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The layout from [Moths WaveNet Starter](https://www.kaggle.com/code/alejopaullier/hms-wavenet-pytorch-train) and certain ideas from [Chris Deottes WaveNet Starter](https://www.kaggle.com/code/cdeotte/wavenet-starter-lb-0-52) were also implemented in this notebook. This notebook is used for inference. The training notebook containing more information about the model can be found [here](https://www.kaggle.com/code/jasperpieterse/hms-1d-eegnet-training).\n\n### <b><span style='color: #196f3d '>Table of Contents</span></b> <a class='anchor' id='top'></a>\n<div style=\" background-color: #ecf0f1 ; padding: 13px 13px; border-radius: 8px; color: white\">\n<li><a href=\"#import_libraries\">Import Libraries</a></li>\n<li><a href=\"#configuration\">Configuration</a></li>\n<li><a href=\"#utils\">Utility Functions</a></li>\n<li><a href=\"#load_meta_data\">Load Test Metadata</a></li>    \n<li><a href=\"#load_data_eeg\">Load Raw Data</a></li>\n<li><a href=\"#preprocess\">Preprocessing Functions</a></li>\n<li><a href=\"#dataset\">Dataset</a></li>\n<li><a href=\"#dataloader\">DataLoader</a></li>\n<li><a href=\"#model\">Model</a></li>\n<li><a href=\"#optimizer\">Optimizer & Scheduler</a></li>\n<li><a href=\"#loss\">Loss Function</a></li>\n<li><a href=\"#lightning\">Pytorch Lightning Model</a></li>\n<li><a href=\"#prediction\">Prediction Function</a></li>\n<li><a href=\"#inference\">Inference</a></li>\n<li><a href=\"#submission\">Submission</a></li>\n    \n</div>","metadata":{}},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Import Libraries</b><a class='anchor' id='import_libraries'></a> [↑](#top) \n\n***\n\nImport all the required libraries for this notebook.","metadata":{}},{"cell_type":"code","source":"import sys\nimport os\nimport gc\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport albumentations as A\nimport librosa\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nimport torchvision.io \nimport torch.multiprocessing as mp\nimport torch.optim.lr_scheduler as scheduler \nimport torchmetrics\nimport pytorch_lightning as pl\n\nsys.path.append('/kaggle/input/kaggle-kl-div')\nfrom kaggle_kl_div import score\nfrom glob import glob\nfrom tqdm import tqdm\nfrom scipy.signal import butter, lfilter\nfrom torch.utils.data import Dataset, DataLoader","metadata":{"papermill":{"duration":0.015982,"end_time":"2023-03-11T22:10:10.234218","exception":false,"start_time":"2023-03-11T22:10:10.218236","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-14T13:40:00.112152Z","iopub.execute_input":"2024-03-14T13:40:00.113119Z","iopub.status.idle":"2024-03-14T13:40:00.120504Z","shell.execute_reply.started":"2024-03-14T13:40:00.113080Z","shell.execute_reply":"2024-03-14T13:40:00.119395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Configuration</b><a class='anchor' id='configuration'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class Config:\n    #Data\n    CUSTOM_FEATURES =['Fp1','T3','C3','O1','Fp2','C4','T4','O2'] # List containing what EEG features to use to create data. Leave empty to use all2\n    NUM_CLASSES = 6 \n    FEATURE_ENGINEERING = True\n    \n    #Training\n    NUM_WORKERS = 2         \n    PRECISION   = 32        #Automatic mixed precision is automatically enabled by PyL if available. \"16-mixed\"\n    BATCH_SIZE  = 32\n    EPOCHS      = 30 \n    PATIENCE    = 20   \n    P_DROPOUT   = 0.0\n    SEED        = 2024\n    LR          = 8e-3\n    FOLDS       = 5 \n    \n    #Model parameters\n    KERNELS = [3,5,7,9]\n    FIXED_KERNEL_SIZE = 5 #Size of the kernel used in the fixed blocks\n    NUM_FEATURE_MAPS = 24 #Number of feature maps (channels) outputed for each parallel convolution (num_channels = num_kernels * num_feature_maps)\n    RESNET_BLOCKS = 8     #Amount of ResNet blocks to be used\n    if CUSTOM_FEATURES:\n        NUM_CHANNELS = len(CUSTOM_FEATURES)\n    else:  \n        NUM_CHANNELS = 20     # Amount of channels used for CNN\n\n\n    #Augmentation\n    PRETRAINED = False    #Whether to use a pretrained EEG net        \n    WEIGHT_DECAY = 0.01\n    USE_MIXUP = False   # Whether to use mixup augmentation\n    MIXUP_ALPHA = 0.1   # Alpha parameter for mixup\n\n    \nclass paths:\n    OUTPUT_DIR   = \"/kaggle/working/\"\n    TRAIN_CSV    = \"/kaggle/input/hms-harmful-brain-activity-classification/train.csv\" \n    TEST_CSV     = \"/kaggle/input/hms-harmful-brain-activity-classification/test.csv\"\n    RAW_EEGS     = \"/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/\"\n    WEIGHTS      = \"/kaggle/input/prayer\" \n\npl.seed_everything(Config.SEED, workers=True)\n\nwarnings.filterwarnings('ignore')\nprint(\"Using float 32 for intermediate calculations\")\ntorch.set_float32_matmul_precision('high')","metadata":{"papermill":{"duration":0.082277,"end_time":"2023-03-11T22:10:20.964863","exception":false,"start_time":"2023-03-11T22:10:20.882586","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-14T13:40:00.122873Z","iopub.execute_input":"2024-03-14T13:40:00.123468Z","iopub.status.idle":"2024-03-14T13:40:00.140169Z","shell.execute_reply.started":"2024-03-14T13:40:00.123434Z","shell.execute_reply":"2024-03-14T13:40:00.139424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Utility Functions</b><a class='anchor' id='utils'></a> [↑](#top) \n","metadata":{}},{"cell_type":"code","source":"def eeg_from_parquet(parquet_path: str, display: bool = False) -> np.ndarray:\n    \"\"\"\n    This function reads a parquet file and extracts the middle 50 seconds of the recording. Then it fills NaN values\n    with the mean value (ignoring NaNs).\n    :param parquet_path: path to parquet file.\n    :param display: whether to display EEG plots or not.\n    :return data: np.array of shape  (time_steps, eeg_features) -> (10_000, 8)\n    \"\"\"\n    #CHOICE: Extract middle 50 seconds of parquet\n    eeg = pd.read_parquet(parquet_path, columns=eeg_features)\n    rows = len(eeg)                       # total amount of samples of eeg\n    offset = (rows - 10_000) // 2         # 50s * 200 Hz= 10_000 samples\n    eeg = eeg.iloc[offset:offset+10_000]  # middle 50 seconds, has the same amount of readings to left and right\n    if display: \n        plt.figure(figsize=(10,5))\n        offset = 0\n        \n    # === Convert to numpy ===\n    data = np.zeros((10_000, len(eeg_features))) # create placeholder of same shape with zeros\n    for index, feature in enumerate(eeg_features):\n        x = eeg[feature].values.astype('float32') # CHOICE: convert to float32, can also try float 16 [gives overflow though]\n        mean = np.nanmean(x) # arithmetic mean along the specified axis, ignoring NaNs\n        nan_percentage = np.isnan(x).mean() # percentage of NaN values in feature\n        # === Fill nan values ===\n        if nan_percentage < 1: # if some values are nan, but not all\n            x = np.nan_to_num(x, nan=mean)\n        else: # if all values are nan\n            x[:] = 0\n        data[:, index] = x\n        \n    # === Plot EEGs ===\n        if display: \n            if index != 0:\n                offset += x.max()\n            plt.plot(range(10_000), x-offset, label=feature)\n            offset -= x.min()\n    if display:\n        plt.legend()\n        name = parquet_path.split('/')[-1].split('.')[0]\n        plt.yticks([])\n        plt.title(f'EEG {name}',size=16)\n        plt.show()    \n\n    return data\n\ndef sep():\n    'Prints a line!!!!'\n    print(\"-\"*100)\n    \ndef show_batch(batch, EEG_IDS, predict_arr=None):\n    \"\"\"Displays 6 random EEG signals from a batch in a 2x3 plot grid.\n\n    Args:\n        batch (array-like): Batch containing EEG signals and potentially labels.\n            Expects (batch_size, num_channels, signal_length) or similar.\n        EEG_IDS (list): List of EEG file IDs corresponding to data in the batch.\n        predict_arr (array-like, optional): Array of predicted labels (for display). Defaults to None\n    \"\"\"\n\n    num_items = 8  # Number of signals to display\n    num_rows = 2\n    num_cols = 4\n\n    # Create a figure for plotting\n    fig = plt.figure(figsize=(14, 8))\n\n    # Randomly select 6 indices from the batch\n    img_indices = np.random.randint(0, batch.shape[0], num_items)\n\n    # Iterate over the selected indices\n    for index, img_index in enumerate(img_indices):\n        img = batch[img_index]\n        lb = None  # Assume no labels\n\n        # Create subplot within the grid\n        ax = fig.add_subplot(num_rows, num_cols, index + 1, xticks=[], yticks=[]) \n\n        # If input is a PyTorch tensor, convert to NumPy array \n        if isinstance(img, torch.Tensor):\n            img = img.detach().cpu().numpy()\n            img = img.transpose(1, 0)  # Adjust dimensions if needed\n\n         # Plot each channel of the EEG signal with an offset for clarity\n        offset = 0\n        for j in range(img.shape[-1]):\n            if j != 0: offset -= img[:, j].min() # Calculate offset \n            ax.plot(img[:, j] + offset, label=f'feature {j+1}')\n            offset += img[:, j].max() + 1 # Update offset for next channel\n\n        # Add labels and titles\n        ax.set_title(f'EEG_Id = {EEG_IDS[img_index]}', size=14)\n\n    return fig ","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:00.141192Z","iopub.execute_input":"2024-03-14T13:40:00.141494Z","iopub.status.idle":"2024-03-14T13:40:00.158724Z","shell.execute_reply.started":"2024-03-14T13:40:00.141472Z","shell.execute_reply":"2024-03-14T13:40:00.157848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Load Test Metadata <b><a class='anchor' id='load_meta_data'></a> [↑](#top) \n\n***\nWe load the metadata and filter it to only use unique EEG ids.","metadata":{}},{"cell_type":"code","source":"test = pd.read_csv(paths.TEST_CSV)\nprint('Test shape:',test.shape)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:00.159846Z","iopub.execute_input":"2024-03-14T13:40:00.160105Z","iopub.status.idle":"2024-03-14T13:40:00.181328Z","shell.execute_reply.started":"2024-03-14T13:40:00.160083Z","shell.execute_reply":"2024-03-14T13:40:00.180487Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Create target dictionaries\ndf = pd.read_csv(paths.TRAIN_CSV)\nTARGETS = df.columns[-6:]\nTARS = {'Seizure':0, 'LPD':1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5} #labels to num\nTARS_INV = {x:y for y,x in TARS.items()}                              #num to labels\nConfig.NUM_CLASSES = len(TARS.keys())","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:00.183330Z","iopub.execute_input":"2024-03-14T13:40:00.184528Z","iopub.status.idle":"2024-03-14T13:40:00.364554Z","shell.execute_reply.started":"2024-03-14T13:40:00.184504Z","shell.execute_reply":"2024-03-14T13:40:00.363355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Load Raw Data</b><a class='anchor' id='load_data_eeg'></a> [↑](#top) \n\n***\n\nLoad the EEG features we will be using and the corresponding raw EEG data from the parquet files.","metadata":{}},{"cell_type":"code","source":"parquet = pd.read_parquet(f'{paths.RAW_EEGS}3911565283.parquet')\neeg_features = parquet.columns\nprint(f'There are {len(eeg_features)} raw EEG features:')\nprint(list(eeg_features) )\n\n#CHOICE: Select features to use. \nif Config.CUSTOM_FEATURES:\n    eeg_features = Config.CUSTOM_FEATURES\n    feature_to_index = {x:y for x,y in zip(eeg_features, range(len(eeg_features)))}\n    print(f'We will use {len(eeg_features)} of these features:')\n    print(list(eeg_features))\n\nelse:\n    feature_to_index = {x:y for x,y in zip(eeg_features, range(len(eeg_features)))}\n    print('We will use all of them')","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:00.366606Z","iopub.execute_input":"2024-03-14T13:40:00.366930Z","iopub.status.idle":"2024-03-14T13:40:00.384781Z","shell.execute_reply.started":"2024-03-14T13:40:00.366903Z","shell.execute_reply":"2024-03-14T13:40:00.383861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"all_eegs = {}\nDISPLAY = 1\nEEG_IDS = test.eeg_id.unique()\nprint('Processing Test EEG parquets...'); print()\nfor i,eeg_id in enumerate(EEG_IDS):\n        \n    # SAVE EEG TO PYTHON DICTIONARY OF NUMPY ARRAYS\n    data = eeg_from_parquet(f'{paths.RAW_EEGS}{eeg_id}.parquet', i<DISPLAY)\n    all_eegs[eeg_id] = data\n    \n#Create new test dataframe with only the eeg_id collumns (which are already unique)\ntest = pd.DataFrame(EEG_IDS, columns=['eeg_id'])","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:00.385872Z","iopub.execute_input":"2024-03-14T13:40:00.386141Z","iopub.status.idle":"2024-03-14T13:40:01.062199Z","shell.execute_reply.started":"2024-03-14T13:40:00.386117Z","shell.execute_reply":"2024-03-14T13:40:01.061166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span>Preprocessing Functions</b><a class='anchor' id='preprocess'></a> [↑](#top) \n\n***\nFunctions of possible augmentations to apply to our data. Will be used in creating the dataset.","metadata":{}},{"cell_type":"code","source":"def butter_lowpass_filter(data, cutoff_freq=20, sampling_rate=200, order=4):\n    #CHOICE: CUTOFF FREQUENCY\n    \"\"\"Applies a Butterworth low-pass filter to the data.\"\"\"\n    nyquist = 0.5 * sampling_rate\n    normal_cutoff = cutoff_freq / nyquist\n    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n    filtered_data = lfilter(b, a, data, axis=0)\n    return filtered_data\n\ndef quantize_data(data, classes):\n    \"\"\"Quantizes data using Mu-law encoding.\n    Args:\n        classes (int): The number of quantization levels.\n    \"\"\"\n    mu_x = mu_law_encoding(data, classes)\n    bins = np.linspace(-1, 1, classes)  # Create equally spaced bins\n    quantized = np.digitize(mu_x, bins) - 1  # Assign data to bins\n    return quantized\n\ndef mu_law_encoding(data, mu):\n    \"\"\"Performs Mu-law encoding on a NumPy array.\"\"\"\n    mu_x = np.sign(data) * np.log(1 + mu * np.abs(data)) / np.log(mu + 1)\n    return mu_x\n\ndef mu_law_expansion(data, mu):\n    \"\"\"Performs Mu-law expansion (decoding) on a NumPy array.\"\"\"\n    s = np.sign(data) * (np.exp(np.abs(data) * np.log(mu + 1)) - 1) / mu\n    return s","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.064455Z","iopub.execute_input":"2024-03-14T13:40:01.064771Z","iopub.status.idle":"2024-03-14T13:40:01.073556Z","shell.execute_reply.started":"2024-03-14T13:40:01.064745Z","shell.execute_reply":"2024-03-14T13:40:01.072622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Dataset</b><a class='anchor' id='dataset'></a> [↑](#top) \n\n***\n\nCreate a custom `Dataset` to load data. Here we can easily implement custom loading mechanisms, data pre-processing, and augmentation techniques. Also allows us to use the Pytorch Dataloader, streamlining data loading and batching.","metadata":{}},{"cell_type":"code","source":"class EEGDataset(torch.utils.data.Dataset):\n    def __init__(self, data, eegs=None, augmentations=None, test=False): \n        self.data = data  \n        self.eegs = eegs \n        self.augmentations = augmentations  \n        self.test = test  # Flag to indicate test mode\n\n    def __len__(self):\n        return len(self.data)  # Return the number of samples in the dataset\n\n    def __getitem__(self, index):\n        # Get a single data sample and its label (if not in test mode)\n        row = self.data.iloc[index]       \n        data = self.eegs[row.eeg_id]  # Load EEG data based on an ID\n\n        if Config.FEATURE_ENGINEERING:\n            if len(Config.CUSTOM_FEATURES) == 8:\n                X = np.zeros((10_000, Config.NUM_CHANNELS), dtype='float32')\n                    \n                # === Feature engineering ===\n                #LL Chain\n                X[:,0] = data[:,feature_to_index['Fp1']] - data[:,feature_to_index['T3']]\n                X[:,1] = data[:,feature_to_index['T3']]  - data[:,feature_to_index['O1']]\n                \n                #LP Chain\n                X[:,2] = data[:,feature_to_index['Fp1']] - data[:,feature_to_index['C3']]\n                X[:,3] = data[:,feature_to_index['C3']]  - data[:,feature_to_index['O1']]\n                \n                #RP Chain\n                X[:,4] = data[:,feature_to_index['Fp2']] - data[:,feature_to_index['C4']]\n                X[:,5] = data[:,feature_to_index['C4']]  - data[:,feature_to_index['O2']]\n                \n                #RR Chain\n                X[:,6] = data[:,feature_to_index['Fp2']] - data[:,feature_to_index['T4']]\n                X[:,7] = data[:,feature_to_index['T4']]  - data[:,feature_to_index['O2']]\n                \n            elif not Config.CUSTOM_FEATURES:\n                Config.NUM_CHANNELS = 16 #update number of channels\n                X = np.zeros((10_000, Config.NUM_CHANNELS), dtype='float16') #Full data cannot be represented with float32 anyway\n\n                #LL Chain\n                X[:,0] = data[:,feature_to_index['Fp1']] - data[:,feature_to_index['F7']]\n                X[:,1] = data[:,feature_to_index['F7']]  - data[:,feature_to_index['T3']]\n                X[:,2] = data[:,feature_to_index['T3']] - data[:,feature_to_index['T5']]\n                X[:,3] = data[:,feature_to_index['T5']]  - data[:,feature_to_index['O1']]\n\n                #LP Chain\n                X[:,4] = data[:,feature_to_index['Fp1']] - data[:,feature_to_index['F3']]\n                X[:,5] = data[:,feature_to_index['C4']]  - data[:,feature_to_index['C3']]\n                X[:,6] = data[:,feature_to_index['Fp2']] - data[:,feature_to_index['P3']]\n                X[:,7] = data[:,feature_to_index['T4']]  - data[:,feature_to_index['O1']]\n\n                #RP Chain\n                X[:,8] = data[:,feature_to_index['Fp2']] - data[:,feature_to_index['F4']]\n                X[:,9] = data[:,feature_to_index['F4']]  - data[:,feature_to_index['C4']]\n                X[:,10] = data[:,feature_to_index['C4']] - data[:,feature_to_index['P4']]\n                X[:,11] = data[:,feature_to_index['P4']]  - data[:,feature_to_index['O2']]\n\n                #RR Chain\n                X[:,12] = data[:,feature_to_index['Fp2']] - data[:,feature_to_index['F8']]\n                X[:,13] = data[:,feature_to_index['F8']]  - data[:,feature_to_index['T4']]\n                X[:,14] = data[:,feature_to_index['T4']] - data[:,feature_to_index['T6']]\n                X[:,15] = data[:,feature_to_index['T6']]  - data[:,feature_to_index['O2']]\n                \n        else:\n\n            X = data\n\n        # === Standarize ===\n        X = np.clip(X,-1024,1024)    \n        X = np.nan_to_num(X, nan=0) / 32.0  \n        \n        # === Preprocess ===\n        X = butter_lowpass_filter(X)  # Apply a low-pass filter \n        X = quantize_data(X, 1)       # Apply quantization\n        samples = torch.from_numpy(X).float()  # Convert to a PyTorch tensor\n        samples = samples.permute(1, 0)  # Adjust tensor shape to get (time_steps, features)\n\n        if not self.test:\n            label = row[TARGETS]         # Get label from metadata\n            label = torch.tensor(label).float()  \n            return samples, label        # Return preprocessed data and label\n        else:\n            return samples               # Return preprocessed data only (for testing)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.074697Z","iopub.execute_input":"2024-03-14T13:40:01.074972Z","iopub.status.idle":"2024-03-14T13:40:01.099023Z","shell.execute_reply.started":"2024-03-14T13:40:01.074950Z","shell.execute_reply":"2024-03-14T13:40:01.098034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> DataLoader</b><a class='anchor' id='dataloader'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"def get_test_dls(df_test):\n    ds_test = EEGDataset(\n        df_test, \n        eegs=all_eegs,\n        augmentations = None,\n        test = True\n    )\n    dl_test = DataLoader(ds_test, batch_size=Config.BATCH_SIZE , shuffle=False, num_workers = 2)    \n    return dl_test, ds_test","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.100090Z","iopub.execute_input":"2024-03-14T13:40:01.100384Z","iopub.status.idle":"2024-03-14T13:40:01.112233Z","shell.execute_reply.started":"2024-03-14T13:40:01.100361Z","shell.execute_reply":"2024-03-14T13:40:01.111504Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Model</b><a class='anchor' id='model'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class ResNet_1D_Block(nn.Module):\n    \"\"\"\n    Implements a ResNet-inspired residual block for 1D convolutional networks.\n\n    Args:\n        in_channels (int): Number of input channels.\n        out_channels (int): Number of output channels.\n        kernel_size (int): Kernel size for the convolutions.\n        stride (int): Stride for the convolutions.\n        padding (int): Padding for the convolutions.\n        downsampling (bool): Whether to apply downsampling.\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, downsampling):\n        super(ResNet_1D_Block, self).__init__()\n        self.bn1 = nn.BatchNorm1d(num_features=in_channels)\n        self.relu = nn.ReLU(inplace=False)                               \n        self.dropout = nn.Dropout(p=Config.P_DROPOUT, inplace=False)    \n\n        self.conv1 = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,\n                               stride=stride, padding=padding, bias=False) \n        self.bn2 = nn.BatchNorm1d(num_features=out_channels)                \n\n        self.conv2 = nn.Conv1d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size,\n                               stride=stride, padding=padding, bias=False)  \n        self.maxpool = nn.MaxPool1d(kernel_size=2, stride=2, padding=0)   \n        self.downsampling = downsampling\n        self.downsample_layer = nn.MaxPool1d(kernel_size=2, stride=2, padding=0) \n\n    def forward(self, x):\n        \"Basically applies a BN, Relu, Dropout and 1D Convolution twice and then maxpools\"\n        identity = x  # Store input for residual connection\n\n        out = self.bn1(x)\n        out = self.relu(out)\n        out = self.dropout(out)\n        out = self.conv1(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n        out = self.dropout(out)\n        out = self.conv2(out)\n\n        out = self.maxpool(out)  \n        if self.downsampling:\n            identity = self.downsample_layer(x)  # Apply downsampling if needed\n\n        out += identity  # Add the orginal input (residual connection)\n        return out\n\n    \nclass EEGNet(nn.Module):\n    \"\"\"\n    EEGNet architecture: A convolutional neural network with ResNet-style blocks designed for EEG signal classification.\n\n    Args:\n        kernels (list): A list of kernel sizes for the initial parallel convolutions.\n        in_channels (int, optional): Number of input channels (e.g., number of electrodes). \n                                    Defaults to 20.\n        fixed_kernel_size (int, optional): Kernel size for subsequent shared convolutions. \n                                           Defaults to 17.\n        NUM_CLASSES (int, optional): Number of output classes for classification. \n                                     Defaults to 6.\n    \"\"\"\n\n    def __init__(self, kernels = Config.KERNELS, num_feature_maps = Config.NUM_FEATURE_MAPS, in_channels=Config.NUM_CHANNELS, \n                       fixed_kernel_size=Config.FIXED_KERNEL_SIZE, num_classes=6):\n        super(EEGNet, self).__init__()\n        self.kernels = kernels\n        self.planes = num_feature_maps  # Initial number of feature maps (=planes) outputed for each kernel (and used in 1D conv and ResNet)\n        self.in_channels = in_channels\n        \n        #----DEFINE LAYERS TO BE USED----\n        self.parallel_conv = nn.ModuleList()  # Parallel convolutions with varying kernel sizes\n        for kernel_size in kernels:\n            conv = nn.Conv1d(in_channels, self.planes, kernel_size, stride=1, padding=0, bias=False)\n            self.parallel_conv.append(conv)\n\n        # Batch normalizations, ReLU activation\n        self.bn1 = nn.BatchNorm1d(self.planes)\n        self.relu = nn.ReLU(inplace=False)\n\n        # Shared convolution and ResNet blocks\n        self.conv1 = nn.Conv1d(self.planes, self.planes, fixed_kernel_size, stride=2, padding=2, bias=False)\n        self.block = self._make_resnet_layer(fixed_kernel_size, stride=1, padding=fixed_kernel_size//2)\n\n        # Downsampling Layers \n        self.bn2 = nn.BatchNorm1d(self.planes)\n        self.avgpool = nn.AvgPool1d(6, stride=6, padding=2)\n        \n        # Gated Recurrent Unit (GRU) for processing sequential patterns in the EEG data\n        self.rnn = nn.GRU(self.in_channels, hidden_size=128, num_layers=1, bidirectional=True)\n        \n        #Final FC layer\n        self.fc = nn.Linear(568, num_classes) \n\n    def _make_resnet_layer(self, kernel_size, stride, blocks=Config.RESNET_BLOCKS, padding=0):\n        \"\"\"Creates a sequence of ResNet blocks.\"\"\"\n        layers = []\n        for _ in range(blocks):\n            downsampling = nn.MaxPool1d(2, stride=2, padding=0)  # Downsampling if needed\n            layers.append(ResNet_1D_Block(self.planes, self.planes, kernel_size, stride, padding, downsampling))\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        \"\"\"Forward pass through the network. First separate parallel convolutions \n           which are then concetaned to go through 1D conv and ResNet block\"\"\"\n        # Parallel convolutions, list containing output of different kernels applied to input\n        out_sep = [conv(x) for conv in self.parallel_conv]  \n\n        # Concatenate outputs\n        out = torch.cat(out_sep, dim=2) \n        \n        # 1D Convolution\n        out = self.bn1(out)\n        out = self.relu(out)\n        out = self.conv1(out)\n        \n        # ResNet Block [Has BN and ReLu build in]\n        out = self.block(out)\n        \n        # Avgerage Pooling\n        out = self.bn2(out)\n        out = self.relu(out)\n        out = self.avgpool(out)\n\n        # Flatten for dense layers\n        out = out.reshape(out.shape[0], -1)  \n\n        # Pass through GRU for sequence representations\n        rnn_out, _ = self.rnn(x.permute(0, 2, 1))   \n        new_rnn_h = rnn_out[:, -1, :]  \n        new_out = torch.cat([out, new_rnn_h], dim=1)  # Combine features\n        result = self.fc(new_out)  # Final classification layer\n\n        return result","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.113431Z","iopub.execute_input":"2024-03-14T13:40:01.113729Z","iopub.status.idle":"2024-03-14T13:40:01.136953Z","shell.execute_reply.started":"2024-03-14T13:40:01.113707Z","shell.execute_reply":"2024-03-14T13:40:01.136051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Optimizer & Scheduler</b><a class='anchor' id='optimizer'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"def get_optimizer(lr, params):\n    model_optimizer = torch.optim.Adam(\n            filter(lambda p: p.requires_grad, params), \n            lr=lr,\n            weight_decay=Config.weight_decay\n        )\n    interval = \"epoch\"\n    \n    lr_scheduler = CosineAnnealingWarmRestarts(\n                            model_optimizer, \n                            T_0=Config.epochs, \n                            T_mult=1, \n                            eta_min=1e-5, \n                            last_epoch=-1\n                        )\n\n    return {\n        \"optimizer\": model_optimizer, \n        \"lr_scheduler\": {\n            \"scheduler\": lr_scheduler,\n            \"interval\": interval,\n            \"monitor\": \"val_loss\",\n            \"frequency\": 1\n        }\n    }","metadata":{"papermill":{"duration":0.01903,"end_time":"2023-03-11T22:10:22.312531","exception":false,"start_time":"2023-03-11T22:10:22.293501","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-14T13:40:01.138142Z","iopub.execute_input":"2024-03-14T13:40:01.138460Z","iopub.status.idle":"2024-03-14T13:40:01.152622Z","shell.execute_reply.started":"2024-03-14T13:40:01.138437Z","shell.execute_reply":"2024-03-14T13:40:01.151757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Loss Function</b><a class='anchor' id='loss'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class KLDivLossWithLogits(nn.KLDivLoss):\n\n    def __init__(self):\n        super().__init__(reduction=\"batchmean\")\n\n    def forward(self, y, t):\n        y = nn.functional.log_softmax(y,  dim=1)\n        loss = super().forward(y, t)\n\n        return loss\n","metadata":{"papermill":{"duration":0.238854,"end_time":"2023-03-11T22:10:24.816235","exception":false,"start_time":"2023-03-11T22:10:24.577381","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-14T13:40:01.153731Z","iopub.execute_input":"2024-03-14T13:40:01.154005Z","iopub.status.idle":"2024-03-14T13:40:01.166682Z","shell.execute_reply.started":"2024-03-14T13:40:01.153968Z","shell.execute_reply":"2024-03-14T13:40:01.165737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Pytorch Lightning Model</b><a class='anchor' id='lightning'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"class EEGModel(pl.LightningModule):\n    \"\"\"\n    Pytorch Lightning module for EEG classification.\n    \"\"\"\n\n    def __init__(self, num_classes = Config.NUM_CLASSES, pretrained = Config.PRETRAINED, fold = -1):\n        super().__init__()\n        self.num_classes = num_classes\n        self.fold = fold\n\n        # Create the EEGNet backbone with specified kernel sizes\n        self.backbone = EEGNet(kernels=Config.KERNELS, in_channels=Config.NUM_CHANNELS, \n                               fixed_kernel_size=Config.FIXED_KERNEL_SIZE, num_classes=Config.NUM_CLASSES)\n\n        # Loss function for multi-class classification\n        self.loss_function = KLDivLossWithLogits() \n\n        self.validation_step_outputs = []  # Storage for validation results\n        self.lin = nn.Softmax(dim=1)       # Softmax for probability outputs \n        self.best_score = 1000.0           # Track best validation score\n\n    def forward(self, images):\n        \"\"\"Forward pass through the EEGNet model.\"\"\"\n        logits = self.backbone(images)  # Extract features and get predictions\n        return logits\n\n    def configure_optimizers(self):\n        \"\"\"Set up the optimizer (using configuration parameters).\"\"\"\n        return get_optimizer(lr=Config.LR, params=self.parameters())\n\n    def train_with_mixup(self, X, y):\n        X, y_a, y_b, lam = mixup_data(X, y, alpha=Config.MIXUP_ALPHA)\n        y_pred = self(X)\n        loss_mixup = mixup_criterion(KLDivLossWithLogits(), y_pred, y_a, y_b, lam)\n        return loss_mixup\n\n    def training_step(self, batch, batch_idx):\n        image, target = batch        \n        if Config.USE_MIXUP:\n            loss = self.train_with_mixup(image, target)\n        else:\n            y_pred = self(image)\n            loss = self.loss_function(y_pred,target)\n\n        self.log(\"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True)\n        return loss        \n\n    def validation_step(self, batch, batch_idx):\n        image, target = batch \n        y_pred = self(image)\n        val_loss = self.loss_function(y_pred, target)\n        self.log(\"val_loss\", val_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True, sync_dist=True)\n        self.validation_step_outputs.append({\"val_loss\": val_loss, \"logits\": y_pred, \"targets\": target})\n\n        return {\"val_loss\": val_loss, \"logits\": y_pred, \"targets\": target}\n\n    def train_dataloader(self):\n        return self._train_dataloader \n    \n    def validation_dataloader(self):\n        return self._validation_dataloader\n    \n    def on_validation_epoch_end(self):\n        outputs = self.validation_step_outputs\n        # print(len(outputs))\n        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n        output_val = nn.Softmax(dim=1)(torch.cat([x['logits'] for x in outputs],dim=0)).cpu().detach().numpy()\n        target_val = torch.cat([x['targets'] for x in outputs],dim=0).cpu().detach().numpy()\n        self.validation_step_outputs = []\n\n        val_df = pd.DataFrame(target_val, columns = list(TARGETS))\n        pred_df = pd.DataFrame(output_val, columns = list(TARGETS))\n\n        val_df['id'] = [f'id_{i}' for i in range(len(val_df))] \n        pred_df['id'] = [f'id_{i}' for i in range(len(pred_df))] \n\n\n        avg_score = score(val_df, pred_df, row_id_column_name = 'id')\n\n        if avg_score < self.best_score:\n            print(f'Fold {self.fold}: Epoch {self.current_epoch} validation loss {avg_loss}')\n            print(f'Fold {self.fold}: Epoch {self.current_epoch} validation KDL score {avg_score}')\n            self.best_score = avg_score\n            # val_df.to_csv(f'{paths.OUTPUT_DIR}/val_df_f{self.fold}.csv',index=False)\n            # pred_df.to_csv(f'{paths.OUTPUT_DIR}/pred_df_f{self.fold}.csv',index=False)\n        \n        return {'val_loss': avg_loss,'val_cmap':avg_score}","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.169090Z","iopub.execute_input":"2024-03-14T13:40:01.169654Z","iopub.status.idle":"2024-03-14T13:40:01.188150Z","shell.execute_reply.started":"2024-03-14T13:40:01.169629Z","shell.execute_reply":"2024-03-14T13:40:01.187356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Prediction Function </b><a class='anchor' id='prediction'></a> [↑](#top) \n\n***\nWe will use a seperate prediction function to generate predictions of the trained model, outside the Pytorch Lightning environment","metadata":{}},{"cell_type":"code","source":"def predict(data_loader, model):\n        \n    model.to('cuda')\n    model.eval()    \n    predictions = []\n    for batch in tqdm(data_loader):\n\n        with torch.no_grad():\n            x = batch\n            x = x.cuda()\n            # inputs = {key:val.reshape(val.shape[0], -1).to(config.device) for key,val in batch.items()}\n            outputs = model(x)\n            outputs = nn.Softmax(dim=1)(outputs)\n        predictions.extend(outputs.detach().cpu().numpy())\n    predictions = np.vstack(predictions)\n    return predictions","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.189296Z","iopub.execute_input":"2024-03-14T13:40:01.189561Z","iopub.status.idle":"2024-03-14T13:40:01.203299Z","shell.execute_reply.started":"2024-03-14T13:40:01.189539Z","shell.execute_reply":"2024-03-14T13:40:01.202394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Inference</b><a class='anchor' id='inference'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"def run_inference(fold_id, Config):\n    logger = None\n    pred_cols = [f'pred_{t}' for t in TARGETS]\n    df_test = test.copy()\n    dl_test, ds_test = get_test_dls(df_test)\n    \n\n    print(f\"Running inference model '{paths.WEIGHTS}/eegnet_best_loss_fold{fold_id}.ckpt'..\")\n    \n    model = EEGModel.load_from_checkpoint(f'{paths.WEIGHTS}/eegnet_best_loss_fold{fold_id}.ckpt',map_location='cuda:0',\n                                          train_dataloader=None,validation_dataloader=None,config=Config)\n    \n    preds = predict(dl_test, model)  \n    gc.collect()\n    return preds","metadata":{"papermill":{"duration":0.025305,"end_time":"2023-03-11T22:10:24.896057","exception":false,"start_time":"2023-03-11T22:10:24.870752","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-14T13:40:01.204571Z","iopub.execute_input":"2024-03-14T13:40:01.204960Z","iopub.status.idle":"2024-03-14T13:40:01.218306Z","shell.execute_reply.started":"2024-03-14T13:40:01.204930Z","shell.execute_reply":"2024-03-14T13:40:01.217547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# <b><span style='color:#196f3d'>|</span> Submission</b><a class='anchor' id='submission'></a> [↑](#top) \n\n***","metadata":{}},{"cell_type":"code","source":"submission_df = test[['eeg_id']].copy()\nsubmission_df[TARGETS] = 0.0\n\ntest_preds = []\nfor en,f in enumerate(range(Config.FOLDS)):\n    preds = run_inference(f, Config)\n    test_preds.append(preds)\n    \ntest_preds = np.mean(test_preds, 0)\nsubmission_df[TARGETS] = test_preds\nsubmission_df.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T13:40:01.219495Z","iopub.execute_input":"2024-03-14T13:40:01.219766Z","iopub.status.idle":"2024-03-14T13:40:03.718001Z","shell.execute_reply.started":"2024-03-14T13:40:01.219744Z","shell.execute_reply":"2024-03-14T13:40:03.717100Z"},"trusted":true},"execution_count":null,"outputs":[]}]}