidth RescaleIntercept \\\n",
"9661 ['00040', '00040'] ['00080', '00080'] -1024.0 \n",
"107285 ['00040', '00040'] ['00080', '00080'] -1024.0 \n",
"81787 ['00040', '00040'] ['00080', '00080'] -1024.0 \n",
"87951 ['00040', '00040'] ['00080', '00080'] -1024.0 \n",
"86533 ['00040', '00040'] ['00080', '00080'] -1024.0 \n",
"\n",
" RescaleSlope PxlMin PxlMax PxlStd PxlMean test test2 \\\n",
"9661 1.0 1.301333 0.126667 -0.761993 1.166719 False True \n",
"107285 1.0 1.301333 0.098667 -0.807341 1.049002 False True \n",
"81787 1.0 1.301333 0.086667 -0.789830 0.920645 False True \n",
"87951 1.0 1.301333 0.130667 -0.782852 0.978311 False True \n",
"86533 1.0 1.301333 0.124000 -0.788199 1.016346 False True \n",
"\n",
" ImageOrientationPatient_0 ImageOrientationPatient_1 \\\n",
"9661 1.0 0.0 \n",
"107285 1.0 0.0 \n",
"81787 1.0 0.0 \n",
"87951 1.0 0.0 \n",
"86533 1.0 0.0 \n",
"\n",
" ImageOrientationPatient_2 ImageOrientationPatient_3 \\\n",
"9661 0.0 0.0 \n",
"107285 0.0 0.0 \n",
"81787 0.0 0.0 \n",
"87951 0.0 0.0 \n",
"86533 0.0 0.0 \n",
"\n",
" ImageOrientationPatient_4 ImageOrientationPatient_5 \\\n",
"9661 0.990268 -0.139173 \n",
"107285 0.933580 -0.358368 \n",
"81787 0.933580 -0.358368 \n",
"87951 0.933580 -0.358368 \n",
"86533 0.933580 -0.358368 \n",
"\n",
" ImagePositionPatient_0 ImagePositionPatient_1 \\\n",
"9661 -125.0 24.416954 \n",
"107285 -125.0 17.558639 \n",
"81787 -125.0 17.558639 \n",
"87951 -125.0 17.558639 \n",
"86533 -125.0 17.558639 \n",
"\n",
" ImagePositionPatient_2 PixelSpacing_0 PixelSpacing_1 \\\n",
"9661 204.228683 0.488281 0.488281 \n",
"107285 177.279488 0.488281 0.488281 \n",
"81787 193.305489 0.488281 0.488281 \n",
"87951 187.979561 0.488281 0.488281 \n",
"86533 182.605538 0.488281 0.488281 \n",
"\n",
" WindowCenter_0 WindowCenter_1 WindowCenter_1_NAN WindowWidth_0 \\\n",
"9661 40.0 40.0 False 80.0 \n",
"107285 40.0 40.0 False 80.0 \n",
"81787 40.0 40.0 False 80.0 \n",
"87951 40.0 40.0 False 80.0 \n",
"86533 40.0 40.0 False 80.0 \n",
"\n",
" WindowWidth_1 WindowWidth_0_le WindowWidth_1_le WindowCenter_1_le \\\n",
"9661 80.0 0 0 1 \n",
"107285 80.0 0 0 1 \n",
"81787 80.0 0 0 1 \n",
"87951 80.0 0 0 1 \n",
"86533 80.0 0 0 1 \n",
"\n",
" BitType_le ImageOrientationPatient_4_f \\\n",
"9661 1 2.536908 \n",
"107285 1 1.781072 \n",
"81787 1 1.781072 \n",
"87951 1 1.781072 \n",
"86533 1 1.781072 \n",
"\n",
" ImageOrientationPatient_4_enc_0 ... ImageOrientationPatient_5_f \\\n",
"9661 0.0 ... 2.405513 \n",
"107285 0.0 ... 0.944214 \n",
"81787 0.0 ... 0.944214 \n",
"87951 0.0 ... 0.944214 \n",
"86533 0.0 ... 0.944214 \n",
"\n",
" ImageOrientationPatient_5_enc_0 ImageOrientationPatient_5_enc_1 \\\n",
"9661 0.0 False \n",
"107285 0.0 False \n",
"81787 0.0 False \n",
"87951 0.0 False \n",
"86533 0.0 False \n",
"\n",
" ImagePositionPatient_0_f ImagePositionPatient_0_enc_0 \\\n",
"9661 -0.72 1.0 \n",
"107285 -0.72 1.0 \n",
"81787 -0.72 1.0 \n",
"87951 -0.72 1.0 \n",
"86533 -0.72 1.0 \n",
"\n",
" ImagePositionPatient_0_enc_1 ImagePositionPatient_0_f_r1 \\\n",
"9661 0.0 1.0 \n",
"107285 0.0 1.0 \n",
"81787 0.0 1.0 \n",
"87951 0.0 1.0 \n",
"86533 0.0 1.0 \n",
"\n",
" ImagePositionPatient_0_f_r05 ImagePositionPatient_1_f \\\n",
"9661 1.0 1.192226 \n",
"107285 1.0 1.100782 \n",
"81787 1.0 1.100782 \n",
"87951 1.0 1.100782 \n",
"86533 1.0 1.100782 \n",
"\n",
" ImagePositionPatient_1_enc_0 ImagePositionPatient_2_f \\\n",
"9661 1.0 0.053336 \n",
"107285 1.0 0.014727 \n",
"81787 1.0 0.037687 \n",
"87951 1.0 0.030057 \n",
"86533 1.0 0.022358 \n",
"\n",
" ImagePositionPatient_2_f_r05 PixelSpacing_1_f PixelSpacing_1_enc_0 \\\n",
"9661 0.0 -0.48 1.0 \n",
"107285 0.0 -0.48 1.0 \n",
"81787 0.0 -0.48 1.0 \n",
"87951 0.0 -0.48 1.0 \n",
"86533 0.0 -0.48 1.0 \n",
"\n",
" PixelSpacing_1_enc_1 WindowCenter_0_le pos_max pos_min pos_size \\\n",
"9661 False 2 1.099715 0.473493 -0.3 \n",
"107285 False 2 1.051518 0.388022 -0.3 \n",
"81787 False 2 1.051518 0.388022 -0.3 \n",
"87951 False 2 1.051518 0.388022 -0.3 \n",
"86533 False 2 1.051518 0.388022 -0.3 \n",
"\n",
" pos_idx1 pos_idx pos_idx2 pos_inc1 pos_inc2 pos_inc1_grp_le \\\n",
"9661 0.000000 17 -0.203390 1.527710 1.522339 3 \n",
"107285 -0.135593 15 -0.067797 1.687011 1.663025 3 \n",
"81787 0.067797 18 -0.271186 1.662964 1.687012 3 \n",
"87951 0.000000 17 -0.203390 1.687011 1.662964 3 \n",
"86533 -0.067797 16 -0.135593 1.663025 1.687011 3 \n",
"\n",
" pos_inc2_grp_le pos_inc1_r1 pos_inc1_r0001 pos_inc1_enc_0 \\\n",
"9661 3 0.0 0.0 0.0 \n",
"107285 3 0.0 0.0 0.0 \n",
"81787 3 0.0 0.0 0.0 \n",
"87951 3 0.0 0.0 0.0 \n",
"86533 3 0.0 0.0 0.0 \n",
"\n",
" pos_inc2_enc_0 pos_inc1_enc_1 pos_inc2_enc_1 pos_size_le \\\n",
"9661 0.0 0.0 0.0 0 \n",
"107285 0.0 0.0 0.0 0 \n",
"81787 0.0 0.0 0.0 0 \n",
"87951 0.0 0.0 0.0 0 \n",
"86533 0.0 0.0 0.0 0 \n",
"\n",
" pos_range pos_rel pos_zeros pos_inc_rng pos_zeros_le \\\n",
"9661 -0.225189 0.193609 0.0 -0.595692 0 \n",
"107285 0.023307 -0.064219 0.0 -0.581939 0 \n",
"81787 0.023307 0.322242 0.0 -0.581939 0 \n",
"87951 0.023307 0.193809 0.0 -0.581939 0 \n",
"86533 0.023307 0.064217 0.0 -0.581939 0 \n",
"\n",
" PxlMin_grp_le PxlMin_zero any epidural intraparenchymal \\\n",
"9661 2 False NaN NaN NaN \n",
"107285 2 False NaN NaN NaN \n",
"81787 2 False NaN NaN NaN \n",
"87951 2 False NaN NaN NaN \n",
"86533 2 False NaN NaN NaN \n",
"\n",
" intraventricular subarachnoid subdural any_series SeriesPP \\\n",
"9661 NaN NaN NaN False -0.5 \n",
"107285 NaN NaN NaN False -0.5 \n",
"81787 NaN NaN NaN False -0.5 \n",
"87951 NaN NaN NaN False -0.5 \n",
"86533 NaN NaN NaN False -0.5 \n",
"\n",
" yuval_idx pred_any \n",
"9661 49823 0.992338 \n",
"107285 74776 0.992465 \n",
"81787 74779 0.992568 \n",
"87951 74778 0.992820 \n",
"86533 74777 0.992826 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_md['pred_any'] = predictions[:,5]\n",
"test_md.sort_values('pred_any').tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 121232, 6)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"test_md['pred_any'] = preds[9,:,1]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8297793f50>]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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kW3UcQKnP0ADwU4VV9RyrqNP+fw/MyU5k6/Eq2iapU0q10wDwU5sOtx/+qQHQm7lZSZypa6agXM8HUKojDQA/telwBaMS9fBPT8zJTgTgU+0GUuoiGgB+qO3wzzPMH5+MSPDO/uWpUYlRjIiNYMsxHQhWqiMNAD+07UQVDS0u7f7xkIgwO0vHAZTqTAPAD7Uf/nl5ts7+5ak52YmUn2vixBmdJUypdhoAfmjT4XLmZCXq4Z99MCfLOh9Au4GUukADwM8UVtVztKJOz/7to9HJQxgaHabXBVKqAw0AP7PJmvxdp3/sm/ZxgC3Hzug4gFIWDQA/8/7hctITIhmdrId/9tWcrCRKahopOttgdylK+QQNAD/S1KqHf/ZH+/kA2g2kVBsNAD+y7fhZ6ptdzB+n/f+XYtywGOKjQnUgWCmLBoAf2XConLAQB1eM0at/XgqHQ5idmajfAJSyeBQAIrJARA6LSIGIPN7F4+Ei8or1+BYRyezw2BPW8sMicrO1LEJEtorIHhHZLyI/9FaDAtmmw+Vcnp1EVFiI3aX4rdlZiZyqqqe0RscBlOo1AETECSwHbgFygKUiktNptQeBs8aYMcAvgJ9a2+YAS4BJwALgaWt/TcB1xphpwHRggYjM9U6TAtOJyjqOVdZxrZ792y/tcyfo5aGV8uwbwGygwBhzzBjTDKwAFndaZzHwgnV7JXC9tI1SLgZWGGOajDHHgQJgtmnTfmnGUOtHj83rwUbr6p/XTRhucyX+bWJKLJGhTnadqra7FKVs50kApAGFHe4XWcu6XMcY0wrUAEk9bSsiThHZDZQD640xW7p6chF5WES2i8j2iooKD8oNTBsOlZOdPIRRSVF2l+LXnA5hUmos+4pr7C5FKdt5EgBdHW/Y+dN6d+t0u60xxmWMmQ6kA7NFZHJXT26MedYYk2uMyU1ODs7uj/rmVrYcq+I6PfvXKyanxbG/pBaXThSvgpwnAVAEjOxwPx0o6W4dEQkB4oAqT7Y1xlQDm2gbI1Bd+KjgDM0uN9dO0ADwhqnpcTS0uDhaoRPEqODmSQBsA8aKSJaIhNE2qLuq0zqrgAes23cBG0zb+fargCXWUUJZwFhgq4gki0g8gIhEAjcAh/rfnMC08XA50eEhXJaZaHcpAWFKWhwAe4u0G0gFt14DwOrTfxR4FzgIvGqM2S8iT4rIImu154EkESkAvgU8bm27H3gVOACsA5YZY1xACrBRRPJoC5j1xpjV3m1aYDDGsPFQOVeNGUpYiJ624Q3ZydFEhTnZq+MAKsh5dEC5MWYNsKbTsu91uN0I3N3Ntk8BT3ValgfM6GuxwehQ2TlKaxr55g3BOf4xENoHgjUAVLDTj5Q+buOFyd+1/9+bpqTFs7+khlaX2+5SlLKNBoCP23ionEmpsQyPjbC7lIAyJT2WxhY3Ryvq7C5FKdtoAPiwmvoWdpw8y3V69I/XTUmLByCvSE8IU8FLA8CHvX+kArfR7p+BkD10CEPCnHpCmApqGgA+bNOhchKHhDF9ZLzdpQQch0OYlBZHngaACmIaAD7K5TZsyq9g3rhknA6d/GUgTEmL42BprQ4Eq6ClAeCj8oqqqaprZr5e/XPATE2Po7HFTYGeEayClAaAj3o/vwIRuGasBsBAmWydEZynZwSrIKUB4KPez69gWno8CUPC7C4lYGUlDSE6PEQHglXQ0gDwQTX1LewprOaacfrpfyA5rDOC9RuAClYaAD7ow4JK3AbmjRtqdykBb2p620Bwiw4EqyCkAeCDNudXEBMRwrR0PfxzoE1Oi6Op1c2R0zoQrIKPBoCPMcbwfn4FV48dSohT/3kG2lQrZHUcQAUjfYfxMUfKz1NW26hH/wySjMQoYsJDyCvWS0Ko4KMB4GM257fNe6wDwIPD4RAmp8Wxt7jW7lKUGnQaAD7m/fwKxgyLJjU+0u5SgsYUHQhWQUoDwIc0trjYeryKefrpf1BNSYujudVN/ulzdpei1KDSAPAhnx47Q1OrW7t/BpnOEayClQaAD9mcX0l4iIM5WTr5+2DKSIpieGw46/aX2V2KUoNKA8CHbD5SweysRCJCnXaXElREhCWXjeL9/ApOntEZwlTw0ADwEcXVDRSUn9f+f5ssnT0KhwgvbzlldylKDRoNAB/RfvinBoA9RsRFcFPOcF7ZXkhji8vucpQaFBoAPmJzfgUpcRGMGRZtdylB6/65GVTXt/BOXqndpSg1KDQAfECry82HBZVcMzYZEZ39yy6Xj05idPIQXvz0pN2lKDUoNAB8wJ6ias41turhnzYTEe6fm8Huwmo9JFQFBQ0AH/DewXKcDuGqMXr5Z7vdOSudyFAnL356wu5SlBpwGgA+YP2B08zJSiQuKtTuUoJebEQot89I463dJdTUt9hdjlIDyqMAEJEFInJYRApE5PEuHg8XkVesx7eISGaHx56wlh8WkZutZSNFZKOIHBSR/SLymLca5G9OVNZxpPw8N+YMt7sUZblv7iiaWt28tqPQ7lKUGlC9BoCIOIHlwC1ADrBURHI6rfYgcNYYMwb4BfBTa9scYAkwCVgAPG3trxX4J2PMRGAusKyLfQaF9QdOA2gA+JBJqXHMykjgT1tO4XYbu8tRasB48g1gNlBgjDlmjGkGVgCLO62zGHjBur0SuF7aDmdZDKwwxjQZY44DBcBsY0ypMWYngDHmHHAQSOt/c/zP+gOnmZgSS3pClN2lqA7un5vB8co6PjpaaXcpSg0YTwIgDej4XbiIz75ZX1jHGNMK1ABJnmxrdRfNALZ4XnZgqKprZvvJKv3074NumTKCxCFhrNim3UAqcHkSAF0dmN75e3F36/S4rYhEA68D3zTGdDkjh4g8LCLbRWR7RUWFB+X6j/cOnsZt4CYNAJ8THuLktikpvHfwNHVNrXaXo9SA8CQAioCRHe6nAyXdrSMiIUAcUNXTtiISStub/5+MMW909+TGmGeNMbnGmNzk5MA6Tv6vB06TGhfBpNRYu0tRXVg0PZXGFveFcRqlAo0nAbANGCsiWSISRtug7qpO66wCHrBu3wVsMMYYa/kS6yihLGAssNUaH3geOGiM+bk3GuJvGppdfHCkghtyhuvZvz5q1qgEUuIiWLWn8+cdpQJDrwFg9ek/CrxL22Dtq8aY/SLypIgsslZ7HkgSkQLgW8Dj1rb7gVeBA8A6YJkxxgVcCdwPXCciu62fW73cNp/2YUEljS1u7f/3YQ6H8LlpqWzOr+BsXbPd5SjldSGerGSMWQOs6bTsex1uNwJ3d7PtU8BTnZZ9SNfjA0Fj/YEyYsJDmJOVZHcpqgeLpqXy7OZjrN1XxhfnjLK7HKW8Ss8EtoHLbXjvYDnzJwwjLET/CXzZpNRYsocO4W3tBlIBSN99bLDr1FnO1DVr948fEGnrBvr0+BlO1zbaXY5SXqUBYIP1B04T6hTmjw+so5oC1aLpqRgDq3WeABVgNABssP7AaeZmJxEboRd/8wejk6OZlBqrRwOpgKMBMMgKys9zrLJOu3/8zKJpqewprNZJ41VA0QAYZO1XmLxhogaAP1k4LRXApwaDjTGcqNRAUpdOA2AQFVbV878fneDOGWmkxkfaXY7qg7T4SHIzEvrVDbS7sJo3dhZ5raYXPz3J/P/axE/WHqLtvEul+kYDYBD9eO1BnCL884LxdpeiLsGi6anknz7P4bJzfd7WGMN3V+bxrVf3sGZv/weTm1pdPL3xKDHhIfz2/aN89/U8Wl3ufu9XBRcNgEGy5dgZ1uwt45F5o0mJ00///ujWKSk4HcJLlzBp/K7Cag6fPkdMRAjfWZnX766b17YXUVbbyDP3zeIb14/l1e1FPPLSThpbXP3arwouGgCDwOU2PLn6AKlxETx8Tbbd5ahLNDQ6nHtyR/Lipyf73O3yytZCosKcvP61K3A6hK//6dLfrJtb3Tyz6SgzR8Vz5ZgkvnXjOJ5cPIn3Dp3m/ue36FSWymMaAIPg9R1F7C+p5bu3TCAyzGl3Oaof/uP2ydw7ZxS/ff8o//LmPlwezBh2vqmVt/NK+NzUVMYNj+Hn90zjQGktT64+cEk1vLmriOLqBv7h+rEXLiT4pcsz+fXSGewurOYLz35CbaOGgOqdBsAAO9/Uyn++e5iZo+JZZB1JovyX0yH8x+2T+fr80fx56ym+sWIXza09972/vaeE+mYXS2a3XRn9+onD+eq8bF7ecoq3dhf36flbXW6WbzzK1PQ45o+7+ETChVNTee6ByzhUdo4VW0/1rWEqKGkADLDlGwuoPN/E9z83SS/7HCBEhO8smMATt0zgnbxSHvrjdhqau+/OWbH1FOOHxzB9ZPyFZd++aTyXZSbwxBt7KSg/7/Fzr9pTwqmqeh69dkyXr6d545KZnZXIS5/qfMaqdxoAA6iwqp7nPzjOnTPTmNbhj18Fhq/OG81P7pzCB0cq+Moft3V5FM6Bklr2FNXwhctGXvSGHep08OulM4kIdXLfc1v4ydpDbDtR1WOXkstt+M2GAiaMiOnxRML752Zwqqqe9/MDawY95X0aAAPEGMP3V+3H6RC+c/MEu8tRA2TJ7FH85M6pfFRwhp+sPfSZx1/ZdoqwEAd3zuw8jTaMiIvgd1/KJTt5CM99cIy7f/sJuf+xnn98ZTer80qoabi4H/+dvaUcq6zjGx36/rty86QRJMeE8+IlHK2kgotH8wGovlu1p4QNh8r594U5jIiLsLscNYDuuWwk+0tqeO7D40xJj2Px9LY3+8YWF2/uKmbBpBHER4V1ue2sjARefmguNQ0tfHCkgvcOlrPxcDlv7irG6RByMxK4bsIwrp0wjN9sOMLYYdEsmDSix3rCQhwsvWwkv95YQGFVPSMTo7zeZhUYNAAGQFVdMz98+wDTR8bzd1dk2l2OGgT/tjCHg6Xn+O7reYxOjmZyWhzr9pVR29h6YfC3J3GRoSycmsrCqam43IZdp86y4VA5Gw6V8+O1h/ix9e3iV0um43D0Ppa0dM4olm86yktbTvLELRP73T4VmLQLaAA8+fZ+zjW28NPPT8XpwR+r8n+hTgfL751JQlQYX31xB1V1zfx56ykykqKY28dZ35wOITczke8smMC6b17Dx49fx1N3TOax68eycKpnR5KlxEVy48ThvLqtUE8OU93SAPCyjYfL+cvuEr42fwzjR8TYXY4aRMkx4fz2vllUnG/igd9vZcvxKu7JHenRJ/aepMZHcu+cDP7xxnF9+kDxpcszOFvfwjs6j4HqhgaAF51vauVf39jLmGHRLLt2tN3lKBtMGxnPU7dPZm9xDU6HcPesdNtquXx0EqOTh/BHHQxW3dAxAC/6f+sOUVrbyMpHriA8RM/4DVZ3546k4nwTLa2GYbH2HQAgItw/N4MfvH2AvKJqpqbrocjqYvoNwEt2nKzij5+e5IHLM5mVkWB3OcpmX58/hsduGGt3Gdw5K52oMCcvfqLfAtRnaQB4gTGGp945yIjYCP75Zr3Us/IdsRGh3D4jjVV7Sjhb12x3OcrHaAB4wSfHzrDzVDVfnz+aIeHaq6Z8yxdyR9LU6tYzg9VnaAB4wfKNBSTHhHN3bu/Heys12HJSYwlzOjhYWmt3KcrHaAD0065TZ/mo4AwPXZ1FRKgO/CrfE+p0MGZYNAc0AFQnGgD9tHxjAfFRodw7J8PuUpTqVk5qLAdL+z6VpQpsGgD9cKCklr8dLOfLV2Rp37/yaRNTYqk830TFuSa7S1E+xKMAEJEFInJYRApE5PEuHg8XkVesx7eISGaHx56wlh8WkZs7LP+9iJSLyD5vNMQOyzcVEB0eotf7UT5vYkrbWek6DqA66jUARMQJLAduAXKApSKS02m1B4GzxpgxwC+An1rb5gBLgEnAAuBpa38Af7CW+aWjFedZs7eU++ZmEBcVanc5SvUoJyUW0ABQF/PkG8BsoMAYc8wY0wysABZ3Wmcx8IJ1eyVwvbRdsHwxsMIY02SMOQ4UWPvDGLMZqPJCG2zxzKajhIc4+MrVWXaXolSv4qPCSImL0IFgdRFPAiANKOxwv8ha1uU6xphWoAZI8nDbHonIwyKyXUS2V1T4xnHMRWfr+cuuYpZcNoqh0eF2l6OURyamxOo3AHURTwKgq8sPdp63rrt1PNm2R8aYZ0Kze7cAAA9iSURBVI0xucaY3OTk5N43GATPfXAcEfjqvGy7S1HKYxNTYjhaUaeXh1YXeBIARUDHM5zSgZLu1hGRECCOtu4dT7b1Ky63YXVeCTfljCAlLtLucpTy2MSUWFxu06dJ6FVg8yQAtgFjRSRLRMJoG9Rd1WmdVcAD1u27gA3GGGMtX2IdJZQFjAW2eqd0e2w9XkXl+WZunZJidylK9Un7QLCOA6h2vQaA1af/KPAucBB41RizX0SeFJFF1mrPA0kiUgB8C3jc2nY/8CpwAFgHLDPGuABE5M/AJ8B4ESkSkQe927SBsXZfKRGhDuaP943uKKU8lZE0hMhQp44DqAs8OnvJGLMGWNNp2fc63G4E7u5m26eAp7pYvrRPlfoAt9uwdl8Z88cN0xO/lN9xOoTxI2I0ANQFeiZwH+w4dZaKc03cMmWE3aUodUkmpsRyoKSWth5aFew0APpgzd5SwkIcXD9xuN2lKHVJclJiqG1spaSm0e5SlA/QAPCQ221Yt6+MeeOSidbuH+WnJrafEVyi3UBKA8Bju4uqKa1p5Fbt/lF+bIJeEkJ1oAHgobV7Swl1inb/KL8WHR5CRlIUB8s0AJSHRwEFO2MMa/aWcfXYZGIj9MJvyr9NHGHP3AANzS62nqji44JKRiZGce+cUbRdMkzZRQPAA3lFNRRXN/DNG8baXYpS/TYxJZZ3D5RR39xKVNjAvgUUna3nrd0lfHikkh0nz9LscuMQcBuorm/m0ev0b8pOGgAeWLOvlBCHcGOOdv8o/zcxJQZj4FDZOWaOShiw56lpaOGuZz6hrLaRnJRY/u7KTK4cM5TcjAT+7S/7+K+/5hMTEcoDOp+GbTQAemGMYe3eMq4YM5T4qDC7y1Gq39qPBDpQUjugAfDk2weoON/EX5ZdyfSR8Rc99p93TeVcYyvfX7Wf2MgQ7piRPmB1qO7pIHAv9pfUcqqqntv06B8VINITIomJCBnQI4H+duA0r+8sYtn80Z9584e2iep/88UZXJ6dxLdfy2P9gdMDVovqngZAL9buK8XpEG7M0QBQgUFErIHggQmA6vpmnnhzLxNGxPTYxx8R6uR3D+QyOTWWZS/v5OOjlQNSj+qeBkAPGltcrNxRxBWjk0gcot0/KnBMTInhUNk53G7vXxLiB6v2c7aumZ/dM42wkJ7fYqLDQ/jDl2eTkRjFN/68ixaX2+v1qO5pAPRg5Y4iTtc28ci80XaXopRX5aTGUt/s4lRVvVf3u25fGX/ZXcKj141hUmqcR9skDAnjOwsmUHm+mc35vjHrX7DQQeButLjcPLPpKNNHxnPF6CS7y1HKq9oHgu97fgsjYiOIiwwlLjKU+Kgw5mYnMn/8sF4/vXdWVdfMv/1lLzkpsSy7dkyftp03LpmEqFDe2FWsJ1sOIg2Abry1u4Ti6gaeXDxJT1ZRAWdSahxfnZfNqTP11DS0UFrTyKGyc5ypa+L3Hx0nPiqUhVNTuGNGGjNHJXT5N9DQ7KKg/Dz5p8+RX36OD/IrqWlo4cUH5xDq7Ft4hIU4WDQtlT9vK6S2sUVPuBwkGgBdcLkNT28sYGJKLNdNGGZ3OUp5ndMhPHHLxM8sb3G5+fBIJW/sKua17UW89OkpRiVGkRIXQWOrm6YWFw0tLhqaXVScb6L9qtJhTgfZyUP40R1TLny76Ks7ZqbzwicnWbu3lC9cNqo/zVMe0gDowtp9pRyrrGP5F2fqp38VVEKdDq6dMIxrJwzjXGML7+4/zTt5JdQ1u4iLDCUyNpyIUCcRIU5S4yMZNzyascNjyEyKIqSPn/o7m5YeR/bQIby+s1gDYJBoAHRijOE3GwoYnTyEBZP10E8VvGIiQrlrVjp3zRqck7REhDtmpPGz9fkUVtUzMjFqUJ43mOlRQJ28d7CcQ2XnWHbtGJwO/fSv1GC6fUYaAG/tLra5kuCgAdCBMYZfbyxgZGIki6al2l2OUkFnZGIUs7MSeWNXsU5bOQiCIgBO1zbS6sEJJh8WVLKnsJqvzRvT7/5MpdSluXNGGscq6sgrqrG7lIAX8O9yZ+uaWfjrD/n3t/b1+ImiodnFf647zIjYCD4/K20QK1RKdXTLlBTCQhy8uSv4uoE+Kqjk3f1lg/Z8AR8ACUPCuCc3nT9vLeSXfzvS5TotLjfLXt7JvpIafrh4EuEhzkGuUinVLi4ylBsnDmfVnpKgujTEucYWvv3aHv7r3cOD1u6ADwCAb980nnty0/nVe0d46dOTFz3mdhu++3oeGw6V8x+3T+bmSXrkj1J2u3NmGlV1zbx/OHguDfHjtYc4XdvIf941tc8n0l2qoAgAEeFHd0zhugnD+N5b+1i37/++Yv1k3SHe2FnMP904jnvnZNhYpVKq3TXjkkkaEsZrOwoH5IJ1vubjgkpe3nKKr1ydzYwBnKOhM/Gnkfbc3Fyzffv2S96+vrmVL/5uCwdKa3nx72ezu7CaH689xN9dkcn3P5ejJ30p5UN+tOYgz24+RnpCJJ+fmc7nZ6YzKsmzcwPcbkNNQwutboPBYP2H2xiaW900t7ppanXT1OrCbWBaenyfr33kLXVNrSz41WZCHA7WPnY1EaHe7YIWkR3GmNwuHwumAIC2C1bd9duPKatppL7ZxaJpqfzyC9Nx6DH/SvmU5lY3a/aWsnJHER8drcQYmJ2ZyI05wwl1Ci7Tdui2y21obHFTUt1AcYef5lbP+9GHx4bz5SuzWDp7FHGRg3sdoh+s2s8Ln5zg1a9ezmWZiV7fvwZAJ0Vn67nnt58wbkQMz96fa1vyK6U8U1LdwJu7inl9ZxHHKuq6XCc5Jpy0+EjSEiJJT4hkeEwEoU4BEQQQAUEID3EQHuogPMRJeIiDuqZWXtpyko8KzhAdHsKSy0by5auySIuPHPB2bT1exT3/8wl/d0UmP1g0aUCeo98BICILgF8BTuA5Y8xPOj0eDvwRmAWcAb5gjDlhPfYE8CDgAr5hjHnXk312xVsBAG2fLkKdot0+SvkRYwxn61sQwOEQHNJ2YbsQh6PfH+T2Fdfwuw+OsTqvFGMMqfGRjIiNYHhcBCNiIxgWE05Tq5sz55s4U9dMlfUzLDaCyamxTE6LY3JqHCMTIz16X2lodnHrf39Aq9vNu9+8hqiwgbkyT78CQEScQD5wI1AEbAOWGmMOdFjn68BUY8wjIrIEuMMY8wURyQH+DMwGUoG/AeOszXrcZ1e8GQBKKdWV4uoGXt1WyMkzdZTVNnK6tomymkYaWlwAxEaEkBQdTuKQMBKiQimpbiT/9DlarcHqmIgQspOjSbe+jaTGRZCWEIUA5eeaqDjXRMX5Rg6U1LLzVDUvf2UOV4wZOmDt6SkAPImc2UCBMeaYtbMVwGKg45v1YuAH1u2VwG+kLQIXAyuMMU3AcREpsPaHB/v0nrWPQ9neAdm1UiqwpAH/2H4nFEgCk9Q21uAQwdHx070biAV3jKG+2UVdcyt1Ta001bhpOuOmyeWi42fsaCAbCHEIdzsdDB0eTtoHkfBBL0WNmAK39NpJ0meeBEAaUNjhfhEwp7t1jDGtIlIDJFnLP+20bftptr3tEwAReRh4GGDUKL1ErFJq8AlCSA8HijhEiA4PITo8BGL+b7nB0OIyFwakQ51CqNNxcYjYyJMA6KrSzv1G3a3T3fKuOuu67IsyxjwLPAttXUDdl9mDAUhOpZTqjQBh1o8v8mTUpAgY2eF+OlDS3ToiEgLEAVU9bOvJPpVSSg0gTwJgGzBWRLJEJAxYAqzqtM4q4AHr9l3ABtM2urwKWCIi4SKSBYwFtnq4T6WUUgOo1y4gq0//UeBd2g7Z/L0xZr+IPAlsN8asAp4HXrQGeatoe0PHWu9V2gZ3W4FlxhgXQFf79H7zlFJKdScoTwRTSqlg0dNhoHoKrFJKBSkNAKWUClIaAEopFaQ0AJRSKkj51SCwiFQAJ3tdsWtDgUovlmOnQGlLoLQDtC2+KFDaAf1rS4YxJrmrB/wqAPpDRLZ3NxLubwKlLYHSDtC2+KJAaQcMXFu0C0gppYKUBoBSSgWpYAqAZ+0uwIsCpS2B0g7QtviiQGkHDFBbgmYMQCml1MWC6RuAUkqpDjQAlFIqSAV8AIjIAhE5LCIFIvK43fX0hYj8XkTKRWRfh2WJIrJeRI5YvxPsrNFTIjJSRDaKyEER2S8ij1nL/ao9IhIhIltFZI/Vjh9ay7NEZIvVjlesy5z7BRFxisguEVlt3ffLtojICRHZKyK7RWS7tcyvXl/tRCReRFaKyCHrb+bygWhLQAeANaH9cuAWIAdYak1U7y/+ACzotOxx4D1jzFjgPeu+P2gF/skYMxGYCyyz/i38rT1NwHXGmGnAdGCBiMwFfgr8wmrHWeBBG2vsq8eAgx3u+3NbrjXGTO9wzLy/vb7a/QpYZ4yZAEyj7d/H+20xxgTsD3A58G6H+08AT9hdVx/bkAns63D/MJBi3U4BDttd4yW26y3gRn9uDxAF7KRtPutKIMRaftHrzpd/aJuN7z3gOmA1bbMY+mtbTgBDOy3zu9cXEAscxzpIZyDbEtDfAOh6Qvu0btb1F8ONMaUA1u9hNtfTZyKSCcwAtuCH7bG6THYD5cB64ChQbYxptVbxp9fZL4HvAG7rfhL+2xYD/FVEdojIw9Yyv3t9AdlABfC/VtfccyIyhAFoS6AHgCcT2qtBJCLRwOvAN40xtXbXcymMMS5jzHTaPj3PBiZ2tdrgVtV3IrIQKDfG7Oi4uItVfb4tliuNMTNp6/JdJiLX2F3QJQoBZgLPGGNmAHUMUNdVoAdAIE4+f1pEUgCs3+U21+MxEQml7c3/T8aYN6zFftseY0w1sIm2MY14EWmfYtVfXmdXAotE5ASwgrZuoF/in23BGFNi/S4H3qQtnP3x9VUEFBljtlj3V9IWCF5vS6AHQCBOPr8KeMC6/QBtfek+T0SEtrmjDxpjft7hIb9qj4gki0i8dTsSuIG2AbqNwF3Waj7fDgBjzBPGmHRjTCZtfxsbjDH34odtEZEhIhLTfhu4CdiHn72+AIwxZUChiIy3Fl1P27zq3m+L3QMegzCgciuQT1s/7b/aXU8fa/8zUAq00Pap4EHa+mjfA45YvxPtrtPDtlxFW1dCHrDb+rnV39oDTAV2We3YB3zPWp4NbAUKgNeAcLtr7WO75gOr/bUtVs17rJ/97X/r/vb66tCe6cB263X2FyBhINqil4JQSqkgFehdQEoppbqhAaCUUkFKA0AppYKUBoBSSgUpDQCllApSGgBKKRWkNACUUipI/X+prQdaZtcg6wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(test_md[['pos_idx','pred_any']].groupby('pos_idx').mean())\n",
"plt.plot([0,60],[0,0])"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8255ef9250>]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(train_md[['pos_idx',all_ich[0]]].groupby('pos_idx').mean())\n",
"plt.plot([0,60],[0,0])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"test_md['pred_any'] = predictions[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8255e69ad0>]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(test_md[['pos_idx','pred_any']].groupby('pos_idx').mean())\n",
"plt.plot([0,60],[0,0])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 [9.30e-05 1.15e-04 1.49e-04 1.64e-03 9.96e-01 9.98e-01 9.99e-01]\n",
"1 [6.76e-06 8.61e-06 1.12e-05 7.63e-05 5.17e-02 3.93e-01 8.82e-01]\n",
"2 [2.12e-05 2.46e-05 3.09e-05 2.84e-04 9.83e-01 9.93e-01 9.95e-01]\n",
"3 [1.40e-05 1.65e-05 1.97e-05 8.68e-05 9.80e-01 9.93e-01 9.95e-01]\n",
"4 [2.81e-05 3.21e-05 4.08e-05 4.00e-04 9.59e-01 9.90e-01 9.92e-01]\n",
"5 [4.51e-05 5.60e-05 7.04e-05 8.55e-04 9.71e-01 9.89e-01 9.91e-01]\n"
]
}
],
"source": [
"# weighted models + weighted ensembling\n",
"#0 [2.14e-04 2.50e-04 3.15e-04 2.15e-03 9.88e-01 9.93e-01 9.94e-01]\n",
"#1 [4.46e-06 5.32e-06 6.88e-06 8.58e-05 1.34e-01 6.16e-01 9.24e-01]\n",
"#2 [4.88e-05 5.54e-05 6.88e-05 3.27e-04 9.65e-01 9.86e-01 9.90e-01]\n",
"#3 [1.78e-05 2.00e-05 2.42e-05 1.04e-04 9.52e-01 9.77e-01 9.81e-01]\n",
"#4 [6.56e-05 7.67e-05 9.50e-05 4.71e-04 9.41e-01 9.85e-01 9.89e-01]\n",
"#5 [9.93e-05 1.21e-04 1.53e-04 9.91e-04 9.42e-01 9.86e-01 9.92e-01]\n",
"\n",
"# weighted models + non-weighted ensembling\n",
"#0 [9.25e-05 1.11e-04 1.41e-04 1.60e-03 9.93e-01 9.97e-01 9.99e-01]\n",
"#1 [8.16e-06 9.69e-06 1.24e-05 9.28e-05 1.31e-01 5.91e-01 8.94e-01]\n",
"#2 [2.38e-05 2.66e-05 3.46e-05 2.46e-04 9.73e-01 9.91e-01 9.94e-01]\n",
"#3 [1.25e-05 1.40e-05 1.71e-05 8.06e-05 9.66e-01 9.90e-01 9.94e-01]\n",
"#4 [3.27e-05 3.80e-05 4.71e-05 3.55e-04 9.51e-01 9.91e-01 9.94e-01]\n",
"#5 [4.51e-05 5.74e-05 7.40e-05 7.90e-04 9.46e-01 9.89e-01 9.94e-01]\n",
"\n",
"# non-weighted models + non-weighted ensembling\n",
"#0 [1.10e-04 1.24e-04 1.55e-04 1.27e-03 9.93e-01 9.97e-01 9.98e-01]\n",
"#1 [8.61e-06 9.98e-06 1.23e-05 8.77e-05 1.36e-01 5.73e-01 8.74e-01]\n",
"#2 [2.34e-05 2.66e-05 3.41e-05 2.12e-04 9.73e-01 9.91e-01 9.95e-01]\n",
"#3 [1.08e-05 1.25e-05 1.50e-05 6.10e-05 9.67e-01 9.92e-01 9.96e-01]\n",
"#4 [3.18e-05 3.68e-05 4.48e-05 3.03e-04 9.51e-01 9.91e-01 9.94e-01]\n",
"#5 [4.72e-05 5.48e-05 6.86e-05 6.83e-04 9.41e-01 9.88e-01 9.92e-01]\n",
"\n",
"# STAGE2 non-weighted models + non-weighted ensembling\n",
"#0 [9.30e-05 1.15e-04 1.49e-04 1.64e-03 9.96e-01 9.98e-01 9.99e-01]\n",
"#1 [6.76e-06 8.61e-06 1.12e-05 7.63e-05 5.17e-02 3.93e-01 8.82e-01]\n",
"#2 [2.12e-05 2.46e-05 3.09e-05 2.84e-04 9.83e-01 9.93e-01 9.95e-01]\n",
"#3 [1.40e-05 1.65e-05 1.97e-05 8.68e-05 9.80e-01 9.93e-01 9.95e-01]\n",
"#4 [2.81e-05 3.21e-05 4.08e-05 4.00e-04 9.59e-01 9.90e-01 9.92e-01]\n",
"#5 [4.51e-05 5.60e-05 7.04e-05 8.55e-04 9.71e-01 9.89e-01 9.91e-01]\n",
"\n",
"np.set_printoptions(precision=2)\n",
"for k in range(6):\n",
" print(k,np.quantile(predictions[:,k],[0.0001,0.001,0.01,0.5,0.99,0.999,0.9999]))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.1376, 0.0029, 0.0464, 0.0378, 0.0449, 0.0591])"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# weighted models + weighted ensembling\n",
"#array([0.1361, 0.0056, 0.0429, 0.0295, 0.0468, 0.0569])\n",
"\n",
"# weighted models + non-weighted ensembling\n",
"#array([0.1335, 0.0055, 0.0423, 0.0298, 0.0466, 0.0556])\n",
"\n",
"# non-weighted models + non-weighted ensembling\n",
"#array([0.1313, 0.0057, 0.0421, 0.0297, 0.0464, 0.0544])\n",
"\n",
"# STAGE2 non-weighted models + non-weighted ensembling\n",
"#array([0.1376, 0.0029, 0.0464, 0.0378, 0.0449, 0.0591])\n",
"\n",
"# STAGE2 weighted models + weighted ensembling\n",
"#array([0.1373, 0.0028, 0.0464, 0.0379, 0.045 , 0.0589])\n",
"\n",
"np.set_printoptions(precision=4)\n",
"predictions.mean(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.13762015847552184"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub.loc[range(0,len(sub),6), 'Label'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"sub = sub.sort_values('ID').reset_index(drop=True)\n",
"best_sub = pd.read_csv(PATH/'submission_stage2_3.csv').sort_values('ID').reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.13749181676694883"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_sub.loc[range(0,len(sub),6), 'Label'].mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"SpearmanrResult(correlation=0.985190415749868, pvalue=0.0)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.stats.spearmanr(sub.loc[range(0,len(sub),6), 'Label'], \n",
" best_sub.loc[range(0,len(sub),6), 'Label'])"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"SpearmanrResult(correlation=0.985190415749868, pvalue=0.0)"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.stats.spearmanr(sub.loc[range(0,len(sub),6), 'Label'], \n",
" best_sub.loc[range(0,len(sub),6), 'Label'])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.9992765979750622"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.corrcoef(sub.sort_values('ID').reset_index(drop=True).loc[range(0,len(sub),6), 'Label'], \n",
" best_sub.sort_values('ID').reset_index(drop=True).loc[range(0,len(sub),6), 'Label'])[0,1]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.999294961658725"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.corrcoef(sub.sort_values('ID').reset_index(drop=True).loc[range(0,len(sub),6), 'Label'], \n",
" best_sub.sort_values('ID').reset_index(drop=True).loc[range(0,len(sub),6), 'Label'])[0,1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submission"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████| 32.1M/32.1M [00:02<00:00, 16.2MB/s]\n",
"Successfully submitted to RSNA Intracranial Hemorrhage Detection"
]
}
],
"source": [
"!~/.local/bin/kaggle competitions submit rsna-intracranial-hemorrhage-detection -f ~/Hemorrhage/sub.csv -m \"GCP, safe final, take 2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kaggle competitions submit rsna-intracranial-hemorrhage-detection -f C:/StudioProjects/Hemorrhage/sub.csv -m \"GCP, d161+d169+d201+s101+yd161, 8TTA, ensemble, bounds\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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",
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