Datasets
Models
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from models.densenet import DenseNet\n",
"from models.MHA_resnet import ResidualNetwork\n",
"from models.resnet_bda import ResidualNetwork_classifier\n",
"from models.resnet_rnn import ResidualNetwork_lstm\n",
"from models.dense_rnn import DenseNet_rnn\n",
"from models.dense_bda import DenseNet_bda\n",
"from models.dense_cam import DenseNet_cam"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model7 = DenseNet_cam(100,30).cuda()\n",
"model7.load_state_dict(torch.load('model_weights/dense_cam_17-aur-0.972-auc 0.623 .pth'))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model1 = DenseNet(100,30).cuda()\n",
"model1.load_state_dict(torch.load('model_weights/densenet-32-aur-0.964-auc 0.615 .pth'))\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model5 = DenseNet_rnn(100,30).cuda()\n",
"model5.load_state_dict(torch.load('model_weights/dense-41-aur-0.97-auc 0.64 .pth'))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_IncompatibleKeys(missing_keys=[], unexpected_keys=['classifier.linear.weight', 'classifier.linear.bias', 'classifier.attention.fc1.weight', 'classifier.attention.fc1.bias', 'classifier.attention.fc2.weight', 'classifier.attention.fc2.bias'])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model6 = DenseNet_bda(100,30).cuda()\n",
"model6.load_state_dict(torch.load('model_weights/dense_bda-1-aur-0.967-auc 0.594 .pth'),strict=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from easydict import EasyDict as edict\n",
"config = edict({'hidden_size':320, 'num_attention_heads':8}) \n",
"model2 = ResidualNetwork(12,config).cuda()\n",
"model2.load_state_dict(torch.load('model_weights/Multi_head_Arrythmia detection-13-aur-0.975-auc 0.62 (1).pth'),strict=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_IncompatibleKeys(missing_keys=[], unexpected_keys=['classifier.linear.weight', 'classifier.linear.bias', 'classifier.attention.fc1.weight', 'classifier.attention.fc1.bias', 'classifier.attention.fc2.weight', 'classifier.attention.fc2.bias'])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model3 = ResidualNetwork_classifier(12).cuda()\n",
"model3.load_state_dict(torch.load('model_weights/RESNET_two_way-41-aur-0.972-auc 0.608 .pth'),strict=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model4 = ResidualNetwork_lstm(12).cuda()\n",
"model4.load_state_dict(torch.load('model_weights/F_CONV_LSTM-25-aur-0.966-auc 0.631 .pth'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torch.nn as nn\n",
"import numpy as np, os, sys, joblib\n",
"import matplotlib.pyplot as pl\n",
"import pandas as pd\n",
"import random, os\n",
"import librosa\n",
"from torch.utils.data.dataset import Dataset\n",
"import torch.nn.functional as F\n",
"from torchvision.transforms import ToTensor\n",
"from torchvision.utils import make_grid\n",
"from torch.utils.data.dataloader import DataLoader\n",
" #from torch.utils.data import random_split\n",
"from torch.optim import lr_scheduler\n",
"import time\n",
"import tqdm\n",
"from evaluate_model import *\n",
"from my_helper_code import *\n",
"from helper_code import *\n",
"#from model import *\n",
"from torch.nn import Conv1d,Conv2d\n",
"from dataset import My_Dataset_separate\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def seed_everything(seed: int):\n",
" \n",
" \n",
" random.seed(seed)\n",
" os.environ['PYTHONHASHSEED'] = str(seed)\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" torch.cuda.manual_seed(seed)\n",
" torch.backends.cudnn.deterministic = True\n",
" torch.backends.cudnn.benchmark = True\n",
" \n",
"seed_everything(0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('dx_mapping_scored.csv')\n",
"labels = df['SNOMEDCTCode'].values\n",
"labels = [str(i) for i in labels]\n",
"classes = list(labels)\n",
"\n",
"test_data_directory = 'test_data'\n",
"test_header_files, test_recording_files = find_challenge_files(test_data_directory)\n",
"test_num_recordings = len(test_recording_files)\n",
"training_classes = list(labels)\n",
"test_classes = list(labels)\n",
"num_classes = len(classes)\n",
"num_classes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"sample_length = 4096\n",
"twelve_leads = ('I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6')\n",
"test_dataset = My_Dataset_separate(test_header_files, test_recording_files, twelve_leads,sample_length,test_classes)\n",
"\n",
"\n",
"test_loader = DataLoader(dataset=test_dataset, batch_size=1,\n",
" shuffle=False, num_workers=0, pin_memory=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from evaluate_model import *\n",
"th = [.2,.3,.4]\n",
"\n",
"def one_zero(data,th):\n",
" result = []\n",
" for i,j in enumerate(data):\n",
" if (data[i] > th) :\n",
" result.append(1)\n",
" else :\n",
" result.append(0)\n",
" return result\n",
"\n",
"def generating_output_files(models,weight, test_classes,test_loader, output_directory,th) :\n",
" for model in models :\n",
" model.eval()\n",
" for inputs, target, header_files in tqdm.tqdm(test_loader):\n",
" header_files = header_files[0] \n",
" input_var = torch.autograd.Variable(inputs.cuda().float())\n",
" target_var = torch.autograd.Variable(target.cuda().float())\n",
" output = 0\n",
" if weight :\n",
" for i,model in enumerate(models):\n",
" output = output+model(input_var)*(weight[i])\n",
" output = output/sum(weight)\n",
" else :\n",
" output = models[0](input_var)\n",
" \n",
" probabilities = output.detach().cpu().numpy().squeeze()\n",
" labels = one_zero(probabilities,th)\n",
" header = load_header(header_files)\n",
" recording_id = get_recording_id(header)\n",
" head, tail = os.path.split(header_files)\n",
" root, extension = os.path.splitext(tail)\n",
" output_file = os.path.join(output_directory, root + '.csv')\n",
" save_outputs(output_file, recording_id, test_classes, labels, probabilities)\n",
" \n",
" \n",
"def test_model(model,weight, test_classes, test_loader, label_directory, output_directory,th) : \n",
" generating_output_files(model,weight, test_classes, test_loader, output_directory,th)\n",
" classes, auroc, auprc, auroc_classes, auprc_classes, accuracy, f_measure, f_measure_classes, challenge_metric,cf_matrix = evaluate_model('test_data','test_outputs')\n",
" print(f'Auroc : {auroc}')\n",
" print(f'Accuracy : {accuracy}')\n",
" print(f'f1 {f_measure}')\n",
" print(f'challenge_metric{challenge_metric}')\n",
" return\t classes, auroc, auprc, auroc_classes, auprc_classes, accuracy, f_measure, f_measure_classes, challenge_metric,cf_matrix\n",
"# tensor([[[0.9949],\n",
"# [0.1105],\n",
"# [0.9002],\n",
"# [0.9921]]], device='cuda:0', grad_fn=<SigmoidBackward0>) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/4413 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"for th 0.2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/4413 [00:02<?, ?it/s]\n"
]
},
{
"ename": "RuntimeError",
"evalue": "mat1 and mat2 shapes cannot be multiplied (320x1 and 320x30)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-8-d1e75f711adc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mth\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'for th {i}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mclasses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauroc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauprc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauroc_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauprc_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf_measure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf_measure_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchallenge_metric\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcf_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel_directory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m.3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-7-f81113d8e1b5>\u001b[0m in \u001b[0;36mtest_model\u001b[1;34m(model, weight, test_classes, test_loader, label_directory, output_directory, th)\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtest_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel_directory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mth\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0mgenerating_output_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 40\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauroc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauprc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauroc_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauprc_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf_measure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf_measure_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchallenge_metric\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcf_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mevaluate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'test_data'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'test_outputs'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'Auroc : {auroc}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-7-f81113d8e1b5>\u001b[0m in \u001b[0;36mgenerating_output_files\u001b[1;34m(models, weight, test_classes, test_loader, output_directory, th)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0mprobabilities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mG:\\Thesis-Git\\models\\dense_cam.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattention\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 81\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmoid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[1;31m# print(out.shape)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\modules\\linear.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mlinear\u001b[1;34m(input, weight, bias)\u001b[0m\n\u001b[0;32m 1846\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhas_torch_function_variadic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1847\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1848\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (320x1 and 320x30)"
]
}
],
"source": [
"\n",
"weight = None\n",
"label_directory = 'test_data'\n",
"output_directory='test_outputs'\n",
"models = [model7]\n",
"cf_matrix_list = []\n",
"for i in th :\n",
" print(f'for th {i}')\n",
" classes, auroc, auprc, auroc_classes, auprc_classes, accuracy, f_measure, f_measure_classes, challenge_metric,cf_matrix = test_model(models,weight, test_classes, test_loader, label_directory, output_directory,.3) \n",
" cf_matrix_list.append(cf_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/4413 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"for th 0.3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████| 4413/4413 [10:37<00:00, 6.92it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading weights...\n",
"Loading label and output files...\n",
"Evaluating model...\n",
"- AUROC and AUPRC...\n",
"- Accuracy...\n",
"- F-measure...\n",
"- Challenge metric...\n",
"Done.\n",
"Auroc : 0.9775901025836848\n",
"Accuracy : 0.6322229775662814\n",
"f1 0.6577955757512897\n",
"challenge_metric0.726762723432914\n"
]
}
],
"source": [
"# model = [model1, model2, model3, model4,model5,model6]\n",
"# weight = [1,1,1,1,1,1]\n",
"# label_directory = 'test_data'\n",
"# output_directory='test_outputs'\n",
"# for i in th:\n",
"# print(f'for th {i}')\n",
"# cf_matrix = test_model(model,weight, test_classes, test_loader, label_directory, output_directory,i) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class ensemble(nn.Module):\n",
" def __init__(self, n_models) :\n",
" super(ensemble, self).__init__()\n",
" self.n_models = n_models\n",
" self.conv1d = nn.Conv1d(self.n_models,1,1)\n",
" \n",
" def forward(self,x) :\n",
" return torch.sigmoid(self.conv1d(x)).squeeze()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"label_files, output_files = find_challenge_files_eval('test_data','test_outputs')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for inputs, target, header_files in tqdm.tqdm(test_loader):\n",
" header_files = header_files[0] \n",
" input_var = torch.autograd.Variable(inputs.cuda().float())\n",
" target_var = torch.autograd.Variable(target.cuda().float())\n",
" output = []\n",
" if weight :\n",
" for i,model in enumerate(models):\n",
" with torch.no_grad:\n",
" output.append(model(input_var))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"weights_file = 'weights.csv'\n",
"sinus_rhythm = set(['426783006'])\n",
"classes, weights = load_weights(weights_file)\n",
"labels = load_labels(label_files, classes)\n",
"\n",
"binary_outputs, scalar_outputs = load_classifier_outputs(output_files, classes)\n",
"cf_matrix = compute_modified_confusion_matrix(labels,binary_outputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"for j in range(26) :\n",
" cf_matrix[j,:] = cf_matrix[j,:]/sum(cf_matrix[j,:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"fig, ax = plt.subplots(figsize=(50,50)) \n",
"ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
"\n",
"ax.set_title('Confusion Matrix\\n\\n');\n",
"ax.set_xlabel('\\nPredicted Values')\n",
"ax.set_ylabel('Actual Values ');\n",
"\n",
"## Ticket labels - List must be in alphabetical order\n",
"# ax.xaxis.set_ticklabels(['False])\n",
"# ax.yaxis.set_ticklabels(['False','True'])\n",
"\n",
"## Display the visualization of the Confusion Matrix.\n",
"plt.savefig('save_as_a_png.png')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import seaborn as sns\n",
"fig, ax = plt.subplots(figsize=(20,20)) \n",
"ax = sns.heatmap(weights, annot=True, cmap='Blues')\n",
"\n",
"ax.set_title('Dx Scores\\n\\n');\n",
"ax.set_xlabel('\\n Predicted Diseases');\n",
"ax.set_ylabel('Actual Values ');\n",
"\n",
"## Ticket labels - List must be in alphabetical order\n",
"# ax.xaxis.set_ticklabels(['False])\n",
"# ax.yaxis.set_ticklabels(['False','True'])\n",
"\n",
"## Display the visualization of the Confusion Matrix.\n",
"plt.savefig('Dx scores.png')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
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},
"nbformat": 4,
"nbformat_minor": 4
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