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os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\nlabels = ['HandStart', 'FirstDigitTouch', 'BothStartLoadPhase', 'LiftOff',\n 'Replace', 'BothReleased']\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2025-04-16T14:54:43.236821Z","iopub.execute_input":"2025-04-16T14:54:43.237485Z","iopub.status.idle":"2025-04-16T14:54:43.252736Z","shell.execute_reply.started":"2025-04-16T14:54:43.237465Z","shell.execute_reply":"2025-04-16T14:54:43.252024Z"},"papermill":{"duration":0.027183,"end_time":"2024-03-28T13:21:23.125623","exception":false,"start_time":"2024-03-28T13:21:23.098440","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"e05868c8","cell_type":"code","source":"import os\nimport random\nfrom glob import glob\nfrom tqdm.notebook import tqdm\nimport gc\n\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport pywt\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler,Normalizer,MinMaxScaler\nimport scipy\nfrom scipy.signal import butter, lfilter, convolve\nfrom scipy.signal import freqz\nfrom scipy.fftpack import fft, ifft\nimport argparse\nimport matplotlib.pyplot as plt\n\nfrom sklearn import metrics\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.nn as nn\nimport torch.optim as optim","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:54:43.253441Z","iopub.execute_input":"2025-04-16T14:54:43.253662Z","iopub.status.idle":"2025-04-16T14:54:49.029569Z","shell.execute_reply.started":"2025-04-16T14:54:43.253635Z","shell.execute_reply":"2025-04-16T14:54:49.028998Z"},"papermill":{"duration":5.700323,"end_time":"2024-03-28T13:21:28.835638","exception":false,"start_time":"2024-03-28T13:21:23.135315","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"be1b04ab","cell_type":"code","source":"torch.manual_seed(2021)\nnp.random.seed(2021)\ndevice = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n_epochs\", type=int, default=1, help=\"number of epochs of training\")\nparser.add_argument(\"--batch_size\", type=int, default=1024, help=\"size of the batches\")\nparser.add_argument(\"--lr\", type=float, default=0.005, help=\"adam's learning rate\")\nparser.add_argument(\"--b1\", type=float, default=0.5, help=\"adam: decay of first order momentum of gradient\")\nparser.add_argument(\"--b2\", type=float, default=0.99, help=\"adam: decay of first order momentum of gradient\")\nparser.add_argument(\"--n_cpu\", type=int, default=4, help=\"number of cpu threads to use during batch generation\")\nparser.add_argument(\"--in_len\", type=int, default=2**10, help=\"length of the input fed to neural net\")\nparser.add_argument(\"--in_channels\", type=int, default=32, help=\"number of signal channels\")\nparser.add_argument(\"--out_channels\", type=int, default=6, help=\"number of classes\")\nparser.add_argument(\"--chunk\", type=int, default=1000, help=\"length of splited chunks\")\nparser.add_argument(\"--lstm_hidden\", type=int, default=1024, help=\"length of splited chunks\")\nopt, unknown = parser.parse_known_args()\nprint(device)","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:54:49.031171Z","iopub.execute_input":"2025-04-16T14:54:49.031479Z","iopub.status.idle":"2025-04-16T14:54:49.104762Z","shell.execute_reply.started":"2025-04-16T14:54:49.031461Z","shell.execute_reply":"2025-04-16T14:54:49.104076Z"},"papermill":{"duration":0.097998,"end_time":"2024-03-28T13:21:28.943909","exception":false,"start_time":"2024-03-28T13:21:28.845911","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"a92f500a","cell_type":"code","source":"# zip the files as ZIPFILE :)\nimport zipfile\nwith zipfile.ZipFile(\"../input/grasp-and-lift-eeg-detection/test.zip\",\"r\") as z:\n z.extractall(\".\")\nwith zipfile.ZipFile(\"../input/grasp-and-lift-eeg-detection/train.zip\",\"r\") as z:\n z.extractall(\".\")","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:54:49.105610Z","iopub.execute_input":"2025-04-16T14:54:49.105826Z","iopub.status.idle":"2025-04-16T14:55:19.705688Z","shell.execute_reply.started":"2025-04-16T14:54:49.105810Z","shell.execute_reply":"2025-04-16T14:55:19.705149Z"},"papermill":{"duration":29.442235,"end_time":"2024-03-28T13:21:58.395612","exception":false,"start_time":"2024-03-28T13:21:28.953377","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"0c19f561","cell_type":"code","source":"def wavelet_denoising(x, wavelet='db2', level=3):\n coeff = pywt.wavedec(x, wavelet, mode=\"per\")\n sigma = (1/0.6745) * madev(coeff[-level])\n uthresh = sigma * np.sqrt(2 * np.log(len(x)))\n coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])\n return pywt.waverec(coeff, wavelet, mode='per')\ndef madev(d, axis=None):\n \"\"\" Mean absolute deviation of a signal \"\"\"\n return np.mean(np.absolute(d - np.mean(d, axis)), axis)","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:55:19.706398Z","iopub.execute_input":"2025-04-16T14:55:19.706593Z","iopub.status.idle":"2025-04-16T14:55:19.712368Z","shell.execute_reply.started":"2025-04-16T14:55:19.706577Z","shell.execute_reply":"2025-04-16T14:55:19.711712Z"},"papermill":{"duration":0.019072,"end_time":"2024-03-28T13:21:58.425065","exception":false,"start_time":"2024-03-28T13:21:58.405993","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"dbe6440f","cell_type":"code","source":"%%time\ndef read_csv(data, events):\n x = pd.read_csv(data)\n y = pd.read_csv(events)\n id = '_'.join(x.iloc[0, 0].split('_')[:-1])\n x = x.iloc[:,1:].values\n y = y.iloc[:,1:].values\n return x, y\n \n\ntrainset = []\ngt = []\nfor filename in tqdm(os.listdir('./train')):\n if 'data' in filename:\n data_file_name = os.path.join('./train', filename)\n id = filename.split('.')[0]\n events_file_name = os.path.join('./train', '_'.join(id.split('_')[:-1]) + '_events.csv')\n x, y = read_csv(data_file_name, events_file_name)\n x = wavelet_denoising(x)\n trainset.append(x.T.astype(np.float32))\n gt.append(y.T.astype(np.float32))","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:55:19.712968Z","iopub.execute_input":"2025-04-16T14:55:19.713202Z","iopub.status.idle":"2025-04-16T14:56:38.152173Z","shell.execute_reply.started":"2025-04-16T14:55:19.713186Z","shell.execute_reply":"2025-04-16T14:56:38.151449Z"},"papermill":{"duration":113.166714,"end_time":"2024-03-28T13:23:51.620981","exception":false,"start_time":"2024-03-28T13:21:58.454267","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"1563b876","cell_type":"markdown","source":"# DataSet For ResCNN","metadata":{"papermill":{"duration":0.009061,"end_time":"2024-03-28T13:21:58.444563","exception":false,"start_time":"2024-03-28T13:21:58.435502","status":"completed"},"tags":[]}},{"id":"19d3556d","cell_type":"code","source":"valid_dataset = trainset[-2:]\nvalid_gt = gt[-2:]\ntrainset = trainset[:-2]\ngt = gt[:-2]","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:56:38.153216Z","iopub.execute_input":"2025-04-16T14:56:38.153432Z","iopub.status.idle":"2025-04-16T14:56:38.157404Z","shell.execute_reply.started":"2025-04-16T14:56:38.153413Z","shell.execute_reply":"2025-04-16T14:56:38.156745Z"},"papermill":{"duration":0.01773,"end_time":"2024-03-28T13:23:51.649263","exception":false,"start_time":"2024-03-28T13:23:51.631533","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"5ed3983d","cell_type":"code","source":"def resample_data(gt, chunk_size=opt.chunk):\n \"\"\"\n split long signals to smaller chunks, discard no-events chunks \n \"\"\"\n total_discard_chunks = 0\n mean_val = []\n threshold = 0.01\n index = []\n \n for i in range(len(gt)):\n for j in range(0, gt[i].shape[1], chunk_size):\n mean_val.append(np.mean(gt[i][:, j:min(gt[i].shape[1],j+chunk_size)]))\n if mean_val[-1] < threshold and j > 0: # discard chunks with low events time\n total_discard_chunks += 1\n index.extend([(i, k) for k in range(j, min(gt[i].shape[1],j+chunk_size), chunk_size//100)])\n else:\n index.extend([(i, k) for k in range(j, min(gt[i].shape[1],j+chunk_size))])\n\n plt.plot([0, len(mean_val)], [threshold, threshold], color='r')\n plt.scatter(range(len(mean_val)), mean_val, s=1)\n plt.show()\n print('Total number of chunks discarded: {} chunks'.format(total_discard_chunks))\n print('{}% data'.format(total_discard_chunks/len(mean_val)))\n del mean_val\n gc.collect()\n return index","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:56:38.158059Z","iopub.execute_input":"2025-04-16T14:56:38.158357Z","iopub.status.idle":"2025-04-16T14:56:38.176323Z","shell.execute_reply.started":"2025-04-16T14:56:38.158328Z","shell.execute_reply":"2025-04-16T14:56:38.175685Z"},"papermill":{"duration":0.023371,"end_time":"2024-03-28T13:23:51.682499","exception":false,"start_time":"2024-03-28T13:23:51.659128","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"2383f6fe","cell_type":"code","source":"%%time\nclass EEGSignalDataset(Dataset):\n def __init__(self, data, gt, soft_label=True, train=True):\n self.data = data\n self.gt = gt\n self.train = train\n self.soft_label = soft_label\n if train:\n self.index = resample_data(gt)\n else:\n self.index = [(i, j) for i in range(len(data)) for j in range(data[i].shape[1])]\n \n def __getitem__(self, i):\n i, j = self.index[i]\n raw_data, label = self.data[i][:,max(0, j-opt.in_len+1):j+1], \\\n self.gt[i][:,j]\n pad = opt.in_len - raw_data.shape[1]\n if pad:\n raw_data = np.pad(raw_data, ((0,0),(pad,0)), 'constant',constant_values=0)\n\n raw_data, label = torch.from_numpy(raw_data.astype(np.float32)),\\\n torch.from_numpy(label.astype(np.float32))\n if self.soft_label:\n label[label < .05] = .05\n return raw_data, label\n \n \n def __len__(self):\n return len(self.index)\n \ndataset = EEGSignalDataset(trainset, gt) \ndataloader = DataLoader(dataset, batch_size = opt.batch_size,\\\n num_workers = opt.n_cpu, shuffle=True)\nprint(len(dataset))\ntestset = EEGSignalDataset(valid_dataset, valid_gt, train=False, soft_label=False) \ntestloader = DataLoader(testset, batch_size = opt.batch_size,\\\n num_workers = opt.n_cpu, shuffle=False)\nvalid_gt = np.concatenate(valid_gt, axis=1)","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:56:38.179140Z","iopub.execute_input":"2025-04-16T14:56:38.179373Z","iopub.status.idle":"2025-04-16T14:56:40.440656Z","shell.execute_reply.started":"2025-04-16T14:56:38.179357Z","shell.execute_reply":"2025-04-16T14:56:40.440022Z"},"papermill":{"duration":2.963691,"end_time":"2024-03-28T13:23:54.656423","exception":false,"start_time":"2024-03-28T13:23:51.692732","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"c611d9bd","cell_type":"markdown","source":"# ResCNN","metadata":{"papermill":{"duration":0.01212,"end_time":"2024-03-28T13:23:54.681024","exception":false,"start_time":"2024-03-28T13:23:54.668904","status":"completed"},"tags":[]}},{"id":"25f9ab21","cell_type":"code","source":"class NNet(nn.Module):\n def __init__(self, in_channels=opt.in_channels, out_channels=opt.out_channels):\n super(NNet, self).__init__()\n self.hidden = 32\n self.net = nn.Sequential(\n nn.BatchNorm1d(opt.in_channels),\n nn.Conv1d(opt.in_channels, opt.in_channels, 5, padding=2),\n nn.Conv1d(opt.in_channels, self.hidden, 16, stride=16),\n nn.LeakyReLU(0.1),\n nn.Conv1d(self.hidden, self.hidden, 7, padding=3),\n )\n for i in range(2):\n self.net.add_module('conv{}'.format(i), \\\n self.__block(self.hidden, self.hidden)) # 16\n \n self.mid = nn.Sequential(\n self.__block(self.hidden, self.hidden),\n self.__block(self.hidden, self.hidden)\n ) # 4\n self.final = nn.Sequential(\n nn.Linear(256, 64),\n nn.LeakyReLU(0.1),\n nn.Linear(64, 6),\n nn.Sigmoid()\n )\n \n def __block(self, inchannels, outchannels):\n return nn.Sequential(\n nn.MaxPool1d(2, 2),\n nn.Conv1d(inchannels, outchannels, 5, padding=2),\n nn.LeakyReLU(0.1),\n nn.BatchNorm1d(outchannels),\n nn.Conv1d(outchannels, outchannels, 5, padding=2),\n nn.LeakyReLU(0.1)\n )\n \n def forward(self, x):\n x = self.net(x)\n y = self.mid(x)\n y = torch.cat((x[..., -4:], y), dim=-1).view(x.shape[0], -1)\n return self.final(y)","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:56:40.441664Z","iopub.execute_input":"2025-04-16T14:56:40.441930Z","iopub.status.idle":"2025-04-16T14:56:40.449811Z","shell.execute_reply.started":"2025-04-16T14:56:40.441904Z","shell.execute_reply":"2025-04-16T14:56:40.448984Z"},"papermill":{"duration":0.02598,"end_time":"2024-03-28T13:23:54.718792","exception":false,"start_time":"2024-03-28T13:23:54.692812","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"f638361e","cell_type":"code","source":"%%time\nnnet = NNet()\nnnet.to(device)\nloss_fnc = nn.BCELoss()\nadam = optim.Adam(nnet.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\nloss_his, train_loss, valid_auc, train_auc = [], [], [], []\nnnet.train()\nfor epoch in range(opt.n_epochs):\n p_bar = tqdm(dataloader)\n for i, (x, y) in enumerate(p_bar):\n x, y = x.to(device), y.to(device)\n pred = nnet(x)\n loss = loss_fnc(pred.squeeze(dim=-1), y)\n adam.zero_grad()\n loss.backward()\n nn.utils.clip_grad_value_(nnet.parameters(), 2.)\n adam.step()\n train_loss.append(loss.item())\n p_bar.set_description('[Loss: {}]'.format(train_loss[-1]))\n if i % 50 == 0:\n loss_his.append(np.mean(train_loss))\n train_loss.clear()\n# if i % 1500 == 0:\n# y[y<0.1]=0\n# train_auc.append(metrics.roc_auc_score(y.detach().cpu().numpy(), \\\n# pred.detach().cpu().numpy()))\n# valid_auc.append(calc_valid_auc(nnet))\n print('[Epoch {}/{}] [Loss: {}]'.format(epoch+1, opt.n_epochs, loss_his[-1]))\n \ntorch.save(nnet.state_dict(), 'model.pt')","metadata":{"execution":{"iopub.status.busy":"2025-04-16T14:56:40.450719Z","iopub.execute_input":"2025-04-16T14:56:40.450976Z","execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":1405.418194,"end_time":"2024-03-28T13:47:20.147947","exception":false,"start_time":"2024-03-28T13:23:54.729753","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"bb477406","cell_type":"code","source":"plt.plot(range(len(loss_his)), loss_his, label='loss')\nplt.legend()\nplt.show()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":0.280995,"end_time":"2024-03-28T13:47:20.440755","exception":false,"start_time":"2024-03-28T13:47:20.159760","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"c98f8313","cell_type":"code","source":"nnet.eval()\ny_pred = []\nwith torch.no_grad():\n for x, _ in tqdm(testloader):\n x = x.to(device)\n pred = nnet(x).detach().cpu().numpy()\n y_pred.append(pred)\ny_pred = np.concatenate(y_pred, axis=0)","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":43.103749,"end_time":"2024-03-28T13:48:03.557093","exception":false,"start_time":"2024-03-28T13:47:20.453344","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"1cb0b37c","cell_type":"code","source":"def plot_roc(y_true, y_pred):\n fig, axs = plt.subplots(3, 2, figsize=(15,15))\n for i, label in enumerate(labels):\n fpr, tpr, _ = metrics.roc_curve(y_true[i], y_pred[i])\n ax = axs[i//2, i%2]\n ax.plot(fpr, tpr,linewidth=3)\n ax.set_title(label+\" ROC\")\n ax.plot([0, 1], [0, 1], 'k--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.0])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n\n plt.show()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":0.025062,"end_time":"2024-03-28T13:48:03.595717","exception":false,"start_time":"2024-03-28T13:48:03.570655","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"98fe8e65","cell_type":"code","source":"plot_roc(valid_gt, y_pred.T)\nprint('auc roc: ', metrics.roc_auc_score(valid_gt.T, y_pred))","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":2.645501,"end_time":"2024-03-28T13:48:06.254244","exception":false,"start_time":"2024-03-28T13:48:03.608743","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"17c6d3ad","cell_type":"code","source":"!pip install tensorboardX -q\n\nfrom tensorboardX import SummaryWriter\n\nnnet = NNet()\nnnet.to(device)\nx.to(device)\n# 创建一个SummaryWriter实例\nwriter = SummaryWriter('./runs/model_visualization')\n\n# 添加模型的计算图\nwriter.add_graph(nnet, x)\n\n# 关闭SummaryWriter\nwriter.close()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":19.206247,"end_time":"2024-03-28T13:48:25.475233","exception":false,"start_time":"2024-03-28T13:48:06.268986","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"c007617c","cell_type":"markdown","source":"# DataSet For LSTM","metadata":{"_kg_hide-input":false,"papermill":{"duration":0.014463,"end_time":"2024-03-28T13:48:25.504661","exception":false,"start_time":"2024-03-28T13:48:25.490198","status":"completed"},"tags":[]}},{"id":"f66604f4","cell_type":"code","source":"%%time\ndef read_csv(data, events):\n x = pd.read_csv(data)\n y = pd.read_csv(events)\n id = '_'.join(x.iloc[0, 0].split('_')[:-1])\n x = x.iloc[:,1:].values\n y = y.iloc[:,1:].values\n return x, y\n \n\ntrainset = []\ngt = []\nfor filename in tqdm(os.listdir('./train')):\n if 'data' in filename:\n data_file_name = os.path.join('./train', filename)\n id = filename.split('.')[0]\n events_file_name = os.path.join('./train', '_'.join(id.split('_')[:-1]) + '_events.csv')\n x, y = read_csv(data_file_name, events_file_name)\n x = wavelet_denoising(x)\n trainset.append(x.astype(np.float32))\n gt.append(y.astype(np.float32))","metadata":{"_kg_hide-input":false,"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":112.267353,"end_time":"2024-03-28T13:50:17.786843","exception":false,"start_time":"2024-03-28T13:48:25.519490","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"1945c7e0","cell_type":"code","source":"valid_dataset = trainset[-2:]\nvalid_gt = gt[-2:]\ntrainset = trainset[:-2]\ngt = gt[:-2]","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.937Z"},"papermill":{"duration":0.022372,"end_time":"2024-03-28T13:50:17.824048","exception":false,"start_time":"2024-03-28T13:50:17.801676","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"c119e5f9","cell_type":"code","source":"gt[0].shape","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.023431,"end_time":"2024-03-28T13:50:17.863389","exception":false,"start_time":"2024-03-28T13:50:17.839958","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"3393cd52","cell_type":"markdown","source":"# DataLoader For LSTM","metadata":{"papermill":{"duration":0.014262,"end_time":"2024-03-28T13:50:17.892049","exception":false,"start_time":"2024-03-28T13:50:17.877787","status":"completed"},"tags":[]}},{"id":"820c69c0","cell_type":"code","source":"def resample_data_time_var(gt, chunk_size=opt.chunk):\n \"\"\"\n split long signals to smaller chunks, discard no-events chunks \n \"\"\"\n total_discard_chunks = 0\n mean_val = []\n threshold = 0.01\n index = []\n \n for i in range(len(gt)):\n for j in range(0, gt[i].shape[0], chunk_size):\n mean_val.append(np.mean(gt[i][j:min(gt[i].shape[0],j+chunk_size), :]))\n if mean_val[-1] < threshold and j > 0: \n total_discard_chunks += 1\n index.extend([(i, k) for k in range(j, min(gt[i].shape[0],j+chunk_size), chunk_size//100)])\n else:\n index.extend([(i, k) for k in range(j, min(gt[i].shape[0],j+chunk_size))])\n\n del mean_val\n gc.collect()\n return index","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.02529,"end_time":"2024-03-28T13:50:17.931598","exception":false,"start_time":"2024-03-28T13:50:17.906308","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"2bc13f9b","cell_type":"code","source":"%%time\nclass EEGSignalDataset(Dataset):\n def __init__(self, data, gt, soft_label=True, train=True):\n self.data = data\n self.gt = gt\n self.train = train\n self.soft_label = soft_label\n if train:\n self.index = resample_data_time_var(gt)\n else:\n self.index = [(i, j) for i in range(len(data)) for j in range(data[i].shape[0])]\n \n def __getitem__(self, i):\n i, j = self.index[i]\n raw_data, label = self.data[i][max(0, j-opt.in_len+1):j+1,:], \\\n self.gt[i][j,:]\n pad = opt.in_len - raw_data.shape[0]\n if pad:\n raw_data = np.pad(raw_data, ((pad,0),(0,0)), 'constant',constant_values=0)\n\n raw_data, label = torch.from_numpy(raw_data.astype(np.float32)),\\\n torch.from_numpy(label.astype(np.float32))\n if self.soft_label:\n label[label < .05] = .05\n return raw_data, label\n \n \n def __len__(self):\n return len(self.index)\n \ndataset = EEGSignalDataset(trainset, gt) \ndataloader = DataLoader(dataset, batch_size = opt.batch_size,\\\n num_workers = opt.n_cpu, shuffle=True)\nprint(len(dataset))\ntestset = EEGSignalDataset(valid_dataset, valid_gt, train=False, soft_label=False) \ntestloader = DataLoader(testset, batch_size = opt.batch_size,\\\n num_workers = opt.n_cpu, shuffle=False)\nvalid_gt = np.concatenate(valid_gt, axis=0)","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":3.07832,"end_time":"2024-03-28T13:50:21.024015","exception":false,"start_time":"2024-03-28T13:50:17.945695","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"fab20944","cell_type":"markdown","source":"# Neural NetWork","metadata":{"papermill":{"duration":0.014266,"end_time":"2024-03-28T13:50:21.053377","exception":false,"start_time":"2024-03-28T13:50:21.039111","status":"completed"},"tags":[]}},{"id":"2008b0f0","cell_type":"code","source":"class LSTMNet(nn.Module):\n def __init__(self, in_channels=opt.in_channels, out_channels=opt.out_channels):\n super(LSTMNet, self).__init__()\n self.hidden = 32\n self.seq = opt.in_len\n self.LSTM = nn.Sequential(\n nn.LSTM(input_size = opt.in_channels,\n hidden_size =opt.in_channels,\n batch_first = True,dropout = 0.8,bidirectional = False),\n ) \n self.MLP = nn.Sequential(\n nn.Linear(160,80),\n nn.LeakyReLU(0.1),\n nn.Linear(80,6),\n nn.Sigmoid()\n )\n self.ConvNet = nn.Sequential(\n nn.BatchNorm1d(opt.in_channels),\n nn.Conv1d(opt.in_channels, opt.in_channels, 5, padding=2),\n nn.Conv1d(opt.in_channels, self.hidden, 16, stride=16),\n nn.LeakyReLU(0.1),\n nn.Conv1d(self.hidden, self.hidden, 7, padding=3),\n self.__block(self.hidden,self.hidden),\n self.__block(self.hidden,self.hidden),\n nn.Conv1d(opt.in_channels, self.hidden, 4, stride=4),\n )\n self.Hidden = nn.Sequential(\n self.__block(self.hidden,self.hidden),\n self.__block(self.hidden,self.hidden)\n )\n def __block(self, inchannels, outchannels):\n return nn.Sequential(\n nn.MaxPool1d(2, 2),\n nn.Conv1d(inchannels, outchannels, 5, padding=2),\n nn.LeakyReLU(0.1),\n nn.BatchNorm1d(outchannels),\n nn.Conv1d(outchannels, outchannels, 5, padding=2),\n nn.LeakyReLU(0.1)\n )\n def forward(self, x):\n x,_ = self.LSTM(x)\n x = x.permute(0,2,1)\n x = self.ConvNet(x)\n y = self.Hidden(x)\n y = torch.cat((x[...,-4:],y),dim = 2).view(x.shape[0],-1)\n y = self.MLP(y)\n return y","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.031523,"end_time":"2024-03-28T13:50:21.099539","exception":false,"start_time":"2024-03-28T13:50:21.068016","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"bde97d5a","cell_type":"code","source":"%%time\nlstmnet = LSTMNet()\nlstmnet.to(device)\nloss_fnc = nn.BCELoss()\nadam = optim.Adam(lstmnet.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\nloss_his, train_loss, valid_auc, train_auc = [], [], [], []\nlstmnet.train()\nfor epoch in range(opt.n_epochs):\n p_bar = tqdm(dataloader)\n for i, (x, y) in enumerate(p_bar):\n x, y = x.to(device), y.to(device)\n pred = lstmnet(x)\n loss = loss_fnc(pred.squeeze(dim=-1), y)\n adam.zero_grad()\n loss.backward()\n nn.utils.clip_grad_value_(lstmnet.parameters(), 2.)\n adam.step()\n train_loss.append(loss.item())\n p_bar.set_description('[Loss: {}]'.format(train_loss[-1]))\n if i % 50 == 0:\n loss_his.append(np.mean(train_loss))\n train_loss.clear()\n# if i % 1500 == 0:\n# y[y<0.1]=0\n# train_auc.append(metrics.roc_auc_score(y.detach().cpu().numpy(), \\\n# pred.detach().cpu().numpy()))\n# valid_auc.append(calc_valid_auc(lstmnet))\n print('[Epoch {}/{}] [Loss: {}]'.format(epoch+1, opt.n_epochs, loss_his[-1]))\n \ntorch.save(lstmnet.state_dict(), 'model.pt')","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":1605.829534,"end_time":"2024-03-28T14:17:06.943154","exception":false,"start_time":"2024-03-28T13:50:21.113620","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"426e6e5d","cell_type":"code","source":"plt.plot(range(len(loss_his)), loss_his, label='loss')\nplt.legend()\nplt.show()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.294086,"end_time":"2024-03-28T14:17:07.252748","exception":false,"start_time":"2024-03-28T14:17:06.958662","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"c1a38fb7","cell_type":"code","source":"lstmnet.eval()\ny_pred = []\nwith torch.no_grad():\n for x, _ in tqdm(testloader):\n x = x.to(device)\n pred = lstmnet(x).detach().cpu().numpy()\n y_pred.append(pred)\ny_pred = np.concatenate(y_pred, axis=0)","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":46.055279,"end_time":"2024-03-28T14:17:53.323486","exception":false,"start_time":"2024-03-28T14:17:07.268207","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"31855a90","cell_type":"code","source":"def plot_roc(y_true, y_pred):\n fig, axs = plt.subplots(3, 2, figsize=(15,15))\n for i, label in enumerate(labels):\n fpr, tpr, _ = metrics.roc_curve(y_true[:,i], y_pred[:,i])\n ax = axs[i//2, i%2]\n ax.plot(fpr, tpr,linewidth=3)\n ax.set_title(label+\" ROC\")\n ax.plot([0, 1], [0, 1], 'k--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.0])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n\n plt.show()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.027265,"end_time":"2024-03-28T14:17:53.367700","exception":false,"start_time":"2024-03-28T14:17:53.340435","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"6c17ffad","cell_type":"code","source":"valid_gt.shape","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.02624,"end_time":"2024-03-28T14:17:53.410437","exception":false,"start_time":"2024-03-28T14:17:53.384197","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"6fdc4cb9","cell_type":"code","source":"plot_roc(valid_gt, y_pred)\nprint('auc roc: ', metrics.roc_auc_score(valid_gt, y_pred))","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":2.486567,"end_time":"2024-03-28T14:17:55.914156","exception":false,"start_time":"2024-03-28T14:17:53.427589","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"2d065f63","cell_type":"markdown","source":"# Model Visualization","metadata":{"papermill":{"duration":0.017219,"end_time":"2024-03-28T14:17:55.950523","exception":false,"start_time":"2024-03-28T14:17:55.933304","status":"completed"},"tags":[]}},{"id":"9e796f58","cell_type":"code","source":"from tensorboardX import SummaryWriter\nlstmnet = LSTMNet()\nlstmnet.to(device)\nx.to(device)\n# 创建一个SummaryWriter实例\nwriter = SummaryWriter('./runs/model_visualization')\n\n# 添加模型的计算图\nwriter.add_graph(lstmnet, x)\n\n# 关闭SummaryWriter\nwriter.close()","metadata":{"execution":{"execution_failed":"2025-04-16T14:59:27.938Z"},"papermill":{"duration":0.412339,"end_time":"2024-03-28T14:17:56.379996","exception":false,"start_time":"2024-03-28T14:17:55.967657","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"4d0726fd-d63d-450f-9a3e-51a757079b9f","cell_type":"code","source":"%%time\n\n!pip install torch==1.13.0 --upgrade &> null\n!pip install ecgmentations &> null # library with 1d signal augmentations\n!pip install git+https://github.com/rostepifanov/nnspt@release_v0.0.2 &> null # library with 1d CNNs","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"cb50e696-a8b3-4293-b47c-0e30f4fb3667","cell_type":"code","source":"import os\nimport torch\nimport random\nimport numpy as np\n\nseed = 1996\n\nrandom.seed(seed)\n\nnp.random.seed(seed)\n\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed(seed)\ntorch.cuda.manual_seed_all(seed)\n\ntorch.backends.cudnn.determenistic = True\ntorch.use_deterministic_algorithms(True)\n\nos.environ['PYTHONHASHSEED'] = str(seed)\n\ndevice = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n\nprint(device)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"21ca843f-fed1-4e42-833e-c7186734df68","cell_type":"code","source":"%%time\n\nfrom pathlib import Path\nfrom zipfile import ZipFile\n\nlabels = [ 'HandStart', 'FirstDigitTouch', 'BothStartLoadPhase', 'LiftOff', 'Replace', 'BothReleased' ]\n\nzipdir = Path.cwd().parent / 'input' / 'grasp-and-lift-eeg-detection'\n\nfor zipfile in zipdir.glob('*.zip'):\n with ZipFile(zipfile, 'r') as zf:\n zf.extractall()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"243c9b26-1e9b-44d2-a1e4-fd8d195e0527","cell_type":"code","source":"%%time\n\nimport pandas as pd\nfrom tqdm import tqdm\n\nxs = []\nys = []\nys_ = []\n\ntraindir = Path('train')\n\nfor datapath in tqdm([*sorted(traindir.glob('*_data.csv'))]):\n eventpath = datapath.parent / ( datapath.stem[:-5] + '_events.csv' )\n\n x = pd.read_csv(datapath)\n y = pd.read_csv(eventpath)\n\n x = x.iloc[:,1:].values\n y_ = y.iloc[:,1:].values\n\n xs.append(x.astype(np.float32))\n ys.append(y_.astype(np.uint8))\n\nxs_train = xs[:-2]\nys_train = ys[:-2]\n\nxs_valid = xs[-2:]\nys_valid = ys[-2:]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"23069bee-6214-4b1d-99e4-87a544a35edd","cell_type":"code","source":"import ecgmentations as E\n\nfrom torch.utils.data import DataLoader, Dataset\n\nclass EEGDataset(Dataset):\n def __init__(self, x, y, augs=dict, train=False):\n self.x = x\n self.y = y\n self.augs = augs\n \n self.train = train\n\n def __getitem__(self, idx):\n eeg = self.x[idx]\n mask = self.y[idx]\n\n if self.train:\n length = mask.shape[0]\n\n size = 5000\n smask = (np.sum(mask[:-size], axis=1) > 0).astype(np.uint8)\n smask = smask * 5 + 1\n p = smask / smask.sum()\n\n jdx = np.random.choice(length-size, p=p)\n\n eeg = eeg[jdx:jdx+size]\n mask = mask[jdx:jdx+size]\n\n auged = self.augs(ecg=eeg, mask=mask)\n eeg, mask = auged['ecg'], auged['mask']\n\n return eeg.T, mask.T\n\n def __len__(self):\n return len(self.x)\n\naugs = E.Sequential([\n E.TimeCrop(length=5000, p=1.0),\n])\n\ndataset = EEGDataset(xs_train, ys_train, augs, True) \ntrain_dataloader = DataLoader(dataset, batch_size=25, num_workers=3, shuffle=True)\n\ndataset = EEGDataset(xs_train, ys_train) \ntrain_dataloader_ = DataLoader(dataset, batch_size=1, num_workers=3, shuffle=False)\n\ndataset = EEGDataset(xs_valid, ys_valid)\nvalid_dataloader = DataLoader(dataset, batch_size=1, num_workers=3, shuffle=False)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"fade9124-cdf4-4dc3-9e58-9cc58beb245e","cell_type":"code","source":"import copy\nimport torch.nn.functional as F\n\nfrom sklearn import metrics\nfrom nnspt.segmentation.unet import Unet\n\nmodel = Unet(in_channels=32, out_channels=6, encoder='timm-efficientnet-b1')\nmodel.to(device)\n\nnepochs = 1000\n\nopt = torch.optim.AdamW(model.parameters(), lr=0.00175)\nshed = torch.optim.lr_scheduler.CosineAnnealingLR(opt, nepochs*len(train_dataloader))\n\nloss_his, train_loss = [], []\n\nbest_score = 0.\nbest_state_dict = copy.deepcopy(model.state_dict())\n\nfor epoch in range(nepochs):\n model.train()\n\n for i, (eeg_batch, mask_batch) in enumerate(train_dataloader):\n eeg_batch, mask_batch = eeg_batch.to(device), mask_batch.to(device)\n\n logits = model(eeg_batch)\n loss = F.binary_cross_entropy_with_logits(logits, mask_batch.float())\n loss.backward()\n\n opt.step()\n shed.step()\n opt.zero_grad()\n\n train_loss.append(loss.item())\n\n if (epoch + 1) % 25 == 0:\n loss_his.append(np.mean(train_loss))\n train_loss.clear()\n\n print('[Epoch {}/{}] [Loss: {}]'.format(epoch+1, nepochs, loss_his[-1]))\n \n model.eval()\n\n y_pred = []\n\n size = 10000\n\n for eeg_batch, _ in tqdm(valid_dataloader):\n for idx in range((eeg_batch.shape[-1] + size - 1) // size):\n with torch.no_grad():\n eeg_batch_ = eeg_batch[:, :, idx*size: (idx+1)*size].to(device)\n\n logits = model(eeg_batch_)\n probs = torch.sigmoid(logits).cpu().numpy()[0]\n\n y_pred.append(probs)\n\n y_pred = np.concatenate(y_pred, axis=1).T\n y_true = np.concatenate(ys_valid, axis=0)\n\n score = metrics.roc_auc_score(y_true, y_pred)\n\n print('[Epoch {}/{}] [Score: {}]'.format(epoch+1, nepochs, score))\n\n if score > best_score:\n best_score = score\n best_state_dict = copy.deepcopy(model.state_dict())\n\nmodel.load_state_dict(best_state_dict)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"e9f34d5e-adf9-48bc-b939-f118c975c41f","cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_roc(y_true, y_pred):\n fig, axs = plt.subplots(3, 2, figsize=(15, 13))\n\n for i, label in enumerate(labels):\n fpr, tpr, _ = metrics.roc_curve(y_true[i], y_pred[i])\n ax = axs[i//2, i%2]\n ax.plot(fpr, tpr)\n ax.set_title(label + ' ROC')\n ax.plot([0, 1], [0, 1], 'k--')\n\n plt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"3051c6ab-0f15-4977-8dee-96b73cbf1085","cell_type":"code","source":"model.eval()\n\ny_pred = []\n\nsize = 10000\n\nfor eeg_batch, _ in tqdm(train_dataloader_):\n for idx in range((eeg_batch.shape[-1] + size - 1) // size):\n with torch.no_grad():\n eeg_batch_ = eeg_batch[:, :, idx*size: (idx+1)*size].to(device)\n\n logits = model(eeg_batch_)\n probs = torch.sigmoid(logits).cpu().numpy()[0]\n\n y_pred.append(probs)\n\ny_pred = np.concatenate(y_pred, axis=1).T\ny_true = np.concatenate(ys_train, axis=0)\n\nplot_roc(y_true.T, y_pred.T)\n\nprint('roc auc: ', metrics.roc_auc_score(y_true, y_pred))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"771ea9e7-6e91-4e3a-ba27-6d863e76ac69","cell_type":"code","source":"model.eval()\n\ny_pred = []\n\nsize = 10000\n\nfor eeg_batch, _ in tqdm(valid_dataloader):\n for idx in range((eeg_batch.shape[-1] + size - 1) // size):\n with torch.no_grad():\n eeg_batch_ = eeg_batch[:, :, idx*size: (idx+1)*size].to(device)\n\n logits = model(eeg_batch_)\n probs = torch.sigmoid(logits).cpu().numpy()[0]\n\n y_pred.append(probs)\n\ny_pred = np.concatenate(y_pred, axis=1).T\ny_true = np.concatenate(ys_valid, axis=0)\n\nplot_roc(y_true.T, y_pred.T)\n\nprint('roc auc: ', metrics.roc_auc_score(y_true, y_pred))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"89786555-6bc6-40f2-8d0a-ffc672005602","cell_type":"code","source":"import pandas as pd\nfrom tqdm import tqdm\n\nxs_test = []\nlengths = {}\n\ntestdir = Path('test')\n\nFNAME = 'subj{}_series{}_{}.csv'\n\nfor subj in range(1, 13):\n for series in [9, 10]:\n datapath = testdir / FNAME.format(subj, series, 'data')\n\n x = pd.read_csv(datapath)\n x = x.iloc[:,1:].values\n\n xs_test.append(x.astype(np.float32))\n lengths['{}_{}'.format(subj, series)] = xs_test[-1].shape[0]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"678ab28d-fa62-42d8-b9e4-c80569b3de25","cell_type":"code","source":"class EEGDatasetTest(Dataset):\n def __init__(self, x):\n self.x = x\n\n def __getitem__(self, idx):\n eeg = self.x[idx]\n\n return eeg.T\n\n def __len__(self):\n return len(self.x)\n\ndataset = EEGDatasetTest(xs_test)\ntest_dataloader = DataLoader(dataset, batch_size=1, num_workers=3, shuffle=False)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"188242f0-9161-4a71-b17d-c566ec4ab462","cell_type":"code","source":"model.eval()\n\ny_pred = []\n\nsize = 10000\n\nfor eeg_batch in tqdm(test_dataloader):\n for idx in range((eeg_batch.shape[-1] + size - 1) // size):\n with torch.no_grad():\n eeg_batch_ = eeg_batch[:, :, idx*size: (idx+1)*size].to(device)\n\n logits = model(eeg_batch_)\n probs = torch.sigmoid(logits).cpu().numpy()[0]\n\n y_pred.append(probs)\n\ny_pred = np.concatenate(y_pred, axis=1).T","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"5851b54f-3791-439b-8d98-aa1f44e9ea99","cell_type":"code","source":"submission = pd.DataFrame(y_pred, index=['subj{}_series{}_{}'.format(sbj, i, j) for sbj in range(1, 13) for i in [9, 10] for j in range(lengths['{}_{}'.format(sbj, i)])], columns=labels)\nsubmission.to_csv('Submission.csv', index_label='id', float_format='%.3f')\n\nsubmission.tail()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"06d2ecbd-d359-48f2-b8f7-d8add2795835","cell_type":"code","source":"!head Submission.csv","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"fd038563-254a-4547-8e51-43774a2473b2","cell_type":"code","source":"!head sample_submission.csv","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}