Optiver Realized Volatility Prediction - 1D CNN.ipynb

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   "source": [
    "import os\n",
    "import random\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader, SequentialSampler, RandomSampler\n",
    "import torch.optim as optim\n",
    "from fastai.layers import SigmoidRange"
   ]
  },
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   "source": [
    "class PreprocessingPipeline:\n",
    "    \n",
    "    def __init__(self, df_train, df_test, n_splits, shuffle, random_state):\n",
    "        \n",
    "        self.df_train = df_train.copy(deep=True)\n",
    "        self.df_test = df_test.copy(deep=True)\n",
    "        self.n_splits = n_splits\n",
    "        self.shuffle = shuffle\n",
    "        self.random_state = random_state\n",
    "        \n",
    "    def _label_encode(self):\n",
    "\n",
    "        # Encoding stock_id for embeddings\n",
    "        le = LabelEncoder()\n",
    "        self.df_train['stock_id_encoded'] = le.fit_transform(self.df_train['stock_id'].values)\n",
    "        self.df_test['stock_id_encoded'] = le.transform(self.df_test['stock_id'].values)\n",
    "    \n",
    "    def _get_folds(self):\n",
    "        \n",
    "        skf = StratifiedKFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state)\n",
    "        for fold, (_, val_idx) in enumerate(skf.split(X=self.df_train, y=self.df_train['stock_id']), 1):\n",
    "            df_train.loc[val_idx, 'fold'] = fold\n",
    "        self.df_train['fold'] = df_train['fold'].astype(np.uint8)\n",
    "            \n",
    "    def transform(self):\n",
    "        \n",
    "        self._label_encode()\n",
    "        self._get_folds()\n",
    "        \n",
    "        return self.df_train, self.df_test\n"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Shape: (428932, 5) - Memory Usage: 8.18 MB\n",
      "Test Set Shape: (3, 3) - Memory Usage: 0.00 MB\n"
     ]
    }
   ],
   "source": [
    "train_test_dtypes = {\n",
    "    'stock_id': np.uint8,\n",
    "    'time_id': np.uint16,\n",
    "    'target': np.float64\n",
    "}\n",
    "\n",
    "df_train = pd.read_csv('../input/optiver-realized-volatility-prediction/train.csv', dtype=train_test_dtypes)\n",
    "df_test = pd.read_csv('../input/optiver-realized-volatility-prediction/test.csv', usecols=['stock_id', 'time_id'], dtype=train_test_dtypes)\n",
    "\n",
    "preprocessing_parameters = {\n",
    "    'df_train': df_train,\n",
    "    'df_test': df_test,\n",
    "    'n_splits': 5,\n",
    "    'shuffle': True,\n",
    "    'random_state': 42\n",
    "}\n",
    "\n",
    "preprocessing_pipeline = PreprocessingPipeline(**preprocessing_parameters)\n",
    "df_train, df_test = preprocessing_pipeline.transform()\n",
    "\n",
    "print(f'Training Set Shape: {df_train.shape} - Memory Usage: {df_train.memory_usage().sum() / 1024 ** 2:.2f} MB')\n",
    "print(f'Test Set Shape: {df_test.shape} - Memory Usage: {df_test.memory_usage().sum() / 1024 ** 2:.2f} MB')"
   ]
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    "def visualize_learning_curve(training_losses, validation_losses, title, path=None):\n",
    "    \n",
    "    \"\"\"\n",
    "    Visualize learning curves of the models\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    training_losses [array-like of shape (n_epochs)]: Array of training losses computed after every epoch\n",
    "    validation_losses [array-like of shape (n_epochs)]: Array of validation losses computed after every epoch\n",
    "    title (str): Title of the plot\n",
    "    path (str or None): Path of the output file (if path is None, plot is displayed with selected backend)\n",
    "    \"\"\"\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(32, 8), dpi=100)\n",
    "\n",
    "    sns.lineplot(\n",
    "        x=np.arange(1, len(training_losses) + 1),\n",
    "        y=training_losses,\n",
    "        ax=ax,\n",
    "        label='train_loss'\n",
    "    )\n",
    "    sns.lineplot(\n",
    "        x=np.arange(1, len(validation_losses) + 1),\n",
    "        y=validation_losses,\n",
    "        ax=ax,\n",
    "        label='val_loss'\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('Epochs/Steps', size=15, labelpad=12.5)\n",
    "    ax.set_ylabel('Loss', size=15, labelpad=12.5)\n",
    "    ax.tick_params(axis='x', labelsize=12.5, pad=10)\n",
    "    ax.tick_params(axis='y', labelsize=12.5, pad=10)\n",
    "    ax.legend(prop={'size': 18})\n",
    "    ax.set_title(title, size=20, pad=15)\n",
    "\n",
    "    if path is None:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.savefig(path)\n"
   ]
  },
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   "source": [
    "def set_seed(seed, deterministic_cudnn=False):\n",
    "\n",
    "    \"\"\"\n",
    "    Set random seed for reproducible results\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    seed (int): Random seed\n",
    "    deterministic_cudnn (bool): Whether to set deterministic cuDNN or not\n",
    "    \"\"\"\n",
    "\n",
    "    if deterministic_cudnn:\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "\n",
    "def rmspe_metric(y_true, y_pred):\n",
    "\n",
    "    \"\"\"\n",
    "    Calculate root mean squared percentage error between ground-truth and predictions\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true [array-like of shape (n_samples)]: Ground-truth\n",
    "    y_pred [array-like of shape (n_samples)]: Predictions\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    rmspe (float): Root mean squared percentage error\n",
    "    \"\"\"\n",
    "\n",
    "    rmspe = np.sqrt(np.mean(np.square((y_true - y_pred) / y_true)))\n",
    "    return rmspe\n",
    "\n",
    "\n",
    "def rmspe_loss(y_true, y_pred):\n",
    "\n",
    "    \"\"\"\n",
    "    Calculate root mean squared percentage error between ground-truth and predictions\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true [torch.tensor of shape (n_samples)]: Ground-truth\n",
    "    y_pred [torch.tensor of shape (n_samples)]: Predictions\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    rmspe (torch.FloatTensor): Root mean squared percentage error\n",
    "    \"\"\"\n",
    "\n",
    "    rmspe = torch.sqrt(torch.mean(torch.square((y_true - y_pred) / y_true)))\n",
    "    return rmspe\n"
   ]
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   "source": [
    "class Optiver2DDataset(Dataset):\n",
    "\n",
    "    def __init__(self, df, flip_probability=0.):\n",
    "\n",
    "        self.df = df\n",
    "        # Normalizing sequences with global means and stds across stocks\n",
    "        book_means = np.array([\n",
    "            0.99969482421875, 1.000321388244629, 0.9995064735412598, 1.0005191564559937,\n",
    "            769.990177708821, 766.7345672818379, 959.3416027831918, 928.2202512713748,\n",
    "            1.0000068043192514, 1.0000055320253616, 5.129816581143487e-08, 9.831598141593519e-08\n",
    "        ])\n",
    "        book_stds = np.array([\n",
    "            0.0036880988627672195, 0.003687119111418724, 0.0037009266670793295, 0.0036990800872445107,\n",
    "            5354.051690318169, 4954.947103063445, 6683.816183660414, 5735.299917793827,\n",
    "            0.003689893218043926, 0.00370745215558702, 6.618708642293018e-07, 1.2508970015188411e-06\n",
    "        ])\n",
    "        # Not normalizing trade price and trade price log returns because of the sparsity\n",
    "        trade_means = np.array([0, 352.9736760331942, 4.1732040971227145, 0])\n",
    "        trade_stds = np.array([1, 1041.9441951057488, 7.79955795393431, 1])\n",
    "\n",
    "        self.transforms = {\n",
    "            'flip': flip_probability,\n",
    "            'normalize': {\n",
    "                'book_means': book_means,\n",
    "                'book_stds': book_stds,\n",
    "                'trade_means': trade_means,\n",
    "                'trade_stds': trade_stds\n",
    "            }\n",
    "        }\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "\n",
    "        \"\"\"\n",
    "        Get the idxth element in the dataset\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        idx (int): Index of the sample (0 <= idx < len(self.df))\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        stock_id_encoded [torch.LongTensor of shape (1)]: Encoded stock_id for stock embeddings\n",
    "        sequences [torch.FloatTensor of shape (600, 16)]: Concatenated sequences from book and trade data\n",
    "        target [torch.Tensor of shape (1)]: Target\n",
    "        \"\"\"\n",
    "\n",
    "        sample = self.df.iloc[idx]\n",
    "        stock_id = int(sample['stock_id'])\n",
    "        time_id = int(sample['time_id'])\n",
    "\n",
    "        # Sequences from book data\n",
    "        book_sequences = np.load(f'../input/optiver-realized-volatility-npy-files/book_train/book_train/stock_{stock_id}/time_{time_id}.npy')\n",
    "        book_wap1 = (book_sequences[:, 0] * book_sequences[:, 5] + book_sequences[:, 1] * book_sequences[:, 4]) /\\\n",
    "                    (book_sequences[:, 4] + book_sequences[:, 5])\n",
    "        book_wap2 = (book_sequences[:, 2] * book_sequences[:, 7] + book_sequences[:, 3] * book_sequences[:, 6]) /\\\n",
    "                    (book_sequences[:, 6] + book_sequences[:, 7])\n",
    "        book_wap1_log = np.log(book_wap1)\n",
    "        book_wap1_log_returns = np.diff(book_wap1_log, prepend=[book_wap1_log[0]])\n",
    "        book_wap2_log = np.log(book_wap2)\n",
    "        book_wap2_log_returns = np.diff(book_wap2_log, prepend=[book_wap2_log[0]])\n",
    "        book_sequences = np.hstack([\n",
    "            book_sequences,\n",
    "            book_wap1.reshape(-1, 1),\n",
    "            book_wap2.reshape(-1, 1),\n",
    "            book_wap1_log_returns.reshape(-1, 1),\n",
    "            book_wap2_log_returns.reshape(-1, 1),\n",
    "        ])\n",
    "        book_sequences = (book_sequences - self.transforms['normalize']['book_means']) / self.transforms['normalize']['book_stds']\n",
    "\n",
    "        # Sequences from trade data\n",
    "        trade_sequences = np.load(f'../input/optiver-realized-volatility-npy-files/trade_train/trade_train/stock_{stock_id}/time_{time_id}.npy')\n",
    "        trade_price_log1p = np.log1p(trade_sequences[:, 0])\n",
    "        trade_price_log_returns = np.diff(trade_price_log1p, prepend=trade_price_log1p[0])\n",
    "        trade_sequences = np.hstack([trade_sequences, trade_price_log_returns.reshape(-1, 1)])\n",
    "\n",
    "        # Concatenate book and trade sequences\n",
    "        sequences = np.hstack([book_sequences, trade_sequences])\n",
    "        sequences = torch.as_tensor(sequences, dtype=torch.float)\n",
    "\n",
    "        # Flip sequences on zeroth dimension\n",
    "        if np.random.rand() < self.transforms['flip']:\n",
    "            sequences = torch.flip(sequences, dims=[0])\n",
    "\n",
    "        stock_id_encoded = torch.as_tensor(sample['stock_id_encoded'], dtype=torch.long)\n",
    "        target = sample['target']\n",
    "        target = torch.as_tensor(target, dtype=torch.float)\n",
    "        return stock_id_encoded, sequences, target\n"
   ]
  },
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   "source": [
    "class Conv1dBlock(nn.Module):\n",
    "\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=(5,), stride=(1,), padding=(2,), skip_connection=False):\n",
    "\n",
    "        super(Conv1dBlock, self).__init__()\n",
    "\n",
    "        self.skip_connection = skip_connection\n",
    "        self.conv_block = nn.Sequential(\n",
    "            nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, padding_mode='replicate', bias=True),\n",
    "            nn.BatchNorm1d(out_channels),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, padding_mode='replicate', bias=True),\n",
    "            nn.BatchNorm1d(out_channels),\n",
    "        )\n",
    "        self.downsample = nn.Sequential(\n",
    "            nn.Conv1d(in_channels, out_channels, kernel_size=(1,), stride=(1,), bias=False),\n",
    "            nn.BatchNorm1d(out_channels)\n",
    "        )\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        output = self.conv_block(x)\n",
    "        if self.skip_connection:\n",
    "            x = self.downsample(x)\n",
    "            output += x\n",
    "        output = self.relu(output)\n",
    "\n",
    "        return output\n",
    "\n",
    "\n",
    "class CNN1DModel(nn.Module):\n",
    "\n",
    "    def __init__(self, in_channels):\n",
    "\n",
    "        super(CNN1DModel, self).__init__()\n",
    "\n",
    "        self.stock_embeddings = nn.Embedding(num_embeddings=113, embedding_dim=16)\n",
    "        self.conv_block1 = Conv1dBlock(in_channels=in_channels, out_channels=32, skip_connection=True)\n",
    "        self.conv_block2 = Conv1dBlock(in_channels=32, out_channels=64, skip_connection=True)\n",
    "        self.conv_block3 = Conv1dBlock(in_channels=64, out_channels=128, skip_connection=True)\n",
    "        self.conv_block4 = Conv1dBlock(in_channels=128, out_channels=64, skip_connection=True)\n",
    "        self.conv_block5 = Conv1dBlock(in_channels=64, out_channels=32, skip_connection=True)\n",
    "        self.conv_block6 = Conv1dBlock(in_channels=32, out_channels=16, skip_connection=True)\n",
    "        self.conv_block7 = Conv1dBlock(in_channels=16, out_channels=8, skip_connection=True)\n",
    "        self.conv_block8 = Conv1dBlock(in_channels=8, out_channels=1, skip_connection=True)\n",
    "        self.pooling = nn.AvgPool1d(kernel_size=(3,), stride=(1,), padding=(1,))\n",
    "        self.linear = nn.Linear(616, 256, bias=True)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(0.25)\n",
    "        self.head = nn.Sequential(\n",
    "            nn.Linear(256, 1, bias=True),\n",
    "            SigmoidRange(0, 0.1)\n",
    "        )\n",
    "\n",
    "    def forward(self, stock_ids, sequences):\n",
    "\n",
    "        x = torch.transpose(sequences, 1, 2)\n",
    "        x = self.conv_block1(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block2(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block3(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block4(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block5(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block6(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block7(x)\n",
    "        x = self.pooling(x)\n",
    "        x = self.conv_block8(x)\n",
    "        x = self.pooling(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        embedded_stock_ids = self.stock_embeddings(stock_ids)\n",
    "        x = torch.cat([x, self.dropout(embedded_stock_ids)], dim=1)\n",
    "        x = self.relu(self.linear(x))\n",
    "        output = self.head(x)\n",
    "        \n",
    "        return output.view(-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7f169b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-15T12:30:00.520929Z",
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   "source": [
    "class Trainer:\n",
    "\n",
    "    def __init__(self, model_name, model_path, model_parameters, training_parameters):\n",
    "\n",
    "        self.model_name = model_name\n",
    "        self.model_path = model_path\n",
    "        self.model_parameters = model_parameters\n",
    "        self.training_parameters = training_parameters\n",
    "\n",
    "    def get_model(self):\n",
    "\n",
    "        model = None\n",
    "\n",
    "        if self.model_name == 'cnn1d':\n",
    "            model = CNN1DModel(**self.model_parameters)\n",
    "\n",
    "        return model\n",
    "\n",
    "    def train_fn(self, train_loader, model, criterion, optimizer, device):\n",
    "\n",
    "        print('\\n')\n",
    "        model.train()\n",
    "        progress_bar = tqdm(train_loader)\n",
    "        losses = []\n",
    "\n",
    "        if self.training_parameters['amp']:\n",
    "            scaler = torch.cuda.amp.GradScaler()\n",
    "        else:\n",
    "            scaler = None\n",
    "\n",
    "        for stock_id_encoded, sequences, target in progress_bar:\n",
    "            \n",
    "            stock_id_encoded, sequences, target = stock_id_encoded.to(device), sequences.to(device), target.to(device)\n",
    "\n",
    "            if scaler is not None:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                    optimizer.zero_grad()\n",
    "                    output = model(stock_id_encoded, sequences)\n",
    "                    loss = criterion(target, output)\n",
    "                scaler.scale(loss).backward()\n",
    "                scaler.step(optimizer)\n",
    "                scaler.update()\n",
    "            else:\n",
    "                optimizer.zero_grad()\n",
    "                output = model(stock_id_encoded, sequences)\n",
    "                loss = criterion(target, output)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "            losses.append(loss.item())\n",
    "            average_loss = np.mean(losses)\n",
    "            progress_bar.set_description(f'train_rmspe: {average_loss:.6f}')\n",
    "\n",
    "        train_loss = np.mean(losses)\n",
    "        return train_loss\n",
    "\n",
    "    def val_fn(self, val_loader, model, criterion, device):\n",
    "\n",
    "        model.eval()\n",
    "        progress_bar = tqdm(val_loader)\n",
    "        losses = []\n",
    "\n",
    "        with torch.no_grad():\n",
    "            \n",
    "            for stock_id_encoded, sequences, target in progress_bar:\n",
    "                \n",
    "                stock_id_encoded, sequences, target = stock_id_encoded.to(device), sequences.to(device), target.to(device)\n",
    "                output = model(stock_id_encoded, sequences)\n",
    "                loss = criterion(target, output)\n",
    "                losses.append(loss.item())\n",
    "                average_loss = np.mean(losses)\n",
    "                progress_bar.set_description(f'val_rmspe: {average_loss:.6f}')\n",
    "\n",
    "        val_loss = np.mean(losses)\n",
    "        return val_loss\n",
    "\n",
    "    def train_and_validate(self, df_train):\n",
    "\n",
    "        print(f'\\n{\"-\" * 26}\\nRunning Model for Training\\n{\"-\" * 26}\\n')\n",
    "\n",
    "        for fold in sorted(df_train['fold'].unique()):\n",
    "\n",
    "            print(f'\\nFold {fold}\\n{\"-\" * 6}')\n",
    "\n",
    "            trn_idx, val_idx = df_train.loc[df_train['fold'] != fold].index, df_train.loc[df_train['fold'] == fold].index\n",
    "            train_dataset = Optiver2DDataset(df=df_train.loc[trn_idx, :], flip_probability=0.)\n",
    "            train_loader = DataLoader(\n",
    "                train_dataset,\n",
    "                batch_size=self.training_parameters['batch_size'],\n",
    "                sampler=RandomSampler(train_dataset),\n",
    "                pin_memory=True,\n",
    "                drop_last=False,\n",
    "                num_workers=self.training_parameters['num_workers'],\n",
    "            )\n",
    "            val_dataset = Optiver2DDataset(df=df_train.loc[val_idx, :], flip_probability=0.)\n",
    "            val_loader = DataLoader(\n",
    "                val_dataset,\n",
    "                batch_size=self.training_parameters['batch_size'],\n",
    "                sampler=SequentialSampler(val_dataset),\n",
    "                pin_memory=True,\n",
    "                drop_last=False,\n",
    "                num_workers=self.training_parameters['num_workers'],\n",
    "            )\n",
    "\n",
    "            set_seed(self.training_parameters['random_state'], deterministic_cudnn=self.training_parameters['deterministic_cudnn'])\n",
    "            device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "            model = self.get_model()\n",
    "            model = model.to(device)\n",
    "\n",
    "            criterion = rmspe_loss\n",
    "            optimizer = optim.Adam(\n",
    "                model.parameters(),\n",
    "                lr=self.training_parameters['learning_rate'],\n",
    "                betas=self.training_parameters['betas'],\n",
    "                weight_decay=self.training_parameters['weight_decay']\n",
    "            )\n",
    "            scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
    "                optimizer,\n",
    "                mode='min',\n",
    "                patience=self.training_parameters['reduce_lr_patience'],\n",
    "                factor=self.training_parameters['reduce_lr_factor'],\n",
    "                min_lr=self.training_parameters['reduce_lr_min'],\n",
    "                verbose=True\n",
    "            )\n",
    "\n",
    "            early_stopping = False\n",
    "            summary = {\n",
    "                'train_loss': [],\n",
    "                'val_loss': []\n",
    "            }\n",
    "\n",
    "            for epoch in range(1, self.training_parameters['epochs'] + 1):\n",
    "\n",
    "                if early_stopping:\n",
    "                    break\n",
    "\n",
    "                train_loss = self.train_fn(train_loader, model, criterion, optimizer, device)\n",
    "                val_loss = self.val_fn(val_loader, model, criterion, device)\n",
    "                print(f'Epoch {epoch} - Training Loss: {train_loss:.6f} - Validation Loss: {val_loss:.6f}')\n",
    "                scheduler.step(val_loss)\n",
    "\n",
    "                best_val_loss = np.min(summary['val_loss']) if len(summary['val_loss']) > 0 else np.inf\n",
    "                if val_loss < best_val_loss:\n",
    "                    model_path = f'{self.model_path}/{self.model_name}_fold{fold}.pt'\n",
    "                    torch.save(model.state_dict(), model_path)\n",
    "                    print(f'Saving model to {model_path} (validation loss decreased from {best_val_loss:.6f} to {val_loss:.6f})')\n",
    "\n",
    "                summary['train_loss'].append(train_loss)\n",
    "                summary['val_loss'].append(val_loss)\n",
    "\n",
    "                best_iteration = np.argmin(summary['val_loss']) + 1\n",
    "                if len(summary['val_loss']) - best_iteration >= self.training_parameters['early_stopping_patience']:\n",
    "                    print(f'Early stopping (validation loss didn\\'t increase for {self.training_parameters[\"early_stopping_patience\"]} epochs/steps)')\n",
    "                    print(f'Best validation loss is {np.min(summary[\"val_loss\"]):.6f}')\n",
    "                    draw_learning_curve(\n",
    "                        training_losses=summary['train_loss'],\n",
    "                        validation_losses=summary['val_loss'],\n",
    "                        title=f'{self.model_name} - Fold {fold} Learning Curve',\n",
    "                        path=f'{self.model_path}/{self.model_name}_fold{fold}_learning_curve.png'\n",
    "                    )\n",
    "                    early_stopping = True\n",
    "\n",
    "    def inference(self, df_train):\n",
    "\n",
    "        print(f'\\n{\"-\" * 27}\\nRunning Model for Inference\\n{\"-\" * 27}')\n",
    "        df_train[f'{self.model_name}_predictions'] = 0\n",
    "\n",
    "        for fold in sorted(df_train['fold'].unique()):\n",
    "\n",
    "            _, val_idx = df_train.loc[df_train['fold'] != fold].index, df_train.loc[df_train['fold'] == fold].index\n",
    "            val_dataset = Optiver2DDataset(df=df_train.loc[val_idx, :], flip_probability=0.)\n",
    "            val_loader = DataLoader(\n",
    "                val_dataset,\n",
    "                batch_size=self.training_parameters['batch_size'],\n",
    "                sampler=SequentialSampler(val_dataset),\n",
    "                pin_memory=True,\n",
    "                drop_last=False,\n",
    "                num_workers=self.training_parameters['num_workers'],\n",
    "            )\n",
    "\n",
    "            set_seed(self.training_parameters['random_state'], deterministic_cudnn=self.training_parameters['deterministic_cudnn'])\n",
    "            device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "            model = self.get_model()\n",
    "            model.load_state_dict(torch.load(f'{self.model_path}/{self.model_name}_fold{fold}.pt'))\n",
    "            model.to(device)\n",
    "            model.eval()\n",
    "\n",
    "            val_predictions = []\n",
    "            with torch.no_grad():\n",
    "                for stock_id, sequences, target in val_loader:\n",
    "                    stock_id, sequences, target = stock_id.to(device), sequences.to(device), target.to(device)\n",
    "                    output = model(stock_id, sequences)\n",
    "                    output = output.detach().cpu().numpy().squeeze().tolist()\n",
    "                    val_predictions += output\n",
    "\n",
    "            df_train.loc[val_idx, f'{self.model_name}_predictions'] = val_predictions\n",
    "            fold_score = rmspe_metric(df_train.loc[val_idx, 'target'], val_predictions)\n",
    "            print(f'Fold {fold} - RMSPE: {fold_score:.6}')\n",
    "\n",
    "            del _, val_idx, val_dataset, val_loader, val_predictions, model\n",
    "\n",
    "        print(f'{\"-\" * 30}')\n",
    "        for stock_id in df_train['stock_id'].unique():\n",
    "            df_stock = df_train.loc[df_train['stock_id'] == stock_id, :]\n",
    "            stock_oof_score = rmspe_metric(df_stock['target'], df_stock[f'{self.model_name}_predictions'])\n",
    "            print(f'Stock {stock_id} - RMSPE: {stock_oof_score:.6}')\n",
    "\n",
    "        oof_score = rmspe_metric(df_train['target'], df_train[f'{self.model_name}_predictions'])\n",
    "        print(f'{\"-\" * 30}\\nOOF RMSPE: {oof_score:.6}\\n{\"-\" * 30}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d39c2590",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-15T12:30:00.547504Z",
     "iopub.status.busy": "2021-08-15T12:30:00.546980Z",
     "iopub.status.idle": "2021-08-15T16:02:28.782623Z",
     "shell.execute_reply": "2021-08-15T16:02:28.782159Z"
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    "papermill": {
     "duration": 12748.249843,
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     "exception": false,
     "start_time": "2021-08-15T12:30:00.532916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------\n",
      "Running Model for Training\n",
      "--------------------------\n",
      "\n",
      "\n",
      "Fold 1\n",
      "------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 - Training Loss: 0.410583 - Validation Loss: 0.321185\n",
      "Saving model to ./cnn1d_fold1.pt (validation loss decreased from inf to 0.321185)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
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       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     "metadata": {},
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     "data": {
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      "text/plain": [
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 - Training Loss: 0.260854 - Validation Loss: 0.259897\n",
      "Saving model to ./cnn1d_fold1.pt (validation loss decreased from 0.321185 to 0.259897)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd6f81c0d092439aa0a10ef5e22596eb",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/336 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 - Training Loss: 0.254241 - Validation Loss: 0.245815\n",
      "Saving model to ./cnn1d_fold1.pt (validation loss decreased from 0.259897 to 0.245815)\n",
      "\n",
      "Fold 2\n",
      "------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddf3fa0295b54282ba58fbd2a141420d",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 - Training Loss: 0.367783 - Validation Loss: 0.250602\n",
      "Saving model to ./cnn1d_fold2.pt (validation loss decreased from inf to 0.250602)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    {
     "data": {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/336 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 - Training Loss: 0.260301 - Validation Loss: 0.250032\n",
      "Saving model to ./cnn1d_fold2.pt (validation loss decreased from 0.250602 to 0.250032)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    {
     "data": {
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      "text/plain": [
       "  0%|          | 0/336 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 - Training Loss: 0.258152 - Validation Loss: 0.261234\n",
      "\n",
      "Fold 3\n",
      "------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "964e4a151ac84a8197b22f756e2a24ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "  0%|          | 0/336 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 - Training Loss: 0.375740 - Validation Loss: 0.249154\n",
      "Saving model to ./cnn1d_fold3.pt (validation loss decreased from inf to 0.249154)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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       "  0%|          | 0/1341 [00:00<?, ?it/s]"
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      "Epoch 2 - Training Loss: 0.262034 - Validation Loss: 0.245680\n",
      "Saving model to ./cnn1d_fold3.pt (validation loss decreased from 0.249154 to 0.245680)\n",
      "\n",
      "\n"
     ]
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     "text": [
      "Epoch 3 - Training Loss: 0.256575 - Validation Loss: 0.250001\n",
      "\n",
      "Fold 4\n",
      "------\n",
      "\n",
      "\n"
     ]
    },
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     "text": [
      "Epoch 1 - Training Loss: 0.376361 - Validation Loss: 0.250872\n",
      "Saving model to ./cnn1d_fold4.pt (validation loss decreased from inf to 0.250872)\n",
      "\n",
      "\n"
     ]
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     "text": [
      "Epoch 2 - Training Loss: 0.260213 - Validation Loss: 0.240199\n",
      "Saving model to ./cnn1d_fold4.pt (validation loss decreased from 0.250872 to 0.240199)\n",
      "\n",
      "\n"
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      "Epoch 3 - Training Loss: 0.258843 - Validation Loss: 0.244607\n",
      "\n",
      "Fold 5\n",
      "------\n",
      "\n",
      "\n"
     ]
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     "text": [
      "Epoch 1 - Training Loss: 0.370173 - Validation Loss: 0.245231\n",
      "Saving model to ./cnn1d_fold5.pt (validation loss decreased from inf to 0.245231)\n",
      "\n",
      "\n"
     ]
    },
    {
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     "text": [
      "Epoch 2 - Training Loss: 0.263025 - Validation Loss: 0.246693\n",
      "\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3 - Training Loss: 0.257934 - Validation Loss: 0.237846\n",
      "Saving model to ./cnn1d_fold5.pt (validation loss decreased from 0.245231 to 0.237846)\n"
     ]
    }
   ],
   "source": [
    "cnn1d_parameters = {\n",
    "    'model_name': 'cnn1d',\n",
    "    'model_path': '.',\n",
    "    'model_parameters': {\n",
    "        'in_channels': 16,\n",
    "    },\n",
    "    'training_parameters': {\n",
    "        'amp': False,\n",
    "        'learning_rate': 0.0005,\n",
    "        'betas': (0.9, 0.999),\n",
    "        'weight_decay': 0,\n",
    "        'epochs': 3,\n",
    "        'batch_size': 256,\n",
    "        'reduce_lr_patience': 5,\n",
    "        'reduce_lr_factor': 0.25,\n",
    "        'reduce_lr_min': 0.000001,\n",
    "        'early_stopping_patience': 20,\n",
    "        'num_workers': 8,\n",
    "        'random_state': 42,\n",
    "        'deterministic_cudnn': False,\n",
    "        'random_state': 42\n",
    "    }\n",
    "}\n",
    "\n",
    "trainer = Trainer(**cnn1d_parameters)\n",
    "trainer.train_and_validate(df_train)"
   ]
  },
  {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Running Model for Inference\n",
      "---------------------------\n",
      "Fold 1 - RMSPE: 0.249176\n",
      "Fold 2 - RMSPE: 0.253398\n",
      "Fold 3 - RMSPE: 0.253229\n",
      "Fold 4 - RMSPE: 0.243575\n",
      "Fold 5 - RMSPE: 0.24081\n",
      "------------------------------\n",
      "Stock 0 - RMSPE: 0.285113\n",
      "Stock 1 - RMSPE: 0.220006\n",
      "Stock 2 - RMSPE: 0.225279\n",
      "Stock 3 - RMSPE: 0.23915\n",
      "Stock 4 - RMSPE: 0.27314\n",
      "Stock 5 - RMSPE: 0.276773\n",
      "Stock 6 - RMSPE: 0.223636\n",
      "Stock 7 - RMSPE: 0.257244\n",
      "Stock 8 - RMSPE: 0.229653\n",
      "Stock 9 - RMSPE: 0.278142\n",
      "Stock 10 - RMSPE: 0.199906\n",
      "Stock 11 - RMSPE: 0.245839\n",
      "Stock 13 - RMSPE: 0.240631\n",
      "Stock 14 - RMSPE: 0.207048\n",
      "Stock 15 - RMSPE: 0.220991\n",
      "Stock 16 - RMSPE: 0.289419\n",
      "Stock 17 - RMSPE: 0.216417\n",
      "Stock 18 - RMSPE: 0.326414\n",
      "Stock 19 - RMSPE: 0.256798\n",
      "Stock 20 - RMSPE: 0.207339\n",
      "Stock 21 - RMSPE: 0.259029\n",
      "Stock 22 - RMSPE: 0.237314\n",
      "Stock 23 - RMSPE: 0.227665\n",
      "Stock 26 - RMSPE: 0.234488\n",
      "Stock 27 - RMSPE: 0.307771\n",
      "Stock 28 - RMSPE: 0.236542\n",
      "Stock 29 - RMSPE: 0.22314\n",
      "Stock 30 - RMSPE: 0.289807\n",
      "Stock 31 - RMSPE: 0.566459\n",
      "Stock 32 - RMSPE: 0.235153\n",
      "Stock 33 - RMSPE: 0.328785\n",
      "Stock 34 - RMSPE: 0.216902\n",
      "Stock 35 - RMSPE: 0.199207\n",
      "Stock 36 - RMSPE: 0.212397\n",
      "Stock 37 - RMSPE: 0.341415\n",
      "Stock 38 - RMSPE: 0.259913\n",
      "Stock 39 - RMSPE: 0.227173\n",
      "Stock 40 - RMSPE: 0.269137\n",
      "Stock 41 - RMSPE: 0.250868\n",
      "Stock 42 - RMSPE: 0.241124\n",
      "Stock 43 - RMSPE: 0.258397\n",
      "Stock 44 - RMSPE: 0.202399\n",
      "Stock 46 - RMSPE: 0.242584\n",
      "Stock 47 - RMSPE: 0.254417\n",
      "Stock 48 - RMSPE: 0.227876\n",
      "Stock 50 - RMSPE: 0.200532\n",
      "Stock 51 - RMSPE: 0.212621\n",
      "Stock 52 - RMSPE: 0.218258\n",
      "Stock 53 - RMSPE: 0.224628\n",
      "Stock 55 - RMSPE: 0.251052\n",
      "Stock 56 - RMSPE: 0.175763\n",
      "Stock 58 - RMSPE: 0.271348\n",
      "Stock 59 - RMSPE: 0.232613\n",
      "Stock 60 - RMSPE: 0.300954\n",
      "Stock 61 - RMSPE: 0.186169\n",
      "Stock 62 - RMSPE: 0.228351\n",
      "Stock 63 - RMSPE: 0.213285\n",
      "Stock 64 - RMSPE: 0.220317\n",
      "Stock 66 - RMSPE: 0.26216\n",
      "Stock 67 - RMSPE: 0.215763\n",
      "Stock 68 - RMSPE: 0.241108\n",
      "Stock 69 - RMSPE: 0.225016\n",
      "Stock 70 - RMSPE: 0.240328\n",
      "Stock 72 - RMSPE: 0.249419\n",
      "Stock 73 - RMSPE: 0.209547\n",
      "Stock 74 - RMSPE: 0.259191\n",
      "Stock 75 - RMSPE: 0.284341\n",
      "Stock 76 - RMSPE: 0.202399\n",
      "Stock 77 - RMSPE: 0.190868\n",
      "Stock 78 - RMSPE: 0.23695\n",
      "Stock 80 - RMSPE: 0.26372\n",
      "Stock 81 - RMSPE: 0.259202\n",
      "Stock 82 - RMSPE: 0.247088\n",
      "Stock 83 - RMSPE: 0.261304\n",
      "Stock 84 - RMSPE: 0.194127\n",
      "Stock 85 - RMSPE: 0.23636\n",
      "Stock 86 - RMSPE: 0.24096\n",
      "Stock 87 - RMSPE: 0.223375\n",
      "Stock 88 - RMSPE: 0.316428\n",
      "Stock 89 - RMSPE: 0.279008\n",
      "Stock 90 - RMSPE: 0.270528\n",
      "Stock 93 - RMSPE: 0.22645\n",
      "Stock 94 - RMSPE: 0.233732\n",
      "Stock 95 - RMSPE: 0.213612\n",
      "Stock 96 - RMSPE: 0.18814\n",
      "Stock 97 - RMSPE: 0.253602\n",
      "Stock 98 - RMSPE: 0.286946\n",
      "Stock 99 - RMSPE: 0.23725\n",
      "Stock 100 - RMSPE: 0.223698\n",
      "Stock 101 - RMSPE: 0.212434\n",
      "Stock 102 - RMSPE: 0.242403\n",
      "Stock 103 - RMSPE: 0.283986\n",
      "Stock 104 - RMSPE: 0.245039\n",
      "Stock 105 - RMSPE: 0.243989\n",
      "Stock 107 - RMSPE: 0.245424\n",
      "Stock 108 - RMSPE: 0.232345\n",
      "Stock 109 - RMSPE: 0.204916\n",
      "Stock 110 - RMSPE: 0.297914\n",
      "Stock 111 - RMSPE: 0.231972\n",
      "Stock 112 - RMSPE: 0.293401\n",
      "Stock 113 - RMSPE: 0.216091\n",
      "Stock 114 - RMSPE: 0.226878\n",
      "Stock 115 - RMSPE: 0.249336\n",
      "Stock 116 - RMSPE: 0.255149\n",
      "Stock 118 - RMSPE: 0.240886\n",
      "Stock 119 - RMSPE: 0.209264\n",
      "Stock 120 - RMSPE: 0.205284\n",
      "Stock 122 - RMSPE: 0.221544\n",
      "Stock 123 - RMSPE: 0.219618\n",
      "Stock 124 - RMSPE: 0.187201\n",
      "Stock 125 - RMSPE: 0.246523\n",
      "Stock 126 - RMSPE: 0.250509\n",
      "------------------------------\n",
      "OOF RMSPE: 0.24809\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "trainer.inference(df_train)"
   ]
  }
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