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It scores 0.607 on GroupKfold 5 Folds split. Kindly update the paths of the datasets accordingly, I have done the training locally.\n\n#### Submission Notebook link: https://www.kaggle.com/nischaydnk/multimodal-1d-2d-eeg-approach-submission\n\n\n##### 1D EEGNet training: https://www.kaggle.com/code/nischaydnk/lightning-1d-eegnet-training-pipeline-hbs\n##### 1D EEGNet Submission: https://www.kaggle.com/code/nischaydnk/hms-submission-1d-eegnet-pipeline-lightning\n\n\n#### PS: This approach is somewhat in continuation of what I have shared earlier in these notebooks .You can also refer my previous work to study more about the 1D approach I have used in the notebook. Also, I have used some of the code from [@moth](https://www.kaggle.com/alejopaullier) baseline with 2D approach [notebook](https://www.kaggle.com/code/alejopaullier/hms-efficientnetb0-pytorch-inference) & ideas shared by [@Chris](https://www.kaggle.com/cdeotte)\n\n\n#### Overall pipeline in this notebook is somewhat like this.\n![img2](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F4712534%2Fc4e39641fa8ec13588b62065ae1e9b58%2FScreenshot%202024-02-10%20at%2010.40.36%20PM.png?generation=1707585942607444&alt=media)","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":[]}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport torch\nimport os\nimport pytorch_lightning as pl\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn import model_selection\nimport torchvision.transforms as transforms\nimport torchvision.io \nimport librosa\nfrom PIL import Image\nimport albumentations as alb\nimport torch.multiprocessing as mp\nimport warnings\nimport os\nfrom pytorch_lightning.callbacks import ModelCheckpoint, BackboneFinetuning, EarlyStopping\n\nwarnings.filterwarnings('ignore')\n","metadata":{"papermill":{"duration":10.633687,"end_time":"2023-03-11T22:10:20.874999","exception":false,"start_time":"2023-03-11T22:10:10.241312","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Kindly update the paths in the following config to run the same model on Kaggle notebooks. You can import other datasets from Moth's notebook, I have shared above.","metadata":{}},{"cell_type":"code","source":"class Config:\n    use_aug = False\n    num_classes = 6\n    batch_size = 32\n\n    epochs = 10\n    PRECISION = 16    \n    PATIENCE = 20    \n    seed = 2024\n    backbone_2d = 'tf_efficientnet_b0'\n    pretrained = True            \n    weight_decay = 1e-2\n    use_mixup = False\n    \n    mixup_alpha = 0.1   \n    num_channels = 8\n    data_root = \"/home/nischay/brain/Data/\"\n    raw_eeg_path = \"/home/nischay/brain/Data/raw_eeg/eegs.npy\" #\"/home/nischay/brain/Data/eegs_20ch.npy\"#None#  \n    raw_spec_path = \"/home/nischay/brain/Data/spec/eeg_specs.npy\" #\"/home/nischay/brain/Data/eegs_20ch.npy\"#None#  \n    kaggle_spec_path = \"/home/nischay/brain/Data/train_spectrograms/\" #\"/home/nischay/brain/Data/eegs_20ch.npy\"#None#  \n    PRE_LOADED_EEGS = \"/home/nischay/brain/Data/spec/eeg_specs.npy\"\n    PRE_LOADED_SPECTOGRAMS = \"/home/nischay/brain/Data/kagglespec/specs.npy\"\n    TRAIN_EEGS = \"/home/nischay//brain/Data/spec/EEG_Spectrograms/\"\n    LR = 7e-4\n    processed_train = None\n    output_dir = '/home/nischay/brain/nbs2/outputs_gp_aux2/mm_exp25_lowbs_contrastnorm'\n    trn_folds = [0,1,2,3,4]\n    \n    ","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_count":null,"outputs":[]},{"cell_type":"code","source":"if not os.path.exists(Config.output_dir):\n    os.makedirs(Config.output_dir)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pl.seed_everything(Config.seed, workers=True)","metadata":{"papermill":{"duration":0.021264,"end_time":"2023-03-11T22:10:20.993713","exception":false,"start_time":"2023-03-11T22:10:20.972449","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def config_to_dict(cfg):\n    return dict((name, getattr(cfg, name)) for name in dir(cfg) if not name.startswith('__'))","metadata":{"papermill":{"duration":0.015742,"end_time":"2023-03-11T22:10:21.016701","exception":false,"start_time":"2023-03-11T22:10:21.000959","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd, numpy as np, os\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(f'{Config.data_root}train.csv')\nprint( df.shape )\n","metadata":{"papermill":{"duration":0.186715,"end_time":"2023-03-11T22:10:21.210686","exception":false,"start_time":"2023-03-11T22:10:21.023971","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nEEG_IDS = df.eeg_id.unique()\n\nTARGETS = df.columns[-6:]\nTARS = {'Seizure':0, 'LPD':1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5}\nTARS_INV = {x:y for y,x in TARS.items()}\n\ntrain_df = df.groupby('eeg_id')[['spectrogram_id','spectrogram_label_offset_seconds']].agg({\n    'spectrogram_id':'first',\n    'spectrogram_label_offset_seconds':'min'\n})\ntrain_df.columns = ['spectogram_id','min']\n\naux = df.groupby('eeg_id')[['spectrogram_id','spectrogram_label_offset_seconds']].agg({\n    'spectrogram_label_offset_seconds':'max'\n})\ntrain_df['max'] = aux\n\naux = df.groupby('eeg_id')[['patient_id']].agg('first')\ntrain_df['patient_id'] = aux\n\naux = df.groupby('eeg_id')[TARGETS].agg('sum')\nfor label in TARGETS:\n    train_df[label] = aux[label].values\n    \ny_data = train_df[TARGETS].values\ny_data = y_data / y_data.sum(axis=1,keepdims=True)\ntrain_df[TARGETS] = y_data\n\naux = df.groupby('eeg_id')[['expert_consensus']].agg('first')\ntrain_df['target'] = aux\n\ntrain = train_df.reset_index()\nprint('Train non-overlapp eeg_id shape:', train_df.shape )\ntrain.head()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Config.num_classes = len(TARS.keys())","metadata":{"papermill":{"duration":0.020696,"end_time":"2023-03-11T22:10:21.239429","exception":false,"start_time":"2023-03-11T22:10:21.218733","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe()","metadata":{"papermill":{"duration":0.036704,"end_time":"2023-03-11T22:10:21.283488","exception":false,"start_time":"2023-03-11T22:10:21.246784","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def eeg_from_parquet(parquet_path, display=False):\n    \n    # EXTRACT MIDDLE 50 SECONDS\n    eeg = pd.read_parquet(parquet_path, columns=FEATS)\n    rows = len(eeg)\n    offset = (rows-10_000)//2\n    eeg = eeg.iloc[offset:offset+10_000]\n    \n    if display: \n        plt.figure(figsize=(10,5))\n        offset = 0\n    \n    # CONVERT TO NUMPY\n    data = np.zeros((10_000,len(FEATS)))\n    for j,col in enumerate(FEATS):\n        \n        # FILL NAN\n        x = eeg[col].values.astype('float32')\n        m = np.nanmean(x)\n        if np.isnan(x).mean()<1: x = np.nan_to_num(x,nan=m)\n        else: x[:] = 0\n            \n        data[:,j] = x\n        \n        if display: \n            if j!=0: offset += x.max()\n            plt.plot(range(10_000),x-offset,label=col)\n            offset -= x.min()\n            \n    if display:\n        plt.legend()\n        name = parquet_path.split('/')[-1]\n        name = name.split('.')[0]\n        plt.title(f'EEG {name}',size=16)\n        plt.show()\n        \n    return data","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{"papermill":{"duration":0.007321,"end_time":"2023-03-11T22:10:21.298932","exception":false,"start_time":"2023-03-11T22:10:21.291611","status":"completed"},"tags":[]}},{"cell_type":"code","source":"%%time\n\nCREATE_EEGS = True\n\ndf = pd.read_parquet(f'{Config.data_root}train_eegs/1000913311.parquet')\nFEATS = df.columns\nprint(f'There are {len(FEATS)} raw eeg features')\nprint( list(FEATS) )\n\nif Config.raw_eeg_path is not None:\n    raw_eegs = np.load(Config.raw_eeg_path, allow_pickle=True).item()\nelse:\n\n    all_eegs = {}\n    DISPLAY = 4\n    EEG_IDS = train.eeg_id.unique()\n    PATH = f'{Config.data_root}train_eegs/'\n    \n    for i,eeg_id in enumerate(EEG_IDS):\n        if (i%100==0)&(i!=0): print(i,', ',end='') \n        \n        # SAVE EEG TO PYTHON DICTIONARY OF NUMPY ARRAYS\n        data = eeg_from_parquet(f'{PATH}{eeg_id}.parquet', display=i<DISPLAY)              \n        all_eegs[eeg_id] = data\n        \n        if i==DISPLAY:\n            if CREATE_EEGS:\n                print(f'Processing {train.eeg_id.nunique()} eeg parquets... ',end='')\n            else:\n                print(f'Reading {len(EEG_IDS)} eeg NumPys from disk.')\n                break\n                \n    if CREATE_EEGS: \n        np.save(f'{Config.data_root}eegs_20ch',all_eegs)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(raw_eegs)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nfrom glob import glob\nREAD_SPEC_FILES = False\n\npaths_spectograms = glob(Config.kaggle_spec_path + \"*.parquet\")\nprint(f'There are {len(paths_spectograms)} spectrogram parquets')\n\nif READ_SPEC_FILES:    \n    all_spectrograms = {}\n    for file_path in tqdm(paths_spectograms):\n        aux = pd.read_parquet(file_path)\n        name = int(file_path.split(\"/\")[-1].split('.')[0])\n        all_spectrograms[name] = aux.iloc[:,1:].values\n        del aux\nelse:\n    all_spectrograms = np.load(Config.PRE_LOADED_SPECTOGRAMS, allow_pickle=True).item()\n    \n# if config.VISUALIZE:\n#     idx = np.random.randint(0,len(paths_spectograms))\n#     spectrogram_path = paths_spectograms[idx]\n#     plot_spectrogram(spectrogram_path)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nREAD_EEG_SPEC_FILES = False\n\npaths_eegs = glob(Config.TRAIN_EEGS + \"*.npy\")\nprint(f'There are {len(paths_eegs)} EEG spectograms')\n\nif READ_EEG_SPEC_FILES:\n    all_eegs = {}\n    for file_path in tqdm(paths_eegs):\n        eeg_id = file_path.split(\"/\")[-1].split(\".\")[0]\n        eeg_spectogram = np.load(file_path)\n        all_eegs[eeg_id] = eeg_spectogram\nelse:\n    all_eegs = np.load(Config.PRE_LOADED_EEGS, allow_pickle=True).item()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nall_eegs[train.loc[0,'eeg_id']].shape\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nraw_eegs[train.loc[0,'eeg_id']].shape\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\n\nclass ResNet_1D_Block(nn.Module):\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=0.1, inplace=False)\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        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\n    def forward(self, x):\n        identity = x\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        identity = self.downsampling(x)\n\n        out += identity\n        return out\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n# import gc\n# iot = torch.randn(2, Config.num_channels, 10000)#.cuda()\n# model = EEGNet(kernels=[3,5,7,9], in_channels=Config.num_channels, fixed_kernel_size=5, num_classes=6)#.cuda()\n# output = model(iot)\n# print(output.shape)\n\n# del iot, model\n# gc.collect()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import timm\n\nclass EEGMegaNet(nn.Module):\n\n    def __init__(self, backbone_2d,in_channels_2d, kernels, pretrained=False, in_channels=20, fixed_kernel_size=17, num_classes=6):\n        super(EEGMegaNet, self).__init__()\n        \n        self.kernels = kernels\n        self.planes = 24\n        self.parallel_conv = nn.ModuleList()\n        self.in_channels = in_channels\n\n\n        \n        self.backbone_2d = timm.create_model(\n            Config.backbone_2d,\n            pretrained=pretrained,\n            drop_rate = 0.1,\n            drop_path_rate = 0.1\n        \n        )\n        \n        self.features_2d = nn.Sequential(*list(self.backbone_2d.children())[:-2] + [nn.AdaptiveAvgPool2d(1),nn.Flatten()])\n        \n        # nn.Sequential(\n            # nn.AdaptiveAvgPool2d(1),\n\n        for i, kernel_size in enumerate(list(self.kernels)):\n            sep_conv = nn.Conv1d(in_channels=in_channels, out_channels=self.planes, kernel_size=(kernel_size),\n                               stride=1, padding=0, bias=False,)\n            self.parallel_conv.append(sep_conv)\n\n        self.bn1 = nn.BatchNorm1d(num_features=self.planes)\n        self.relu = nn.ReLU(inplace=False)\n        self.conv1 = nn.Conv1d(in_channels=self.planes, out_channels=self.planes, kernel_size=fixed_kernel_size,\n                               stride=2, padding=2, bias=False)\n        self.block = self._make_resnet_layer(kernel_size=fixed_kernel_size, stride=1, padding=fixed_kernel_size//2)\n        self.bn2 = nn.BatchNorm1d(num_features=self.planes)\n        self.avgpool = nn.AvgPool1d(kernel_size=4, stride=4, padding=2)\n        self.rnn = nn.GRU(input_size=self.in_channels, hidden_size=128, num_layers=1, bidirectional=True)\n        \n        self.fc1 = nn.Linear(in_features=1280, out_features=128)\n        self.fc2 = nn.Linear(in_features=736, out_features=128)\n        self.fc = nn.Linear(in_features=256, out_features=num_classes)\n\n        self.fc1d = nn.Linear(in_features=128, out_features=num_classes)\n        self.fc2d = nn.Linear(in_features=128, out_features=num_classes)\n        \n        \n        self.rnn1 = nn.GRU(input_size=156, hidden_size=156, num_layers=1, bidirectional=True)\n\n    def _make_resnet_layer(self, kernel_size, stride, blocks=8, padding=0):\n        layers = []\n        downsample = None\n        base_width = self.planes\n\n        for i in range(blocks):\n            downsampling = nn.Sequential(\n                    nn.MaxPool1d(kernel_size=2, stride=2, padding=0)\n                )\n            layers.append(ResNet_1D_Block(in_channels=self.planes, out_channels=self.planes, kernel_size=kernel_size,\n                                       stride=stride, padding=padding, downsampling=downsampling))\n\n        return nn.Sequential(*layers)\n\n    def _reshape_input(self, spec):\n        \"\"\"\n        Reshapes input (128, 256, 8) -> (512, 512, 3) monotone image.\n        \"\"\" \n        # === Get spectograms ===\n        spectograms = [spec[:, :, :, i:i+1] for i in range(4)]\n        spectograms = torch.cat(spectograms, dim=1)\n        \n        # === Get EEG spectograms ===\n        eegs = [spec[:, :, :, i:i+1] for i in range(4,8)]\n        eegs = torch.cat(eegs, dim=1)\n        \n        # === Reshape (512,512,3) ===\n        spec = spectograms\n            \n        spec = torch.cat([spec,spec,spec], dim=3)\n        spec = spec.permute(0, 3, 1, 2)\n        return spec\n\n    def forward(self, x, spec):\n\n        spec = self._reshape_input(spec)\n        spec = self.features_2d(spec)\n        # print(spec.shape) #2, 1280, 16, 8\n        out_sep = []\n\n        for i in range(len(self.kernels)):\n            sep = self.parallel_conv[i](x)\n            out_sep.append(sep)\n\n        out = torch.cat(out_sep, dim=2)\n        out = self.bn1(out)\n        out = self.relu(out)\n        out = self.conv1(out)  \n\n        out = self.block(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n        out = self.avgpool(out)  \n\n\n        \n        out = out.reshape(out.shape[0], -1)  \n\n        rnn_out, _ = self.rnn(x.permute(0,2, 1))\n        new_rnn_h = rnn_out[:, -1, :]  \n\n        new_out = torch.cat([out, new_rnn_h], dim=1)  \n        new_out = self.fc2(new_out)  \n        out1d = self.fc1d(new_out)\n        \n        spec = self.fc1(spec)  \n        out2d = self.fc2d(spec)\n        \n        result = torch.cat([new_out, spec], dim=1)  \n        result = self.fc(result)\n        \n        \n        return result, new_out, spec, out1d, out2d","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import gc\niot = torch.randn(2, Config.num_channels, 10000)#.cuda()\nspec = torch.randn(2, 128, 256, 8)#.cuda()\n\nmodel = EEGMegaNet(backbone_2d=Config.backbone_2d,in_channels_2d=8,\n                   kernels=[3,5,7,9],pretrained=False,\n                   in_channels=Config.num_channels, fixed_kernel_size=5,\n                   num_classes=6)#.cuda()\noutput,_,_,_,_ = model(iot, spec)\nprint(output.shape)\n\ndel iot, model\ngc.collect()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df_train.iloc[:,17:]\n# !pip install braindecode","metadata":{"papermill":{"duration":0.014813,"end_time":"2023-03-11T22:10:21.446786","exception":false,"start_time":"2023-03-11T22:10:21.431973","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import albumentations as A\ndef get_train_transform():\n    return A.Compose([\n        A.HorizontalFlip(p=0.5),\n        A.OneOf([\n                A.Cutout(max_h_size=5, max_w_size=16),\n                A.CoarseDropout(max_holes=4),\n            ], p=0.5),\n    ])\n\nfrom torch_audiomentations import Compose, Gain, PolarityInversion\n\nimport torch_audiomentations as tA\n\ndef get_transforms(*, data):\n    \n    if data == 'train':\n        return tA.Compose(\n                transforms=[\n                     # tA.ShuffleChannels(p=0.25,mode=\"per_channel\",p_mode=\"per_channel\",),\n                     tA.AddColoredNoise(p=0.15,mode=\"per_channel\",p_mode=\"per_channel\", max_snr_in_db = 15, sample_rate=200),\n                     tA.Shift(p=0.5,mode=\"per_example\",p_mode=\"per_example\",max_shift=0.025, min_shift=-0.025,sample_rate=200),\n                ])\n\n    elif data == 'valid':\n        return tA.Compose([\n        ])\n\nfrom braindecode.augmentation import AugmentedDataLoader, SignFlip, FrequencyShift, ChannelsDropout,ChannelsShuffle\nfrom braindecode.augmentation import Transform\n# from braindecode.\nfreq_shift = FrequencyShift(\n    probability=.3,\n    sfreq=200,\n    max_delta_freq=2.  # the frequency shifts are sampled now between -2 and 2 Hz\n)\n\nch_drop = ChannelsDropout(probability=0.2, p_drop=0.2)\nsign_flip = SignFlip(probability=.1)\nch_shuffle = ChannelsShuffle(probability=0.25)\n\ntransforms = [\n    freq_shift,\n    sign_flip,\n    # ch_drop,\n    # ch_shuffle\n    \n]\n\n\nclass Compose(Transform):\n    \"\"\"Transform composition.\n\n    Callable class allowing to cast a sequence of Transform objects into a\n    single one.\n\n    Parameters\n    ----------\n    transforms: list\n        Sequence of Transforms to be composed.\n    \"\"\"\n\n    def __init__(self, transforms):\n        self.transforms = transforms\n        super().__init__()\n\n    def forward(self, X, y):\n        for transform in self.transforms:\n            X = transform(X, None)\n        return X, y\n\ndef get_transforms(*, data):\n    \n    if data == 'train':\n        return Compose(\n                transforms=transforms)\n\n    elif data == 'valid':\n        return Compose([\n        ])\n\n","metadata":{"papermill":{"duration":0.015477,"end_time":"2023-03-11T22:10:21.469737","exception":false,"start_time":"2023-03-11T22:10:21.454260","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from scipy.signal import butter, lfilter\n\ndef quantize_data(data, classes):\n    mu_x = mu_law_encoding(data, classes)\n    # bins = np.linspace(-1, 1, classes)\n    # quantized = np.digitize(mu_x, bins) - 1\n    return mu_x#quantized\n\ndef mu_law_encoding(data, mu):\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    s = np.sign(data) * (np.exp(np.abs(data) * np.log(mu + 1)) - 1) / mu\n    return s\n\ndef butter_lowpass_filter(data, cutoff_freq=20, sampling_rate=200, order=4):\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\nclass EEGDataset(torch.utils.data.Dataset):\n\n    def __init__(self, data, eegs=None, augmentations = None, test = False): \n\n        self.data = data\n        self.eegs = eegs\n        self.augmentations = augmentations\n        self.test = test\n\n        \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n\n        row = self.data.iloc[index]      \n        data = self.eegs[row.eeg_id]\n\n        data = np.clip(data,-1024,1024)\n        data = np.nan_to_num(data, nan=0) / 32.0\n        \n        data = butter_lowpass_filter(data)\n        data = quantize_data(data,1)\n\n        samples = torch.from_numpy(data).float()\n        \n        # samples,_ = self.augmentations(samples.unsqueeze(0), None)\n        # samples = samples.squeeze()\n        # samples = np.stack(samples, axis=0)\n        # samples = torch.from_numpy(samples).float()\n    \n        samples = samples.permute(1,0)\n        if not self.test:\n            label = row[TARGETS] \n            label = torch.tensor(label).float()  \n            return samples, label\n        else:\n            return samples\n# ================================","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nclass EEGDataset(torch.utils.data.Dataset):\n\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\n        \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n        row = self.data.iloc[index]      \n        data = self.eegs[row.eeg_id]\n        sample = np.zeros((data.shape[0], 8))  # Assuming data.shape[1] == 8 for 8 channels\n        \n        # Mapping of channel names to indices\n        FEAT2IDX = {'Fp1': 0, 'T3': 1, 'C3': 2, 'O1': 3, 'Fp2': 4, 'C4': 5, 'T4': 6, 'O2': 7}\n        \n        # Compute differences\n        sample[:,0] = data[:,FEAT2IDX['Fp1']] - data[:,FEAT2IDX['T3']]\n        sample[:,1] = data[:,FEAT2IDX['T3']] - data[:,FEAT2IDX['O1']]\n        \n        sample[:,2] = data[:,FEAT2IDX['Fp1']] - data[:,FEAT2IDX['C3']]\n        sample[:,3] = data[:,FEAT2IDX['C3']] - data[:,FEAT2IDX['O1']]\n        \n        sample[:,4] = data[:,FEAT2IDX['Fp2']] - data[:,FEAT2IDX['C4']]\n        sample[:,5] = data[:,FEAT2IDX['C4']] - data[:,FEAT2IDX['O2']]\n        \n        sample[:,6] = data[:,FEAT2IDX['Fp2']] - data[:,FEAT2IDX['T4']]\n        sample[:,7] = data[:,FEAT2IDX['T4']] - data[:,FEAT2IDX['O2']]\n\n        # sample = np.concatenate([sample,data],1)\n        \n        # Feature Engineering on sample instead of data\n        \n        sample = (sample - np.mean(sample, axis=0)) / np.std(sample, axis=0)\n\n        sample = np.clip(sample, -1024, 1024)\n        sample = np.nan_to_num(sample, nan=0)# / 32.0\n        \n        sample = butter_lowpass_filter(sample)\n        sample = quantize_data(sample, 1)\n\n\n        # samples = self.augmentations(samples.unsqueeze(0))\n        # samples = samples.squeeze()\n        # samples = self.augmentations(sample)\n        \n        samples = torch.from_numpy(sample).float()\n\n        \n        # samples = np.stack(samples, axis=0)\n        # samples = torch.from_numpy(samples).float()\n        # samples = samples[::2,:]\n\n        \n        samples = samples.permute(1, 0)\n        if not self.test:\n            label = row[TARGETS]  # Assuming 'TARGETS' is defined somewhere as the label column name\n            label = torch.tensor(label).float()  \n            return samples, label\n        else:\n            return samples\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport numpy as np\nimport pandas as pd\nfrom scipy.signal import butter, lfilter\n\nclass EEGDataset(torch.utils.data.Dataset):\n    def __init__(self, data, eegs=None, specs=None, eeg_specs=None, spec_aug = False, augmentations=None, test=False): \n        self.data = data\n        self.eegs = eegs\n        self.specs = specs  # Spectrograms for each ID\n        self.eeg_specs = eeg_specs  # EEG spectrograms for each ID\n        self.augmentations = augmentations\n        self.test = test\n        self.spec_aug = spec_aug\n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n        row = self.data.iloc[index]\n        \n        # Processing EEG signal data\n        eeg_data = self.eegs[row.eeg_id]\n        sample = np.zeros((eeg_data.shape[0], 8))  # Assuming eeg_data.shape[1] == 8 for 8 channels\n        FEAT2IDX = {'Fp1': 0, 'T3': 1, 'C3': 2, 'O1': 3, 'Fp2': 4, 'C4': 5, 'T4': 6, 'O2': 7}\n        \n        # Compute differences\n        for i, (start, end) in enumerate([('Fp1', 'T3'), ('T3', 'O1'), ('Fp1', 'C3'), ('C3', 'O1'), \n                                          ('Fp2', 'C4'), ('C4', 'O2'), ('Fp2', 'T4'), ('T4', 'O2')]):\n            sample[:, i] = eeg_data[:, FEAT2IDX[start]] - eeg_data[:, FEAT2IDX[end]]\n        \n        sample = self.process_sample(sample)\n        \n        samples = torch.from_numpy(sample).float()\n        samples = samples.permute(1, 0)\n        \n        # Processing spectrogram data\n        spec = self.__spec_data_generation(row)\n        if self.spec_aug:\n            spec = self.__transform(spec) \n        \n        if not self.test:\n            label = row[TARGETS]  # Assuming 'TARGETS' is defined somewhere as the label column name\n            label = torch.tensor(label).float()  \n            return samples, spec, label\n        else:\n            return samples, spec\n    \n    def process_sample(self, sample):\n        # Normalize the sample data\n        sample = (sample - np.mean(sample, axis=0)) / np.std(sample, axis=0)\n        sample = np.clip(sample, -1024, 1024)\n        sample = np.nan_to_num(sample, nan=0)\n        sample = butter_lowpass_filter(sample)\n        sample = quantize_data(sample, 1)\n        return sample\n\n    def __spec_data_generation(self, row):\n        \"\"\"\n        Generates data containing batch_size samples. This method directly\n        uses class attributes for spectrograms and EEG spectrograms.\n        \"\"\"\n        X = np.zeros((128, 256, 8), dtype='float32')        \n        if not self.test:\n            # Assuming your DataFrame has a column that combines min and max values for slicing\n            r = int((row['min'] + row['max']) // 4)\n        else:\n            r = 0  # Adjust as necessary for test mode\n        \n        for region in range(4):\n            img = self.specs[row.spectogram_id][r:r+300, region*100:(region+1)*100].T\n            \n            # Log transform spectogram\n            img = np.clip(img, np.exp(-4), np.exp(8))\n            img = np.log(img)\n\n            # Standarize per image\n            ep = 1e-6\n            mu = np.nanmean(img.flatten())\n            std = np.nanstd(img.flatten())\n            img = (img-mu)/(std+ep)\n            img = np.nan_to_num(img, nan=0.0)\n            X[14:-14, :, region] = img[:, 22:-22] / 2.0\n        \n        # Process EEG spectrogram - assuming a single channel example\n        img = self.eeg_specs[row.eeg_id]\n        X[:, :, 4:] = img                \n        return X\n\n    def __transform(self, img):\n        transforms = A.Compose([\n            A.HorizontalFlip(p=0.5),\n        ])\n        return transforms(image=img)['image']\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from augs_gwnet import *\n\ntransforms = dict(\n    train=Compose([\n        BandPass(lower=12, upper=512),\n        GaussianNoiseSNR(min_snr=15, max_snr=30, p=0.5),\n    ]),\n    test=BandPass(lower=12, upper=512),\n    # tta=BandPass(lower=12, upper=512)\n)\n\ndef get_transforms(*, data):\n    \n    if data == 'train':\n        return Compose(\n                # transforms=transforms\n            [\n                # BandPass(lower=12, upper=512),\n                # GaussianNoiseSNR(min_snr=15, max_snr=30, p=0.5),\n                        FlipWave(p=0.5)\n]\n        )\n\n    elif data == 'valid':\n        return Compose([\n            # BandPass(lower=12, upper=512),\n            \n        ])","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import *\n\ngkf = GroupKFold(n_splits=5)\ntrain['fold'] = 0\nfor fold, (tr_idx, val_idx) in enumerate(gkf.split(train, train.target, train.patient_id)):   \n    train.loc[val_idx, 'fold'] = fold\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train.to_csv('/home/nischay/brain/Data/5gkf_fold.csv',index=False)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.020799,"end_time":"2023-03-11T22:10:21.498018","exception":false,"start_time":"2023-03-11T22:10:21.477219","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_fold_dls(df_train, df_valid):\n\n    \n    ds_train = EEGDataset(\n        df_train, \n        eegs=raw_eegs,\n        specs=all_spectrograms,\n        eeg_specs=all_eegs,\n        augmentations = get_transforms(data='train'),\n        test = False\n    )\n    \n    ds_val = EEGDataset(\n        df_valid, \n        eegs=raw_eegs,\n        specs=all_spectrograms,\n        eeg_specs=all_eegs,\n        augmentations = get_transforms(data='valid'),\n        test = False\n    )\n    dl_train = DataLoader(ds_train, batch_size=Config.batch_size , shuffle=True, num_workers = 2)    \n    dl_val = DataLoader(ds_val, batch_size=Config.batch_size, num_workers = 2)\n    return dl_train, dl_val, ds_train, ds_val","metadata":{"papermill":{"duration":0.016902,"end_time":"2023-03-11T22:10:21.522443","exception":false,"start_time":"2023-03-11T22:10:21.505541","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ndef show_batch(img_ds, num_items, num_rows, num_cols, EEG_IDS, predict_arr=None):\n    fig = plt.figure(figsize=(12, 6))    \n    img_index = np.random.randint(0, len(img_ds)-1, num_items)\n    for index, img_index in enumerate(img_index):  # list first items\n        img,spec, lb = img_ds[img_index]    \n        print(spec.shape)\n        ax = fig.add_subplot(num_rows, num_cols, index + 1, xticks=[], yticks=[])\n        if isinstance(img, torch.Tensor):\n            img = img.detach().numpy()\n            img = img.transpose(1,0)\n            # print(img.shape)\n        offset = 0\n        for j in range(img.shape[-1]):\n            if j != 0: offset -= img[:, j].min()\n            ax.plot(img[:, j] + offset, label=f'feature {j+1}')\n            offset += img[:, j].max() + 1  # Adding 1 for visual separation\n\n        ax.legend()\n        ax.set_title(f'EEG_Id = {EEG_IDS[img_index]}', size=14)\n\n    plt.tight_layout()\n    plt.show()\n","metadata":{"papermill":{"duration":0.017145,"end_time":"2023-03-11T22:10:21.547015","exception":false,"start_time":"2023-03-11T22:10:21.529870","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy_train = train[train['fold']!=0].copy()\ndummy_valid = train[train['fold']==0].copy()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\ndl_train, dl_val, ds_train, ds_val = get_fold_dls(dummy_train, dummy_valid)\nshow_batch(ds_val, 8, 2, 4, EEG_IDS)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\ndl_train, dl_val, ds_train, ds_val = get_fold_dls(dummy_train, dummy_valid)\nshow_batch(ds_train, 8, 2, 4, EEG_IDS)","metadata":{"papermill":{"duration":0.729218,"end_time":"2023-03-11T22:10:22.283711","exception":false,"start_time":"2023-03-11T22:10:21.554493","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Config.num_channels = 16","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, ReduceLROnPlateau, OneCycleLR\n\ndef 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-7, \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_count":null,"outputs":[]},{"cell_type":"code","source":"from torchtoolbox.tools import mixup_data, mixup_criterion\nimport torch.nn as nn\nfrom torch.nn.functional import cross_entropy\nimport torchmetrics\nimport timm","metadata":{"papermill":{"duration":0.28664,"end_time":"2023-03-11T22:10:22.608728","exception":false,"start_time":"2023-03-11T22:10:22.322088","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sklearn.metrics\nimport sys\nsys.path.append('/home/nischay/brain/scripts')\n\nfrom kaggle_kl_div import score\n","metadata":{"papermill":{"duration":0.039494,"end_time":"2023-03-11T22:10:22.663948","exception":false,"start_time":"2023-03-11T22:10:22.624454","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy = dummy_valid.copy()\ndummy[TARGETS] = np.random.rand(dummy.shape[0],len(TARGETS))\n\ndummy[TARGETS] = dummy[TARGETS].div(dummy[TARGETS].sum(axis=1), axis=0)\n","metadata":{"papermill":{"duration":0.125551,"end_time":"2023-03-11T22:10:22.806868","exception":false,"start_time":"2023-03-11T22:10:22.681317","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy_valid.head(3)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy.head(3)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score(dummy_valid[['eeg_id']+list(TARGETS)], dummy[['eeg_id']+list(TARGETS)],row_id_column_name='eeg_id')","metadata":{"papermill":{"duration":1.063221,"end_time":"2023-03-11T22:10:23.885220","exception":false,"start_time":"2023-03-11T22:10:22.821999","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# mixup_criterion(KLDivLossWithLogits)","metadata":{"papermill":{"duration":0.672346,"end_time":"2023-03-11T22:10:24.567443","exception":false,"start_time":"2023-03-11T22:10:23.895097","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"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_count":null,"outputs":[]},{"cell_type":"code","source":"import random\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class EEGModel(pl.LightningModule):\n    def __init__(self, num_classes = Config.num_classes, pretrained = Config.pretrained, fold = fold):\n        super().__init__()\n        self.num_classes = num_classes\n        self.fold = fold\n        # self.backbone = EEGNet(kernels=[3,5,7,9], in_channels=Config.num_channels, fixed_kernel_size=5, num_classes=Config.num_classes)\n        self.backbone = EEGMegaNet(backbone_2d=Config.backbone_2d,\n                                   in_channels_2d=8,\n                                   kernels=[3,5,7,9],pretrained=True,\n                                   in_channels=Config.num_channels,\n                                   fixed_kernel_size=5, num_classes=6)\n\n\n        self.contrastive_loss = nn.CosineEmbeddingLoss()  # Using cosine similarity for contrastive loss\n\n        self.loss_function = KLDivLossWithLogits() #nn.KLDivLoss() #nn.BCEWithLogitsLoss() \n        self.validation_step_outputs = []\n        self.lin = nn.Softmax(dim=1)\n        self.best_score = 1000.0\n    def forward(self,eeg, spec):\n        logits = self.backbone(eeg, spec)\n        # logits = self.lin(logits)\n        return logits\n        \n    def configure_optimizers(self):\n        return get_optimizer(lr=Config.LR, params=self.parameters())\n\n    def training_step(self, batch, batch_idx):\n        eeg, spec, target = batch\n        y_pred, embedding_1d, embedding_2d,yp1,yp2 = self(eeg, spec)\n        classification_loss = self.loss_function(y_pred, target)\n        \n        classification_loss1 = self.loss_function(yp1, target)\n        classification_loss2 = self.loss_function(yp2, target)\n        \n\n        # Calculate contrastive loss \n        \n        embedding_1d = torch.nn.functional.normalize(embedding_1d, p=2, dim=1)\n        embedding_2d = torch.nn.functional.normalize(embedding_2d, p=2, dim=1)\n\n        \n        contrastive_target = torch.ones(embedding_1d.size(0)).to(self.device)  # Assuming all pairs are similar\n        contrastive_loss = self.contrastive_loss(embedding_1d, embedding_2d, contrastive_target)\n\n        total_loss = classification_loss + classification_loss1*0.5 + classification_loss2*0.5 + contrastive_loss*0.5  # Aux losses\n\n        self.log(\"train_loss\", total_loss, on_step=True, on_epoch=True, prog_bar=True)\n        return total_loss     \n\n    def validation_step(self, batch, batch_idx):\n        eeg, spec, target = batch \n        # print(target)\n        y_pred,_,_,y1d,y2d = self(eeg, spec)\n\n        y_pred = y_pred*0.5 + y1d*0.25 + y2d*0.25\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)\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        avg_score = avg_loss\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'{Config.output_dir}/val_df_f{self.fold}.csv',index=False)\n            # pred_df.to_csv(f'{Config.output_dir}/pred_df_f{self.fold}.csv',index=False)\n        \n        return {'val_loss': avg_loss,'val_cmap':avg_score}\n    \n","metadata":{"papermill":{"duration":0.032359,"end_time":"2023-03-11T22:10:24.859359","exception":false,"start_time":"2023-03-11T22:10:24.827000","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\ntqdm.pandas()","metadata":{},"execution_count":null,"outputs":[]},{"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,x2, y = batch\n            x = x.cuda()\n            x2 = x2.cuda()\n            \n            # inputs = {key:val.reshape(val.shape[0], -1).to(config.device) for key,val in batch.items()}\n            outputs,_,_,y1,y2 = model(x, x2)\n            \n            outputs = outputs*0.5 + y1*0.25 + y2*0.25\n            \n            outputs = nn.Softmax(dim=1)(outputs)\n        predictions.extend(outputs.detach().cpu().numpy())\n    predictions = np.vstack(predictions)\n    return predictions\n\ndef predict2(ds_test, model):\n    \n    model.to('cuda')\n    model.eval()    \n    predictions = []\n    for en in tqdm(range(len(ds_test))):\n        # print(en)\n        x,_ = ds_test[en]\n        x = x.unsqueeze(0).cuda()\n        # print(images.shape)\n        with torch.no_grad():\n            outputs,_,_ = model(x)\n            outputs = nn.Softmax(dim=1)(outputs)\n            outputs = outputs.detach().cpu().numpy()\n\n        predictions.append(outputs)\n        \n    return predictions","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pytorch_lightning.loggers import WandbLogger\nimport gc\ntorch.set_float32_matmul_precision('high')\ndef run_training(fold_id, Config):\n    print(f\"Running training for fold {fold_id}...\")\n    logger = None\n    pred_cols = [f'pred_{t}' for t in TARGETS]\n    \n    df_train = train[train['fold']!=fold_id].copy()\n    df_valid = train[train['fold']==fold_id].copy()\n\n    print(len(df_train),'train length')\n    print(len(df_valid),'valid length')\n    \n    dl_train, dl_val, ds_train, ds_val = get_fold_dls(df_train, df_valid)\n    \n    eeg_model = EEGModel(num_classes = Config.num_classes, pretrained = Config.pretrained, fold = fold_id)\n\n    \n    early_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=0.00, patience=Config.PATIENCE, verbose= True, mode=\"min\")\n    checkpoint_callback = ModelCheckpoint(monitor='val_loss',\n                                          dirpath= f\"{Config.output_dir}/\",\n                                      save_top_k=1,\n                                      save_last= True,\n                                      save_weights_only=False,\n                                      filename= f'eegnet_best_loss_fold{fold_id}',\n                                      verbose= True,\n                                      mode='min')\n    \n    callbacks_to_use = [checkpoint_callback,early_stop_callback]\n\n\n    trainer = pl.Trainer(\n        # gpus=3,\n        devices=[1],\n        \n        val_check_interval=0.5,\n        deterministic=True,\n        max_epochs=Config.epochs,\n        # max_epochs=3,\n        \n        logger=logger,\n        callbacks=callbacks_to_use,\n        precision=Config.PRECISION,\n        accelerator=\"gpu\" \n    )\n    \n\n    print(\"Running trainer.fit\")\n    trainer.fit(eeg_model, train_dataloaders = dl_train, val_dataloaders = dl_val)                \n    # trainer.\n\n    model = EEGModel.load_from_checkpoint(f'{Config.output_dir}/eegnet_best_loss_fold{fold_id}.ckpt',train_dataloader=None,validation_dataloader=None,config=Config)    \n    preds = predict(dl_val, model)  \n    print(preds.shape)\n    df_valid[pred_cols] = preds\n    df_valid.to_csv(f'{Config.output_dir}/pred_df_f{fold_id}.csv',index=False)\n    gc.collect()\n    # torch.cuda.empty_cache()\n    return preds\n    ","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_count":null,"outputs":[]},{"cell_type":"code","source":"# list(train[train['fold']==0].index)\n\n\n##########################  \n########################## \n##- 0.8069934784657877 -## \n##- 0.7036157331012920 -## \n##= 0.7465863334992089 -## \n##= 0.7355040572184077 -## \n##= 0.6818253941807645 =##  \n########################## \n########################## \n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# run_training()\n\noof_df = train.copy()\npred_cols = [f'pred_{t}' for t in TARGETS]\noof_df[pred_cols] = 0.0\nfor f in Config.trn_folds:\n    val_idx = list(train[train['fold']==f].index)\n    print(len(val_idx))\n    val_preds = run_training(f, Config)    \n    # val_df = pd.read_csv(f'{Config.output_dir}/val_df_f{f}.csv')\n    # pred_df = pd.read_csv(f'{Config.output_dir}/pred_df_f{f}.csv')\n    oof_df.loc[val_idx, pred_cols] = val_preds\n    ","metadata":{"papermill":{"duration":655.665744,"end_time":"2023-03-11T22:21:20.573290","exception":false,"start_time":"2023-03-11T22:10:24.907546","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof_df","metadata":{"papermill":{"duration":0.012965,"end_time":"2023-03-11T22:21:21.693668","exception":false,"start_time":"2023-03-11T22:21:21.680703","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof_pred_df= oof_df[['eeg_id'] + list(['pred_'+i for i in TARGETS])]\noof_pred_df.columns = ['eeg_id'] + list(TARGETS)\n\noof_true_df = oof_df[oof_pred_df.columns].copy()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof_score = score(solution=oof_true_df, submission=oof_pred_df, row_id_column_name='eeg_id')\nprint('OOF Score for solution =',oof_score)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nConfig.output_dir\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\noof_df.to_csv(f'{Config.output_dir}/oof.csv',index=False)\n# pred_df[TARGETS].values.shape\n# a.sum(axis=1)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_idx = list(train[train['fold']==0].index)\noof_df.loc[val_idx, TARGETS]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n# F0:0.803\n# F1:0.719\n# F2: 0.761\n# F3: 0.743\n# F4: 0.717\n\n### 0.OOF Score for solution = 0.749223413337422\n\n\n\n# F0:0.797\n# F1:0.703\n# F2: 0.781\n# F3: 0.696\n# F4: 0.705\n\n### 0.OOF Score for solution = 0.736\n\n\n# F0:0.776\n# F1:0.691\n# F2: 0.759\n# F3: 0.743\n# F4: 0.718\n\n### 0.OOF Score for solution = 0.7376\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.01258,"end_time":"2023-03-11T22:21:21.719118","exception":false,"start_time":"2023-03-11T22:21:21.706538","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}