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/4-L2-train-and-submit.ipynb
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| a | b/4-L2-train-and-submit.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 1, |
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| 6 | "metadata": {}, |
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| 7 | "outputs": [], |
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| 8 | "source": [ |
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| 9 | "experiment_name = 'sm'" |
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| 10 | ] |
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| 11 | }, |
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| 12 | { |
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| 13 | "cell_type": "code", |
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| 14 | "execution_count": 2, |
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| 15 | "metadata": {}, |
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| 16 | "outputs": [], |
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| 17 | "source": [ |
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| 18 | "from pathlib import Path\n", |
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| 19 | "from fastai.data_block import get_files\n", |
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| 20 | "import pickle\n", |
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| 21 | "import torch\n", |
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| 22 | "import numpy as np, pandas as pd" |
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| 23 | ] |
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| 24 | }, |
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| 25 | { |
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| 26 | "cell_type": "code", |
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| 27 | "execution_count": 3, |
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| 28 | "metadata": {}, |
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| 29 | "outputs": [], |
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| 30 | "source": [ |
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| 31 | "class defaultlist(list):\n", |
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| 32 | " def __init__(self, fx):\n", |
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| 33 | " self._fx = fx\n", |
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| 34 | " def _fill(self, index):\n", |
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| 35 | " while len(self) <= index:\n", |
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| 36 | " self.append(self._fx())\n", |
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| 37 | " def __setitem__(self, index, value):\n", |
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| 38 | " self._fill(index)\n", |
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| 39 | " list.__setitem__(self, index, value)\n", |
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| 40 | " def __getitem__(self, index):\n", |
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| 41 | " self._fill(index)\n", |
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| 42 | " return list.__getitem__(self, index)" |
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| 43 | ] |
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| 44 | }, |
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| 45 | { |
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| 46 | "cell_type": "code", |
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| 47 | "execution_count": 4, |
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| 48 | "metadata": {}, |
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| 49 | "outputs": [], |
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| 50 | "source": [ |
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| 51 | "data_dir = Path('data/predictions')\n", |
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| 52 | "base_classes = [ 'any', 'epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', 'subdural' ]" |
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| 53 | ] |
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| 54 | }, |
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| 55 | { |
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| 56 | "cell_type": "code", |
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| 57 | "execution_count": 5, |
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| 58 | "metadata": {}, |
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| 59 | "outputs": [], |
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| 60 | "source": [ |
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| 61 | "stage = \"stage_2\"" |
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| 62 | ] |
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| 63 | }, |
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| 64 | { |
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| 65 | "cell_type": "code", |
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| 66 | "execution_count": 6, |
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| 67 | "metadata": {}, |
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| 68 | "outputs": [], |
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| 69 | "source": [ |
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| 70 | "fn_to_study_ix = pickle.load(open(f'data/{stage}_fn_to_study_ix.pickle', 'rb'))\n", |
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| 71 | "study_ix_to_fn = pickle.load(open(f'data/{stage}_study_ix_to_fn.pickle', 'rb'))\n", |
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| 72 | "study_to_data = pickle.load(open(f'data/{stage}_study_to_data.pickle', 'rb'))\n", |
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| 73 | "df = pd.read_csv(f\"data/{stage}_train_dicom_diags_norm.csv\")\n", |
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| 74 | "df = df.set_index(['fid'])\n" |
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| 75 | ] |
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| 76 | }, |
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| 77 | { |
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| 78 | "cell_type": "code", |
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| 79 | "execution_count": 7, |
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| 80 | "metadata": {}, |
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| 81 | "outputs": [], |
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| 82 | "source": [ |
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| 83 | "labels_df = df.loc[:,'any':'subdural']\n", |
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| 84 | "labels_df.index.name = 'fn'" |
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| 85 | ] |
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| 86 | }, |
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| 87 | { |
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| 88 | "cell_type": "code", |
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| 89 | "execution_count": 8, |
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| 90 | "metadata": { |
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| 91 | "scrolled": true |
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| 92 | }, |
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| 93 | "outputs": [ |
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| 94 | { |
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| 95 | "data": { |
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| 96 | "text/plain": [ |
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| 97 | "(['data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0413_subdural_loss_fold1_of_5',\n", |
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| 98 | " 'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0406_subdural_loss_fold2_of_5',\n", |
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| 99 | " 'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0403_subdural_loss_fold3_of_5',\n", |
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| 100 | " 'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0399_subdural_loss_fold4_of_5',\n", |
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| 101 | " 'data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0386_subdural_loss_fold5_of_5',\n", |
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| 102 | " 'data/predictions/densenet121_sz512_cv0.0749_weighted_loss_fold1_of_5',\n", |
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| 103 | " 'data/predictions/densenet121_sz512_cv0.0743_weighted_loss_fold2_of_5',\n", |
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| 104 | " 'data/predictions/densenet121_sz512_cv0.0748_weighted_loss_fold3_of_5',\n", |
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| 105 | " 'data/predictions/densenet121_sz512_cv0.0738_weighted_loss_fold4_of_5',\n", |
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| 106 | " 'data/predictions/densenet121_sz512_cv0.0739_weighted_loss_fold5_of_5',\n", |
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| 107 | " 'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0375_subdural_loss_fold1_of_5',\n", |
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| 108 | " 'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0369_subdural_loss_fold2_of_5',\n", |
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| 109 | " 'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0371_subdural_loss_fold3_of_5',\n", |
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| 110 | " 'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0366_subdural_loss_fold4_of_5',\n", |
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| 111 | " 'data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0365_subdural_loss_fold5_of_5',\n", |
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| 112 | " 'data/predictions/resnet101_sz512_cv0.0758_weighted_loss_fold1_of_5',\n", |
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| 113 | " 'data/predictions/resnet101_sz512_cv0.0754_weighted_loss_fold2_of_5',\n", |
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| 114 | " 'data/predictions/resnet101_sz512_cv0.0761_weighted_loss_fold3_of_5',\n", |
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| 115 | " 'data/predictions/resnet101_sz512_cv0.0751_weighted_loss_fold4_of_5',\n", |
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| 116 | " 'data/predictions/resnet101_sz512_cv0.0749_weighted_loss_fold5_of_5',\n", |
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| 117 | " 'data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0556_subdural_loss_fold1_of_5',\n", |
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| 118 | " 'data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0550_subdural_loss_fold2_of_5',\n", |
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| 119 | " 'data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0528_subdural_loss_fold3_of_5',\n", |
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| 120 | " 'data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0530_subdural_loss_fold4_of_5',\n", |
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| 121 | " 'data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0522_subdural_loss_fold5_of_5',\n", |
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| 122 | " 'data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold1_of_5',\n", |
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| 123 | " 'data/predictions/resnet18_sz512_cv0.0773_weighted_loss_fold2_of_5',\n", |
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| 124 | " 'data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold3_of_5',\n", |
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| 125 | " 'data/predictions/resnet18_sz512_cv0.0762_weighted_loss_fold4_of_5',\n", |
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| 126 | " 'data/predictions/resnet18_sz512_cv0.0768_weighted_loss_fold5_of_5',\n", |
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| 127 | " 'data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0496_subdural_loss_fold1_of_5',\n", |
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| 128 | " 'data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0484_subdural_loss_fold2_of_5',\n", |
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| 129 | " 'data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0474_subdural_loss_fold3_of_5',\n", |
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| 130 | " 'data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0466_subdural_loss_fold4_of_5',\n", |
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| 131 | " 'data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0453_subdural_loss_fold5_of_5',\n", |
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| 132 | " 'data/predictions/resnet34_sz512_cv0.0765_weighted_loss_fold1_of_5',\n", |
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| 133 | " 'data/predictions/resnet34_sz512_cv0.0763_weighted_loss_fold2_of_5',\n", |
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| 134 | " 'data/predictions/resnet34_sz512_cv0.0782_weighted_loss_fold3_of_5',\n", |
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| 135 | " 'data/predictions/resnet34_sz512_cv0.0750_weighted_loss_fold4_of_5',\n", |
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| 136 | " 'data/predictions/resnet34_sz512_cv0.0759_weighted_loss_fold5_of_5',\n", |
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| 137 | " 'data/predictions/resnet50_sz512_cv0.0761_weighted_loss_fold1_of_5',\n", |
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| 138 | " 'data/predictions/resnet50_sz512_cv0.0754_weighted_loss_fold2_of_5',\n", |
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| 139 | " 'data/predictions/resnet50_sz512_cv0.0764_weighted_loss_fold3_of_5',\n", |
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| 140 | " 'data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold4_of_5',\n", |
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| 141 | " 'data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold5_of_5',\n", |
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| 142 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0387_subdural_loss_fold1_of_5',\n", |
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| 143 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0378_subdural_loss_fold2_of_5',\n", |
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| 144 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0381_subdural_loss_fold3_of_5',\n", |
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| 145 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0376_subdural_loss_fold4_of_5',\n", |
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| 146 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0372_subdural_loss_fold5_of_5'],\n", |
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| 147 | " ['data/predictions/densenet121',\n", |
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| 148 | " 'data/predictions/densenet121-53_train_cpu_window_uint8',\n", |
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| 149 | " 'data/predictions/resnet101',\n", |
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| 150 | " 'data/predictions/resnet101-53_train_cpu_window_uint8',\n", |
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| 151 | " 'data/predictions/resnet18',\n", |
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| 152 | " 'data/predictions/resnet18-53_train_cpu_window_uint8',\n", |
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| 153 | " 'data/predictions/resnet34',\n", |
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| 154 | " 'data/predictions/resnet34-53_train_cpu_window_uint8',\n", |
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| 155 | " 'data/predictions/resnet50',\n", |
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| 156 | " 'data/predictions/resnext50_32x4d-53_train_cpu_window_uint8'])" |
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| 157 | ] |
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| 158 | }, |
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| 159 | "execution_count": 8, |
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| 160 | "metadata": {}, |
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| 161 | "output_type": "execute_result" |
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| 162 | } |
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| 163 | ], |
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| 164 | "source": [ |
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| 165 | "csvs = sorted([str(fn) for fn in get_files(data_dir, extensions='.csv')])\n", |
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| 166 | "csv = csvs[0]\n", |
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| 167 | "def where_test(csv):\n", |
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| 168 | " res = csv.find('_test') \n", |
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| 169 | " return res if res!= -1 else csv.find('_valid') \n", |
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| 170 | "where_fold = lambda csv: csv.index('fold')\n", |
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| 171 | "where_stem = lambda csv: len(csv)-csv[::-1].index('/')\n", |
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| 172 | "where_sz = lambda csv: csv.index('_sz')\n", |
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| 173 | "model_fns = sorted(np.unique([csv[:where_test(csv)] for csv in csvs]),\n", |
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| 174 | " key=lambda x:x[where_stem(x):where_sz(x)]+x[where_fold(x):where_test(x)])\n", |
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| 175 | "archs = np.unique([model_fn[:where_sz(model_fn)] for model_fn in model_fns]).tolist()\n", |
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| 176 | "n_folds = len(model_fns)//len(archs)\n", |
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| 177 | "model_fns, archs, " |
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| 178 | ] |
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| 179 | }, |
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| 180 | { |
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| 181 | "cell_type": "code", |
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| 182 | "execution_count": 9, |
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| 183 | "metadata": {}, |
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| 184 | "outputs": [ |
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| 185 | { |
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| 186 | "name": "stdout", |
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| 187 | "output_type": "stream", |
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| 188 | "text": [ |
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| 189 | "0 data/predictions/densenet121_sz512_cv0.0749_weighted_loss_fold1_of_5\n", |
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| 190 | "1 data/predictions/densenet121_sz512_cv0.0743_weighted_loss_fold2_of_5\n", |
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| 191 | "2 data/predictions/densenet121_sz512_cv0.0748_weighted_loss_fold3_of_5\n", |
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| 192 | "3 data/predictions/densenet121_sz512_cv0.0738_weighted_loss_fold4_of_5\n", |
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| 193 | "4 data/predictions/densenet121_sz512_cv0.0739_weighted_loss_fold5_of_5\n", |
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| 194 | "0 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0413_subdural_loss_fold1_of_5\n", |
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| 195 | "1 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0406_subdural_loss_fold2_of_5\n", |
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| 196 | "2 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0403_subdural_loss_fold3_of_5\n", |
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| 197 | "3 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0399_subdural_loss_fold4_of_5\n", |
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| 198 | "4 data/predictions/densenet121-53_train_cpu_window_uint8_sz512_cv0.0386_subdural_loss_fold5_of_5\n", |
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| 199 | "0 data/predictions/resnet101_sz512_cv0.0758_weighted_loss_fold1_of_5\n", |
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| 200 | "1 data/predictions/resnet101_sz512_cv0.0754_weighted_loss_fold2_of_5\n", |
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| 201 | "2 data/predictions/resnet101_sz512_cv0.0761_weighted_loss_fold3_of_5\n", |
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| 202 | "3 data/predictions/resnet101_sz512_cv0.0751_weighted_loss_fold4_of_5\n", |
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| 203 | "4 data/predictions/resnet101_sz512_cv0.0749_weighted_loss_fold5_of_5\n", |
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| 204 | "0 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0375_subdural_loss_fold1_of_5\n", |
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| 205 | "1 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0369_subdural_loss_fold2_of_5\n", |
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| 206 | "2 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0371_subdural_loss_fold3_of_5\n", |
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| 207 | "3 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0366_subdural_loss_fold4_of_5\n", |
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| 208 | "4 data/predictions/resnet101-53_train_cpu_window_uint8_sz512_cv0.0365_subdural_loss_fold5_of_5\n", |
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| 209 | "0 data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold1_of_5\n", |
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| 210 | "1 data/predictions/resnet18_sz512_cv0.0773_weighted_loss_fold2_of_5\n", |
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| 211 | "2 data/predictions/resnet18_sz512_cv0.0786_weighted_loss_fold3_of_5\n", |
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| 212 | "3 data/predictions/resnet18_sz512_cv0.0762_weighted_loss_fold4_of_5\n", |
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| 213 | "4 data/predictions/resnet18_sz512_cv0.0768_weighted_loss_fold5_of_5\n", |
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| 214 | "0 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0556_subdural_loss_fold1_of_5\n", |
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| 215 | "1 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0550_subdural_loss_fold2_of_5\n", |
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| 216 | "2 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0528_subdural_loss_fold3_of_5\n", |
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| 217 | "3 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0530_subdural_loss_fold4_of_5\n", |
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| 218 | "4 data/predictions/resnet18-53_train_cpu_window_uint8_sz512_cv0.0522_subdural_loss_fold5_of_5\n", |
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| 219 | "0 data/predictions/resnet34_sz512_cv0.0765_weighted_loss_fold1_of_5\n", |
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| 220 | "1 data/predictions/resnet34_sz512_cv0.0763_weighted_loss_fold2_of_5\n", |
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| 221 | "2 data/predictions/resnet34_sz512_cv0.0782_weighted_loss_fold3_of_5\n", |
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| 222 | "3 data/predictions/resnet34_sz512_cv0.0750_weighted_loss_fold4_of_5\n", |
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| 223 | "4 data/predictions/resnet34_sz512_cv0.0759_weighted_loss_fold5_of_5\n", |
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| 224 | "0 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0496_subdural_loss_fold1_of_5\n", |
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| 225 | "1 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0484_subdural_loss_fold2_of_5\n", |
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| 226 | "2 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0474_subdural_loss_fold3_of_5\n", |
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| 227 | "3 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0466_subdural_loss_fold4_of_5\n", |
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| 228 | "4 data/predictions/resnet34-53_train_cpu_window_uint8_sz512_cv0.0453_subdural_loss_fold5_of_5\n", |
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| 229 | "0 data/predictions/resnet50_sz512_cv0.0761_weighted_loss_fold1_of_5\n", |
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| 230 | "1 data/predictions/resnet50_sz512_cv0.0754_weighted_loss_fold2_of_5\n", |
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| 231 | "2 data/predictions/resnet50_sz512_cv0.0764_weighted_loss_fold3_of_5\n", |
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| 232 | "3 data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold4_of_5\n", |
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| 233 | "4 data/predictions/resnet50_sz512_cv0.0751_weighted_loss_fold5_of_5\n", |
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| 234 | "0 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0387_subdural_loss_fold1_of_5\n", |
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| 235 | "1 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0378_subdural_loss_fold2_of_5\n", |
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| 236 | "2 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0381_subdural_loss_fold3_of_5\n", |
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| 237 | "3 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0376_subdural_loss_fold4_of_5\n", |
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| 238 | "4 data/predictions/resnext50_32x4d-53_train_cpu_window_uint8_sz512_cv0.0372_subdural_loss_fold5_of_5\n" |
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| 239 | ] |
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| 240 | } |
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| 241 | ], |
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| 242 | "source": [ |
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| 243 | "arch_names = [arch[where_stem(arch):] for arch in archs]\n", |
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| 244 | "test_folds_dfs, train_arch_dfs = defaultlist(list),[]\n", |
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| 245 | "for arch in archs:\n", |
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| 246 | " train_model_dfs = []\n", |
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| 247 | " _arch = arch[where_stem(arch):]\n", |
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| 248 | " for i,model_fn in enumerate([model for model in model_fns if model.find(arch+\"_\")!=-1]):\n", |
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| 249 | " print(i,model_fn)\n", |
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| 250 | " t_df = pd.read_csv(model_fn + '_valid_fns.csv')\n", |
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| 251 | " t = torch.load(model_fn + '_valid.pth')\n", |
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| 252 | " t_mean, t_std = t.mean(dim=0), t.std(dim=0)\n", |
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| 253 | " train_model_dfs.append(pd.DataFrame({**{'fn' : t_df.values.squeeze(-1),},\n", |
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| 254 | " **{f'X{_arch}_{c}_mean' : t_mean[:,6+i].numpy() for i,c in enumerate(base_classes)},\n", |
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| 255 | " **{f'X{_arch}_{c}_std' : t_std[:,6+i].numpy() for i,c in enumerate(base_classes)}}))\n", |
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| 256 | " \n", |
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| 257 | " t_df = pd.read_csv(model_fn + '_test_fns.csv')\n", |
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| 258 | " t = torch.load(model_fn + '_test.pth')\n", |
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| 259 | " t_mean, t_std = t.mean(dim=0), t.std(dim=0)\n", |
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| 260 | " test_folds_dfs[i].append(pd.DataFrame({**{'fn' : t_df.values.squeeze(-1),},\n", |
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| 261 | " **{f'X{_arch}_{c}_mean' : t_mean[:,6+i].numpy() for i,c in enumerate(base_classes)},\n", |
||
| 262 | " **{f'X{_arch}_{c}_std' : t_std[:,6+i].numpy() for i,c in enumerate(base_classes)}}))\n", |
||
| 263 | "\n", |
||
| 264 | " train_arch_dfs.append(pd.concat(train_model_dfs, axis=0))" |
||
| 265 | ] |
||
| 266 | }, |
||
| 267 | { |
||
| 268 | "cell_type": "code", |
||
| 269 | "execution_count": 10, |
||
| 270 | "metadata": {}, |
||
| 271 | "outputs": [], |
||
| 272 | "source": [ |
||
| 273 | "tta_df = train_arch_dfs[0]\n", |
||
| 274 | "for arch_df in train_arch_dfs[1:]:\n", |
||
| 275 | " tta_df = tta_df.merge(arch_df,on='fn')" |
||
| 276 | ] |
||
| 277 | }, |
||
| 278 | { |
||
| 279 | "cell_type": "code", |
||
| 280 | "execution_count": 11, |
||
| 281 | "metadata": {}, |
||
| 282 | "outputs": [], |
||
| 283 | "source": [ |
||
| 284 | "tta_df = tta_df.merge(labels_df,on='fn')" |
||
| 285 | ] |
||
| 286 | }, |
||
| 287 | { |
||
| 288 | "cell_type": "code", |
||
| 289 | "execution_count": 12, |
||
| 290 | "metadata": {}, |
||
| 291 | "outputs": [ |
||
| 292 | { |
||
| 293 | "data": { |
||
| 294 | "text/html": [ |
||
| 295 | "<div>\n", |
||
| 296 | "<style scoped>\n", |
||
| 297 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 298 | " vertical-align: middle;\n", |
||
| 299 | " }\n", |
||
| 300 | "\n", |
||
| 301 | " .dataframe tbody tr th {\n", |
||
| 302 | " vertical-align: top;\n", |
||
| 303 | " }\n", |
||
| 304 | "\n", |
||
| 305 | " .dataframe thead th {\n", |
||
| 306 | " text-align: right;\n", |
||
| 307 | " }\n", |
||
| 308 | "</style>\n", |
||
| 309 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 310 | " <thead>\n", |
||
| 311 | " <tr style=\"text-align: right;\">\n", |
||
| 312 | " <th></th>\n", |
||
| 313 | " <th>fn</th>\n", |
||
| 314 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 315 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 316 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 317 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 318 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 319 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 320 | " <th>Xdensenet121_any_std</th>\n", |
||
| 321 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 322 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 323 | " <th>...</th>\n", |
||
| 324 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_std</th>\n", |
||
| 325 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_std</th>\n", |
||
| 326 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std</th>\n", |
||
| 327 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std</th>\n", |
||
| 328 | " <th>any</th>\n", |
||
| 329 | " <th>epidural</th>\n", |
||
| 330 | " <th>intraparenchymal</th>\n", |
||
| 331 | " <th>intraventricular</th>\n", |
||
| 332 | " <th>subarachnoid</th>\n", |
||
| 333 | " <th>subdural</th>\n", |
||
| 334 | " </tr>\n", |
||
| 335 | " </thead>\n", |
||
| 336 | " <tbody>\n", |
||
| 337 | " <tr>\n", |
||
| 338 | " <th>0</th>\n", |
||
| 339 | " <td>ID_0000950d7</td>\n", |
||
| 340 | " <td>0.000216</td>\n", |
||
| 341 | " <td>0.000009</td>\n", |
||
| 342 | " <td>0.000040</td>\n", |
||
| 343 | " <td>0.000017</td>\n", |
||
| 344 | " <td>0.000131</td>\n", |
||
| 345 | " <td>0.000070</td>\n", |
||
| 346 | " <td>0.000077</td>\n", |
||
| 347 | " <td>2.602864e-06</td>\n", |
||
| 348 | " <td>0.000013</td>\n", |
||
| 349 | " <td>...</td>\n", |
||
| 350 | " <td>0.000009</td>\n", |
||
| 351 | " <td>2.093691e-06</td>\n", |
||
| 352 | " <td>0.000017</td>\n", |
||
| 353 | " <td>0.000002</td>\n", |
||
| 354 | " <td>0</td>\n", |
||
| 355 | " <td>0</td>\n", |
||
| 356 | " <td>0</td>\n", |
||
| 357 | " <td>0</td>\n", |
||
| 358 | " <td>0</td>\n", |
||
| 359 | " <td>0</td>\n", |
||
| 360 | " </tr>\n", |
||
| 361 | " <tr>\n", |
||
| 362 | " <th>1</th>\n", |
||
| 363 | " <td>ID_0000aee4b</td>\n", |
||
| 364 | " <td>0.000652</td>\n", |
||
| 365 | " <td>0.000132</td>\n", |
||
| 366 | " <td>0.000049</td>\n", |
||
| 367 | " <td>0.000030</td>\n", |
||
| 368 | " <td>0.000237</td>\n", |
||
| 369 | " <td>0.000415</td>\n", |
||
| 370 | " <td>0.000478</td>\n", |
||
| 371 | " <td>7.363618e-05</td>\n", |
||
| 372 | " <td>0.000018</td>\n", |
||
| 373 | " <td>...</td>\n", |
||
| 374 | " <td>0.000003</td>\n", |
||
| 375 | " <td>4.035392e-07</td>\n", |
||
| 376 | " <td>0.000253</td>\n", |
||
| 377 | " <td>0.000225</td>\n", |
||
| 378 | " <td>0</td>\n", |
||
| 379 | " <td>0</td>\n", |
||
| 380 | " <td>0</td>\n", |
||
| 381 | " <td>0</td>\n", |
||
| 382 | " <td>0</td>\n", |
||
| 383 | " <td>0</td>\n", |
||
| 384 | " </tr>\n", |
||
| 385 | " <tr>\n", |
||
| 386 | " <th>2</th>\n", |
||
| 387 | " <td>ID_0001de0e8</td>\n", |
||
| 388 | " <td>0.000875</td>\n", |
||
| 389 | " <td>0.000079</td>\n", |
||
| 390 | " <td>0.000283</td>\n", |
||
| 391 | " <td>0.000013</td>\n", |
||
| 392 | " <td>0.000218</td>\n", |
||
| 393 | " <td>0.000636</td>\n", |
||
| 394 | " <td>0.001155</td>\n", |
||
| 395 | " <td>8.438517e-05</td>\n", |
||
| 396 | " <td>0.000151</td>\n", |
||
| 397 | " <td>...</td>\n", |
||
| 398 | " <td>0.000247</td>\n", |
||
| 399 | " <td>1.205952e-05</td>\n", |
||
| 400 | " <td>0.001305</td>\n", |
||
| 401 | " <td>0.000090</td>\n", |
||
| 402 | " <td>0</td>\n", |
||
| 403 | " <td>0</td>\n", |
||
| 404 | " <td>0</td>\n", |
||
| 405 | " <td>0</td>\n", |
||
| 406 | " <td>0</td>\n", |
||
| 407 | " <td>0</td>\n", |
||
| 408 | " </tr>\n", |
||
| 409 | " <tr>\n", |
||
| 410 | " <th>3</th>\n", |
||
| 411 | " <td>ID_0002003a8</td>\n", |
||
| 412 | " <td>0.001583</td>\n", |
||
| 413 | " <td>0.000008</td>\n", |
||
| 414 | " <td>0.000745</td>\n", |
||
| 415 | " <td>0.000176</td>\n", |
||
| 416 | " <td>0.000233</td>\n", |
||
| 417 | " <td>0.000478</td>\n", |
||
| 418 | " <td>0.000657</td>\n", |
||
| 419 | " <td>8.040458e-07</td>\n", |
||
| 420 | " <td>0.000355</td>\n", |
||
| 421 | " <td>...</td>\n", |
||
| 422 | " <td>0.000542</td>\n", |
||
| 423 | " <td>3.861410e-04</td>\n", |
||
| 424 | " <td>0.000488</td>\n", |
||
| 425 | " <td>0.000947</td>\n", |
||
| 426 | " <td>0</td>\n", |
||
| 427 | " <td>0</td>\n", |
||
| 428 | " <td>0</td>\n", |
||
| 429 | " <td>0</td>\n", |
||
| 430 | " <td>0</td>\n", |
||
| 431 | " <td>0</td>\n", |
||
| 432 | " </tr>\n", |
||
| 433 | " <tr>\n", |
||
| 434 | " <th>4</th>\n", |
||
| 435 | " <td>ID_000229f2a</td>\n", |
||
| 436 | " <td>0.000251</td>\n", |
||
| 437 | " <td>0.000041</td>\n", |
||
| 438 | " <td>0.000084</td>\n", |
||
| 439 | " <td>0.000039</td>\n", |
||
| 440 | " <td>0.000162</td>\n", |
||
| 441 | " <td>0.000084</td>\n", |
||
| 442 | " <td>0.000072</td>\n", |
||
| 443 | " <td>1.584314e-05</td>\n", |
||
| 444 | " <td>0.000014</td>\n", |
||
| 445 | " <td>...</td>\n", |
||
| 446 | " <td>0.001312</td>\n", |
||
| 447 | " <td>1.682060e-06</td>\n", |
||
| 448 | " <td>0.000491</td>\n", |
||
| 449 | " <td>0.000120</td>\n", |
||
| 450 | " <td>0</td>\n", |
||
| 451 | " <td>0</td>\n", |
||
| 452 | " <td>0</td>\n", |
||
| 453 | " <td>0</td>\n", |
||
| 454 | " <td>0</td>\n", |
||
| 455 | " <td>0</td>\n", |
||
| 456 | " </tr>\n", |
||
| 457 | " </tbody>\n", |
||
| 458 | "</table>\n", |
||
| 459 | "<p>5 rows × 127 columns</p>\n", |
||
| 460 | "</div>" |
||
| 461 | ], |
||
| 462 | "text/plain": [ |
||
| 463 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 464 | "0 ID_0000950d7 0.000216 0.000009 \n", |
||
| 465 | "1 ID_0000aee4b 0.000652 0.000132 \n", |
||
| 466 | "2 ID_0001de0e8 0.000875 0.000079 \n", |
||
| 467 | "3 ID_0002003a8 0.001583 0.000008 \n", |
||
| 468 | "4 ID_000229f2a 0.000251 0.000041 \n", |
||
| 469 | "\n", |
||
| 470 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 471 | "0 0.000040 0.000017 \n", |
||
| 472 | "1 0.000049 0.000030 \n", |
||
| 473 | "2 0.000283 0.000013 \n", |
||
| 474 | "3 0.000745 0.000176 \n", |
||
| 475 | "4 0.000084 0.000039 \n", |
||
| 476 | "\n", |
||
| 477 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 478 | "0 0.000131 0.000070 \n", |
||
| 479 | "1 0.000237 0.000415 \n", |
||
| 480 | "2 0.000218 0.000636 \n", |
||
| 481 | "3 0.000233 0.000478 \n", |
||
| 482 | "4 0.000162 0.000084 \n", |
||
| 483 | "\n", |
||
| 484 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 485 | "0 0.000077 2.602864e-06 \n", |
||
| 486 | "1 0.000478 7.363618e-05 \n", |
||
| 487 | "2 0.001155 8.438517e-05 \n", |
||
| 488 | "3 0.000657 8.040458e-07 \n", |
||
| 489 | "4 0.000072 1.584314e-05 \n", |
||
| 490 | "\n", |
||
| 491 | " Xdensenet121_intraparenchymal_std ... \\\n", |
||
| 492 | "0 0.000013 ... \n", |
||
| 493 | "1 0.000018 ... \n", |
||
| 494 | "2 0.000151 ... \n", |
||
| 495 | "3 0.000355 ... \n", |
||
| 496 | "4 0.000014 ... \n", |
||
| 497 | "\n", |
||
| 498 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_std \\\n", |
||
| 499 | "0 0.000009 \n", |
||
| 500 | "1 0.000003 \n", |
||
| 501 | "2 0.000247 \n", |
||
| 502 | "3 0.000542 \n", |
||
| 503 | "4 0.001312 \n", |
||
| 504 | "\n", |
||
| 505 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_std \\\n", |
||
| 506 | "0 2.093691e-06 \n", |
||
| 507 | "1 4.035392e-07 \n", |
||
| 508 | "2 1.205952e-05 \n", |
||
| 509 | "3 3.861410e-04 \n", |
||
| 510 | "4 1.682060e-06 \n", |
||
| 511 | "\n", |
||
| 512 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std \\\n", |
||
| 513 | "0 0.000017 \n", |
||
| 514 | "1 0.000253 \n", |
||
| 515 | "2 0.001305 \n", |
||
| 516 | "3 0.000488 \n", |
||
| 517 | "4 0.000491 \n", |
||
| 518 | "\n", |
||
| 519 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std any epidural \\\n", |
||
| 520 | "0 0.000002 0 0 \n", |
||
| 521 | "1 0.000225 0 0 \n", |
||
| 522 | "2 0.000090 0 0 \n", |
||
| 523 | "3 0.000947 0 0 \n", |
||
| 524 | "4 0.000120 0 0 \n", |
||
| 525 | "\n", |
||
| 526 | " intraparenchymal intraventricular subarachnoid subdural \n", |
||
| 527 | "0 0 0 0 0 \n", |
||
| 528 | "1 0 0 0 0 \n", |
||
| 529 | "2 0 0 0 0 \n", |
||
| 530 | "3 0 0 0 0 \n", |
||
| 531 | "4 0 0 0 0 \n", |
||
| 532 | "\n", |
||
| 533 | "[5 rows x 127 columns]" |
||
| 534 | ] |
||
| 535 | }, |
||
| 536 | "execution_count": 12, |
||
| 537 | "metadata": {}, |
||
| 538 | "output_type": "execute_result" |
||
| 539 | } |
||
| 540 | ], |
||
| 541 | "source": [ |
||
| 542 | "tta_df.head()" |
||
| 543 | ] |
||
| 544 | }, |
||
| 545 | { |
||
| 546 | "cell_type": "code", |
||
| 547 | "execution_count": 13, |
||
| 548 | "metadata": {}, |
||
| 549 | "outputs": [ |
||
| 550 | { |
||
| 551 | "data": { |
||
| 552 | "text/plain": [ |
||
| 553 | "(752802, 752802)" |
||
| 554 | ] |
||
| 555 | }, |
||
| 556 | "execution_count": 13, |
||
| 557 | "metadata": {}, |
||
| 558 | "output_type": "execute_result" |
||
| 559 | } |
||
| 560 | ], |
||
| 561 | "source": [ |
||
| 562 | "len(tta_df), len(labels_df)" |
||
| 563 | ] |
||
| 564 | }, |
||
| 565 | { |
||
| 566 | "cell_type": "markdown", |
||
| 567 | "metadata": {}, |
||
| 568 | "source": [ |
||
| 569 | "# Begin boosting data preparation" |
||
| 570 | ] |
||
| 571 | }, |
||
| 572 | { |
||
| 573 | "cell_type": "code", |
||
| 574 | "execution_count": 14, |
||
| 575 | "metadata": {}, |
||
| 576 | "outputs": [ |
||
| 577 | { |
||
| 578 | "data": { |
||
| 579 | "text/html": [ |
||
| 580 | "<div>\n", |
||
| 581 | "<style scoped>\n", |
||
| 582 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 583 | " vertical-align: middle;\n", |
||
| 584 | " }\n", |
||
| 585 | "\n", |
||
| 586 | " .dataframe tbody tr th {\n", |
||
| 587 | " vertical-align: top;\n", |
||
| 588 | " }\n", |
||
| 589 | "\n", |
||
| 590 | " .dataframe thead th {\n", |
||
| 591 | " text-align: right;\n", |
||
| 592 | " }\n", |
||
| 593 | "</style>\n", |
||
| 594 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 595 | " <thead>\n", |
||
| 596 | " <tr style=\"text-align: right;\">\n", |
||
| 597 | " <th></th>\n", |
||
| 598 | " <th>fn</th>\n", |
||
| 599 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 600 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 601 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 602 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 603 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 604 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 605 | " <th>Xdensenet121_any_std</th>\n", |
||
| 606 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 607 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 608 | " <th>...</th>\n", |
||
| 609 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std</th>\n", |
||
| 610 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std</th>\n", |
||
| 611 | " <th>any</th>\n", |
||
| 612 | " <th>epidural</th>\n", |
||
| 613 | " <th>intraparenchymal</th>\n", |
||
| 614 | " <th>intraventricular</th>\n", |
||
| 615 | " <th>subarachnoid</th>\n", |
||
| 616 | " <th>subdural</th>\n", |
||
| 617 | " <th>study</th>\n", |
||
| 618 | " <th>z</th>\n", |
||
| 619 | " </tr>\n", |
||
| 620 | " </thead>\n", |
||
| 621 | " <tbody>\n", |
||
| 622 | " <tr>\n", |
||
| 623 | " <th>0</th>\n", |
||
| 624 | " <td>ID_0000950d7</td>\n", |
||
| 625 | " <td>0.000216</td>\n", |
||
| 626 | " <td>0.000009</td>\n", |
||
| 627 | " <td>0.000040</td>\n", |
||
| 628 | " <td>0.000017</td>\n", |
||
| 629 | " <td>0.000131</td>\n", |
||
| 630 | " <td>0.000070</td>\n", |
||
| 631 | " <td>0.000077</td>\n", |
||
| 632 | " <td>2.602864e-06</td>\n", |
||
| 633 | " <td>0.000013</td>\n", |
||
| 634 | " <td>...</td>\n", |
||
| 635 | " <td>0.000017</td>\n", |
||
| 636 | " <td>0.000002</td>\n", |
||
| 637 | " <td>0</td>\n", |
||
| 638 | " <td>0</td>\n", |
||
| 639 | " <td>0</td>\n", |
||
| 640 | " <td>0</td>\n", |
||
| 641 | " <td>0</td>\n", |
||
| 642 | " <td>0</td>\n", |
||
| 643 | " <td>ID_84296c3845</td>\n", |
||
| 644 | " <td>32</td>\n", |
||
| 645 | " </tr>\n", |
||
| 646 | " <tr>\n", |
||
| 647 | " <th>1</th>\n", |
||
| 648 | " <td>ID_0000aee4b</td>\n", |
||
| 649 | " <td>0.000652</td>\n", |
||
| 650 | " <td>0.000132</td>\n", |
||
| 651 | " <td>0.000049</td>\n", |
||
| 652 | " <td>0.000030</td>\n", |
||
| 653 | " <td>0.000237</td>\n", |
||
| 654 | " <td>0.000415</td>\n", |
||
| 655 | " <td>0.000478</td>\n", |
||
| 656 | " <td>7.363618e-05</td>\n", |
||
| 657 | " <td>0.000018</td>\n", |
||
| 658 | " <td>...</td>\n", |
||
| 659 | " <td>0.000253</td>\n", |
||
| 660 | " <td>0.000225</td>\n", |
||
| 661 | " <td>0</td>\n", |
||
| 662 | " <td>0</td>\n", |
||
| 663 | " <td>0</td>\n", |
||
| 664 | " <td>0</td>\n", |
||
| 665 | " <td>0</td>\n", |
||
| 666 | " <td>0</td>\n", |
||
| 667 | " <td>ID_1e59488a44</td>\n", |
||
| 668 | " <td>7</td>\n", |
||
| 669 | " </tr>\n", |
||
| 670 | " <tr>\n", |
||
| 671 | " <th>2</th>\n", |
||
| 672 | " <td>ID_0001de0e8</td>\n", |
||
| 673 | " <td>0.000875</td>\n", |
||
| 674 | " <td>0.000079</td>\n", |
||
| 675 | " <td>0.000283</td>\n", |
||
| 676 | " <td>0.000013</td>\n", |
||
| 677 | " <td>0.000218</td>\n", |
||
| 678 | " <td>0.000636</td>\n", |
||
| 679 | " <td>0.001155</td>\n", |
||
| 680 | " <td>8.438517e-05</td>\n", |
||
| 681 | " <td>0.000151</td>\n", |
||
| 682 | " <td>...</td>\n", |
||
| 683 | " <td>0.001305</td>\n", |
||
| 684 | " <td>0.000090</td>\n", |
||
| 685 | " <td>0</td>\n", |
||
| 686 | " <td>0</td>\n", |
||
| 687 | " <td>0</td>\n", |
||
| 688 | " <td>0</td>\n", |
||
| 689 | " <td>0</td>\n", |
||
| 690 | " <td>0</td>\n", |
||
| 691 | " <td>ID_9d97cf289b</td>\n", |
||
| 692 | " <td>22</td>\n", |
||
| 693 | " </tr>\n", |
||
| 694 | " <tr>\n", |
||
| 695 | " <th>3</th>\n", |
||
| 696 | " <td>ID_0002003a8</td>\n", |
||
| 697 | " <td>0.001583</td>\n", |
||
| 698 | " <td>0.000008</td>\n", |
||
| 699 | " <td>0.000745</td>\n", |
||
| 700 | " <td>0.000176</td>\n", |
||
| 701 | " <td>0.000233</td>\n", |
||
| 702 | " <td>0.000478</td>\n", |
||
| 703 | " <td>0.000657</td>\n", |
||
| 704 | " <td>8.040458e-07</td>\n", |
||
| 705 | " <td>0.000355</td>\n", |
||
| 706 | " <td>...</td>\n", |
||
| 707 | " <td>0.000488</td>\n", |
||
| 708 | " <td>0.000947</td>\n", |
||
| 709 | " <td>0</td>\n", |
||
| 710 | " <td>0</td>\n", |
||
| 711 | " <td>0</td>\n", |
||
| 712 | " <td>0</td>\n", |
||
| 713 | " <td>0</td>\n", |
||
| 714 | " <td>0</td>\n", |
||
| 715 | " <td>ID_805824f65e</td>\n", |
||
| 716 | " <td>17</td>\n", |
||
| 717 | " </tr>\n", |
||
| 718 | " <tr>\n", |
||
| 719 | " <th>4</th>\n", |
||
| 720 | " <td>ID_000229f2a</td>\n", |
||
| 721 | " <td>0.000251</td>\n", |
||
| 722 | " <td>0.000041</td>\n", |
||
| 723 | " <td>0.000084</td>\n", |
||
| 724 | " <td>0.000039</td>\n", |
||
| 725 | " <td>0.000162</td>\n", |
||
| 726 | " <td>0.000084</td>\n", |
||
| 727 | " <td>0.000072</td>\n", |
||
| 728 | " <td>1.584314e-05</td>\n", |
||
| 729 | " <td>0.000014</td>\n", |
||
| 730 | " <td>...</td>\n", |
||
| 731 | " <td>0.000491</td>\n", |
||
| 732 | " <td>0.000120</td>\n", |
||
| 733 | " <td>0</td>\n", |
||
| 734 | " <td>0</td>\n", |
||
| 735 | " <td>0</td>\n", |
||
| 736 | " <td>0</td>\n", |
||
| 737 | " <td>0</td>\n", |
||
| 738 | " <td>0</td>\n", |
||
| 739 | " <td>ID_f54eba3225</td>\n", |
||
| 740 | " <td>30</td>\n", |
||
| 741 | " </tr>\n", |
||
| 742 | " </tbody>\n", |
||
| 743 | "</table>\n", |
||
| 744 | "<p>5 rows × 129 columns</p>\n", |
||
| 745 | "</div>" |
||
| 746 | ], |
||
| 747 | "text/plain": [ |
||
| 748 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 749 | "0 ID_0000950d7 0.000216 0.000009 \n", |
||
| 750 | "1 ID_0000aee4b 0.000652 0.000132 \n", |
||
| 751 | "2 ID_0001de0e8 0.000875 0.000079 \n", |
||
| 752 | "3 ID_0002003a8 0.001583 0.000008 \n", |
||
| 753 | "4 ID_000229f2a 0.000251 0.000041 \n", |
||
| 754 | "\n", |
||
| 755 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 756 | "0 0.000040 0.000017 \n", |
||
| 757 | "1 0.000049 0.000030 \n", |
||
| 758 | "2 0.000283 0.000013 \n", |
||
| 759 | "3 0.000745 0.000176 \n", |
||
| 760 | "4 0.000084 0.000039 \n", |
||
| 761 | "\n", |
||
| 762 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 763 | "0 0.000131 0.000070 \n", |
||
| 764 | "1 0.000237 0.000415 \n", |
||
| 765 | "2 0.000218 0.000636 \n", |
||
| 766 | "3 0.000233 0.000478 \n", |
||
| 767 | "4 0.000162 0.000084 \n", |
||
| 768 | "\n", |
||
| 769 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 770 | "0 0.000077 2.602864e-06 \n", |
||
| 771 | "1 0.000478 7.363618e-05 \n", |
||
| 772 | "2 0.001155 8.438517e-05 \n", |
||
| 773 | "3 0.000657 8.040458e-07 \n", |
||
| 774 | "4 0.000072 1.584314e-05 \n", |
||
| 775 | "\n", |
||
| 776 | " Xdensenet121_intraparenchymal_std ... \\\n", |
||
| 777 | "0 0.000013 ... \n", |
||
| 778 | "1 0.000018 ... \n", |
||
| 779 | "2 0.000151 ... \n", |
||
| 780 | "3 0.000355 ... \n", |
||
| 781 | "4 0.000014 ... \n", |
||
| 782 | "\n", |
||
| 783 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_std \\\n", |
||
| 784 | "0 0.000017 \n", |
||
| 785 | "1 0.000253 \n", |
||
| 786 | "2 0.001305 \n", |
||
| 787 | "3 0.000488 \n", |
||
| 788 | "4 0.000491 \n", |
||
| 789 | "\n", |
||
| 790 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_std any epidural \\\n", |
||
| 791 | "0 0.000002 0 0 \n", |
||
| 792 | "1 0.000225 0 0 \n", |
||
| 793 | "2 0.000090 0 0 \n", |
||
| 794 | "3 0.000947 0 0 \n", |
||
| 795 | "4 0.000120 0 0 \n", |
||
| 796 | "\n", |
||
| 797 | " intraparenchymal intraventricular subarachnoid subdural study \\\n", |
||
| 798 | "0 0 0 0 0 ID_84296c3845 \n", |
||
| 799 | "1 0 0 0 0 ID_1e59488a44 \n", |
||
| 800 | "2 0 0 0 0 ID_9d97cf289b \n", |
||
| 801 | "3 0 0 0 0 ID_805824f65e \n", |
||
| 802 | "4 0 0 0 0 ID_f54eba3225 \n", |
||
| 803 | "\n", |
||
| 804 | " z \n", |
||
| 805 | "0 32 \n", |
||
| 806 | "1 7 \n", |
||
| 807 | "2 22 \n", |
||
| 808 | "3 17 \n", |
||
| 809 | "4 30 \n", |
||
| 810 | "\n", |
||
| 811 | "[5 rows x 129 columns]" |
||
| 812 | ] |
||
| 813 | }, |
||
| 814 | "execution_count": 14, |
||
| 815 | "metadata": {}, |
||
| 816 | "output_type": "execute_result" |
||
| 817 | } |
||
| 818 | ], |
||
| 819 | "source": [ |
||
| 820 | "#add study and z position to dataframe to join\n", |
||
| 821 | "tta_df['study'] = tta_df.fn.apply(lambda fn: fn_to_study_ix[fn][0] )\n", |
||
| 822 | "tta_df['z'] = tta_df.fn.apply(lambda fn: int(fn_to_study_ix[fn][1]) )\n", |
||
| 823 | "tta_df.head()" |
||
| 824 | ] |
||
| 825 | }, |
||
| 826 | { |
||
| 827 | "cell_type": "code", |
||
| 828 | "execution_count": 15, |
||
| 829 | "metadata": {}, |
||
| 830 | "outputs": [ |
||
| 831 | { |
||
| 832 | "data": { |
||
| 833 | "text/html": [ |
||
| 834 | "<div>\n", |
||
| 835 | "<style scoped>\n", |
||
| 836 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 837 | " vertical-align: middle;\n", |
||
| 838 | " }\n", |
||
| 839 | "\n", |
||
| 840 | " .dataframe tbody tr th {\n", |
||
| 841 | " vertical-align: top;\n", |
||
| 842 | " }\n", |
||
| 843 | "\n", |
||
| 844 | " .dataframe thead th {\n", |
||
| 845 | " text-align: right;\n", |
||
| 846 | " }\n", |
||
| 847 | "</style>\n", |
||
| 848 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 849 | " <thead>\n", |
||
| 850 | " <tr style=\"text-align: right;\">\n", |
||
| 851 | " <th></th>\n", |
||
| 852 | " <th>fn</th>\n", |
||
| 853 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 854 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 855 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 856 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 857 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 858 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 859 | " <th>Xdensenet121_any_std</th>\n", |
||
| 860 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 861 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 862 | " <th>...</th>\n", |
||
| 863 | " <th>Xresnet50_intraparenchymal_mean-1</th>\n", |
||
| 864 | " <th>Xresnet50_intraventricular_mean-1</th>\n", |
||
| 865 | " <th>Xresnet50_subarachnoid_mean-1</th>\n", |
||
| 866 | " <th>Xresnet50_subdural_mean-1</th>\n", |
||
| 867 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean-1</th>\n", |
||
| 868 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean-1</th>\n", |
||
| 869 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean-1</th>\n", |
||
| 870 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean-1</th>\n", |
||
| 871 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean-1</th>\n", |
||
| 872 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean-1</th>\n", |
||
| 873 | " </tr>\n", |
||
| 874 | " </thead>\n", |
||
| 875 | " <tbody>\n", |
||
| 876 | " <tr>\n", |
||
| 877 | " <th>0</th>\n", |
||
| 878 | " <td>ID_0000950d7</td>\n", |
||
| 879 | " <td>0.000216</td>\n", |
||
| 880 | " <td>0.000009</td>\n", |
||
| 881 | " <td>0.000040</td>\n", |
||
| 882 | " <td>0.000017</td>\n", |
||
| 883 | " <td>0.000131</td>\n", |
||
| 884 | " <td>0.000070</td>\n", |
||
| 885 | " <td>0.000077</td>\n", |
||
| 886 | " <td>2.602864e-06</td>\n", |
||
| 887 | " <td>0.000013</td>\n", |
||
| 888 | " <td>...</td>\n", |
||
| 889 | " <td>0.000003</td>\n", |
||
| 890 | " <td>2.410099e-07</td>\n", |
||
| 891 | " <td>0.000009</td>\n", |
||
| 892 | " <td>0.000010</td>\n", |
||
| 893 | " <td>0.000023</td>\n", |
||
| 894 | " <td>0.000012</td>\n", |
||
| 895 | " <td>0.000041</td>\n", |
||
| 896 | " <td>0.000015</td>\n", |
||
| 897 | " <td>0.000040</td>\n", |
||
| 898 | " <td>0.000004</td>\n", |
||
| 899 | " </tr>\n", |
||
| 900 | " <tr>\n", |
||
| 901 | " <th>1</th>\n", |
||
| 902 | " <td>ID_0000aee4b</td>\n", |
||
| 903 | " <td>0.000652</td>\n", |
||
| 904 | " <td>0.000132</td>\n", |
||
| 905 | " <td>0.000049</td>\n", |
||
| 906 | " <td>0.000030</td>\n", |
||
| 907 | " <td>0.000237</td>\n", |
||
| 908 | " <td>0.000415</td>\n", |
||
| 909 | " <td>0.000478</td>\n", |
||
| 910 | " <td>7.363618e-05</td>\n", |
||
| 911 | " <td>0.000018</td>\n", |
||
| 912 | " <td>...</td>\n", |
||
| 913 | " <td>0.000032</td>\n", |
||
| 914 | " <td>2.871577e-05</td>\n", |
||
| 915 | " <td>0.000192</td>\n", |
||
| 916 | " <td>0.000307</td>\n", |
||
| 917 | " <td>0.000320</td>\n", |
||
| 918 | " <td>0.000031</td>\n", |
||
| 919 | " <td>0.000005</td>\n", |
||
| 920 | " <td>0.000004</td>\n", |
||
| 921 | " <td>0.000129</td>\n", |
||
| 922 | " <td>0.000094</td>\n", |
||
| 923 | " </tr>\n", |
||
| 924 | " <tr>\n", |
||
| 925 | " <th>2</th>\n", |
||
| 926 | " <td>ID_0001de0e8</td>\n", |
||
| 927 | " <td>0.000875</td>\n", |
||
| 928 | " <td>0.000079</td>\n", |
||
| 929 | " <td>0.000283</td>\n", |
||
| 930 | " <td>0.000013</td>\n", |
||
| 931 | " <td>0.000218</td>\n", |
||
| 932 | " <td>0.000636</td>\n", |
||
| 933 | " <td>0.001155</td>\n", |
||
| 934 | " <td>8.438517e-05</td>\n", |
||
| 935 | " <td>0.000151</td>\n", |
||
| 936 | " <td>...</td>\n", |
||
| 937 | " <td>0.000386</td>\n", |
||
| 938 | " <td>4.940505e-05</td>\n", |
||
| 939 | " <td>0.001212</td>\n", |
||
| 940 | " <td>0.001620</td>\n", |
||
| 941 | " <td>0.002983</td>\n", |
||
| 942 | " <td>0.000075</td>\n", |
||
| 943 | " <td>0.000413</td>\n", |
||
| 944 | " <td>0.000028</td>\n", |
||
| 945 | " <td>0.001168</td>\n", |
||
| 946 | " <td>0.000841</td>\n", |
||
| 947 | " </tr>\n", |
||
| 948 | " <tr>\n", |
||
| 949 | " <th>3</th>\n", |
||
| 950 | " <td>ID_0002003a8</td>\n", |
||
| 951 | " <td>0.001583</td>\n", |
||
| 952 | " <td>0.000008</td>\n", |
||
| 953 | " <td>0.000745</td>\n", |
||
| 954 | " <td>0.000176</td>\n", |
||
| 955 | " <td>0.000233</td>\n", |
||
| 956 | " <td>0.000478</td>\n", |
||
| 957 | " <td>0.000657</td>\n", |
||
| 958 | " <td>8.040458e-07</td>\n", |
||
| 959 | " <td>0.000355</td>\n", |
||
| 960 | " <td>...</td>\n", |
||
| 961 | " <td>0.000253</td>\n", |
||
| 962 | " <td>2.354449e-05</td>\n", |
||
| 963 | " <td>0.000079</td>\n", |
||
| 964 | " <td>0.000145</td>\n", |
||
| 965 | " <td>0.000816</td>\n", |
||
| 966 | " <td>0.000072</td>\n", |
||
| 967 | " <td>0.000268</td>\n", |
||
| 968 | " <td>0.000263</td>\n", |
||
| 969 | " <td>0.000419</td>\n", |
||
| 970 | " <td>0.000390</td>\n", |
||
| 971 | " </tr>\n", |
||
| 972 | " <tr>\n", |
||
| 973 | " <th>4</th>\n", |
||
| 974 | " <td>ID_000229f2a</td>\n", |
||
| 975 | " <td>0.000251</td>\n", |
||
| 976 | " <td>0.000041</td>\n", |
||
| 977 | " <td>0.000084</td>\n", |
||
| 978 | " <td>0.000039</td>\n", |
||
| 979 | " <td>0.000162</td>\n", |
||
| 980 | " <td>0.000084</td>\n", |
||
| 981 | " <td>0.000072</td>\n", |
||
| 982 | " <td>1.584314e-05</td>\n", |
||
| 983 | " <td>0.000014</td>\n", |
||
| 984 | " <td>...</td>\n", |
||
| 985 | " <td>0.000087</td>\n", |
||
| 986 | " <td>1.064072e-05</td>\n", |
||
| 987 | " <td>0.000406</td>\n", |
||
| 988 | " <td>0.000361</td>\n", |
||
| 989 | " <td>0.000489</td>\n", |
||
| 990 | " <td>0.000057</td>\n", |
||
| 991 | " <td>0.000044</td>\n", |
||
| 992 | " <td>0.000004</td>\n", |
||
| 993 | " <td>0.000202</td>\n", |
||
| 994 | " <td>0.000139</td>\n", |
||
| 995 | " </tr>\n", |
||
| 996 | " </tbody>\n", |
||
| 997 | "</table>\n", |
||
| 998 | "<p>5 rows × 251 columns</p>\n", |
||
| 999 | "</div>" |
||
| 1000 | ], |
||
| 1001 | "text/plain": [ |
||
| 1002 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 1003 | "0 ID_0000950d7 0.000216 0.000009 \n", |
||
| 1004 | "1 ID_0000aee4b 0.000652 0.000132 \n", |
||
| 1005 | "2 ID_0001de0e8 0.000875 0.000079 \n", |
||
| 1006 | "3 ID_0002003a8 0.001583 0.000008 \n", |
||
| 1007 | "4 ID_000229f2a 0.000251 0.000041 \n", |
||
| 1008 | "\n", |
||
| 1009 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 1010 | "0 0.000040 0.000017 \n", |
||
| 1011 | "1 0.000049 0.000030 \n", |
||
| 1012 | "2 0.000283 0.000013 \n", |
||
| 1013 | "3 0.000745 0.000176 \n", |
||
| 1014 | "4 0.000084 0.000039 \n", |
||
| 1015 | "\n", |
||
| 1016 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 1017 | "0 0.000131 0.000070 \n", |
||
| 1018 | "1 0.000237 0.000415 \n", |
||
| 1019 | "2 0.000218 0.000636 \n", |
||
| 1020 | "3 0.000233 0.000478 \n", |
||
| 1021 | "4 0.000162 0.000084 \n", |
||
| 1022 | "\n", |
||
| 1023 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 1024 | "0 0.000077 2.602864e-06 \n", |
||
| 1025 | "1 0.000478 7.363618e-05 \n", |
||
| 1026 | "2 0.001155 8.438517e-05 \n", |
||
| 1027 | "3 0.000657 8.040458e-07 \n", |
||
| 1028 | "4 0.000072 1.584314e-05 \n", |
||
| 1029 | "\n", |
||
| 1030 | " Xdensenet121_intraparenchymal_std ... Xresnet50_intraparenchymal_mean-1 \\\n", |
||
| 1031 | "0 0.000013 ... 0.000003 \n", |
||
| 1032 | "1 0.000018 ... 0.000032 \n", |
||
| 1033 | "2 0.000151 ... 0.000386 \n", |
||
| 1034 | "3 0.000355 ... 0.000253 \n", |
||
| 1035 | "4 0.000014 ... 0.000087 \n", |
||
| 1036 | "\n", |
||
| 1037 | " Xresnet50_intraventricular_mean-1 Xresnet50_subarachnoid_mean-1 \\\n", |
||
| 1038 | "0 2.410099e-07 0.000009 \n", |
||
| 1039 | "1 2.871577e-05 0.000192 \n", |
||
| 1040 | "2 4.940505e-05 0.001212 \n", |
||
| 1041 | "3 2.354449e-05 0.000079 \n", |
||
| 1042 | "4 1.064072e-05 0.000406 \n", |
||
| 1043 | "\n", |
||
| 1044 | " Xresnet50_subdural_mean-1 \\\n", |
||
| 1045 | "0 0.000010 \n", |
||
| 1046 | "1 0.000307 \n", |
||
| 1047 | "2 0.001620 \n", |
||
| 1048 | "3 0.000145 \n", |
||
| 1049 | "4 0.000361 \n", |
||
| 1050 | "\n", |
||
| 1051 | " Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean-1 \\\n", |
||
| 1052 | "0 0.000023 \n", |
||
| 1053 | "1 0.000320 \n", |
||
| 1054 | "2 0.002983 \n", |
||
| 1055 | "3 0.000816 \n", |
||
| 1056 | "4 0.000489 \n", |
||
| 1057 | "\n", |
||
| 1058 | " Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean-1 \\\n", |
||
| 1059 | "0 0.000012 \n", |
||
| 1060 | "1 0.000031 \n", |
||
| 1061 | "2 0.000075 \n", |
||
| 1062 | "3 0.000072 \n", |
||
| 1063 | "4 0.000057 \n", |
||
| 1064 | "\n", |
||
| 1065 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean-1 \\\n", |
||
| 1066 | "0 0.000041 \n", |
||
| 1067 | "1 0.000005 \n", |
||
| 1068 | "2 0.000413 \n", |
||
| 1069 | "3 0.000268 \n", |
||
| 1070 | "4 0.000044 \n", |
||
| 1071 | "\n", |
||
| 1072 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean-1 \\\n", |
||
| 1073 | "0 0.000015 \n", |
||
| 1074 | "1 0.000004 \n", |
||
| 1075 | "2 0.000028 \n", |
||
| 1076 | "3 0.000263 \n", |
||
| 1077 | "4 0.000004 \n", |
||
| 1078 | "\n", |
||
| 1079 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean-1 \\\n", |
||
| 1080 | "0 0.000040 \n", |
||
| 1081 | "1 0.000129 \n", |
||
| 1082 | "2 0.001168 \n", |
||
| 1083 | "3 0.000419 \n", |
||
| 1084 | "4 0.000202 \n", |
||
| 1085 | "\n", |
||
| 1086 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean-1 \n", |
||
| 1087 | "0 0.000004 \n", |
||
| 1088 | "1 0.000094 \n", |
||
| 1089 | "2 0.000841 \n", |
||
| 1090 | "3 0.000390 \n", |
||
| 1091 | "4 0.000139 \n", |
||
| 1092 | "\n", |
||
| 1093 | "[5 rows x 251 columns]" |
||
| 1094 | ] |
||
| 1095 | }, |
||
| 1096 | "execution_count": 15, |
||
| 1097 | "metadata": {}, |
||
| 1098 | "output_type": "execute_result" |
||
| 1099 | } |
||
| 1100 | ], |
||
| 1101 | "source": [ |
||
| 1102 | "#now join top and bottom\n", |
||
| 1103 | "tta_df['z+'] = tta_df.z+1\n", |
||
| 1104 | "tta_df['z-'] = tta_df.z-1\n", |
||
| 1105 | "#only merge means to avoid too many columns\n", |
||
| 1106 | "merge_cols = [c for c in tta_df.columns if 'mean' in c] \n", |
||
| 1107 | "merged_df = pd.merge(tta_df, tta_df[merge_cols+ ['study','z+']], how='left', left_on=['study', 'z'], right_on=['study','z+'], suffixes=('','+1'))\n", |
||
| 1108 | "merged_df = pd.merge(merged_df, tta_df[merge_cols+ ['study','z-']], how='left', left_on=['study', 'z'], right_on=['study','z-'], suffixes=('','-1'))\n", |
||
| 1109 | "merged_df.drop(['z++1','z--1'], axis=1, inplace=True) #after merge these columns are not needed\n", |
||
| 1110 | "len(merged_df)\n", |
||
| 1111 | "merged_df.head()\n" |
||
| 1112 | ] |
||
| 1113 | }, |
||
| 1114 | { |
||
| 1115 | "cell_type": "code", |
||
| 1116 | "execution_count": 16, |
||
| 1117 | "metadata": {}, |
||
| 1118 | "outputs": [ |
||
| 1119 | { |
||
| 1120 | "data": { |
||
| 1121 | "text/html": [ |
||
| 1122 | "<div>\n", |
||
| 1123 | "<style scoped>\n", |
||
| 1124 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1125 | " vertical-align: middle;\n", |
||
| 1126 | " }\n", |
||
| 1127 | "\n", |
||
| 1128 | " .dataframe tbody tr th {\n", |
||
| 1129 | " vertical-align: top;\n", |
||
| 1130 | " }\n", |
||
| 1131 | "\n", |
||
| 1132 | " .dataframe thead th {\n", |
||
| 1133 | " text-align: right;\n", |
||
| 1134 | " }\n", |
||
| 1135 | "</style>\n", |
||
| 1136 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1137 | " <thead>\n", |
||
| 1138 | " <tr style=\"text-align: right;\">\n", |
||
| 1139 | " <th></th>\n", |
||
| 1140 | " <th>fn</th>\n", |
||
| 1141 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 1142 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 1143 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 1144 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 1145 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 1146 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 1147 | " <th>Xdensenet121_any_std</th>\n", |
||
| 1148 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 1149 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 1150 | " <th>...</th>\n", |
||
| 1151 | " <th>Xresnet50_intraparenchymal_mean_max</th>\n", |
||
| 1152 | " <th>Xresnet50_intraventricular_mean_max</th>\n", |
||
| 1153 | " <th>Xresnet50_subarachnoid_mean_max</th>\n", |
||
| 1154 | " <th>Xresnet50_subdural_mean_max</th>\n", |
||
| 1155 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n", |
||
| 1156 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n", |
||
| 1157 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n", |
||
| 1158 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n", |
||
| 1159 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n", |
||
| 1160 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n", |
||
| 1161 | " </tr>\n", |
||
| 1162 | " </thead>\n", |
||
| 1163 | " <tbody>\n", |
||
| 1164 | " <tr>\n", |
||
| 1165 | " <th>0</th>\n", |
||
| 1166 | " <td>ID_0000950d7</td>\n", |
||
| 1167 | " <td>0.000216</td>\n", |
||
| 1168 | " <td>0.000009</td>\n", |
||
| 1169 | " <td>0.000040</td>\n", |
||
| 1170 | " <td>0.000017</td>\n", |
||
| 1171 | " <td>0.000131</td>\n", |
||
| 1172 | " <td>0.000070</td>\n", |
||
| 1173 | " <td>0.000077</td>\n", |
||
| 1174 | " <td>2.602864e-06</td>\n", |
||
| 1175 | " <td>0.000013</td>\n", |
||
| 1176 | " <td>...</td>\n", |
||
| 1177 | " <td>0.486492</td>\n", |
||
| 1178 | " <td>0.905775</td>\n", |
||
| 1179 | " <td>0.150154</td>\n", |
||
| 1180 | " <td>0.099021</td>\n", |
||
| 1181 | " <td>0.857974</td>\n", |
||
| 1182 | " <td>0.001780</td>\n", |
||
| 1183 | " <td>0.361130</td>\n", |
||
| 1184 | " <td>0.789858</td>\n", |
||
| 1185 | " <td>0.168123</td>\n", |
||
| 1186 | " <td>0.073204</td>\n", |
||
| 1187 | " </tr>\n", |
||
| 1188 | " <tr>\n", |
||
| 1189 | " <th>1</th>\n", |
||
| 1190 | " <td>ID_0000aee4b</td>\n", |
||
| 1191 | " <td>0.000652</td>\n", |
||
| 1192 | " <td>0.000132</td>\n", |
||
| 1193 | " <td>0.000049</td>\n", |
||
| 1194 | " <td>0.000030</td>\n", |
||
| 1195 | " <td>0.000237</td>\n", |
||
| 1196 | " <td>0.000415</td>\n", |
||
| 1197 | " <td>0.000478</td>\n", |
||
| 1198 | " <td>7.363618e-05</td>\n", |
||
| 1199 | " <td>0.000018</td>\n", |
||
| 1200 | " <td>...</td>\n", |
||
| 1201 | " <td>0.002535</td>\n", |
||
| 1202 | " <td>0.002878</td>\n", |
||
| 1203 | " <td>0.006184</td>\n", |
||
| 1204 | " <td>0.003424</td>\n", |
||
| 1205 | " <td>0.027332</td>\n", |
||
| 1206 | " <td>0.000609</td>\n", |
||
| 1207 | " <td>0.003878</td>\n", |
||
| 1208 | " <td>0.008006</td>\n", |
||
| 1209 | " <td>0.009463</td>\n", |
||
| 1210 | " <td>0.006090</td>\n", |
||
| 1211 | " </tr>\n", |
||
| 1212 | " <tr>\n", |
||
| 1213 | " <th>2</th>\n", |
||
| 1214 | " <td>ID_0001de0e8</td>\n", |
||
| 1215 | " <td>0.000875</td>\n", |
||
| 1216 | " <td>0.000079</td>\n", |
||
| 1217 | " <td>0.000283</td>\n", |
||
| 1218 | " <td>0.000013</td>\n", |
||
| 1219 | " <td>0.000218</td>\n", |
||
| 1220 | " <td>0.000636</td>\n", |
||
| 1221 | " <td>0.001155</td>\n", |
||
| 1222 | " <td>8.438517e-05</td>\n", |
||
| 1223 | " <td>0.000151</td>\n", |
||
| 1224 | " <td>...</td>\n", |
||
| 1225 | " <td>0.002166</td>\n", |
||
| 1226 | " <td>0.003913</td>\n", |
||
| 1227 | " <td>0.004683</td>\n", |
||
| 1228 | " <td>0.005613</td>\n", |
||
| 1229 | " <td>0.018091</td>\n", |
||
| 1230 | " <td>0.002842</td>\n", |
||
| 1231 | " <td>0.012398</td>\n", |
||
| 1232 | " <td>0.009452</td>\n", |
||
| 1233 | " <td>0.009165</td>\n", |
||
| 1234 | " <td>0.005672</td>\n", |
||
| 1235 | " </tr>\n", |
||
| 1236 | " <tr>\n", |
||
| 1237 | " <th>3</th>\n", |
||
| 1238 | " <td>ID_0002003a8</td>\n", |
||
| 1239 | " <td>0.001583</td>\n", |
||
| 1240 | " <td>0.000008</td>\n", |
||
| 1241 | " <td>0.000745</td>\n", |
||
| 1242 | " <td>0.000176</td>\n", |
||
| 1243 | " <td>0.000233</td>\n", |
||
| 1244 | " <td>0.000478</td>\n", |
||
| 1245 | " <td>0.000657</td>\n", |
||
| 1246 | " <td>8.040458e-07</td>\n", |
||
| 1247 | " <td>0.000355</td>\n", |
||
| 1248 | " <td>...</td>\n", |
||
| 1249 | " <td>0.001896</td>\n", |
||
| 1250 | " <td>0.001544</td>\n", |
||
| 1251 | " <td>0.004229</td>\n", |
||
| 1252 | " <td>0.002354</td>\n", |
||
| 1253 | " <td>0.021156</td>\n", |
||
| 1254 | " <td>0.000476</td>\n", |
||
| 1255 | " <td>0.005249</td>\n", |
||
| 1256 | " <td>0.028012</td>\n", |
||
| 1257 | " <td>0.004112</td>\n", |
||
| 1258 | " <td>0.020228</td>\n", |
||
| 1259 | " </tr>\n", |
||
| 1260 | " <tr>\n", |
||
| 1261 | " <th>4</th>\n", |
||
| 1262 | " <td>ID_000229f2a</td>\n", |
||
| 1263 | " <td>0.000251</td>\n", |
||
| 1264 | " <td>0.000041</td>\n", |
||
| 1265 | " <td>0.000084</td>\n", |
||
| 1266 | " <td>0.000039</td>\n", |
||
| 1267 | " <td>0.000162</td>\n", |
||
| 1268 | " <td>0.000084</td>\n", |
||
| 1269 | " <td>0.000072</td>\n", |
||
| 1270 | " <td>1.584314e-05</td>\n", |
||
| 1271 | " <td>0.000014</td>\n", |
||
| 1272 | " <td>...</td>\n", |
||
| 1273 | " <td>0.011392</td>\n", |
||
| 1274 | " <td>0.059925</td>\n", |
||
| 1275 | " <td>0.010892</td>\n", |
||
| 1276 | " <td>0.015900</td>\n", |
||
| 1277 | " <td>0.042269</td>\n", |
||
| 1278 | " <td>0.001678</td>\n", |
||
| 1279 | " <td>0.012257</td>\n", |
||
| 1280 | " <td>0.029033</td>\n", |
||
| 1281 | " <td>0.014108</td>\n", |
||
| 1282 | " <td>0.011063</td>\n", |
||
| 1283 | " </tr>\n", |
||
| 1284 | " </tbody>\n", |
||
| 1285 | "</table>\n", |
||
| 1286 | "<p>5 rows × 311 columns</p>\n", |
||
| 1287 | "</div>" |
||
| 1288 | ], |
||
| 1289 | "text/plain": [ |
||
| 1290 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 1291 | "0 ID_0000950d7 0.000216 0.000009 \n", |
||
| 1292 | "1 ID_0000aee4b 0.000652 0.000132 \n", |
||
| 1293 | "2 ID_0001de0e8 0.000875 0.000079 \n", |
||
| 1294 | "3 ID_0002003a8 0.001583 0.000008 \n", |
||
| 1295 | "4 ID_000229f2a 0.000251 0.000041 \n", |
||
| 1296 | "\n", |
||
| 1297 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 1298 | "0 0.000040 0.000017 \n", |
||
| 1299 | "1 0.000049 0.000030 \n", |
||
| 1300 | "2 0.000283 0.000013 \n", |
||
| 1301 | "3 0.000745 0.000176 \n", |
||
| 1302 | "4 0.000084 0.000039 \n", |
||
| 1303 | "\n", |
||
| 1304 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 1305 | "0 0.000131 0.000070 \n", |
||
| 1306 | "1 0.000237 0.000415 \n", |
||
| 1307 | "2 0.000218 0.000636 \n", |
||
| 1308 | "3 0.000233 0.000478 \n", |
||
| 1309 | "4 0.000162 0.000084 \n", |
||
| 1310 | "\n", |
||
| 1311 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 1312 | "0 0.000077 2.602864e-06 \n", |
||
| 1313 | "1 0.000478 7.363618e-05 \n", |
||
| 1314 | "2 0.001155 8.438517e-05 \n", |
||
| 1315 | "3 0.000657 8.040458e-07 \n", |
||
| 1316 | "4 0.000072 1.584314e-05 \n", |
||
| 1317 | "\n", |
||
| 1318 | " Xdensenet121_intraparenchymal_std ... \\\n", |
||
| 1319 | "0 0.000013 ... \n", |
||
| 1320 | "1 0.000018 ... \n", |
||
| 1321 | "2 0.000151 ... \n", |
||
| 1322 | "3 0.000355 ... \n", |
||
| 1323 | "4 0.000014 ... \n", |
||
| 1324 | "\n", |
||
| 1325 | " Xresnet50_intraparenchymal_mean_max Xresnet50_intraventricular_mean_max \\\n", |
||
| 1326 | "0 0.486492 0.905775 \n", |
||
| 1327 | "1 0.002535 0.002878 \n", |
||
| 1328 | "2 0.002166 0.003913 \n", |
||
| 1329 | "3 0.001896 0.001544 \n", |
||
| 1330 | "4 0.011392 0.059925 \n", |
||
| 1331 | "\n", |
||
| 1332 | " Xresnet50_subarachnoid_mean_max Xresnet50_subdural_mean_max \\\n", |
||
| 1333 | "0 0.150154 0.099021 \n", |
||
| 1334 | "1 0.006184 0.003424 \n", |
||
| 1335 | "2 0.004683 0.005613 \n", |
||
| 1336 | "3 0.004229 0.002354 \n", |
||
| 1337 | "4 0.010892 0.015900 \n", |
||
| 1338 | "\n", |
||
| 1339 | " Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max \\\n", |
||
| 1340 | "0 0.857974 \n", |
||
| 1341 | "1 0.027332 \n", |
||
| 1342 | "2 0.018091 \n", |
||
| 1343 | "3 0.021156 \n", |
||
| 1344 | "4 0.042269 \n", |
||
| 1345 | "\n", |
||
| 1346 | " Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max \\\n", |
||
| 1347 | "0 0.001780 \n", |
||
| 1348 | "1 0.000609 \n", |
||
| 1349 | "2 0.002842 \n", |
||
| 1350 | "3 0.000476 \n", |
||
| 1351 | "4 0.001678 \n", |
||
| 1352 | "\n", |
||
| 1353 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max \\\n", |
||
| 1354 | "0 0.361130 \n", |
||
| 1355 | "1 0.003878 \n", |
||
| 1356 | "2 0.012398 \n", |
||
| 1357 | "3 0.005249 \n", |
||
| 1358 | "4 0.012257 \n", |
||
| 1359 | "\n", |
||
| 1360 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max \\\n", |
||
| 1361 | "0 0.789858 \n", |
||
| 1362 | "1 0.008006 \n", |
||
| 1363 | "2 0.009452 \n", |
||
| 1364 | "3 0.028012 \n", |
||
| 1365 | "4 0.029033 \n", |
||
| 1366 | "\n", |
||
| 1367 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max \\\n", |
||
| 1368 | "0 0.168123 \n", |
||
| 1369 | "1 0.009463 \n", |
||
| 1370 | "2 0.009165 \n", |
||
| 1371 | "3 0.004112 \n", |
||
| 1372 | "4 0.014108 \n", |
||
| 1373 | "\n", |
||
| 1374 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max \n", |
||
| 1375 | "0 0.073204 \n", |
||
| 1376 | "1 0.006090 \n", |
||
| 1377 | "2 0.005672 \n", |
||
| 1378 | "3 0.020228 \n", |
||
| 1379 | "4 0.011063 \n", |
||
| 1380 | "\n", |
||
| 1381 | "[5 rows x 311 columns]" |
||
| 1382 | ] |
||
| 1383 | }, |
||
| 1384 | "execution_count": 16, |
||
| 1385 | "metadata": {}, |
||
| 1386 | "output_type": "execute_result" |
||
| 1387 | } |
||
| 1388 | ], |
||
| 1389 | "source": [ |
||
| 1390 | "#now add maximum value for each merge_col by study\n", |
||
| 1391 | "group_df = merged_df[merge_cols+['study']].groupby('study').agg('max')\n", |
||
| 1392 | "merged_df = pd.merge(merged_df, group_df, how='left', on='study', suffixes=('','_max'))\n", |
||
| 1393 | "merged_df.head()" |
||
| 1394 | ] |
||
| 1395 | }, |
||
| 1396 | { |
||
| 1397 | "cell_type": "code", |
||
| 1398 | "execution_count": 17, |
||
| 1399 | "metadata": {}, |
||
| 1400 | "outputs": [ |
||
| 1401 | { |
||
| 1402 | "data": { |
||
| 1403 | "text/html": [ |
||
| 1404 | "<div>\n", |
||
| 1405 | "<style scoped>\n", |
||
| 1406 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1407 | " vertical-align: middle;\n", |
||
| 1408 | " }\n", |
||
| 1409 | "\n", |
||
| 1410 | " .dataframe tbody tr th {\n", |
||
| 1411 | " vertical-align: top;\n", |
||
| 1412 | " }\n", |
||
| 1413 | "\n", |
||
| 1414 | " .dataframe thead th {\n", |
||
| 1415 | " text-align: right;\n", |
||
| 1416 | " }\n", |
||
| 1417 | "</style>\n", |
||
| 1418 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1419 | " <thead>\n", |
||
| 1420 | " <tr style=\"text-align: right;\">\n", |
||
| 1421 | " <th></th>\n", |
||
| 1422 | " <th>fn</th>\n", |
||
| 1423 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 1424 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 1425 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 1426 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 1427 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 1428 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 1429 | " <th>Xdensenet121_any_std</th>\n", |
||
| 1430 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 1431 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 1432 | " <th>...</th>\n", |
||
| 1433 | " <th>Xresnet50_intraparenchymal_mean_max</th>\n", |
||
| 1434 | " <th>Xresnet50_intraventricular_mean_max</th>\n", |
||
| 1435 | " <th>Xresnet50_subarachnoid_mean_max</th>\n", |
||
| 1436 | " <th>Xresnet50_subdural_mean_max</th>\n", |
||
| 1437 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n", |
||
| 1438 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n", |
||
| 1439 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n", |
||
| 1440 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n", |
||
| 1441 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n", |
||
| 1442 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n", |
||
| 1443 | " </tr>\n", |
||
| 1444 | " </thead>\n", |
||
| 1445 | " <tbody>\n", |
||
| 1446 | " <tr>\n", |
||
| 1447 | " <th>0</th>\n", |
||
| 1448 | " <td>ID_0000950d7</td>\n", |
||
| 1449 | " <td>0.000216</td>\n", |
||
| 1450 | " <td>0.000009</td>\n", |
||
| 1451 | " <td>0.000040</td>\n", |
||
| 1452 | " <td>0.000017</td>\n", |
||
| 1453 | " <td>0.000131</td>\n", |
||
| 1454 | " <td>0.000070</td>\n", |
||
| 1455 | " <td>0.000077</td>\n", |
||
| 1456 | " <td>2.602864e-06</td>\n", |
||
| 1457 | " <td>0.000013</td>\n", |
||
| 1458 | " <td>...</td>\n", |
||
| 1459 | " <td>0.486492</td>\n", |
||
| 1460 | " <td>0.905775</td>\n", |
||
| 1461 | " <td>0.150154</td>\n", |
||
| 1462 | " <td>0.099021</td>\n", |
||
| 1463 | " <td>0.857974</td>\n", |
||
| 1464 | " <td>0.001780</td>\n", |
||
| 1465 | " <td>0.361130</td>\n", |
||
| 1466 | " <td>0.789858</td>\n", |
||
| 1467 | " <td>0.168123</td>\n", |
||
| 1468 | " <td>0.073204</td>\n", |
||
| 1469 | " </tr>\n", |
||
| 1470 | " <tr>\n", |
||
| 1471 | " <th>1</th>\n", |
||
| 1472 | " <td>ID_0000aee4b</td>\n", |
||
| 1473 | " <td>0.000652</td>\n", |
||
| 1474 | " <td>0.000132</td>\n", |
||
| 1475 | " <td>0.000049</td>\n", |
||
| 1476 | " <td>0.000030</td>\n", |
||
| 1477 | " <td>0.000237</td>\n", |
||
| 1478 | " <td>0.000415</td>\n", |
||
| 1479 | " <td>0.000478</td>\n", |
||
| 1480 | " <td>7.363618e-05</td>\n", |
||
| 1481 | " <td>0.000018</td>\n", |
||
| 1482 | " <td>...</td>\n", |
||
| 1483 | " <td>0.002535</td>\n", |
||
| 1484 | " <td>0.002878</td>\n", |
||
| 1485 | " <td>0.006184</td>\n", |
||
| 1486 | " <td>0.003424</td>\n", |
||
| 1487 | " <td>0.027332</td>\n", |
||
| 1488 | " <td>0.000609</td>\n", |
||
| 1489 | " <td>0.003878</td>\n", |
||
| 1490 | " <td>0.008006</td>\n", |
||
| 1491 | " <td>0.009463</td>\n", |
||
| 1492 | " <td>0.006090</td>\n", |
||
| 1493 | " </tr>\n", |
||
| 1494 | " <tr>\n", |
||
| 1495 | " <th>2</th>\n", |
||
| 1496 | " <td>ID_0001de0e8</td>\n", |
||
| 1497 | " <td>0.000875</td>\n", |
||
| 1498 | " <td>0.000079</td>\n", |
||
| 1499 | " <td>0.000283</td>\n", |
||
| 1500 | " <td>0.000013</td>\n", |
||
| 1501 | " <td>0.000218</td>\n", |
||
| 1502 | " <td>0.000636</td>\n", |
||
| 1503 | " <td>0.001155</td>\n", |
||
| 1504 | " <td>8.438517e-05</td>\n", |
||
| 1505 | " <td>0.000151</td>\n", |
||
| 1506 | " <td>...</td>\n", |
||
| 1507 | " <td>0.002166</td>\n", |
||
| 1508 | " <td>0.003913</td>\n", |
||
| 1509 | " <td>0.004683</td>\n", |
||
| 1510 | " <td>0.005613</td>\n", |
||
| 1511 | " <td>0.018091</td>\n", |
||
| 1512 | " <td>0.002842</td>\n", |
||
| 1513 | " <td>0.012398</td>\n", |
||
| 1514 | " <td>0.009452</td>\n", |
||
| 1515 | " <td>0.009165</td>\n", |
||
| 1516 | " <td>0.005672</td>\n", |
||
| 1517 | " </tr>\n", |
||
| 1518 | " <tr>\n", |
||
| 1519 | " <th>3</th>\n", |
||
| 1520 | " <td>ID_0002003a8</td>\n", |
||
| 1521 | " <td>0.001583</td>\n", |
||
| 1522 | " <td>0.000008</td>\n", |
||
| 1523 | " <td>0.000745</td>\n", |
||
| 1524 | " <td>0.000176</td>\n", |
||
| 1525 | " <td>0.000233</td>\n", |
||
| 1526 | " <td>0.000478</td>\n", |
||
| 1527 | " <td>0.000657</td>\n", |
||
| 1528 | " <td>8.040458e-07</td>\n", |
||
| 1529 | " <td>0.000355</td>\n", |
||
| 1530 | " <td>...</td>\n", |
||
| 1531 | " <td>0.001896</td>\n", |
||
| 1532 | " <td>0.001544</td>\n", |
||
| 1533 | " <td>0.004229</td>\n", |
||
| 1534 | " <td>0.002354</td>\n", |
||
| 1535 | " <td>0.021156</td>\n", |
||
| 1536 | " <td>0.000476</td>\n", |
||
| 1537 | " <td>0.005249</td>\n", |
||
| 1538 | " <td>0.028012</td>\n", |
||
| 1539 | " <td>0.004112</td>\n", |
||
| 1540 | " <td>0.020228</td>\n", |
||
| 1541 | " </tr>\n", |
||
| 1542 | " <tr>\n", |
||
| 1543 | " <th>4</th>\n", |
||
| 1544 | " <td>ID_000229f2a</td>\n", |
||
| 1545 | " <td>0.000251</td>\n", |
||
| 1546 | " <td>0.000041</td>\n", |
||
| 1547 | " <td>0.000084</td>\n", |
||
| 1548 | " <td>0.000039</td>\n", |
||
| 1549 | " <td>0.000162</td>\n", |
||
| 1550 | " <td>0.000084</td>\n", |
||
| 1551 | " <td>0.000072</td>\n", |
||
| 1552 | " <td>1.584314e-05</td>\n", |
||
| 1553 | " <td>0.000014</td>\n", |
||
| 1554 | " <td>...</td>\n", |
||
| 1555 | " <td>0.011392</td>\n", |
||
| 1556 | " <td>0.059925</td>\n", |
||
| 1557 | " <td>0.010892</td>\n", |
||
| 1558 | " <td>0.015900</td>\n", |
||
| 1559 | " <td>0.042269</td>\n", |
||
| 1560 | " <td>0.001678</td>\n", |
||
| 1561 | " <td>0.012257</td>\n", |
||
| 1562 | " <td>0.029033</td>\n", |
||
| 1563 | " <td>0.014108</td>\n", |
||
| 1564 | " <td>0.011063</td>\n", |
||
| 1565 | " </tr>\n", |
||
| 1566 | " </tbody>\n", |
||
| 1567 | "</table>\n", |
||
| 1568 | "<p>5 rows × 311 columns</p>\n", |
||
| 1569 | "</div>" |
||
| 1570 | ], |
||
| 1571 | "text/plain": [ |
||
| 1572 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 1573 | "0 ID_0000950d7 0.000216 0.000009 \n", |
||
| 1574 | "1 ID_0000aee4b 0.000652 0.000132 \n", |
||
| 1575 | "2 ID_0001de0e8 0.000875 0.000079 \n", |
||
| 1576 | "3 ID_0002003a8 0.001583 0.000008 \n", |
||
| 1577 | "4 ID_000229f2a 0.000251 0.000041 \n", |
||
| 1578 | "\n", |
||
| 1579 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 1580 | "0 0.000040 0.000017 \n", |
||
| 1581 | "1 0.000049 0.000030 \n", |
||
| 1582 | "2 0.000283 0.000013 \n", |
||
| 1583 | "3 0.000745 0.000176 \n", |
||
| 1584 | "4 0.000084 0.000039 \n", |
||
| 1585 | "\n", |
||
| 1586 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 1587 | "0 0.000131 0.000070 \n", |
||
| 1588 | "1 0.000237 0.000415 \n", |
||
| 1589 | "2 0.000218 0.000636 \n", |
||
| 1590 | "3 0.000233 0.000478 \n", |
||
| 1591 | "4 0.000162 0.000084 \n", |
||
| 1592 | "\n", |
||
| 1593 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 1594 | "0 0.000077 2.602864e-06 \n", |
||
| 1595 | "1 0.000478 7.363618e-05 \n", |
||
| 1596 | "2 0.001155 8.438517e-05 \n", |
||
| 1597 | "3 0.000657 8.040458e-07 \n", |
||
| 1598 | "4 0.000072 1.584314e-05 \n", |
||
| 1599 | "\n", |
||
| 1600 | " Xdensenet121_intraparenchymal_std ... \\\n", |
||
| 1601 | "0 0.000013 ... \n", |
||
| 1602 | "1 0.000018 ... \n", |
||
| 1603 | "2 0.000151 ... \n", |
||
| 1604 | "3 0.000355 ... \n", |
||
| 1605 | "4 0.000014 ... \n", |
||
| 1606 | "\n", |
||
| 1607 | " Xresnet50_intraparenchymal_mean_max Xresnet50_intraventricular_mean_max \\\n", |
||
| 1608 | "0 0.486492 0.905775 \n", |
||
| 1609 | "1 0.002535 0.002878 \n", |
||
| 1610 | "2 0.002166 0.003913 \n", |
||
| 1611 | "3 0.001896 0.001544 \n", |
||
| 1612 | "4 0.011392 0.059925 \n", |
||
| 1613 | "\n", |
||
| 1614 | " Xresnet50_subarachnoid_mean_max Xresnet50_subdural_mean_max \\\n", |
||
| 1615 | "0 0.150154 0.099021 \n", |
||
| 1616 | "1 0.006184 0.003424 \n", |
||
| 1617 | "2 0.004683 0.005613 \n", |
||
| 1618 | "3 0.004229 0.002354 \n", |
||
| 1619 | "4 0.010892 0.015900 \n", |
||
| 1620 | "\n", |
||
| 1621 | " Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max \\\n", |
||
| 1622 | "0 0.857974 \n", |
||
| 1623 | "1 0.027332 \n", |
||
| 1624 | "2 0.018091 \n", |
||
| 1625 | "3 0.021156 \n", |
||
| 1626 | "4 0.042269 \n", |
||
| 1627 | "\n", |
||
| 1628 | " Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max \\\n", |
||
| 1629 | "0 0.001780 \n", |
||
| 1630 | "1 0.000609 \n", |
||
| 1631 | "2 0.002842 \n", |
||
| 1632 | "3 0.000476 \n", |
||
| 1633 | "4 0.001678 \n", |
||
| 1634 | "\n", |
||
| 1635 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max \\\n", |
||
| 1636 | "0 0.361130 \n", |
||
| 1637 | "1 0.003878 \n", |
||
| 1638 | "2 0.012398 \n", |
||
| 1639 | "3 0.005249 \n", |
||
| 1640 | "4 0.012257 \n", |
||
| 1641 | "\n", |
||
| 1642 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max \\\n", |
||
| 1643 | "0 0.789858 \n", |
||
| 1644 | "1 0.008006 \n", |
||
| 1645 | "2 0.009452 \n", |
||
| 1646 | "3 0.028012 \n", |
||
| 1647 | "4 0.029033 \n", |
||
| 1648 | "\n", |
||
| 1649 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max \\\n", |
||
| 1650 | "0 0.168123 \n", |
||
| 1651 | "1 0.009463 \n", |
||
| 1652 | "2 0.009165 \n", |
||
| 1653 | "3 0.004112 \n", |
||
| 1654 | "4 0.014108 \n", |
||
| 1655 | "\n", |
||
| 1656 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max \n", |
||
| 1657 | "0 0.073204 \n", |
||
| 1658 | "1 0.006090 \n", |
||
| 1659 | "2 0.005672 \n", |
||
| 1660 | "3 0.020228 \n", |
||
| 1661 | "4 0.011063 \n", |
||
| 1662 | "\n", |
||
| 1663 | "[5 rows x 311 columns]" |
||
| 1664 | ] |
||
| 1665 | }, |
||
| 1666 | "execution_count": 17, |
||
| 1667 | "metadata": {}, |
||
| 1668 | "output_type": "execute_result" |
||
| 1669 | } |
||
| 1670 | ], |
||
| 1671 | "source": [ |
||
| 1672 | "#manage NANs created by merging\n", |
||
| 1673 | "merged_cols = [c for c in merged_df.columns if c.endswith('1')]\n", |
||
| 1674 | "\n", |
||
| 1675 | "for c in merged_cols:\n", |
||
| 1676 | " merged_df[c].fillna(merged_df[c.replace('-1', '').replace('+1', '')], inplace=True)\n", |
||
| 1677 | " \n", |
||
| 1678 | "merged_df.head()" |
||
| 1679 | ] |
||
| 1680 | }, |
||
| 1681 | { |
||
| 1682 | "cell_type": "code", |
||
| 1683 | "execution_count": 18, |
||
| 1684 | "metadata": {}, |
||
| 1685 | "outputs": [ |
||
| 1686 | { |
||
| 1687 | "data": { |
||
| 1688 | "text/plain": [ |
||
| 1689 | "2.0 40785\n", |
||
| 1690 | "0.0 40527\n", |
||
| 1691 | "3.0 40439\n", |
||
| 1692 | "1.0 40068\n", |
||
| 1693 | "5.0 39983\n", |
||
| 1694 | "4.0 39953\n", |
||
| 1695 | "11.0 39882\n", |
||
| 1696 | "6.0 39779\n", |
||
| 1697 | "9.0 39644\n", |
||
| 1698 | "7.0 39625\n", |
||
| 1699 | "10.0 39378\n", |
||
| 1700 | "8.0 39360\n", |
||
| 1701 | "16.0 39355\n", |
||
| 1702 | "13.0 39301\n", |
||
| 1703 | "12.0 39183\n", |
||
| 1704 | "17.0 39136\n", |
||
| 1705 | "15.0 38904\n", |
||
| 1706 | "14.0 38781\n", |
||
| 1707 | "18.0 38719\n", |
||
| 1708 | "Name: fold, dtype: int64" |
||
| 1709 | ] |
||
| 1710 | }, |
||
| 1711 | "execution_count": 18, |
||
| 1712 | "metadata": {}, |
||
| 1713 | "output_type": "execute_result" |
||
| 1714 | } |
||
| 1715 | ], |
||
| 1716 | "source": [ |
||
| 1717 | "#add fold for training\n", |
||
| 1718 | "merged_df['fold'] = merged_df.study.apply(lambda s: study_to_data[s]['fold'])\n", |
||
| 1719 | "merged_df.fold.value_counts()" |
||
| 1720 | ] |
||
| 1721 | }, |
||
| 1722 | { |
||
| 1723 | "cell_type": "markdown", |
||
| 1724 | "metadata": {}, |
||
| 1725 | "source": [ |
||
| 1726 | "# Prepare test data frame" |
||
| 1727 | ] |
||
| 1728 | }, |
||
| 1729 | { |
||
| 1730 | "cell_type": "code", |
||
| 1731 | "execution_count": 19, |
||
| 1732 | "metadata": {}, |
||
| 1733 | "outputs": [ |
||
| 1734 | { |
||
| 1735 | "data": { |
||
| 1736 | "text/plain": [ |
||
| 1737 | "['Xdensenet121_any_mean',\n", |
||
| 1738 | " 'Xdensenet121_epidural_mean',\n", |
||
| 1739 | " 'Xdensenet121_intraparenchymal_mean',\n", |
||
| 1740 | " 'Xdensenet121_intraventricular_mean',\n", |
||
| 1741 | " 'Xdensenet121_subarachnoid_mean',\n", |
||
| 1742 | " 'Xdensenet121_subdural_mean',\n", |
||
| 1743 | " 'Xdensenet121-53_train_cpu_window_uint8_any_mean',\n", |
||
| 1744 | " 'Xdensenet121-53_train_cpu_window_uint8_epidural_mean',\n", |
||
| 1745 | " 'Xdensenet121-53_train_cpu_window_uint8_intraparenchymal_mean',\n", |
||
| 1746 | " 'Xdensenet121-53_train_cpu_window_uint8_intraventricular_mean',\n", |
||
| 1747 | " 'Xdensenet121-53_train_cpu_window_uint8_subarachnoid_mean',\n", |
||
| 1748 | " 'Xdensenet121-53_train_cpu_window_uint8_subdural_mean',\n", |
||
| 1749 | " 'Xresnet101_any_mean',\n", |
||
| 1750 | " 'Xresnet101_epidural_mean',\n", |
||
| 1751 | " 'Xresnet101_intraparenchymal_mean',\n", |
||
| 1752 | " 'Xresnet101_intraventricular_mean',\n", |
||
| 1753 | " 'Xresnet101_subarachnoid_mean',\n", |
||
| 1754 | " 'Xresnet101_subdural_mean',\n", |
||
| 1755 | " 'Xresnet101-53_train_cpu_window_uint8_any_mean',\n", |
||
| 1756 | " 'Xresnet101-53_train_cpu_window_uint8_epidural_mean',\n", |
||
| 1757 | " 'Xresnet101-53_train_cpu_window_uint8_intraparenchymal_mean',\n", |
||
| 1758 | " 'Xresnet101-53_train_cpu_window_uint8_intraventricular_mean',\n", |
||
| 1759 | " 'Xresnet101-53_train_cpu_window_uint8_subarachnoid_mean',\n", |
||
| 1760 | " 'Xresnet101-53_train_cpu_window_uint8_subdural_mean',\n", |
||
| 1761 | " 'Xresnet18_any_mean',\n", |
||
| 1762 | " 'Xresnet18_epidural_mean',\n", |
||
| 1763 | " 'Xresnet18_intraparenchymal_mean',\n", |
||
| 1764 | " 'Xresnet18_intraventricular_mean',\n", |
||
| 1765 | " 'Xresnet18_subarachnoid_mean',\n", |
||
| 1766 | " 'Xresnet18_subdural_mean',\n", |
||
| 1767 | " 'Xresnet18-53_train_cpu_window_uint8_any_mean',\n", |
||
| 1768 | " 'Xresnet18-53_train_cpu_window_uint8_epidural_mean',\n", |
||
| 1769 | " 'Xresnet18-53_train_cpu_window_uint8_intraparenchymal_mean',\n", |
||
| 1770 | " 'Xresnet18-53_train_cpu_window_uint8_intraventricular_mean',\n", |
||
| 1771 | " 'Xresnet18-53_train_cpu_window_uint8_subarachnoid_mean',\n", |
||
| 1772 | " 'Xresnet18-53_train_cpu_window_uint8_subdural_mean',\n", |
||
| 1773 | " 'Xresnet34_any_mean',\n", |
||
| 1774 | " 'Xresnet34_epidural_mean',\n", |
||
| 1775 | " 'Xresnet34_intraparenchymal_mean',\n", |
||
| 1776 | " 'Xresnet34_intraventricular_mean',\n", |
||
| 1777 | " 'Xresnet34_subarachnoid_mean',\n", |
||
| 1778 | " 'Xresnet34_subdural_mean',\n", |
||
| 1779 | " 'Xresnet34-53_train_cpu_window_uint8_any_mean',\n", |
||
| 1780 | " 'Xresnet34-53_train_cpu_window_uint8_epidural_mean',\n", |
||
| 1781 | " 'Xresnet34-53_train_cpu_window_uint8_intraparenchymal_mean',\n", |
||
| 1782 | " 'Xresnet34-53_train_cpu_window_uint8_intraventricular_mean',\n", |
||
| 1783 | " 'Xresnet34-53_train_cpu_window_uint8_subarachnoid_mean',\n", |
||
| 1784 | " 'Xresnet34-53_train_cpu_window_uint8_subdural_mean',\n", |
||
| 1785 | " 'Xresnet50_any_mean',\n", |
||
| 1786 | " 'Xresnet50_epidural_mean',\n", |
||
| 1787 | " 'Xresnet50_intraparenchymal_mean',\n", |
||
| 1788 | " 'Xresnet50_intraventricular_mean',\n", |
||
| 1789 | " 'Xresnet50_subarachnoid_mean',\n", |
||
| 1790 | " 'Xresnet50_subdural_mean',\n", |
||
| 1791 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean',\n", |
||
| 1792 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean',\n", |
||
| 1793 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean',\n", |
||
| 1794 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean',\n", |
||
| 1795 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean',\n", |
||
| 1796 | " 'Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean']" |
||
| 1797 | ] |
||
| 1798 | }, |
||
| 1799 | "execution_count": 19, |
||
| 1800 | "metadata": {}, |
||
| 1801 | "output_type": "execute_result" |
||
| 1802 | } |
||
| 1803 | ], |
||
| 1804 | "source": [ |
||
| 1805 | "merge_cols" |
||
| 1806 | ] |
||
| 1807 | }, |
||
| 1808 | { |
||
| 1809 | "cell_type": "code", |
||
| 1810 | "execution_count": 20, |
||
| 1811 | "metadata": {}, |
||
| 1812 | "outputs": [ |
||
| 1813 | { |
||
| 1814 | "data": { |
||
| 1815 | "text/html": [ |
||
| 1816 | "<div>\n", |
||
| 1817 | "<style scoped>\n", |
||
| 1818 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1819 | " vertical-align: middle;\n", |
||
| 1820 | " }\n", |
||
| 1821 | "\n", |
||
| 1822 | " .dataframe tbody tr th {\n", |
||
| 1823 | " vertical-align: top;\n", |
||
| 1824 | " }\n", |
||
| 1825 | "\n", |
||
| 1826 | " .dataframe thead th {\n", |
||
| 1827 | " text-align: right;\n", |
||
| 1828 | " }\n", |
||
| 1829 | "</style>\n", |
||
| 1830 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1831 | " <thead>\n", |
||
| 1832 | " <tr style=\"text-align: right;\">\n", |
||
| 1833 | " <th></th>\n", |
||
| 1834 | " <th>fn</th>\n", |
||
| 1835 | " <th>Xdensenet121_any_mean</th>\n", |
||
| 1836 | " <th>Xdensenet121_epidural_mean</th>\n", |
||
| 1837 | " <th>Xdensenet121_intraparenchymal_mean</th>\n", |
||
| 1838 | " <th>Xdensenet121_intraventricular_mean</th>\n", |
||
| 1839 | " <th>Xdensenet121_subarachnoid_mean</th>\n", |
||
| 1840 | " <th>Xdensenet121_subdural_mean</th>\n", |
||
| 1841 | " <th>Xdensenet121_any_std</th>\n", |
||
| 1842 | " <th>Xdensenet121_epidural_std</th>\n", |
||
| 1843 | " <th>Xdensenet121_intraparenchymal_std</th>\n", |
||
| 1844 | " <th>...</th>\n", |
||
| 1845 | " <th>Xresnet50_intraparenchymal_mean_max</th>\n", |
||
| 1846 | " <th>Xresnet50_intraventricular_mean_max</th>\n", |
||
| 1847 | " <th>Xresnet50_subarachnoid_mean_max</th>\n", |
||
| 1848 | " <th>Xresnet50_subdural_mean_max</th>\n", |
||
| 1849 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max</th>\n", |
||
| 1850 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max</th>\n", |
||
| 1851 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max</th>\n", |
||
| 1852 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max</th>\n", |
||
| 1853 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max</th>\n", |
||
| 1854 | " <th>Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max</th>\n", |
||
| 1855 | " </tr>\n", |
||
| 1856 | " </thead>\n", |
||
| 1857 | " <tbody>\n", |
||
| 1858 | " <tr>\n", |
||
| 1859 | " <th>0</th>\n", |
||
| 1860 | " <td>ID_0fbf6a978</td>\n", |
||
| 1861 | " <td>0.652952</td>\n", |
||
| 1862 | " <td>0.064774</td>\n", |
||
| 1863 | " <td>0.077681</td>\n", |
||
| 1864 | " <td>0.005225</td>\n", |
||
| 1865 | " <td>0.292178</td>\n", |
||
| 1866 | " <td>0.338067</td>\n", |
||
| 1867 | " <td>0.096585</td>\n", |
||
| 1868 | " <td>3.203979e-02</td>\n", |
||
| 1869 | " <td>0.054162</td>\n", |
||
| 1870 | " <td>...</td>\n", |
||
| 1871 | " <td>0.996345</td>\n", |
||
| 1872 | " <td>0.973147</td>\n", |
||
| 1873 | " <td>0.959216</td>\n", |
||
| 1874 | " <td>0.376672</td>\n", |
||
| 1875 | " <td>0.998088</td>\n", |
||
| 1876 | " <td>0.032759</td>\n", |
||
| 1877 | " <td>0.990556</td>\n", |
||
| 1878 | " <td>0.993277</td>\n", |
||
| 1879 | " <td>0.982379</td>\n", |
||
| 1880 | " <td>0.296742</td>\n", |
||
| 1881 | " </tr>\n", |
||
| 1882 | " <tr>\n", |
||
| 1883 | " <th>1</th>\n", |
||
| 1884 | " <td>ID_d62ec3412</td>\n", |
||
| 1885 | " <td>0.000902</td>\n", |
||
| 1886 | " <td>0.000007</td>\n", |
||
| 1887 | " <td>0.000197</td>\n", |
||
| 1888 | " <td>0.000157</td>\n", |
||
| 1889 | " <td>0.000337</td>\n", |
||
| 1890 | " <td>0.000279</td>\n", |
||
| 1891 | " <td>0.000446</td>\n", |
||
| 1892 | " <td>3.052288e-06</td>\n", |
||
| 1893 | " <td>0.000050</td>\n", |
||
| 1894 | " <td>...</td>\n", |
||
| 1895 | " <td>0.005606</td>\n", |
||
| 1896 | " <td>0.011685</td>\n", |
||
| 1897 | " <td>0.056988</td>\n", |
||
| 1898 | " <td>0.012356</td>\n", |
||
| 1899 | " <td>0.180219</td>\n", |
||
| 1900 | " <td>0.004658</td>\n", |
||
| 1901 | " <td>0.034183</td>\n", |
||
| 1902 | " <td>0.070213</td>\n", |
||
| 1903 | " <td>0.048766</td>\n", |
||
| 1904 | " <td>0.028650</td>\n", |
||
| 1905 | " </tr>\n", |
||
| 1906 | " <tr>\n", |
||
| 1907 | " <th>2</th>\n", |
||
| 1908 | " <td>ID_cb544194b</td>\n", |
||
| 1909 | " <td>0.013207</td>\n", |
||
| 1910 | " <td>0.000106</td>\n", |
||
| 1911 | " <td>0.000226</td>\n", |
||
| 1912 | " <td>0.000060</td>\n", |
||
| 1913 | " <td>0.003247</td>\n", |
||
| 1914 | " <td>0.008092</td>\n", |
||
| 1915 | " <td>0.005803</td>\n", |
||
| 1916 | " <td>3.079419e-05</td>\n", |
||
| 1917 | " <td>0.000056</td>\n", |
||
| 1918 | " <td>...</td>\n", |
||
| 1919 | " <td>0.003033</td>\n", |
||
| 1920 | " <td>0.001944</td>\n", |
||
| 1921 | " <td>0.181010</td>\n", |
||
| 1922 | " <td>0.168275</td>\n", |
||
| 1923 | " <td>0.316041</td>\n", |
||
| 1924 | " <td>0.002888</td>\n", |
||
| 1925 | " <td>0.028160</td>\n", |
||
| 1926 | " <td>0.006408</td>\n", |
||
| 1927 | " <td>0.157353</td>\n", |
||
| 1928 | " <td>0.208944</td>\n", |
||
| 1929 | " </tr>\n", |
||
| 1930 | " <tr>\n", |
||
| 1931 | " <th>3</th>\n", |
||
| 1932 | " <td>ID_0d62513ec</td>\n", |
||
| 1933 | " <td>0.004513</td>\n", |
||
| 1934 | " <td>0.000018</td>\n", |
||
| 1935 | " <td>0.000599</td>\n", |
||
| 1936 | " <td>0.000379</td>\n", |
||
| 1937 | " <td>0.001197</td>\n", |
||
| 1938 | " <td>0.002964</td>\n", |
||
| 1939 | " <td>0.001034</td>\n", |
||
| 1940 | " <td>5.030724e-06</td>\n", |
||
| 1941 | " <td>0.000253</td>\n", |
||
| 1942 | " <td>...</td>\n", |
||
| 1943 | " <td>0.001040</td>\n", |
||
| 1944 | " <td>0.001171</td>\n", |
||
| 1945 | " <td>0.003586</td>\n", |
||
| 1946 | " <td>0.014528</td>\n", |
||
| 1947 | " <td>0.013856</td>\n", |
||
| 1948 | " <td>0.000814</td>\n", |
||
| 1949 | " <td>0.003383</td>\n", |
||
| 1950 | " <td>0.003476</td>\n", |
||
| 1951 | " <td>0.002704</td>\n", |
||
| 1952 | " <td>0.016037</td>\n", |
||
| 1953 | " </tr>\n", |
||
| 1954 | " <tr>\n", |
||
| 1955 | " <th>4</th>\n", |
||
| 1956 | " <td>ID_fc45b2151</td>\n", |
||
| 1957 | " <td>0.000032</td>\n", |
||
| 1958 | " <td>0.000001</td>\n", |
||
| 1959 | " <td>0.000014</td>\n", |
||
| 1960 | " <td>0.000009</td>\n", |
||
| 1961 | " <td>0.000016</td>\n", |
||
| 1962 | " <td>0.000031</td>\n", |
||
| 1963 | " <td>0.000014</td>\n", |
||
| 1964 | " <td>3.315683e-07</td>\n", |
||
| 1965 | " <td>0.000004</td>\n", |
||
| 1966 | " <td>...</td>\n", |
||
| 1967 | " <td>0.001387</td>\n", |
||
| 1968 | " <td>0.001326</td>\n", |
||
| 1969 | " <td>0.004180</td>\n", |
||
| 1970 | " <td>0.003761</td>\n", |
||
| 1971 | " <td>0.019404</td>\n", |
||
| 1972 | " <td>0.000497</td>\n", |
||
| 1973 | " <td>0.003189</td>\n", |
||
| 1974 | " <td>0.000700</td>\n", |
||
| 1975 | " <td>0.010232</td>\n", |
||
| 1976 | " <td>0.004891</td>\n", |
||
| 1977 | " </tr>\n", |
||
| 1978 | " </tbody>\n", |
||
| 1979 | "</table>\n", |
||
| 1980 | "<p>5 rows × 305 columns</p>\n", |
||
| 1981 | "</div>" |
||
| 1982 | ], |
||
| 1983 | "text/plain": [ |
||
| 1984 | " fn Xdensenet121_any_mean Xdensenet121_epidural_mean \\\n", |
||
| 1985 | "0 ID_0fbf6a978 0.652952 0.064774 \n", |
||
| 1986 | "1 ID_d62ec3412 0.000902 0.000007 \n", |
||
| 1987 | "2 ID_cb544194b 0.013207 0.000106 \n", |
||
| 1988 | "3 ID_0d62513ec 0.004513 0.000018 \n", |
||
| 1989 | "4 ID_fc45b2151 0.000032 0.000001 \n", |
||
| 1990 | "\n", |
||
| 1991 | " Xdensenet121_intraparenchymal_mean Xdensenet121_intraventricular_mean \\\n", |
||
| 1992 | "0 0.077681 0.005225 \n", |
||
| 1993 | "1 0.000197 0.000157 \n", |
||
| 1994 | "2 0.000226 0.000060 \n", |
||
| 1995 | "3 0.000599 0.000379 \n", |
||
| 1996 | "4 0.000014 0.000009 \n", |
||
| 1997 | "\n", |
||
| 1998 | " Xdensenet121_subarachnoid_mean Xdensenet121_subdural_mean \\\n", |
||
| 1999 | "0 0.292178 0.338067 \n", |
||
| 2000 | "1 0.000337 0.000279 \n", |
||
| 2001 | "2 0.003247 0.008092 \n", |
||
| 2002 | "3 0.001197 0.002964 \n", |
||
| 2003 | "4 0.000016 0.000031 \n", |
||
| 2004 | "\n", |
||
| 2005 | " Xdensenet121_any_std Xdensenet121_epidural_std \\\n", |
||
| 2006 | "0 0.096585 3.203979e-02 \n", |
||
| 2007 | "1 0.000446 3.052288e-06 \n", |
||
| 2008 | "2 0.005803 3.079419e-05 \n", |
||
| 2009 | "3 0.001034 5.030724e-06 \n", |
||
| 2010 | "4 0.000014 3.315683e-07 \n", |
||
| 2011 | "\n", |
||
| 2012 | " Xdensenet121_intraparenchymal_std ... \\\n", |
||
| 2013 | "0 0.054162 ... \n", |
||
| 2014 | "1 0.000050 ... \n", |
||
| 2015 | "2 0.000056 ... \n", |
||
| 2016 | "3 0.000253 ... \n", |
||
| 2017 | "4 0.000004 ... \n", |
||
| 2018 | "\n", |
||
| 2019 | " Xresnet50_intraparenchymal_mean_max Xresnet50_intraventricular_mean_max \\\n", |
||
| 2020 | "0 0.996345 0.973147 \n", |
||
| 2021 | "1 0.005606 0.011685 \n", |
||
| 2022 | "2 0.003033 0.001944 \n", |
||
| 2023 | "3 0.001040 0.001171 \n", |
||
| 2024 | "4 0.001387 0.001326 \n", |
||
| 2025 | "\n", |
||
| 2026 | " Xresnet50_subarachnoid_mean_max Xresnet50_subdural_mean_max \\\n", |
||
| 2027 | "0 0.959216 0.376672 \n", |
||
| 2028 | "1 0.056988 0.012356 \n", |
||
| 2029 | "2 0.181010 0.168275 \n", |
||
| 2030 | "3 0.003586 0.014528 \n", |
||
| 2031 | "4 0.004180 0.003761 \n", |
||
| 2032 | "\n", |
||
| 2033 | " Xresnext50_32x4d-53_train_cpu_window_uint8_any_mean_max \\\n", |
||
| 2034 | "0 0.998088 \n", |
||
| 2035 | "1 0.180219 \n", |
||
| 2036 | "2 0.316041 \n", |
||
| 2037 | "3 0.013856 \n", |
||
| 2038 | "4 0.019404 \n", |
||
| 2039 | "\n", |
||
| 2040 | " Xresnext50_32x4d-53_train_cpu_window_uint8_epidural_mean_max \\\n", |
||
| 2041 | "0 0.032759 \n", |
||
| 2042 | "1 0.004658 \n", |
||
| 2043 | "2 0.002888 \n", |
||
| 2044 | "3 0.000814 \n", |
||
| 2045 | "4 0.000497 \n", |
||
| 2046 | "\n", |
||
| 2047 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraparenchymal_mean_max \\\n", |
||
| 2048 | "0 0.990556 \n", |
||
| 2049 | "1 0.034183 \n", |
||
| 2050 | "2 0.028160 \n", |
||
| 2051 | "3 0.003383 \n", |
||
| 2052 | "4 0.003189 \n", |
||
| 2053 | "\n", |
||
| 2054 | " Xresnext50_32x4d-53_train_cpu_window_uint8_intraventricular_mean_max \\\n", |
||
| 2055 | "0 0.993277 \n", |
||
| 2056 | "1 0.070213 \n", |
||
| 2057 | "2 0.006408 \n", |
||
| 2058 | "3 0.003476 \n", |
||
| 2059 | "4 0.000700 \n", |
||
| 2060 | "\n", |
||
| 2061 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subarachnoid_mean_max \\\n", |
||
| 2062 | "0 0.982379 \n", |
||
| 2063 | "1 0.048766 \n", |
||
| 2064 | "2 0.157353 \n", |
||
| 2065 | "3 0.002704 \n", |
||
| 2066 | "4 0.010232 \n", |
||
| 2067 | "\n", |
||
| 2068 | " Xresnext50_32x4d-53_train_cpu_window_uint8_subdural_mean_max \n", |
||
| 2069 | "0 0.296742 \n", |
||
| 2070 | "1 0.028650 \n", |
||
| 2071 | "2 0.208944 \n", |
||
| 2072 | "3 0.016037 \n", |
||
| 2073 | "4 0.004891 \n", |
||
| 2074 | "\n", |
||
| 2075 | "[5 rows x 305 columns]" |
||
| 2076 | ] |
||
| 2077 | }, |
||
| 2078 | "execution_count": 20, |
||
| 2079 | "metadata": {}, |
||
| 2080 | "output_type": "execute_result" |
||
| 2081 | } |
||
| 2082 | ], |
||
| 2083 | "source": [ |
||
| 2084 | "#fill and create test dataframe\n", |
||
| 2085 | "test_dfs = []\n", |
||
| 2086 | "\n", |
||
| 2087 | "for i in range(len(test_folds_dfs)):\n", |
||
| 2088 | " current_test_fold = test_folds_dfs[i]\n", |
||
| 2089 | " test_df = current_test_fold[0]\n", |
||
| 2090 | " for arch_df in current_test_fold[1:]:\n", |
||
| 2091 | " test_df = test_df.merge(arch_df,on='fn')\n", |
||
| 2092 | " test_dfs.append(test_df)\n", |
||
| 2093 | "\n", |
||
| 2094 | "for i in range(len(test_dfs)):\n", |
||
| 2095 | " #add study and z position to dataframe to join\n", |
||
| 2096 | " test_dfs[i]['study'] = test_dfs[i].fn.apply(lambda fn: fn_to_study_ix[fn][0] )\n", |
||
| 2097 | " test_dfs[i]['z'] = test_dfs[i].fn.apply(lambda fn: int(fn_to_study_ix[fn][1]) )\n", |
||
| 2098 | " \n", |
||
| 2099 | " #now join top and bottom\n", |
||
| 2100 | " test_dfs[i]['z+'] = test_dfs[i].z+1\n", |
||
| 2101 | " test_dfs[i]['z-'] = test_dfs[i].z-1\n", |
||
| 2102 | " \n", |
||
| 2103 | " #only merge means to avoid too many columns\n", |
||
| 2104 | " merge_cols = [c for c in tta_df.columns if 'mean' in c] \n", |
||
| 2105 | " test_dfs[i] = pd.merge(test_dfs[i], test_dfs[i][merge_cols+ ['study','z+']], how='left', left_on=['study', 'z'], right_on=['study','z+'], suffixes=('','+1'))\n", |
||
| 2106 | " test_dfs[i] = pd.merge(test_dfs[i], test_dfs[i][merge_cols+ ['study','z-']], how='left', left_on=['study', 'z'], right_on=['study','z-'], suffixes=('','-1'))\n", |
||
| 2107 | " test_dfs[i].drop(['z++1','z--1'], axis=1, inplace=True) #after merge these columns are not needed\n", |
||
| 2108 | " \n", |
||
| 2109 | " #now add maximum value for each merge_col by study\n", |
||
| 2110 | " group_df = test_dfs[i][merge_cols+['study']].groupby('study').agg('max')\n", |
||
| 2111 | " test_dfs[i] = pd.merge(test_dfs[i], group_df, how='left', on='study', suffixes=('','_max'))\n", |
||
| 2112 | " \n", |
||
| 2113 | " #manage NANs created by merging\n", |
||
| 2114 | " merged_cols = [c for c in test_dfs[i].columns if c.endswith('1')]\n", |
||
| 2115 | "\n", |
||
| 2116 | " for c in merged_cols:\n", |
||
| 2117 | " test_dfs[i][c].fillna(test_dfs[i][c.replace('-1', '').replace('+1', '')], inplace=True)\n", |
||
| 2118 | "\n", |
||
| 2119 | "test_dfs[0].head()" |
||
| 2120 | ] |
||
| 2121 | }, |
||
| 2122 | { |
||
| 2123 | "cell_type": "markdown", |
||
| 2124 | "metadata": {}, |
||
| 2125 | "source": [ |
||
| 2126 | "# Train XG Boost" |
||
| 2127 | ] |
||
| 2128 | }, |
||
| 2129 | { |
||
| 2130 | "cell_type": "code", |
||
| 2131 | "execution_count": 21, |
||
| 2132 | "metadata": { |
||
| 2133 | "scrolled": true |
||
| 2134 | }, |
||
| 2135 | "outputs": [ |
||
| 2136 | { |
||
| 2137 | "data": { |
||
| 2138 | "text/plain": [ |
||
| 2139 | "(633568, 119234)" |
||
| 2140 | ] |
||
| 2141 | }, |
||
| 2142 | "execution_count": 21, |
||
| 2143 | "metadata": {}, |
||
| 2144 | "output_type": "execute_result" |
||
| 2145 | } |
||
| 2146 | ], |
||
| 2147 | "source": [ |
||
| 2148 | "folds = [1,5,12] #three random folds\n", |
||
| 2149 | "\n", |
||
| 2150 | "train_df = merged_df.query('fold not in @folds')\n", |
||
| 2151 | "val_df = merged_df.query('fold in @folds')\n", |
||
| 2152 | "\n", |
||
| 2153 | "len(train_df),len(val_df)\n" |
||
| 2154 | ] |
||
| 2155 | }, |
||
| 2156 | { |
||
| 2157 | "cell_type": "code", |
||
| 2158 | "execution_count": 22, |
||
| 2159 | "metadata": {}, |
||
| 2160 | "outputs": [ |
||
| 2161 | { |
||
| 2162 | "name": "stdout", |
||
| 2163 | "output_type": "stream", |
||
| 2164 | "text": [ |
||
| 2165 | "any Xresnet34_any_mean 0.10142146307769834\n", |
||
| 2166 | "epidural Xresnet34_epidural_mean 0.012571424002310823\n", |
||
| 2167 | "intraparenchymal Xresnet34_intraparenchymal_mean 0.04222755602983461\n", |
||
| 2168 | "intraventricular Xresnet34_intraventricular_mean 0.025140569354671405\n", |
||
| 2169 | "subarachnoid Xresnet34_subarachnoid_mean 0.06708897203821126\n", |
||
| 2170 | "subdural Xresnet34_subdural_mean 0.08416194895275723\n" |
||
| 2171 | ] |
||
| 2172 | } |
||
| 2173 | ], |
||
| 2174 | "source": [ |
||
| 2175 | "#first check log loss for current model predictions\n", |
||
| 2176 | "from sklearn.metrics import log_loss\n", |
||
| 2177 | "\n", |
||
| 2178 | "resnet34_vars = [c for c in merged_df.columns if c.startswith('Xresnet34') and c.endswith('mean')]\n", |
||
| 2179 | "y_true = val_df[base_classes]\n", |
||
| 2180 | "y_pred = val_df[resnet34_vars]\n", |
||
| 2181 | "\n", |
||
| 2182 | "for tc,pc in zip(y_true.columns,y_pred.columns):\n", |
||
| 2183 | " loss = log_loss( y_true[tc], y_pred[pc] )\n", |
||
| 2184 | " print(tc,pc,loss)\n" |
||
| 2185 | ] |
||
| 2186 | }, |
||
| 2187 | { |
||
| 2188 | "cell_type": "code", |
||
| 2189 | "execution_count": 23, |
||
| 2190 | "metadata": {}, |
||
| 2191 | "outputs": [], |
||
| 2192 | "source": [ |
||
| 2193 | "import xgboost as xgb\n", |
||
| 2194 | "import os\n", |
||
| 2195 | "from sklearn.multioutput import MultiOutputRegressor\n", |
||
| 2196 | "#select only variables from deep learning model\n", |
||
| 2197 | "model_vars = [c for c in merged_df.columns if c.startswith('X')]\n", |
||
| 2198 | "model_vars.append('z')" |
||
| 2199 | ] |
||
| 2200 | }, |
||
| 2201 | { |
||
| 2202 | "cell_type": "code", |
||
| 2203 | "execution_count": 24, |
||
| 2204 | "metadata": {}, |
||
| 2205 | "outputs": [], |
||
| 2206 | "source": [ |
||
| 2207 | "x_train = train_df[model_vars]\n", |
||
| 2208 | "y_train = train_df[base_classes]" |
||
| 2209 | ] |
||
| 2210 | }, |
||
| 2211 | { |
||
| 2212 | "cell_type": "code", |
||
| 2213 | "execution_count": 25, |
||
| 2214 | "metadata": {}, |
||
| 2215 | "outputs": [ |
||
| 2216 | { |
||
| 2217 | "name": "stdout", |
||
| 2218 | "output_type": "stream", |
||
| 2219 | "text": [ |
||
| 2220 | "Training xgboost\n", |
||
| 2221 | "Done training\n" |
||
| 2222 | ] |
||
| 2223 | } |
||
| 2224 | ], |
||
| 2225 | "source": [ |
||
| 2226 | "xgb_clf = MultiOutputRegressor(xgb.XGBRegressor(objective='binary:logistic',tree_method='gpu_hist',\n", |
||
| 2227 | " n_estimators=60, reg_lambda=14.0005, \n", |
||
| 2228 | " max_depth=5,learning_rate=0.1188,gamma=0.0, reg_alpha=0.2043,\n", |
||
| 2229 | " min_child_weight=0.0,max_delta_step=0.0,subsample=1.0,colsample_bytree=1.0,\n", |
||
| 2230 | " colsample_bylevel=1.0,silent=0,nthread=-1,\n", |
||
| 2231 | " scale_pos_weight=1.0,base_score=0.05,seed=1337,missing=None,))\n", |
||
| 2232 | "print('Training xgboost')\n", |
||
| 2233 | "xgb_clf.fit(x_train, y_train) \n", |
||
| 2234 | "\n", |
||
| 2235 | "#save trained xgb model\n", |
||
| 2236 | "pickle.dump(xgb_clf, open('data/xgb_%s.pickle'%experiment_name, 'wb'))\n", |
||
| 2237 | "\n", |
||
| 2238 | "print('Done training')" |
||
| 2239 | ] |
||
| 2240 | }, |
||
| 2241 | { |
||
| 2242 | "cell_type": "code", |
||
| 2243 | "execution_count": 26, |
||
| 2244 | "metadata": {}, |
||
| 2245 | "outputs": [], |
||
| 2246 | "source": [ |
||
| 2247 | "from catboost import CatBoostClassifier" |
||
| 2248 | ] |
||
| 2249 | }, |
||
| 2250 | { |
||
| 2251 | "cell_type": "code", |
||
| 2252 | "execution_count": 27, |
||
| 2253 | "metadata": {}, |
||
| 2254 | "outputs": [ |
||
| 2255 | { |
||
| 2256 | "name": "stderr", |
||
| 2257 | "output_type": "stream", |
||
| 2258 | "text": [ |
||
| 2259 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
||
| 2260 | ] |
||
| 2261 | }, |
||
| 2262 | { |
||
| 2263 | "name": "stdout", |
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| 2264 | "output_type": "stream", |
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| 2440 | { |
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| 2620 | ] |
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| 2621 | }, |
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| 2622 | { |
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| 2623 | "name": "stdout", |
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| 2625 | "text": [ |
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| 2725 | "447:\tlearn: 0.0903587\ttotal: 4.23s\tremaining: 491ms\n", |
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| 2726 | "448:\tlearn: 0.0903461\ttotal: 4.24s\tremaining: 482ms\n", |
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| 2727 | "449:\tlearn: 0.0903364\ttotal: 4.25s\tremaining: 472ms\n", |
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| 2728 | "450:\tlearn: 0.0903315\ttotal: 4.26s\tremaining: 463ms\n", |
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| 2729 | "451:\tlearn: 0.0903254\ttotal: 4.27s\tremaining: 454ms\n", |
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| 2730 | "452:\tlearn: 0.0903187\ttotal: 4.28s\tremaining: 444ms\n", |
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| 2731 | "453:\tlearn: 0.0903098\ttotal: 4.29s\tremaining: 435ms\n", |
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| 2732 | "454:\tlearn: 0.0903034\ttotal: 4.3s\tremaining: 425ms\n", |
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| 2733 | "455:\tlearn: 0.0902943\ttotal: 4.31s\tremaining: 416ms\n", |
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| 2734 | "456:\tlearn: 0.0902880\ttotal: 4.32s\tremaining: 406ms\n", |
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| 2735 | "457:\tlearn: 0.0902839\ttotal: 4.33s\tremaining: 397ms\n", |
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| 2736 | "458:\tlearn: 0.0902773\ttotal: 4.34s\tremaining: 387ms\n", |
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| 2737 | "459:\tlearn: 0.0902665\ttotal: 4.34s\tremaining: 378ms\n", |
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| 2738 | "460:\tlearn: 0.0902596\ttotal: 4.36s\tremaining: 368ms\n", |
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| 2742 | "464:\tlearn: 0.0902413\ttotal: 4.39s\tremaining: 331ms\n", |
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| 2743 | "465:\tlearn: 0.0902311\ttotal: 4.4s\tremaining: 321ms\n", |
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| 2744 | "466:\tlearn: 0.0902198\ttotal: 4.41s\tremaining: 312ms\n", |
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| 2745 | "467:\tlearn: 0.0902098\ttotal: 4.42s\tremaining: 302ms\n", |
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| 2746 | "468:\tlearn: 0.0902016\ttotal: 4.43s\tremaining: 293ms\n", |
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| 2747 | "469:\tlearn: 0.0901831\ttotal: 4.44s\tremaining: 283ms\n", |
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| 2749 | "471:\tlearn: 0.0901688\ttotal: 4.46s\tremaining: 264ms\n", |
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| 2750 | "472:\tlearn: 0.0901635\ttotal: 4.47s\tremaining: 255ms\n", |
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| 2751 | "473:\tlearn: 0.0901541\ttotal: 4.47s\tremaining: 246ms\n", |
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| 2752 | "474:\tlearn: 0.0901461\ttotal: 4.49s\tremaining: 236ms\n", |
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| 2753 | "475:\tlearn: 0.0901386\ttotal: 4.49s\tremaining: 227ms\n", |
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| 2754 | "476:\tlearn: 0.0901324\ttotal: 4.5s\tremaining: 217ms\n", |
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| 2755 | "477:\tlearn: 0.0901269\ttotal: 4.51s\tremaining: 208ms\n", |
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| 2756 | "478:\tlearn: 0.0901183\ttotal: 4.52s\tremaining: 198ms\n", |
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| 2757 | "479:\tlearn: 0.0901111\ttotal: 4.53s\tremaining: 189ms\n", |
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| 2758 | "480:\tlearn: 0.0901017\ttotal: 4.54s\tremaining: 179ms\n", |
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| 2759 | "481:\tlearn: 0.0900939\ttotal: 4.55s\tremaining: 170ms\n", |
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| 2760 | "482:\tlearn: 0.0900876\ttotal: 4.56s\tremaining: 160ms\n", |
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| 2762 | "484:\tlearn: 0.0900718\ttotal: 4.58s\tremaining: 142ms\n", |
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| 2763 | "485:\tlearn: 0.0900682\ttotal: 4.59s\tremaining: 132ms\n", |
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| 2764 | "486:\tlearn: 0.0900608\ttotal: 4.6s\tremaining: 123ms\n", |
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| 2765 | "487:\tlearn: 0.0900561\ttotal: 4.61s\tremaining: 113ms\n", |
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| 2766 | "488:\tlearn: 0.0900452\ttotal: 4.62s\tremaining: 104ms\n", |
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| 2767 | "489:\tlearn: 0.0900373\ttotal: 4.63s\tremaining: 94.4ms\n", |
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| 2769 | "491:\tlearn: 0.0900145\ttotal: 4.64s\tremaining: 75.5ms\n", |
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| 2770 | "492:\tlearn: 0.0900041\ttotal: 4.65s\tremaining: 66.1ms\n", |
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| 2771 | "493:\tlearn: 0.0899984\ttotal: 4.66s\tremaining: 56.6ms\n", |
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| 2772 | "494:\tlearn: 0.0899915\ttotal: 4.67s\tremaining: 47.2ms\n", |
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| 2773 | "495:\tlearn: 0.0899834\ttotal: 4.68s\tremaining: 37.8ms\n", |
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| 2774 | "496:\tlearn: 0.0899760\ttotal: 4.69s\tremaining: 28.3ms\n", |
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| 2775 | "497:\tlearn: 0.0899698\ttotal: 4.7s\tremaining: 18.9ms\n", |
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| 2776 | "498:\tlearn: 0.0899580\ttotal: 4.71s\tremaining: 9.44ms\n", |
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| 2777 | "499:\tlearn: 0.0899515\ttotal: 4.72s\tremaining: 0us\n" |
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| 2778 | ] |
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| 2779 | }, |
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| 2780 | { |
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| 2781 | "name": "stderr", |
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| 2782 | "output_type": "stream", |
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| 2783 | "text": [ |
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| 2784 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
||
| 2785 | ] |
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| 2786 | }, |
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| 2787 | { |
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| 2788 | "name": "stdout", |
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| 2789 | "output_type": "stream", |
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| 2790 | "text": [ |
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| 2791 | "0:\tlearn: 0.5965772\ttotal: 13.1ms\tremaining: 6.56s\n", |
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| 2792 | "1:\tlearn: 0.5142973\ttotal: 24.5ms\tremaining: 6.11s\n", |
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| 2793 | "2:\tlearn: 0.4387375\ttotal: 35.3ms\tremaining: 5.84s\n", |
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| 2794 | "3:\tlearn: 0.3714045\ttotal: 45.5ms\tremaining: 5.64s\n", |
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| 2797 | "6:\tlearn: 0.2261139\ttotal: 75.8ms\tremaining: 5.34s\n", |
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| 2799 | "8:\tlearn: 0.1658817\ttotal: 96.2ms\tremaining: 5.25s\n", |
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| 2800 | "9:\tlearn: 0.1431357\ttotal: 107ms\tremaining: 5.23s\n", |
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| 2965 | ] |
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| 2966 | }, |
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| 2967 | { |
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| 2968 | "name": "stdout", |
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| 2969 | "output_type": "stream", |
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| 2970 | "text": [ |
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| 3147 | ] |
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| 3148 | }, |
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| 3149 | { |
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| 3150 | "name": "stdout", |
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| 3218 | "415:\tlearn: 0.0107993\ttotal: 3.89s\tremaining: 786ms\n", |
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| 3219 | "416:\tlearn: 0.0107959\ttotal: 3.9s\tremaining: 777ms\n", |
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| 3220 | "417:\tlearn: 0.0107893\ttotal: 3.91s\tremaining: 767ms\n", |
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| 3221 | "418:\tlearn: 0.0107880\ttotal: 3.92s\tremaining: 758ms\n", |
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| 3223 | "420:\tlearn: 0.0107739\ttotal: 3.94s\tremaining: 739ms\n", |
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| 3224 | "421:\tlearn: 0.0107627\ttotal: 3.95s\tremaining: 730ms\n", |
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| 3225 | "422:\tlearn: 0.0107565\ttotal: 3.96s\tremaining: 720ms\n", |
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| 3226 | "423:\tlearn: 0.0107485\ttotal: 3.97s\tremaining: 711ms\n", |
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| 3227 | "424:\tlearn: 0.0107402\ttotal: 3.98s\tremaining: 702ms\n", |
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| 3228 | "425:\tlearn: 0.0107376\ttotal: 3.98s\tremaining: 692ms\n", |
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| 3229 | "426:\tlearn: 0.0107318\ttotal: 3.99s\tremaining: 683ms\n", |
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| 3230 | "427:\tlearn: 0.0107296\ttotal: 4s\tremaining: 673ms\n", |
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| 3231 | "428:\tlearn: 0.0107206\ttotal: 4.01s\tremaining: 664ms\n", |
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| 3232 | "429:\tlearn: 0.0107143\ttotal: 4.02s\tremaining: 655ms\n", |
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| 3233 | "430:\tlearn: 0.0107059\ttotal: 4.03s\tremaining: 645ms\n", |
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| 3234 | "431:\tlearn: 0.0107019\ttotal: 4.04s\tremaining: 636ms\n", |
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| 3235 | "432:\tlearn: 0.0106964\ttotal: 4.05s\tremaining: 627ms\n", |
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| 3236 | "433:\tlearn: 0.0106857\ttotal: 4.06s\tremaining: 617ms\n", |
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| 3238 | "435:\tlearn: 0.0106781\ttotal: 4.08s\tremaining: 599ms\n", |
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| 3239 | "436:\tlearn: 0.0106680\ttotal: 4.09s\tremaining: 589ms\n", |
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| 3240 | "437:\tlearn: 0.0106662\ttotal: 4.09s\tremaining: 580ms\n", |
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| 3241 | "438:\tlearn: 0.0106552\ttotal: 4.1s\tremaining: 570ms\n", |
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| 3242 | "439:\tlearn: 0.0106520\ttotal: 4.11s\tremaining: 561ms\n", |
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| 3244 | "441:\tlearn: 0.0106385\ttotal: 4.13s\tremaining: 542ms\n", |
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| 3245 | "442:\tlearn: 0.0106361\ttotal: 4.14s\tremaining: 533ms\n", |
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| 3246 | "443:\tlearn: 0.0106268\ttotal: 4.15s\tremaining: 523ms\n", |
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| 3247 | "444:\tlearn: 0.0106193\ttotal: 4.16s\tremaining: 514ms\n", |
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| 3250 | "447:\tlearn: 0.0106090\ttotal: 4.19s\tremaining: 486ms\n", |
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| 3251 | "448:\tlearn: 0.0106059\ttotal: 4.2s\tremaining: 477ms\n", |
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| 3252 | "449:\tlearn: 0.0106021\ttotal: 4.21s\tremaining: 467ms\n", |
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| 3253 | "450:\tlearn: 0.0105967\ttotal: 4.21s\tremaining: 458ms\n", |
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| 3254 | "451:\tlearn: 0.0105905\ttotal: 4.22s\tremaining: 449ms\n", |
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| 3255 | "452:\tlearn: 0.0105857\ttotal: 4.23s\tremaining: 439ms\n", |
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| 3256 | "453:\tlearn: 0.0105801\ttotal: 4.24s\tremaining: 430ms\n", |
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| 3259 | "456:\tlearn: 0.0105674\ttotal: 4.27s\tremaining: 402ms\n", |
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| 3260 | "457:\tlearn: 0.0105669\ttotal: 4.28s\tremaining: 392ms\n", |
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| 3261 | "458:\tlearn: 0.0105579\ttotal: 4.29s\tremaining: 383ms\n", |
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| 3262 | "459:\tlearn: 0.0105501\ttotal: 4.3s\tremaining: 374ms\n", |
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| 3263 | "460:\tlearn: 0.0105423\ttotal: 4.31s\tremaining: 364ms\n", |
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| 3264 | "461:\tlearn: 0.0105394\ttotal: 4.32s\tremaining: 355ms\n", |
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| 3265 | "462:\tlearn: 0.0105319\ttotal: 4.33s\tremaining: 346ms\n", |
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| 3266 | "463:\tlearn: 0.0105220\ttotal: 4.33s\tremaining: 336ms\n", |
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| 3267 | "464:\tlearn: 0.0105198\ttotal: 4.34s\tremaining: 327ms\n", |
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| 3268 | "465:\tlearn: 0.0105149\ttotal: 4.35s\tremaining: 318ms\n", |
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| 3269 | "466:\tlearn: 0.0105090\ttotal: 4.36s\tremaining: 308ms\n", |
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| 3270 | "467:\tlearn: 0.0105049\ttotal: 4.37s\tremaining: 299ms\n", |
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| 3271 | "468:\tlearn: 0.0105026\ttotal: 4.38s\tremaining: 290ms\n", |
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| 3272 | "469:\tlearn: 0.0104989\ttotal: 4.39s\tremaining: 280ms\n", |
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| 3273 | "470:\tlearn: 0.0104975\ttotal: 4.4s\tremaining: 271ms\n", |
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| 3274 | "471:\tlearn: 0.0104883\ttotal: 4.41s\tremaining: 262ms\n", |
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| 3275 | "472:\tlearn: 0.0104823\ttotal: 4.42s\tremaining: 252ms\n", |
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| 3276 | "473:\tlearn: 0.0104704\ttotal: 4.43s\tremaining: 243ms\n", |
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| 3280 | "477:\tlearn: 0.0104453\ttotal: 4.46s\tremaining: 205ms\n", |
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| 3281 | "478:\tlearn: 0.0104369\ttotal: 4.47s\tremaining: 196ms\n", |
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| 3282 | "479:\tlearn: 0.0104321\ttotal: 4.48s\tremaining: 187ms\n", |
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| 3283 | "480:\tlearn: 0.0104302\ttotal: 4.49s\tremaining: 177ms\n", |
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| 3284 | "481:\tlearn: 0.0104263\ttotal: 4.5s\tremaining: 168ms\n", |
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| 3285 | "482:\tlearn: 0.0104221\ttotal: 4.51s\tremaining: 159ms\n", |
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| 3286 | "483:\tlearn: 0.0104117\ttotal: 4.52s\tremaining: 149ms\n", |
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| 3287 | "484:\tlearn: 0.0104063\ttotal: 4.53s\tremaining: 140ms\n", |
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| 3288 | "485:\tlearn: 0.0104012\ttotal: 4.54s\tremaining: 131ms\n", |
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| 3289 | "486:\tlearn: 0.0103961\ttotal: 4.54s\tremaining: 121ms\n", |
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| 3290 | "487:\tlearn: 0.0103887\ttotal: 4.55s\tremaining: 112ms\n", |
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| 3291 | "488:\tlearn: 0.0103808\ttotal: 4.56s\tremaining: 103ms\n", |
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| 3292 | "489:\tlearn: 0.0103732\ttotal: 4.57s\tremaining: 93.3ms\n", |
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| 3293 | "490:\tlearn: 0.0103687\ttotal: 4.58s\tremaining: 84ms\n", |
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| 3294 | "491:\tlearn: 0.0103655\ttotal: 4.59s\tremaining: 74.7ms\n", |
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| 3295 | "492:\tlearn: 0.0103565\ttotal: 4.6s\tremaining: 65.3ms\n", |
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| 3296 | "493:\tlearn: 0.0103532\ttotal: 4.61s\tremaining: 56ms\n", |
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| 3297 | "494:\tlearn: 0.0103472\ttotal: 4.62s\tremaining: 46.7ms\n", |
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| 3298 | "495:\tlearn: 0.0103409\ttotal: 4.63s\tremaining: 37.3ms\n", |
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| 3299 | "496:\tlearn: 0.0103375\ttotal: 4.64s\tremaining: 28ms\n", |
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| 3300 | "497:\tlearn: 0.0103352\ttotal: 4.65s\tremaining: 18.7ms\n", |
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| 3301 | "498:\tlearn: 0.0103330\ttotal: 4.66s\tremaining: 9.33ms\n", |
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| 3302 | "499:\tlearn: 0.0103305\ttotal: 4.67s\tremaining: 0us\n" |
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| 3303 | ] |
||
| 3304 | }, |
||
| 3305 | { |
||
| 3306 | "name": "stderr", |
||
| 3307 | "output_type": "stream", |
||
| 3308 | "text": [ |
||
| 3309 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
||
| 3310 | ] |
||
| 3311 | }, |
||
| 3312 | { |
||
| 3313 | "name": "stdout", |
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| 3314 | "output_type": "stream", |
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| 3315 | "text": [ |
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| 3316 | "0:\tlearn: 0.6124470\ttotal: 11.2ms\tremaining: 5.58s\n", |
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| 3317 | "1:\tlearn: 0.5409320\ttotal: 21.1ms\tremaining: 5.26s\n", |
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| 3318 | "2:\tlearn: 0.4787689\ttotal: 30.8ms\tremaining: 5.09s\n", |
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| 3319 | "3:\tlearn: 0.4274331\ttotal: 43ms\tremaining: 5.34s\n", |
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| 3320 | "4:\tlearn: 0.3791450\ttotal: 54.6ms\tremaining: 5.41s\n", |
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| 3321 | "5:\tlearn: 0.3359890\ttotal: 64.7ms\tremaining: 5.33s\n", |
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| 3322 | "6:\tlearn: 0.3008023\ttotal: 75.1ms\tremaining: 5.29s\n", |
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| 3323 | "7:\tlearn: 0.2689132\ttotal: 84.7ms\tremaining: 5.21s\n", |
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| 3324 | "8:\tlearn: 0.2440483\ttotal: 94.1ms\tremaining: 5.13s\n", |
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| 3325 | "9:\tlearn: 0.2205727\ttotal: 104ms\tremaining: 5.08s\n", |
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| 3492 | { |
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| 3674 | { |
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| 3781 | "453:\tlearn: 0.0368287\ttotal: 4.22s\tremaining: 428ms\n", |
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| 3787 | "459:\tlearn: 0.0368072\ttotal: 4.28s\tremaining: 372ms\n", |
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| 3791 | "463:\tlearn: 0.0367852\ttotal: 4.31s\tremaining: 335ms\n", |
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| 3793 | "465:\tlearn: 0.0367781\ttotal: 4.33s\tremaining: 316ms\n", |
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| 3794 | "466:\tlearn: 0.0367695\ttotal: 4.34s\tremaining: 307ms\n", |
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| 3806 | "478:\tlearn: 0.0367199\ttotal: 4.45s\tremaining: 195ms\n", |
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| 3807 | "479:\tlearn: 0.0367143\ttotal: 4.46s\tremaining: 186ms\n", |
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| 3814 | "486:\tlearn: 0.0366767\ttotal: 4.53s\tremaining: 121ms\n", |
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| 3818 | "490:\tlearn: 0.0366614\ttotal: 4.56s\tremaining: 83.7ms\n", |
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| 3819 | "491:\tlearn: 0.0366578\ttotal: 4.57s\tremaining: 74.4ms\n", |
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| 3820 | "492:\tlearn: 0.0366509\ttotal: 4.58s\tremaining: 65.1ms\n", |
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| 3821 | "493:\tlearn: 0.0366466\ttotal: 4.59s\tremaining: 55.8ms\n", |
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| 3822 | "494:\tlearn: 0.0366437\ttotal: 4.6s\tremaining: 46.5ms\n", |
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| 3823 | "495:\tlearn: 0.0366401\ttotal: 4.61s\tremaining: 37.2ms\n", |
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| 3824 | "496:\tlearn: 0.0366357\ttotal: 4.62s\tremaining: 27.9ms\n", |
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| 3825 | "497:\tlearn: 0.0366326\ttotal: 4.63s\tremaining: 18.6ms\n", |
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| 3826 | "498:\tlearn: 0.0366258\ttotal: 4.64s\tremaining: 9.3ms\n", |
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| 3827 | "499:\tlearn: 0.0366237\ttotal: 4.65s\tremaining: 0us\n" |
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| 3828 | ] |
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| 3829 | }, |
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| 3830 | { |
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| 3831 | "name": "stderr", |
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| 3832 | "output_type": "stream", |
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| 3833 | "text": [ |
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| 3834 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
||
| 3835 | ] |
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| 3836 | }, |
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| 3837 | { |
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| 3838 | "name": "stdout", |
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| 3839 | "output_type": "stream", |
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| 3840 | "text": [ |
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| 3841 | "0:\tlearn: 0.5974800\ttotal: 10.8ms\tremaining: 5.38s\n", |
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| 3842 | "1:\tlearn: 0.5163507\ttotal: 20.4ms\tremaining: 5.07s\n", |
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| 3843 | "2:\tlearn: 0.4474815\ttotal: 29.7ms\tremaining: 4.92s\n", |
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| 3844 | "3:\tlearn: 0.3925685\ttotal: 38.8ms\tremaining: 4.81s\n", |
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| 3845 | "4:\tlearn: 0.3338620\ttotal: 48.3ms\tremaining: 4.78s\n", |
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| 3846 | "5:\tlearn: 0.2860227\ttotal: 57.9ms\tremaining: 4.77s\n", |
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| 3847 | "6:\tlearn: 0.2460400\ttotal: 68ms\tremaining: 4.79s\n", |
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| 3849 | "8:\tlearn: 0.1857879\ttotal: 88.2ms\tremaining: 4.81s\n", |
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| 4015 | ] |
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| 4016 | }, |
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| 4017 | { |
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| 4018 | "name": "stdout", |
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| 4019 | "output_type": "stream", |
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| 4197 | ] |
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| 4198 | }, |
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| 4199 | { |
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| 4200 | "name": "stdout", |
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| 4201 | "output_type": "stream", |
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| 4202 | "text": [ |
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| 4258 | "405:\tlearn: 0.0231067\ttotal: 3.77s\tremaining: 872ms\n", |
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| 4259 | "406:\tlearn: 0.0231039\ttotal: 3.77s\tremaining: 863ms\n", |
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| 4260 | "407:\tlearn: 0.0230979\ttotal: 3.79s\tremaining: 854ms\n", |
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| 4261 | "408:\tlearn: 0.0230939\ttotal: 3.79s\tremaining: 844ms\n", |
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| 4262 | "409:\tlearn: 0.0230905\ttotal: 3.8s\tremaining: 835ms\n", |
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| 4263 | "410:\tlearn: 0.0230848\ttotal: 3.81s\tremaining: 826ms\n", |
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| 4264 | "411:\tlearn: 0.0230819\ttotal: 3.82s\tremaining: 817ms\n", |
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| 4265 | "412:\tlearn: 0.0230768\ttotal: 3.83s\tremaining: 807ms\n", |
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| 4267 | "414:\tlearn: 0.0230713\ttotal: 3.85s\tremaining: 789ms\n", |
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| 4271 | "418:\tlearn: 0.0230544\ttotal: 3.89s\tremaining: 752ms\n", |
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| 4273 | "420:\tlearn: 0.0230445\ttotal: 3.91s\tremaining: 733ms\n", |
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| 4277 | "424:\tlearn: 0.0230331\ttotal: 3.94s\tremaining: 696ms\n", |
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| 4280 | "427:\tlearn: 0.0230239\ttotal: 3.97s\tremaining: 668ms\n", |
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| 4281 | "428:\tlearn: 0.0230213\ttotal: 3.98s\tremaining: 659ms\n", |
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| 4284 | "431:\tlearn: 0.0230103\ttotal: 4.01s\tremaining: 631ms\n", |
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| 4291 | "438:\tlearn: 0.0229884\ttotal: 4.07s\tremaining: 566ms\n", |
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| 4299 | "446:\tlearn: 0.0229593\ttotal: 4.15s\tremaining: 492ms\n", |
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| 4300 | "447:\tlearn: 0.0229565\ttotal: 4.16s\tremaining: 483ms\n", |
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| 4302 | "449:\tlearn: 0.0229483\ttotal: 4.18s\tremaining: 464ms\n", |
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| 4304 | "451:\tlearn: 0.0229427\ttotal: 4.19s\tremaining: 445ms\n", |
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| 4306 | "453:\tlearn: 0.0229332\ttotal: 4.21s\tremaining: 427ms\n", |
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| 4307 | "454:\tlearn: 0.0229319\ttotal: 4.22s\tremaining: 418ms\n", |
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| 4308 | "455:\tlearn: 0.0229302\ttotal: 4.23s\tremaining: 408ms\n", |
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| 4309 | "456:\tlearn: 0.0229272\ttotal: 4.24s\tremaining: 399ms\n", |
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| 4310 | "457:\tlearn: 0.0229241\ttotal: 4.25s\tremaining: 390ms\n", |
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| 4311 | "458:\tlearn: 0.0229217\ttotal: 4.26s\tremaining: 381ms\n", |
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| 4312 | "459:\tlearn: 0.0229193\ttotal: 4.27s\tremaining: 371ms\n", |
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| 4313 | "460:\tlearn: 0.0229163\ttotal: 4.28s\tremaining: 362ms\n", |
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| 4315 | "462:\tlearn: 0.0229103\ttotal: 4.3s\tremaining: 343ms\n", |
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| 4316 | "463:\tlearn: 0.0229034\ttotal: 4.3s\tremaining: 334ms\n", |
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| 4317 | "464:\tlearn: 0.0229001\ttotal: 4.31s\tremaining: 325ms\n", |
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| 4318 | "465:\tlearn: 0.0228976\ttotal: 4.32s\tremaining: 315ms\n", |
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| 4319 | "466:\tlearn: 0.0228902\ttotal: 4.33s\tremaining: 306ms\n", |
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| 4320 | "467:\tlearn: 0.0228884\ttotal: 4.34s\tremaining: 297ms\n", |
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| 4321 | "468:\tlearn: 0.0228852\ttotal: 4.35s\tremaining: 288ms\n", |
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| 4324 | "471:\tlearn: 0.0228723\ttotal: 4.38s\tremaining: 260ms\n", |
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| 4325 | "472:\tlearn: 0.0228686\ttotal: 4.39s\tremaining: 250ms\n", |
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| 4326 | "473:\tlearn: 0.0228638\ttotal: 4.4s\tremaining: 241ms\n", |
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| 4330 | "477:\tlearn: 0.0228507\ttotal: 4.43s\tremaining: 204ms\n", |
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| 4331 | "478:\tlearn: 0.0228481\ttotal: 4.44s\tremaining: 195ms\n", |
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| 4333 | "480:\tlearn: 0.0228420\ttotal: 4.46s\tremaining: 176ms\n", |
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| 4337 | "484:\tlearn: 0.0228319\ttotal: 4.5s\tremaining: 139ms\n", |
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| 4338 | "485:\tlearn: 0.0228273\ttotal: 4.51s\tremaining: 130ms\n", |
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| 4339 | "486:\tlearn: 0.0228246\ttotal: 4.52s\tremaining: 121ms\n", |
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| 4340 | "487:\tlearn: 0.0228200\ttotal: 4.53s\tremaining: 111ms\n", |
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| 4341 | "488:\tlearn: 0.0228172\ttotal: 4.54s\tremaining: 102ms\n", |
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| 4342 | "489:\tlearn: 0.0228160\ttotal: 4.54s\tremaining: 92.8ms\n", |
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| 4343 | "490:\tlearn: 0.0228115\ttotal: 4.55s\tremaining: 83.5ms\n", |
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| 4344 | "491:\tlearn: 0.0228064\ttotal: 4.56s\tremaining: 74.2ms\n", |
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| 4345 | "492:\tlearn: 0.0228032\ttotal: 4.57s\tremaining: 64.9ms\n", |
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| 4346 | "493:\tlearn: 0.0228016\ttotal: 4.58s\tremaining: 55.7ms\n", |
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| 4347 | "494:\tlearn: 0.0227979\ttotal: 4.59s\tremaining: 46.4ms\n", |
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| 4348 | "495:\tlearn: 0.0227933\ttotal: 4.6s\tremaining: 37.1ms\n", |
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| 4349 | "496:\tlearn: 0.0227898\ttotal: 4.61s\tremaining: 27.8ms\n", |
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| 4350 | "497:\tlearn: 0.0227867\ttotal: 4.62s\tremaining: 18.6ms\n", |
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| 4351 | "498:\tlearn: 0.0227847\ttotal: 4.63s\tremaining: 9.28ms\n", |
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| 4352 | "499:\tlearn: 0.0227827\ttotal: 4.64s\tremaining: 0us\n" |
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| 4353 | ] |
||
| 4354 | }, |
||
| 4355 | { |
||
| 4356 | "name": "stderr", |
||
| 4357 | "output_type": "stream", |
||
| 4358 | "text": [ |
||
| 4359 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
||
| 4360 | ] |
||
| 4361 | }, |
||
| 4362 | { |
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| 4363 | "name": "stdout", |
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| 4364 | "output_type": "stream", |
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| 4365 | "text": [ |
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| 4366 | "0:\tlearn: 0.6197599\ttotal: 11.4ms\tremaining: 5.69s\n", |
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| 4367 | "1:\tlearn: 0.5539440\ttotal: 21.2ms\tremaining: 5.29s\n", |
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| 4368 | "2:\tlearn: 0.4974472\ttotal: 31.5ms\tremaining: 5.21s\n", |
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| 4369 | "3:\tlearn: 0.4421587\ttotal: 41.7ms\tremaining: 5.17s\n", |
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| 4370 | "4:\tlearn: 0.4004615\ttotal: 51.6ms\tremaining: 5.11s\n", |
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| 4371 | "5:\tlearn: 0.3558608\ttotal: 61.7ms\tremaining: 5.08s\n", |
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| 4372 | "6:\tlearn: 0.3247709\ttotal: 71.8ms\tremaining: 5.05s\n", |
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| 4373 | "7:\tlearn: 0.2907828\ttotal: 81.9ms\tremaining: 5.04s\n", |
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| 4374 | "8:\tlearn: 0.2654862\ttotal: 91.8ms\tremaining: 5.01s\n", |
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| 4375 | "9:\tlearn: 0.2403010\ttotal: 101ms\tremaining: 4.96s\n", |
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| 4424 | "58:\tlearn: 0.0655000\ttotal: 562ms\tremaining: 4.2s\n", |
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| 4428 | "62:\tlearn: 0.0648428\ttotal: 598ms\tremaining: 4.15s\n", |
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| 4441 | "75:\tlearn: 0.0633832\ttotal: 719ms\tremaining: 4.01s\n", |
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| 4443 | "77:\tlearn: 0.0632714\ttotal: 738ms\tremaining: 3.99s\n", |
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| 4541 | ] |
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| 4543 | { |
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| 4725 | { |
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| 4827 | "449:\tlearn: 0.0583930\ttotal: 4.17s\tremaining: 464ms\n", |
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| 4828 | "450:\tlearn: 0.0583843\ttotal: 4.18s\tremaining: 455ms\n", |
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| 4829 | "451:\tlearn: 0.0583751\ttotal: 4.19s\tremaining: 445ms\n", |
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| 4831 | "453:\tlearn: 0.0583575\ttotal: 4.21s\tremaining: 427ms\n", |
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| 4835 | "457:\tlearn: 0.0583283\ttotal: 4.25s\tremaining: 389ms\n", |
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| 4844 | "466:\tlearn: 0.0582691\ttotal: 4.33s\tremaining: 306ms\n", |
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| 4846 | "468:\tlearn: 0.0582556\ttotal: 4.35s\tremaining: 288ms\n", |
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| 4851 | "473:\tlearn: 0.0582208\ttotal: 4.4s\tremaining: 241ms\n", |
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| 4855 | "477:\tlearn: 0.0581876\ttotal: 4.43s\tremaining: 204ms\n", |
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| 4856 | "478:\tlearn: 0.0581809\ttotal: 4.44s\tremaining: 195ms\n", |
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| 4857 | "479:\tlearn: 0.0581743\ttotal: 4.45s\tremaining: 186ms\n", |
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| 4864 | "486:\tlearn: 0.0581273\ttotal: 4.52s\tremaining: 121ms\n", |
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| 4867 | "489:\tlearn: 0.0581071\ttotal: 4.54s\tremaining: 92.8ms\n", |
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| 4868 | "490:\tlearn: 0.0581019\ttotal: 4.55s\tremaining: 83.5ms\n", |
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| 4869 | "491:\tlearn: 0.0580959\ttotal: 4.56s\tremaining: 74.2ms\n", |
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| 4870 | "492:\tlearn: 0.0580895\ttotal: 4.57s\tremaining: 64.9ms\n", |
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| 4871 | "493:\tlearn: 0.0580769\ttotal: 4.58s\tremaining: 55.6ms\n", |
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| 4873 | "495:\tlearn: 0.0580688\ttotal: 4.6s\tremaining: 37.1ms\n", |
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| 4878 | ] |
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| 4879 | }, |
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| 4880 | { |
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| 4881 | "name": "stderr", |
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| 4882 | "output_type": "stream", |
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| 4883 | "text": [ |
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| 4884 | "Warning: less than 75% gpu memory available for training. Free: 4064.625 Total: 11019.4375\n" |
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| 4885 | ] |
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| 4886 | }, |
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| 4887 | { |
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| 4888 | "name": "stdout", |
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| 5066 | { |
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| 5246 | }, |
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| 5247 | { |
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| 5248 | "name": "stdout", |
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| 5403 | ] |
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| 5404 | }, |
||
| 5405 | { |
||
| 5406 | "data": { |
||
| 5407 | "text/plain": [ |
||
| 5408 | "MultiOutputRegressor(estimator=<catboost.core.CatBoostClassifier object at 0x7f8db10a8c18>,\n", |
||
| 5409 | " n_jobs=None)" |
||
| 5410 | ] |
||
| 5411 | }, |
||
| 5412 | "execution_count": 27, |
||
| 5413 | "metadata": {}, |
||
| 5414 | "output_type": "execute_result" |
||
| 5415 | } |
||
| 5416 | ], |
||
| 5417 | "source": [ |
||
| 5418 | "cb_clf = MultiOutputRegressor(CatBoostClassifier(task_type='GPU',depth=5,l2_leaf_reg=30,iterations=500))\n", |
||
| 5419 | "cb_clf.fit(x_train,y_train)" |
||
| 5420 | ] |
||
| 5421 | }, |
||
| 5422 | { |
||
| 5423 | "cell_type": "markdown", |
||
| 5424 | "metadata": {}, |
||
| 5425 | "source": [ |
||
| 5426 | "# Run predictions on trained XGB model" |
||
| 5427 | ] |
||
| 5428 | }, |
||
| 5429 | { |
||
| 5430 | "cell_type": "code", |
||
| 5431 | "execution_count": 28, |
||
| 5432 | "metadata": {}, |
||
| 5433 | "outputs": [ |
||
| 5434 | { |
||
| 5435 | "name": "stdout", |
||
| 5436 | "output_type": "stream", |
||
| 5437 | "text": [ |
||
| 5438 | "0 any 0.09340231172025013\n", |
||
| 5439 | "1 epidural 0.012141253590899853\n", |
||
| 5440 | "2 intraparenchymal 0.039596951835288635\n", |
||
| 5441 | "3 intraventricular 0.02329420861999448\n", |
||
| 5442 | "4 subarachnoid 0.062225838595083294\n", |
||
| 5443 | "5 subdural 0.07753075725012087\n", |
||
| 5444 | "0.05737051904741248\n" |
||
| 5445 | ] |
||
| 5446 | } |
||
| 5447 | ], |
||
| 5448 | "source": [ |
||
| 5449 | "#predictions from model and evaluation\n", |
||
| 5450 | "x_test = val_df[model_vars]\n", |
||
| 5451 | "y_pred = xgb_clf.predict(x_test)\n", |
||
| 5452 | "y_true = val_df[base_classes]\n", |
||
| 5453 | "\n", |
||
| 5454 | "l = 0\n", |
||
| 5455 | "for i,c in enumerate(y_true.columns):\n", |
||
| 5456 | " loss = log_loss( y_true[c], y_pred[:,i] )\n", |
||
| 5457 | " l += loss if i != 0 else loss*2\n", |
||
| 5458 | " print(i,c,loss)\n", |
||
| 5459 | "print(l/7.)" |
||
| 5460 | ] |
||
| 5461 | }, |
||
| 5462 | { |
||
| 5463 | "cell_type": "code", |
||
| 5464 | "execution_count": 29, |
||
| 5465 | "metadata": {}, |
||
| 5466 | "outputs": [ |
||
| 5467 | { |
||
| 5468 | "name": "stdout", |
||
| 5469 | "output_type": "stream", |
||
| 5470 | "text": [ |
||
| 5471 | "0 any 0.0936741594705755\n", |
||
| 5472 | "1 epidural 0.012621494943967658\n", |
||
| 5473 | "2 intraparenchymal 0.039732457939622046\n", |
||
| 5474 | "3 intraventricular 0.023184423126184128\n", |
||
| 5475 | "4 subarachnoid 0.06195888386520253\n", |
||
| 5476 | "5 subdural 0.0781302903061038\n", |
||
| 5477 | "0.057567981303175884\n" |
||
| 5478 | ] |
||
| 5479 | } |
||
| 5480 | ], |
||
| 5481 | "source": [ |
||
| 5482 | "#predictions from model and evaluation\n", |
||
| 5483 | "x_test = val_df[model_vars]\n", |
||
| 5484 | "_y_pred = [c.predict_proba(x_test)[:,1:] for c in cb_clf.estimators_]\n", |
||
| 5485 | "y_pred = np.concatenate(_y_pred,axis=-1)\n", |
||
| 5486 | "y_true = val_df[base_classes]\n", |
||
| 5487 | "\n", |
||
| 5488 | "l = 0\n", |
||
| 5489 | "for i,c in enumerate(y_true.columns):\n", |
||
| 5490 | " loss = log_loss( y_true[c], y_pred[:,i] )\n", |
||
| 5491 | " l += loss if i != 0 else loss*2\n", |
||
| 5492 | " print(i,c,loss)\n", |
||
| 5493 | "print(l/7.)" |
||
| 5494 | ] |
||
| 5495 | }, |
||
| 5496 | { |
||
| 5497 | "cell_type": "markdown", |
||
| 5498 | "metadata": {}, |
||
| 5499 | "source": [ |
||
| 5500 | "# Prepare submission with trained model" |
||
| 5501 | ] |
||
| 5502 | }, |
||
| 5503 | { |
||
| 5504 | "cell_type": "code", |
||
| 5505 | "execution_count": 30, |
||
| 5506 | "metadata": {}, |
||
| 5507 | "outputs": [ |
||
| 5508 | { |
||
| 5509 | "name": "stdout", |
||
| 5510 | "output_type": "stream", |
||
| 5511 | "text": [ |
||
| 5512 | "finish\n" |
||
| 5513 | ] |
||
| 5514 | } |
||
| 5515 | ], |
||
| 5516 | "source": [ |
||
| 5517 | "sub_path = 'data/submission_L2.csv'\n", |
||
| 5518 | "\n", |
||
| 5519 | "data = {}\n", |
||
| 5520 | "for test_df in test_dfs:\n", |
||
| 5521 | " x_test = test_df[model_vars]\n", |
||
| 5522 | " xgb_probas = xgb_clf.predict(x_test)\n", |
||
| 5523 | " cb_probas = np.concatenate([c.predict_proba(x_test)[:,1:] for c in cb_clf.estimators_],axis=-1)\n", |
||
| 5524 | " \n", |
||
| 5525 | " probas = (xgb_probas + cb_probas) / 2.\n", |
||
| 5526 | " \n", |
||
| 5527 | " for idx, row in test_df.iterrows():\n", |
||
| 5528 | " file_id = row.fn\n", |
||
| 5529 | " for klass_index, klass in enumerate(base_classes):\n", |
||
| 5530 | " key = file_id + '_' + klass\n", |
||
| 5531 | " if key not in data:\n", |
||
| 5532 | " data[key] = probas[idx, klass_index]\n", |
||
| 5533 | " else:\n", |
||
| 5534 | " data[key] += probas[idx, klass_index]\n", |
||
| 5535 | "\n", |
||
| 5536 | "print('finish') \n", |
||
| 5537 | " \n", |
||
| 5538 | "final_prediction = []\n", |
||
| 5539 | "for key in data:\n", |
||
| 5540 | " final_prediction.append([key, data[key] / 5])\n", |
||
| 5541 | "\n", |
||
| 5542 | "df = pd.DataFrame(final_prediction, columns=['ID','Label'])\n", |
||
| 5543 | "df.to_csv(sub_path, index=False)\n" |
||
| 5544 | ] |
||
| 5545 | }, |
||
| 5546 | { |
||
| 5547 | "cell_type": "code", |
||
| 5548 | "execution_count": 31, |
||
| 5549 | "metadata": {}, |
||
| 5550 | "outputs": [ |
||
| 5551 | { |
||
| 5552 | "data": { |
||
| 5553 | "text/plain": [ |
||
| 5554 | "'\"L2 Stacking of densenet121 densenet121-53_train_cpu_window_uint8 resnet101 resnet101-53_train_cpu_window_uint8 resnet18 resnet18-53_train_cpu_window_uint8 resnet34 resnet34-53_train_cpu_window_uint8 resnet50 resnext50_32x4d-53_train_cpu_window_uint8 ensembled with xgboost + catboost\"'" |
||
| 5555 | ] |
||
| 5556 | }, |
||
| 5557 | "execution_count": 31, |
||
| 5558 | "metadata": {}, |
||
| 5559 | "output_type": "execute_result" |
||
| 5560 | } |
||
| 5561 | ], |
||
| 5562 | "source": [ |
||
| 5563 | "msg = '\"L2 Stacking of ' + \" \".join(arch_names) + ' ensembled with xgboost + catboost\"'\n", |
||
| 5564 | "msg" |
||
| 5565 | ] |
||
| 5566 | }, |
||
| 5567 | { |
||
| 5568 | "cell_type": "code", |
||
| 5569 | "execution_count": null, |
||
| 5570 | "metadata": {}, |
||
| 5571 | "outputs": [], |
||
| 5572 | "source": [ |
||
| 5573 | "!kaggle competitions submit -c rsna-intracranial-hemorrhage-detection -f {sub_path} -m {msg}" |
||
| 5574 | ] |
||
| 5575 | } |
||
| 5576 | ], |
||
| 5577 | "metadata": { |
||
| 5578 | "kernelspec": { |
||
| 5579 | "display_name": "Python 3", |
||
| 5580 | "language": "python", |
||
| 5581 | "name": "python3" |
||
| 5582 | }, |
||
| 5583 | "language_info": { |
||
| 5584 | "codemirror_mode": { |
||
| 5585 | "name": "ipython", |
||
| 5586 | "version": 3 |
||
| 5587 | }, |
||
| 5588 | "file_extension": ".py", |
||
| 5589 | "mimetype": "text/x-python", |
||
| 5590 | "name": "python", |
||
| 5591 | "nbconvert_exporter": "python", |
||
| 5592 | "pygments_lexer": "ipython3", |
||
| 5593 | "version": "3.7.3" |
||
| 5594 | } |
||
| 5595 | }, |
||
| 5596 | "nbformat": 4, |
||
| 5597 | "nbformat_minor": 2 |
||
| 5598 | } |