{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":41880,"databundleVersionId":5677426,"sourceType":"competition"}],"dockerImageVersionId":30408,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport numpy as np \nimport pandas as pd\nimport os\nfrom tqdm import tqdm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torch.nn import CrossEntropyLoss\nimport torch.nn.functional as F\n\nMAIN_DIR = \"/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/\"\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nFEATURES = [\"AccV\", \"AccML\", \"AccAP\"]\nTARGETS = [\"StartHesitation\", \"Turn\", \"Walking\"]\n\nN_EPOCHS = 1","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2025-08-05T17:21:55.707408Z","iopub.execute_input":"2025-08-05T17:21:55.708106Z","iopub.status.idle":"2025-08-05T17:21:58.875989Z","shell.execute_reply.started":"2025-08-05T17:21:55.708079Z","shell.execute_reply":"2025-08-05T17:21:58.875126Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# 1. About the Data / Loading","metadata":{}},{"cell_type":"markdown","source":"CONTEXT : *People with FOG are filmed while performing certain tasks that are likely to increase its occurrence. Experts then review the video to score each frame, indicating when FOG occurred. While scoring in this manner is relatively reliable and sensitive, it is extremely time-consuming and requires specific expertise. Another method involves augmenting FOG-provoking testing with wearable devices. With more sensors, the detection of FOG becomes easier, however, compliance and usability may be reduced. Therefore, a combination of these two methods may be the best approach.*","metadata":{}},{"cell_type":"markdown","source":"The data series include three datasets, collected under distinct circumstances:\n\n- **tDCS FOG (tdcsfog)** dataset: data series collected in the lab, as subjects completed a FOG-provoking protocol.\n- **DeFOG (defog)** dataset: data series collected in the subject's home, as subjects completed a FOG-provoking protocol\n- **Daily Living (daily)** dataset: comprising one week of continuous 24/7 recordings from sixty-five subjects. Forty-five subjects exhibit FOG symptoms and also have series in the defog dataset, while the other twenty subjects do not exhibit FOG symptoms and do not have series elsewhere in the data.","metadata":{}},{"cell_type":"markdown","source":"**tDCS FOG** & **DeFOG** are annotated => Supervised Learning\n**Daily Living** is not annotated => Unsupervised/Semi-supervised Learning","metadata":{}},{"cell_type":"code","source":"# Reduce Memory Usage\n# reference : https://www.kaggle.com/code/arjanso/reducing-dataframe-memory-size-by-65 @ARJANGROEN\n\ndef reduce_memory_usage(df):\n \n start_mem = df.memory_usage().sum() / 1024**2\n print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n \n for col in df.columns:\n col_type = df[col].dtype.name\n if ((col_type != 'datetime64[ns]') & (col_type != 'category')):\n if (col_type != 'object'):\n c_min = df[col].min()\n c_max = df[col].max()\n\n if str(col_type)[:3] == 'int':\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n\n else:\n if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n df[col] = df[col].astype(np.float16)\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n else:\n pass\n else:\n df[col] = df[col].astype('category')\n mem_usg = df.memory_usage().sum() / 1024**2 \n print(\"Memory usage became: \",mem_usg,\" MB\")\n \n return df","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2025-08-05T17:26:21.366138Z","iopub.execute_input":"2025-08-05T17:26:21.367044Z","iopub.status.idle":"2025-08-05T17:26:21.377519Z","shell.execute_reply.started":"2025-08-05T17:26:21.367007Z","shell.execute_reply":"2025-08-05T17:26:21.376554Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"def read_data(\n dataset,\n datatype,\n subject_id = None):\n \n metadata = pd.read_csv(MAIN_DIR + dataset + \"_metadata.csv\")\n \n DATA_ROOT = MAIN_DIR + datatype + \"/\" + dataset\n \n if subject_id is not None:\n files = [file for file in files if subject_id in file]\n \n df_res = pd.DataFrame()\n for root, dirs, files in os.walk(DATA_ROOT):\n for name in tqdm(files):\n f = os.path.join(root, name)\n query_datatype = pd.read_csv(f)\n query_datatype[\"file\"] = name.replace(\".csv\", \"\")\n df_res = pd.concat([df_res,query_datatype])\n \n df_res = metadata.merge(df_res,\n how = 'inner',\n left_on = 'Id',\n right_on = 'file')\n df_res = df_res.drop([\"file\"], axis = 1)\n\n df_res = reduce_memory_usage(df_res)\n \n return df_res\n ","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:26:25.980638Z","iopub.execute_input":"2025-08-05T17:26:25.981361Z","iopub.status.idle":"2025-08-05T17:26:25.988288Z","shell.execute_reply.started":"2025-08-05T17:26:25.981324Z","shell.execute_reply":"2025-08-05T17:26:25.987433Z"},"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"#yehudit\ndef read_data_(\n dataset=\"defog\",\n datatype=\"train\",\n subject_id = None):\n \n \n metadata = pd.read_csv(MAIN_DIR + dataset + \"_metadata.csv\")\n \n \n \n DATA_ROOT = MAIN_DIR + datatype + \"/\" + dataset\n \n if subject_id is not None:\n files = [file for file in files if subject_id in file]\n \n df_res = pd.DataFrame()\n for root, dirs, files in os.walk(DATA_ROOT):\n \n for name in tqdm(files):\n f = os.path.join(root, name)\n query_datatype = pd.read_csv(f)\n if dataset==\"defog\" and datatype==\"train\":\n new_query_datatype= query_datatype.loc[query_datatype['Valid'] == True]\n else:\n new_query_datatype = query_datatype \n new_query_datatype[\"file\"] = name.replace(\".csv\", \"\")\n df_res = pd.concat([df_res,new_query_datatype])\n \n if dataset == \"tdcsfog\" :\n metadata = metadata.drop('Test', axis=1)\n df_res = metadata.merge(df_res,\n how = 'inner',\n left_on = 'Id',\n right_on = 'file')\n \n df_res = df_res.drop([\"file\"], axis = 1)\n \n if (dataset == \"defog\") and (datatype==\"train\") : \n df_res = df_res.drop('Valid', axis=1)\n df_res = df_res.drop('Task', axis=1)\n \n \n df_res = reduce_memory_usage(df_res)\n \n return df_res\n","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:26:29.589669Z","iopub.execute_input":"2025-08-05T17:26:29.59002Z","iopub.status.idle":"2025-08-05T17:26:29.599338Z","shell.execute_reply.started":"2025-08-05T17:26:29.589991Z","shell.execute_reply":"2025-08-05T17:26:29.598375Z"},"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# #yehudit\n# def read_data_test_tdcs(\n# dataset=\"defog\",\n# datatype=\"train\",\n# subject_id = None):\n \n# metadata = pd.read_csv(MAIN_DIR + dataset + \"_metadata.csv\")\n \n \n \n# DATA_ROOT = MAIN_DIR + datatype + \"/\" + dataset\n \n# if subject_id is not None:\n# files = [file for file in files if subject_id in file]\n \n# df_res = pd.DataFrame()\n# for root, dirs, files in os.walk(DATA_ROOT):\n \n# for name in tqdm(files):\n# if name != \"0a89f859b5.csv\":\n# f = os.path.join(root, name)\n# query_datatype = pd.read_csv(f)\n# if dataset==\"defog\" and datatype==\"train\":\n# new_query_datatype= query_datatype.loc[query_datatype['Valid'] == True]\n# else:\n# new_query_datatype = query_datatype \n# new_query_datatype[\"file\"] = name.replace(\".csv\", \"\")\n# df_res = pd.concat([df_res,new_query_datatype])\n \n# if dataset == \"tdcsfog\" :\n# metadata = metadata.drop('Test', axis=1)\n# df_res = metadata.merge(df_res,\n# how = 'inner',\n# left_on = 'Id',\n# right_on = 'file')\n \n# df_res = df_res.drop([\"file\"], axis = 1)\n \n# if (dataset == \"defog\") and (datatype==\"train\") : \n# df_res = df_res.drop('Valid', axis=1)\n# df_res = df_res.drop('Task', axis=1)\n \n \n# df_res = reduce_memory_usage(df_res)\n \n# return df_res\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-05T17:26:34.534262Z","iopub.execute_input":"2025-08-05T17:26:34.534635Z","iopub.status.idle":"2025-08-05T17:26:34.540447Z","shell.execute_reply.started":"2025-08-05T17:26:34.534603Z","shell.execute_reply":"2025-08-05T17:26:34.539395Z"}},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"# ****create combined train dataset****","metadata":{}},{"cell_type":"code","source":"df_defog = read_data_( dataset=\"defog\",datatype=\"train\")\ndf_tdcsfog = read_data_( dataset=\"tdcsfog\",datatype=\"train\")\ncombined_ds_train = pd.concat([df_defog,df_tdcsfog])","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:26:38.864571Z","iopub.execute_input":"2025-08-05T17:26:38.865297Z","iopub.status.idle":"2025-08-05T17:29:14.804609Z","shell.execute_reply.started":"2025-08-05T17:26:38.865259Z","shell.execute_reply":"2025-08-05T17:29:14.803543Z"},"trusted":true},"outputs":[{"name":"stderr","text":" 0%| | 0/91 [00:00 1: \n break\n count += 1","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:29:40.045863Z","iopub.execute_input":"2025-08-05T17:29:40.046204Z","iopub.status.idle":"2025-08-05T17:29:40.145458Z","shell.execute_reply.started":"2025-08-05T17:29:40.046174Z","shell.execute_reply":"2025-08-05T17:29:40.144504Z"},"trusted":true},"outputs":[{"name":"stdout","text":"FEATURES EXAMPLES\ntorch.Size([8, 2, 3])\ntensor([[[-9.7021e-01, 6.1615e-02, -2.6562e-01],\n [-9.8438e-01, 4.4495e-02, -2.6562e-01]],\n\n [[-9.7021e-01, 6.1615e-02, -2.6562e-01],\n [-9.8438e-01, 4.4495e-02, -2.6562e-01]],\n\n [[-9.7021e-01, 6.1615e-02, -2.6562e-01],\n [-9.8438e-01, 4.4495e-02, -2.6562e-01]],\n\n [[-9.8438e-01, 4.4495e-02, -2.6562e-01],\n [-9.8438e-01, 2.9022e-02, -2.6562e-01]],\n\n [[-9.8438e-01, 2.9022e-02, -2.6562e-01],\n [-9.8438e-01, 1.5625e-02, -2.6562e-01]],\n\n [[-9.8438e-01, 1.5625e-02, -2.6562e-01],\n [-9.8486e-01, 1.5327e-02, -2.6562e-01]],\n\n [[-9.8486e-01, 1.5327e-02, -2.6562e-01],\n [-1.0000e+00, 1.5140e-04, -2.6562e-01]],\n\n [[-1.0000e+00, 1.5140e-04, -2.6562e-01],\n [-1.0000e+00, 1.5625e-02, -2.6562e-01]]])\nTARGET EXAMPLES\ntensor([0., 0., 0., 0., 0., 0., 0., 0.])\n\n\nFEATURES EXAMPLES\ntorch.Size([8, 2, 3])\ntensor([[[-1.0000e+00, 1.5625e-02, -2.6562e-01],\n [-1.0000e+00, 1.5625e-02, -2.6562e-01]],\n\n [[-1.0000e+00, 1.5625e-02, -2.6562e-01],\n [-1.0000e+00, 1.5625e-02, -2.6562e-01]],\n\n [[-1.0000e+00, 1.5625e-02, -2.6562e-01],\n [-1.0000e+00, 1.5625e-02, -2.6562e-01]],\n\n [[-1.0000e+00, 1.5625e-02, -2.6562e-01],\n [-1.0000e+00, 1.6153e-04, -2.6562e-01]],\n\n [[-1.0000e+00, 1.6153e-04, -2.6562e-01],\n [-1.0000e+00, 0.0000e+00, -2.6562e-01]],\n\n [[-1.0000e+00, 0.0000e+00, -2.6562e-01],\n [-1.0000e+00, 0.0000e+00, -2.6562e-01]],\n\n [[-1.0000e+00, 0.0000e+00, -2.6562e-01],\n [-1.0000e+00, 0.0000e+00, -2.6562e-01]],\n\n [[-1.0000e+00, 0.0000e+00, -2.6562e-01],\n [-1.0000e+00, 0.0000e+00, -2.6562e-01]]])\nTARGET EXAMPLES\ntensor([0., 0., 0., 0., 0., 0., 0., 0.])\n\n\nFEATURES EXAMPLES\ntorch.Size([8, 2, 3])\ntensor([[[-1.0000, 0.0000, -0.2656],\n [-1.0000, 0.0000, -0.2656]],\n\n [[-1.0000, 0.0000, -0.2656],\n [-1.0000, 0.0000, -0.2656]],\n\n [[-1.0000, 0.0000, -0.2656],\n [-1.0000, 0.0000, -0.2656]],\n\n [[-1.0000, 0.0000, -0.2656],\n [-1.0000, 0.0000, -0.2656]],\n\n [[-1.0000, 0.0000, -0.2656],\n [-0.9873, 0.0000, -0.2656]],\n\n [[-0.9873, 0.0000, -0.2656],\n [-0.9966, 0.0000, -0.2656]],\n\n [[-0.9966, 0.0000, -0.2656],\n [-0.9883, 0.0000, -0.2656]],\n\n [[-0.9883, 0.0000, -0.2656],\n [-0.9956, 0.0000, -0.2656]]])\nTARGET EXAMPLES\ntensor([0., 0., 0., 0., 0., 0., 0., 0.])\n\n\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"class LSTMNet(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers, num_classes):\n super().__init__()\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n self.fc1 = nn.Linear(hidden_size, num_classes)\n\n def forward(self, x):\n\n hidden_state = torch.zeros((self.num_layers, x.size(0), self.hidden_size), dtype=torch.float32)\n cell_state = torch.zeros((self.num_layers, x.size(0), self.hidden_size), dtype=torch.float32)\n\n out, _ = self.lstm(x, (hidden_state, cell_state))\n out = out[:, -1,:]\n out = self.fc1(out)\n return out\n","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:29:45.82927Z","iopub.execute_input":"2025-08-05T17:29:45.829919Z","iopub.status.idle":"2025-08-05T17:29:45.837201Z","shell.execute_reply.started":"2025-08-05T17:29:45.829888Z","shell.execute_reply":"2025-08-05T17:29:45.83627Z"},"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"markdown","source":"# 4. Training","metadata":{}},{"cell_type":"code","source":"def train(model, dataloader, loss_fn, optimizer):\n model.train()\n total_loss = 0\n for epoch in range(N_EPOCHS):\n mean_precision = []\n for (features, targets) in tqdm(dataloader):\n optimizer.zero_grad()\n preds = model(features)\n loss = loss_fn(preds, targets.long())\n mean_precision.append(loss.item())\n loss.backward()\n optimizer.step()\n \n print(\"Average Precision : \", np.mean(mean_precision))\n \n return model\n\ndef predict(model, dataloader): \n model.eval()\n predictions = np.empty(len(dataset_test))\n count = 0\n for features in tqdm(dataloader):\n preds = model(features)\n preds = torch.argmax(preds, dim = 1)\n preds = preds.numpy()\n predictions[count : count + len(preds)] = preds\n count += len(preds)\n \n return predictions","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:29:49.573612Z","iopub.execute_input":"2025-08-05T17:29:49.574376Z","iopub.status.idle":"2025-08-05T17:29:49.582325Z","shell.execute_reply.started":"2025-08-05T17:29:49.574342Z","shell.execute_reply":"2025-08-05T17:29:49.581155Z"},"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"INPUT_SIZE = len(FEATURES)\nHIDDEN_SIZE = 10\nNUM_LAYERS = 1\nNUM_CLASSES = 4\nPARAMS = {\n \"input_size\" : INPUT_SIZE,\n \"hidden_size\" : HIDDEN_SIZE,\n \"num_layers\" : NUM_LAYERS,\n \"num_classes\" : NUM_CLASSES\n}\nmodel = LSTMNet(**PARAMS)\n\nloss_fn = CrossEntropyLoss()\n\noptimizer = optim.Adam(model.parameters(), lr=0.001)","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:29:51.70908Z","iopub.execute_input":"2025-08-05T17:29:51.709959Z","iopub.status.idle":"2025-08-05T17:29:51.718704Z","shell.execute_reply.started":"2025-08-05T17:29:51.709921Z","shell.execute_reply":"2025-08-05T17:29:51.717736Z"},"trusted":true},"outputs":[],"execution_count":14},{"cell_type":"code","source":"model = train(\n model, \n dataloader_train,\n loss_fn,\n optimizer\n)","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:29:55.046956Z","iopub.execute_input":"2025-08-05T17:29:55.047639Z","iopub.status.idle":"2025-08-05T17:29:55.801899Z","shell.execute_reply.started":"2025-08-05T17:29:55.047604Z","shell.execute_reply":"2025-08-05T17:29:55.800929Z"},"trusted":true},"outputs":[{"name":"stderr","text":"100%|██████████| 125/125 [00:00<00:00, 167.65it/s]","output_type":"stream"},{"name":"stdout","text":"Average Precision : 1.1088211631774902\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"preds_tdcsfog = predict(model, dataloader_test)","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:01.166015Z","iopub.execute_input":"2025-08-05T17:30:01.166949Z","iopub.status.idle":"2025-08-05T17:30:03.297849Z","shell.execute_reply.started":"2025-08-05T17:30:01.166912Z","shell.execute_reply":"2025-08-05T17:30:03.296779Z"},"trusted":true},"outputs":[{"name":"stderr","text":"100%|██████████| 5/5 [00:02<00:00, 2.35it/s]\n","output_type":"stream"}],"execution_count":16},{"cell_type":"markdown","source":"# 3. First Submission","metadata":{"execution":{"iopub.status.busy":"2023-03-28T09:27:08.999527Z","iopub.execute_input":"2023-03-28T09:27:09.000095Z","iopub.status.idle":"2023-03-28T09:27:09.013584Z","shell.execute_reply.started":"2023-03-28T09:27:09.000046Z","shell.execute_reply":"2023-03-28T09:27:09.011646Z"}}},{"cell_type":"code","source":"# dataset_train = read_data(dataset = \"tdcsfog\", datatype = \"test\")\n# dataloader_train = DataLoader(dataset_train, batch_size = 8, shuffle = False)\n# dataset_test = read_data(dataset = \"defog\", datatype = \"test\")\n# dataloader_test = DataLoader(dataset_test, batch_size = 1000, shuffle = False)\n\ntest_tdcsfog = read_data(dataset = \"tdcsfog\", datatype = \"test\")\ntest_defog = read_data(dataset = \"defog\", datatype = \"test\")\n\nlen_test_tdcsfog = len(test_tdcsfog)\nlen_test_defog = len(test_defog)\nprint(len(test_tdcsfog))\nprint(len(test_defog))","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:09.64835Z","iopub.execute_input":"2025-08-05T17:30:09.648718Z","iopub.status.idle":"2025-08-05T17:30:10.138396Z","shell.execute_reply.started":"2025-08-05T17:30:09.648685Z","shell.execute_reply":"2025-08-05T17:30:10.137388Z"},"trusted":true},"outputs":[{"name":"stderr","text":"100%|██████████| 1/1 [00:00<00:00, 145.92it/s]\n","output_type":"stream"},{"name":"stdout","text":"Memory usage of dataframe is 0.36 MB\nMemory usage became: 0.09409904479980469 MB\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 1/1 [00:00<00:00, 3.38it/s]","output_type":"stream"},{"name":"stdout","text":"Memory usage of dataframe is 19.34 MB\nMemory usage became: 5.910381317138672 MB\n4682\n281688\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"test_tdcsfog[\"y_pred\"] = preds_tdcsfog\ntest_defog[\"y_pred\"] = np.zeros(len_test_defog)","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:13.591287Z","iopub.execute_input":"2025-08-05T17:30:13.592128Z","iopub.status.idle":"2025-08-05T17:30:13.597829Z","shell.execute_reply.started":"2025-08-05T17:30:13.592093Z","shell.execute_reply":"2025-08-05T17:30:13.596847Z"},"trusted":true},"outputs":[],"execution_count":18},{"cell_type":"code","source":"sub_fmt = pd.read_csv(\"/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:15.509604Z","iopub.execute_input":"2025-08-05T17:30:15.510189Z","iopub.status.idle":"2025-08-05T17:30:15.695699Z","shell.execute_reply.started":"2025-08-05T17:30:15.510156Z","shell.execute_reply":"2025-08-05T17:30:15.694649Z"},"trusted":true},"outputs":[],"execution_count":19},{"cell_type":"code","source":"sub = pd.DataFrame()\nfor data in [test_tdcsfog, test_defog]:\n temp = data.copy()\n temp[\"Id\"] = temp.apply(lambda x : str(x.Id) + \"_\" + str(x.Time), axis = 1)\n temp['StartHesitation'] = np.where(temp['y_pred']==1, 1, 0)\n temp['Turn'] = np.where(temp['y_pred']==2, 1, 0)\n temp['Walking'] = np.where(temp['y_pred']==3, 1, 0)\n temp = temp[[\"Id\"] + TARGETS]\n sub = pd.concat([sub, temp])","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:18.601077Z","iopub.execute_input":"2025-08-05T17:30:18.601912Z","iopub.status.idle":"2025-08-05T17:30:23.172047Z","shell.execute_reply.started":"2025-08-05T17:30:18.601879Z","shell.execute_reply":"2025-08-05T17:30:23.170914Z"},"trusted":true},"outputs":[],"execution_count":20},{"cell_type":"code","source":"test_tdcsfog.head()","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:26.259183Z","iopub.execute_input":"2025-08-05T17:30:26.259961Z","iopub.status.idle":"2025-08-05T17:30:26.277347Z","shell.execute_reply.started":"2025-08-05T17:30:26.25993Z","shell.execute_reply":"2025-08-05T17:30:26.276363Z"},"trusted":true},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" Id Subject Visit Test Medication Time AccV AccML \\\n0 003f117e14 4dc2f8 3 2 on 0 -9.531250 0.566406 \n1 003f117e14 4dc2f8 3 2 on 1 -9.539062 0.563965 \n2 003f117e14 4dc2f8 3 2 on 2 -9.531250 0.561523 \n3 003f117e14 4dc2f8 3 2 on 3 -9.531250 0.564453 \n4 003f117e14 4dc2f8 3 2 on 4 -9.539062 0.562012 \n\n AccAP y_pred \n0 -1.413086 0.0 \n1 -1.440430 0.0 \n2 -1.429688 0.0 \n3 -1.415039 0.0 \n4 -1.429688 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
IdSubjectVisitTestMedicationTimeAccVAccMLAccAPy_pred
0003f117e144dc2f832on0-9.5312500.566406-1.4130860.0
1003f117e144dc2f832on1-9.5390620.563965-1.4404300.0
2003f117e144dc2f832on2-9.5312500.561523-1.4296880.0
3003f117e144dc2f832on3-9.5312500.564453-1.4150390.0
4003f117e144dc2f832on4-9.5390620.562012-1.4296880.0
\n
"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"sub.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-05T17:30:33.860697Z","iopub.execute_input":"2025-08-05T17:30:33.861084Z","iopub.status.idle":"2025-08-05T17:30:33.893507Z","shell.execute_reply.started":"2025-08-05T17:30:33.861051Z","shell.execute_reply":"2025-08-05T17:30:33.89231Z"}},"outputs":[{"name":"stdout","text":"\nInt64Index: 286370 entries, 0 to 281687\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Id 286370 non-null object\n 1 StartHesitation 286370 non-null int64 \n 2 Turn 286370 non-null int64 \n 3 Walking 286370 non-null int64 \ndtypes: int64(3), object(1)\nmemory usage: 10.9+ MB\n","output_type":"stream"}],"execution_count":22},{"cell_type":"code","source":"sub_fmt.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-05T17:30:40.277799Z","iopub.execute_input":"2025-08-05T17:30:40.278142Z","iopub.status.idle":"2025-08-05T17:30:40.287818Z","shell.execute_reply.started":"2025-08-05T17:30:40.278113Z","shell.execute_reply":"2025-08-05T17:30:40.286873Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":" Id StartHesitation Turn Walking\n0 003f117e14_0 0 0 0\n1 003f117e14_1 0 0 0\n2 003f117e14_2 0 0 0\n3 003f117e14_3 0 0 0\n4 003f117e14_4 0 0 0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
IdStartHesitationTurnWalking
0003f117e14_0000
1003f117e14_1000
2003f117e14_2000
3003f117e14_3000
4003f117e14_4000
\n
"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"len(sub_fmt)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-05T17:30:43.824408Z","iopub.execute_input":"2025-08-05T17:30:43.824769Z","iopub.status.idle":"2025-08-05T17:30:43.831442Z","shell.execute_reply.started":"2025-08-05T17:30:43.824739Z","shell.execute_reply":"2025-08-05T17:30:43.829854Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"286370"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"sub.to_csv(\"/kaggle/working/submission.csv\", index = False)","metadata":{"execution":{"iopub.status.busy":"2025-08-05T17:30:46.452932Z","iopub.execute_input":"2025-08-05T17:30:46.453287Z","iopub.status.idle":"2025-08-05T17:30:46.871549Z","shell.execute_reply.started":"2025-08-05T17:30:46.453256Z","shell.execute_reply":"2025-08-05T17:30:46.870195Z"},"trusted":true},"outputs":[],"execution_count":25},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}