notebooks/python/Lab_Recurrent_Models.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CBravoR/AdvancedAnalyticsLabs/blob/master/notebooks/python/Lab_Recurrent_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vp0p_-0iXesy"
      },
      "source": [
        "# Recurrent models\n",
        "\n",
        "In this lab, we will start working with the time series of ATM failures, in order to classify them. There are many other applications possible, such as clustering, regression, or even anomaly detection using autoencoders.\n",
        "\n",
        "As always, let's first import the packages we will use and the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rl1J04DQjGKg"
      },
      "outputs": [],
      "source": [
        "# Install necessasary packages, if not done before\n",
        "!pip install torchview\n",
        "!pip install livelossplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "352wYg3NXbZq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import PIL\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "\n",
        "# For validation\n",
        "from sklearn.metrics import roc_auc_score, confusion_matrix, roc_curve, auc\n",
        "\n",
        "# Plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from IPython.display import Image\n",
        "from torchview import draw_graph\n",
        "import graphviz\n",
        "from livelossplot import PlotLosses\n",
        "graphviz.set_jupyter_format('png')\n",
        "%matplotlib inline\n",
        "\n",
        "# Import Pytorch lybraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import TensorDataset, DataLoader, random_split\n",
        "from torch.optim.lr_scheduler import _LRScheduler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TOPF72a6YSXu"
      },
      "outputs": [],
      "source": [
        "!gdown https://drive.google.com/uc?id=1rQfs6djY8MjeMPqHxl0cHbqTCwLB3giD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3QGgovzZvHO"
      },
      "outputs": [],
      "source": [
        "!unzip ATM.zip\n",
        "!rm ATM.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dot-GLJHZQsU"
      },
      "source": [
        "Now we read the data. It comes from [this Github](https://github.com/victormvy/sigma-convkernels/blob/main/main.py) and [its related paper](https://arxiv.org/ftp/arxiv/papers/2305/2305.10059.pdf) that uses the logs from ATM to detect failures. The failures can be either because of foreign body, generic failure, jam, preventive maintenance, replacement of any part or bad usage. There are 38 measurements the ATM keeps track of over a day, divided into series of 144 points. So, our input is a matrix of size (38, 144) for every point, predicting if the machine will be functional one week into the future.\n",
        "\n",
        "The following image, from the paper, describes the data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMmD_7CmVFIS"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ckyNW9bEZSnl"
      },
      "outputs": [],
      "source": [
        "# Read the data\n",
        "data = np.load('sigma_pdm.npy', allow_pickle=True)\n",
        "\n",
        "X = data[:, :-4, :]\n",
        "y = data[:, -4:, -1]\n",
        "\n",
        "del data\n",
        "\n",
        "# Move to pandas\n",
        "y_df = pd.DataFrame(y, columns = ['ID', 'Day', 'Type', 'Status'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aKoYOB2nTWxJ"
      },
      "outputs": [],
      "source": [
        "# Create Pandas DataFrame for X\n",
        "x_df = pd.DataFrame.from_records(X)\n",
        "x_df\n",
        "\n",
        "# Add the target\n",
        "x_df['target'] = y_df['Status'].values\n",
        "\n",
        "# Split the data\n",
        "seed = 20240202\n",
        "x_df['if_test'] = np.random.RandomState(seed=seed).binomial(1, 0.3, size=x_df.shape[0])\n",
        "x_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uBBIaDh-UF3j"
      },
      "outputs": [],
      "source": [
        "# Delete the original data\n",
        "del X, y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fx5vDDz3dXGR"
      },
      "source": [
        "Now we can plot one of the series for one of the cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vj9TDNPtjGKx"
      },
      "outputs": [],
      "source": [
        "# For case 3, plot series 9.\n",
        "plt.plot(x_df[2][8])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97K1bMJC34Z8"
      },
      "source": [
        "Now we are ready to start training models. Let's define torch's device so it runs the most efficient way possible."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZPQ5MpXbjGKy"
      },
      "outputs": [],
      "source": [
        "print(f\"Is CUDA supported by this system? {torch.cuda.is_available()}\")\n",
        "print(f\"CUDA version: {torch.version.cuda}\")\n",
        "\n",
        "# Storing ID of current CUDA device\n",
        "cuda_id = torch.cuda.current_device()\n",
        "print(f\"ID of current CUDA device: {torch.cuda.current_device()}\")\n",
        "\n",
        "print(f\"Name of current CUDA device: {torch.cuda.get_device_name(cuda_id)}\")\n",
        "\n",
        "# Making the code device-agnostic\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(f\"The default device is set to {device}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_JOdShOYTvc"
      },
      "source": [
        "## Long-Short Term Memory (LSTM) Networks\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvJMgihGBoNP"
      },
      "source": [
        "First, we will train an LSTM. The LSTM is a fairly complex model, so VRAM will be a signficant constraint. Let's first set up the train and test dataset. We will normalize the data and One Hot Encode the labels using sklearn's [OneHotEncode](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) function. We will also set the class weights as we have done before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BhG5-p25jGK0"
      },
      "outputs": [],
      "source": [
        "def scale_dataframe_sequences(x_df, test_var=None):\n",
        "    # Prepare the scaler\n",
        "    scaler = StandardScaler()\n",
        "\n",
        "    # Split the data if test_var given\n",
        "    if test_var is not None:\n",
        "        x_train = x_df.loc[test_var==0, :]\n",
        "        x_test = x_df.loc[test_var==1, :]\n",
        "    else:\n",
        "        x_train = x_df\n",
        "\n",
        "    # Dictionary to store the scaled variables\n",
        "    scaled_data = {}\n",
        "\n",
        "    if test_var is not None:\n",
        "        scaled_data_test = {}\n",
        "\n",
        "    # Get the number of elements in a sequence\n",
        "    seq_len = x_train.iloc[:, 0][0].shape[0]\n",
        "\n",
        "    for column in x_df.columns:\n",
        "        # Get data from the DataFrame and reshape to 2D array\n",
        "        data = np.stack(x_train[column].values).reshape(-1, seq_len)\n",
        "\n",
        "        # Scale the data\n",
        "        scaled_data[column] = scaler.fit_transform(data).reshape(-1, 1, seq_len).tolist()\n",
        "\n",
        "        # Apply to test set if needed\n",
        "        if test_var is not None:\n",
        "            data_test = np.stack(x_test[column].values).reshape(-1, seq_len)\n",
        "            scaled_data_test[column] = scaler.transform(data_test).reshape(-1, 1, seq_len).tolist()\n",
        "\n",
        "    # Create a new DataFrame with the scaled data\n",
        "    scaled_df = pd.DataFrame(scaled_data)\n",
        "    scaled_df = scaled_df.map(lambda x: np.array(x[0]))\n",
        "    if test_var is not None:\n",
        "        scaled_df_test = pd.DataFrame(scaled_data_test)\n",
        "        scaled_df_test = scaled_df_test.map(lambda x: np.array(x[0]))\n",
        "        return scaled_df, scaled_df_test\n",
        "    else:\n",
        "        return scaled_df, _\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j6FOITwNRlXl"
      },
      "outputs": [],
      "source": [
        "# Create train and test datasets.\n",
        "x_columns = x_df.columns[np.r_[0:37]]\n",
        "\n",
        "# Normalize train and test\n",
        "x_train, x_test = scale_dataframe_sequences(x_df.loc[:, x_columns], x_df['if_test'])\n",
        "\n",
        "# Encode test set\n",
        "enc = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
        "y_train = enc.fit_transform(x_df.loc[x_df['if_test']==0, 'target'].values.reshape(-1, 1))[:, 1]\n",
        "y_test = enc.transform(x_df.loc[x_df['if_test']==1, 'target'].values.reshape(-1, 1))[:, 1]\n",
        "\n",
        "# Class weights\n",
        "pos_weight = np.sum(1 - y_train) / np.sum(y_train)\n",
        "pos_weight = torch.tensor(pos_weight,dtype=torch.float32).to(device)\n",
        "pos_weight"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXvwmqlSKbz9"
      },
      "source": [
        "\n",
        "Now we will create the LSTM. First, let's try to be naïve and see what happens if we simply try to train our LSTM using 128 cells and just passing the sequence. As we saw in the lectures, Google's LeNet showed that stacking parallel layers of convolution (so, not sequential models) seemed like a good idea. Would this idea work with LSTM?\n",
        "\n",
        "Let's find out. For this, we will create a pytorch model and it's corresponding forward pass. We will use an LSTM to create an output embedding of the time series, and then pass that to a dense layer with droput that will classify the sequence. Finally, we will determine if the sequence is a suggesting a failure or not with a sigmoid output layer.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mNEHdDWcjGK2"
      },
      "outputs": [],
      "source": [
        "# Define the LSTM model\n",
        "class LSTMClassifier(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, layer_dim, classifier_dim, output_dim):\n",
        "        super(LSTMClassifier, self).__init__()\n",
        "\n",
        "        # Hidden dimensions\n",
        "        self.hidden_dim = hidden_dim\n",
        "\n",
        "        # Number of hidden layers\n",
        "        self.layer_dim = layer_dim\n",
        "\n",
        "        # Building your LSTM\n",
        "        # batch_first=True causes input/output tensors to be of shape\n",
        "        # (batch_dim, seq_dim, feature_dim)\n",
        "        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)\n",
        "\n",
        "        # Classifier network\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_dim, classifier_dim),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(classifier_dim, output_dim),\n",
        "            #nn.Softmax(dim=1) # No need for softmax with logit loss.\n",
        "       )\n",
        "\n",
        "    # Forward method\n",
        "    def forward(self, x):\n",
        "        # Initialize hidden state with zeros\n",
        "        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).to(device)\n",
        "\n",
        "        # Initialize cell state\n",
        "        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        out, (hn, cn) = self.lstm(x, (h0, c0))\n",
        "        out = self.classifier(out[:, -1, :])\n",
        "        return out\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NbNZoYpjGK2"
      },
      "source": [
        "Let's initatiate the model and see the diagram."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZvJi6VlsjGK2"
      },
      "outputs": [],
      "source": [
        "# Initialize the model\n",
        "n_vars = 37\n",
        "atm_lstm_model = LSTMClassifier(n_vars, 128, 1, 256, 1).to(device)\n",
        "print(atm_lstm_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tfYkC42kjGK2"
      },
      "outputs": [],
      "source": [
        "# Draw the model\n",
        "model_graph = draw_graph(atm_lstm_model, input_size=(1, 128, n_vars),\n",
        "                         device=device,\n",
        "                         expand_nested=True)\n",
        "model_graph.visual_graph\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gSVdvASLTvb"
      },
      "source": [
        "Now we are ready to train. Recurrent models are very tricky in terms of their training, and the gradients usually present a very erratic behaviour. It is a good idea to clip the gradients, that is, to lower the value of the gradient so it does not explode. There is a Torch utility called [```clip_grad_norm```](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html), which allows to set a maximum value against the mean for the gradient.\n",
        "\n",
        "I would generally advice to use a value of around 1 to 2 if you are seeing erratic training behaviour.\n",
        "\n",
        "We will use the series-oriented RMSprop as our optimizer, and implemente norm clipping in the training loop."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sZgctUJIjGK3"
      },
      "outputs": [],
      "source": [
        "# Set up optimizer and loss\n",
        "learning_rate = 0.0001\n",
        "optimizer = optim.RMSprop(atm_lstm_model.parameters(), lr=learning_rate)\n",
        "loss_fn = nn.BCEWithLogitsLoss(pos_weight=pos_weight).to(device)\n",
        "\n",
        "# Set global run parameters\n",
        "best_vloss = 10000000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6HBjt1qjGK4"
      },
      "source": [
        "We also need to create the data loaders. These are pretty simple: Just read from the pandas dataset and split them into train and test. We can do that easily with the following code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fr8A3euFjGK6"
      },
      "outputs": [],
      "source": [
        "def create_train_val_dataloaders(x_train_df, y_train, batch_size=64, val_size=0.33, seed=42):\n",
        "    \"\"\"\n",
        "    Create DataLoader instances for training and validation datasets suited for LSTM models.\n",
        "\n",
        "    Parameters:\n",
        "    x_train_df (pandas.DataFrame): DataFrame where each cell is a sequence (1D array or list) of length seq_len.\n",
        "    y_train (numpy.ndarray): The labels for training.\n",
        "    batch_size (int): The batch size for both train and validation loaders.\n",
        "    val_size (float): The fraction of the data to be used for validation.\n",
        "\n",
        "    Returns:\n",
        "    train_loader (DataLoader): DataLoader for the training set.\n",
        "    val_loader (DataLoader): DataLoader for the validation set.\n",
        "    \"\"\"\n",
        "\n",
        "    # Set the random seed for reproducibility\n",
        "    torch.manual_seed(seed)\n",
        "\n",
        "    # Convert the DataFrame of sequences into a correctly shaped 3D numpy array\n",
        "    # sequences.shape should be (number of samples, seq_len, number of features per timestep)\n",
        "    sequences = np.stack(x_train_df.apply(lambda s: np.stack(s.values).reshape(s.values[0].shape[0], -1), axis=1).values)\n",
        "\n",
        "    # Check if y_train is a numpy array, if not convert it\n",
        "    if not isinstance(y_train, np.ndarray):\n",
        "        y_train = np.array(y_train)\n",
        "\n",
        "    if y_train.ndim == 1:\n",
        "        y_train = y_train[:, None]  # Convert to 2D array if necessary\n",
        "\n",
        "    # Convert to PyTorch tensors\n",
        "    x_train_tensor = torch.from_numpy(sequences).float()\n",
        "    y_train_tensor = torch.from_numpy(y_train).float()\n",
        "\n",
        "    # Create the TensorDataset\n",
        "    train_data = TensorDataset(x_train_tensor, y_train_tensor)\n",
        "\n",
        "    # Split the dataset into train and validation sets\n",
        "    total_samples = len(train_data)\n",
        "    train_size = int((1 - val_size) * total_samples)\n",
        "    val_size = total_samples - train_size\n",
        "    train_dataset, val_dataset = random_split(train_data, [train_size, val_size])\n",
        "\n",
        "    # Create the dataloaders\n",
        "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "    return train_loader, val_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0wW_Du5BjGK6"
      },
      "outputs": [],
      "source": [
        "# Create the data loaders\n",
        "batch_size = 2048 # A100\n",
        "train_loader, val_loader = create_train_val_dataloaders(x_train, y_train.reshape(-1,1),\n",
        "                                                       batch_size=batch_size)\n",
        "dataloaders = {\n",
        "    \"train\": train_loader,\n",
        "    \"validation\": val_loader\n",
        "}\n",
        "\n",
        "# Softmax function for the output\n",
        "softmax_func = np.vectorize(lambda x: 1/(1+np.exp(-1 * x)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41NBhJMAjGK7"
      },
      "source": [
        "Now we are finally ready to train."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TuNxYU9kjGK8"
      },
      "outputs": [],
      "source": [
        "# Set run parameters\n",
        "n_epochs = 200\n",
        "liveloss = PlotLosses()\n",
        "\n",
        "# Train!\n",
        "for epoch in range(n_epochs):\n",
        "   # Run the epoch\n",
        "  logs = {}\n",
        "\n",
        "  # Run a train epoch, and then a validation epoch.\n",
        "  for phase in ['train', 'validation']:\n",
        "      if phase == 'train':\n",
        "          atm_lstm_model.train()\n",
        "      else:\n",
        "          atm_lstm_model.eval()\n",
        "\n",
        "      running_loss = 0.0\n",
        "      running_corrects = 0\n",
        "\n",
        "      for i, data in enumerate(dataloaders[phase]):\n",
        "          inputs, labels = data\n",
        "          # print(labels)\n",
        "          inputs = inputs.to(device)\n",
        "          labels = labels.to(device)\n",
        "\n",
        "          outputs = atm_lstm_model(inputs).to(device)\n",
        "          loss = loss_fn(outputs, labels)\n",
        "\n",
        "          if phase == 'train':\n",
        "              optimizer.zero_grad()\n",
        "              loss.backward()\n",
        "\n",
        "              # Clip the gradient norm\n",
        "              nn.utils.clip_grad_norm_(atm_lstm_model.parameters(), 2)\n",
        "\n",
        "              # Backpropagate\n",
        "              optimizer.step()\n",
        "\n",
        "          preds = softmax_func(outputs.detach().cpu().numpy())\n",
        "          preds = np.round(preds)\n",
        "          running_loss += loss.detach() * inputs.size(0)\n",
        "          running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "          if i % 10 == 9:\n",
        "            batch_loss = running_loss / (10 * (i+1))\n",
        "            print(f'{phase} batch {i+1} loss: {batch_loss:.3f}')\n",
        "            tb_x = epoch * len(dataloaders[phase]) + i + 1\n",
        "\n",
        "          # Delete the used VRAM\n",
        "          torch.cuda.empty_cache()\n",
        "\n",
        "      epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
        "      epoch_acc = running_corrects / len(dataloaders[phase].dataset)\n",
        "\n",
        "      prefix = ''\n",
        "      if phase == 'validation':\n",
        "          prefix = 'val_'\n",
        "\n",
        "          # Track best performance, and save the model's state\n",
        "          if epoch_loss < best_vloss:\n",
        "              best_vloss = epoch_loss\n",
        "              model_path = 'best_model.ph'\n",
        "              print(f'New best model found. Saving it as {model_path}')\n",
        "              torch.save(atm_lstm_model.state_dict(), model_path)\n",
        "\n",
        "      logs[prefix + 'log loss'] = epoch_loss.item()\n",
        "      logs[prefix + 'accuracy'] = epoch_acc.item()\n",
        "\n",
        "  liveloss.update(logs)\n",
        "  liveloss.send()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fOOwFP1MaJ4"
      },
      "source": [
        "Training is progressing! Let's apply it to the test set and see what we get. You may also want to keep training, as the model is still learning after 200 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LuFDOrHJ0tPe"
      },
      "outputs": [],
      "source": [
        "def create_test_dataloader(x_test_df, y_test=None, batch_size=64, seed=42):\n",
        "    \"\"\"\n",
        "    Create DataLoader instance for the test dataset.\n",
        "\n",
        "    Parameters:\n",
        "    x_test_df (pandas.DataFrame): DataFrame where each cell is a sequence (1D array or list) of length seq_len.\n",
        "    y_test (numpy.ndarray, optional): The labels for testing. Pass None if there are no labels.\n",
        "    batch_size (int): The batch size for the test loader.\n",
        "\n",
        "    Returns:\n",
        "    test_loader (DataLoader): DataLoader for the test set.\n",
        "    \"\"\"\n",
        "\n",
        "    # Set the random seed for reproducibility\n",
        "    torch.manual_seed(seed)\n",
        "\n",
        "    # Convert the DataFrame of sequences into a correctly shaped 3D numpy array\n",
        "    # sequences.shape should be (number of samples, seq_len, number of features per timestep)\n",
        "    sequences = np.stack(x_test_df.apply(lambda s: np.stack(s.values).reshape(s.values[0].shape[0], -1), axis=1).values)\n",
        "\n",
        "    # Convert to PyTorch tensors\n",
        "    x_test_tensor = torch.from_numpy(sequences).float()\n",
        "\n",
        "    # Create a TensorDataset from the input data\n",
        "    if y_test is not None:\n",
        "        # Check if y_test is a numpy array, if not convert it\n",
        "        if not isinstance(y_test, np.ndarray):\n",
        "            y_test = np.array(y_test)\n",
        "\n",
        "        # Convert labels to a PyTorch tensor\n",
        "        y_test_tensor = torch.from_numpy(y_test).float()\n",
        "\n",
        "        # Assert that the number of samples matches\n",
        "        assert len(x_test_tensor) == len(y_test_tensor), \"The number of input samples and labels must be the same.\"\n",
        "        test_data = TensorDataset(x_test_tensor, y_test_tensor)\n",
        "    else:\n",
        "        test_data = TensorDataset(x_test_tensor)\n",
        "\n",
        "    # Create the DataLoader for the test set\n",
        "    test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "    return test_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2uGJrOgAjGK-"
      },
      "outputs": [],
      "source": [
        "test_loader = create_test_dataloader(x_test, y_test.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C4GpibuVjGK-"
      },
      "outputs": [],
      "source": [
        "atm_lstm_model.load_state_dict(torch.load('best_model.ph'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOh60-rUGQrc"
      },
      "outputs": [],
      "source": [
        "# Wrapper to save memory by not recomputing gradients.\n",
        "with torch.no_grad():\n",
        "    # Set the model in evaluation mode.\n",
        "    atm_lstm_model.eval()\n",
        "\n",
        "    # Calculate running loss and accuracy\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    test_labels = np.array([])\n",
        "    test_probs = np.array([])\n",
        "    test_predictions = np.array([])\n",
        "\n",
        "    # Apply to the test set\n",
        "    for i, data in enumerate(test_loader):\n",
        "        inputs, labels = data\n",
        "        test_labels = np.append(test_labels, labels.cpu().numpy())\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = atm_lstm_model(inputs)\n",
        "        test_probs = np.append(test_probs, outputs.cpu().numpy())\n",
        "        outputs = outputs.to(device)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        preds = softmax_func(outputs.cpu().numpy())\n",
        "        preds = np.round(preds)\n",
        "        test_predictions = np.append(test_predictions, preds)\n",
        "        running_loss += loss.detach() * inputs.size(0)\n",
        "        running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "test_loss = running_loss / len(test_loader.dataset)\n",
        "test_acc = running_corrects / len(test_loader.dataset)\n",
        "\n",
        "print(f'The test set accuracy is {test_acc*100:.2f}%')\n",
        "print(f'The test set loss is {test_loss:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "72gAC9XpGri-"
      },
      "outputs": [],
      "source": [
        "# Calculate confusion matrix\n",
        "confusion_matrix_net = confusion_matrix(y_true = test_labels,\n",
        "                    y_pred = test_predictions)\n",
        "\n",
        "# Turn matrix to percentages\n",
        "confusion_matrix_net = confusion_matrix_net.astype('float') / confusion_matrix_net.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Turn to dataframe\n",
        "df_cm = pd.DataFrame(\n",
        "        confusion_matrix_net,\n",
        "        index=np.unique(test_labels),\n",
        "        columns=np.unique(test_labels),\n",
        ")\n",
        "\n",
        "# Parameters of the image\n",
        "figsize = (10,7)\n",
        "fontsize=14\n",
        "\n",
        "# Create image\n",
        "fig = plt.figure(figsize=figsize)\n",
        "heatmap = sns.heatmap(df_cm, annot=True, fmt='.2f')\n",
        "\n",
        "# Make it nicer\n",
        "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "\n",
        "# Add labels\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# Plot!\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k35SBXAwjGLA"
      },
      "outputs": [],
      "source": [
        "# Calculate the ROC curve points\n",
        "fpr, tpr, thresholds = roc_curve(test_labels, test_probs)\n",
        "\n",
        "# Save the AUC in a variable to display it. Round it first\n",
        "auc = np.round(roc_auc_score(y_true = test_labels,\n",
        "                             y_score = test_probs),\n",
        "              decimals = 3)\n",
        "\n",
        "# Create and show the plot\n",
        "plt.plot(fpr,tpr,label=\"ATM Failures, auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZlv4i1hjGLA"
      },
      "source": [
        "The model is not learning much with this architecture. Let's see if we can improve upon this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NutqVO_2jGLA"
      },
      "source": [
        "## LSTM with multiple layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycb_xjwljGLB"
      },
      "source": [
        "To improve training, we can try to  chain LSTMs. This can help as you won't need to add just one very large (thus intractable) LSTM and instead can train two smaller ones. This is easily done in pytorch simply by setting the ```layer_dim``` parameter to a higher number. Let's try to stack two LSTMs and see the performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D7_r381LLawo"
      },
      "outputs": [],
      "source": [
        "# Initialize the model\n",
        "n_vars = 37\n",
        "atm_lstm_model = LSTMClassifier(n_vars, 256, 2, 256, 1).to(device)\n",
        "print(atm_lstm_model)\n",
        "\n",
        "# Draw the model\n",
        "model_graph = draw_graph(atm_lstm_model, input_size=(1, 256, n_vars),\n",
        "                         device=device,\n",
        "                         expand_nested=True)\n",
        "model_graph.visual_graph\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DLoYlPUGxIg"
      },
      "source": [
        "As we can see, this model now passes a much more reduced number of features to the LSTM layer, thus allowing for a reduced complexity. Of course, this will only be as good as the features are, so you'll need to experiment to get this right.\n",
        "\n",
        "Let's train the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYXlDNTRx8-2"
      },
      "source": [
        "We will be very aggressive with the norm clipping now. You can identify the need for this if the losses are very unstable. Experiment with this value for your own applications! We'll also use Adam as our optimizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HAPOj-R4jGLC"
      },
      "outputs": [],
      "source": [
        "# Set up optimizer and loss\n",
        "learning_rate = 0.001\n",
        "optimizer = optim.RMSprop(atm_lstm_model.parameters(), lr=learning_rate)\n",
        "loss_fn = nn.BCEWithLogitsLoss(pos_weight=pos_weight).to(device)\n",
        "\n",
        "# Set global run parameters\n",
        "best_vloss = 10000000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jdyX6khzjGLC"
      },
      "outputs": [],
      "source": [
        "# Create the data loaders\n",
        "train_loader, val_loader = create_train_val_dataloaders(x_train, y_train.reshape(-1,1),\n",
        "                                                       batch_size=batch_size)\n",
        "dataloaders = {\n",
        "    \"train\": train_loader,\n",
        "    \"validation\": val_loader\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3OfKnDbjGLD"
      },
      "outputs": [],
      "source": [
        "# Set run parameters\n",
        "n_epochs = 300\n",
        "liveloss = PlotLosses()\n",
        "\n",
        "\n",
        "# Train!\n",
        "for epoch in range(n_epochs):\n",
        "   # Run the epoch\n",
        "  logs = {}\n",
        "\n",
        "  # Run a train epoch, and then a validation epoch.\n",
        "  for phase in ['train', 'validation']:\n",
        "      if phase == 'train':\n",
        "          atm_lstm_model.train()\n",
        "      else:\n",
        "          atm_lstm_model.eval()\n",
        "\n",
        "      running_loss = 0.0\n",
        "      running_corrects = 0\n",
        "\n",
        "      for i, data in enumerate(dataloaders[phase]):\n",
        "          inputs, labels = data\n",
        "          inputs = inputs.to(device)\n",
        "          labels = labels.to(device)\n",
        "\n",
        "          outputs = atm_lstm_model(inputs).to(device)\n",
        "          loss = loss_fn(outputs, labels)\n",
        "\n",
        "          if phase == 'train':\n",
        "              optimizer.zero_grad()\n",
        "              loss.backward()\n",
        "              # Clip the gradient norm\n",
        "              nn.utils.clip_grad_norm_(atm_lstm_model.parameters(), 0.1)\n",
        "              # Backpropagate\n",
        "              optimizer.step()\n",
        "\n",
        "          preds = softmax_func(outputs.detach().cpu().numpy())\n",
        "          preds = np.round(preds)\n",
        "          running_loss += loss.detach() * inputs.size(0)\n",
        "          running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "          if i % 10 == 9:\n",
        "            batch_loss = running_loss / (10 * (i+1))\n",
        "            print(f'{phase} batch {i+1} loss: {batch_loss:.3f}')\n",
        "            tb_x = epoch * len(dataloaders[phase]) + i + 1\n",
        "\n",
        "          # Delete the used VRAM\n",
        "          torch.cuda.empty_cache()\n",
        "\n",
        "      epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
        "      epoch_acc = running_corrects / len(dataloaders[phase].dataset)\n",
        "\n",
        "      prefix = ''\n",
        "      if phase == 'validation':\n",
        "          prefix = 'val_'\n",
        "\n",
        "          # Track best performance, and save the model's state\n",
        "          if epoch_loss < best_vloss:\n",
        "              best_vloss = epoch_loss\n",
        "              model_path = 'best_model.ph'\n",
        "              print(f'New best model found. Saving it as {model_path}')\n",
        "              torch.save(atm_lstm_model.state_dict(), model_path)\n",
        "\n",
        "      logs[prefix + 'log loss'] = epoch_loss.item()\n",
        "      logs[prefix + 'accuracy'] = epoch_acc.item()\n",
        "\n",
        "  liveloss.update(logs)\n",
        "  liveloss.send()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rx0ZhYzWxTf1"
      },
      "source": [
        "That's better! We can see the LSTM now reaches a much better loss. Remember, this was done without **any** data cleaning, just standardization! Also of note is how long it took the optimizer to find a proper direction of descent. It pays to have a good patience!\n",
        "\n",
        "We can see that learning stalls around 100 epochs. This is why we use callbacks, we can now simply recover the optimal model and train again with a lower rate if needed.\n",
        "\n",
        "Finally, I did not **acid test** this model. It is very easy to add redundant layers to the model or even redundant series, as our model has 37 series to get patterns from. Try playing around with the model and continue training. It should not be hard to improve these results further."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYN6HKCjxoHq"
      },
      "source": [
        "Let's see the performance now over the test set. We start by loading the best model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3ClcS2DjGLE"
      },
      "outputs": [],
      "source": [
        "atm_lstm_model.load_state_dict(torch.load('best_model.ph'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9I65_c3fjGLE"
      },
      "outputs": [],
      "source": [
        "# Wrapper to save memory by not recomputing gradients.\n",
        "with torch.no_grad():\n",
        "    # Set the model in evaluation mode.\n",
        "    atm_lstm_model.eval()\n",
        "\n",
        "    # Calculate running loss and accuracy\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    test_labels = np.array([])\n",
        "    test_probs = np.array([])\n",
        "    test_predictions = np.array([])\n",
        "\n",
        "    # Apply to the test set\n",
        "    for i, data in enumerate(test_loader):\n",
        "        inputs, labels = data\n",
        "        test_labels = np.append(test_labels, labels.cpu().numpy())\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = atm_lstm_model(inputs)\n",
        "        test_probs = np.append(test_probs, outputs.cpu().numpy())\n",
        "        outputs = outputs.to(device)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        preds = softmax_func(outputs.cpu().numpy())\n",
        "        preds = np.round(preds)\n",
        "        test_predictions = np.append(test_predictions, preds)\n",
        "        running_loss += loss.detach() * inputs.size(0)\n",
        "        running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "test_loss = running_loss / len(test_loader.dataset)\n",
        "test_acc = running_corrects / len(test_loader.dataset)\n",
        "\n",
        "print(f'The test set accuracy is {test_acc*100:.2f}%')\n",
        "print(f'The test set loss is {test_loss:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QLQCeHU1jGLF"
      },
      "outputs": [],
      "source": [
        "# Calculate confusion matrix\n",
        "confusion_matrix_net = confusion_matrix(y_true = test_labels,\n",
        "                    y_pred = test_predictions)\n",
        "\n",
        "# Turn matrix to percentages\n",
        "confusion_matrix_net = confusion_matrix_net.astype('float') / confusion_matrix_net.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Turn to dataframe\n",
        "df_cm = pd.DataFrame(\n",
        "        confusion_matrix_net,\n",
        "        index=np.unique(test_labels),\n",
        "        columns=np.unique(test_labels),\n",
        ")\n",
        "\n",
        "# Parameters of the image\n",
        "figsize = (10,7)\n",
        "fontsize=14\n",
        "\n",
        "# Create image\n",
        "fig = plt.figure(figsize=figsize)\n",
        "heatmap = sns.heatmap(df_cm, annot=True, fmt='.2f')\n",
        "\n",
        "# Make it nicer\n",
        "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "\n",
        "# Add labels\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# Plot!\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NrQHgs01X5b"
      },
      "source": [
        "This is a pretty good model! The ROC curve will give us a better view of what is happening."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "W3gjKFDMjGLG"
      },
      "outputs": [],
      "source": [
        "# Calculate the ROC curve points\n",
        "fpr, tpr, thresholds = roc_curve(test_labels, test_probs)\n",
        "\n",
        "# Save the AUC in a variable to display it. Round it first\n",
        "auc = np.round(roc_auc_score(y_true = test_labels,\n",
        "                             y_score = test_probs),\n",
        "              decimals = 3)\n",
        "\n",
        "# Create and show the plot\n",
        "plt.plot(fpr,tpr,label=\"ATM Failures - LSTM, auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wk2sAqh_jGLG"
      },
      "source": [
        "We are obtaining an amazing AUC! Given how unbalanced our model is though, the AUC may be misleading. An alternative would be to calculate the AUPRC and use that. In any case, our model is looking very good!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2Vlv_O_2GYz"
      },
      "source": [
        "## GRU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2_y20ug1ncL"
      },
      "source": [
        "Now we will train a GRU. In theory, a GRU will be able to reach the same results as the LSTM but using less parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHazPX4b10ML"
      },
      "source": [
        "GRUs are more efficient, so you can either use the same size that will run faster, or increase the size to try to learn more. Let's do the former."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mGhDAKwejGLH"
      },
      "outputs": [],
      "source": [
        "# Define the LSTM model\n",
        "class GRUClassifier(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, layer_dim, classifier_dim, output_dim):\n",
        "        super(GRUClassifier, self).__init__()\n",
        "\n",
        "        # Hidden dimensions\n",
        "        self.hidden_dim = hidden_dim\n",
        "\n",
        "        # Number of hidden layers\n",
        "        self.layer_dim = layer_dim\n",
        "\n",
        "        # Building your GRU\n",
        "        # batch_first=True causes input/output tensors to be of shape\n",
        "        # (batch_dim, seq_dim, feature_dim)\n",
        "        self.gru = nn.GRU(input_dim, hidden_dim, layer_dim, batch_first=True)\n",
        "\n",
        "        # Classifier network\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_dim, classifier_dim),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(classifier_dim, output_dim),\n",
        "            #nn.Softmax(dim=1) # No need for softmax with logit loss.\n",
        "       )\n",
        "\n",
        "    # Forward method\n",
        "    def forward(self, x):\n",
        "        # Initialize hidden state with zeros\n",
        "        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        out, hn = self.gru(x, h0)\n",
        "        out = self.classifier(out[:, -1, :])\n",
        "        return out\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aeWDJASY2SX8"
      },
      "outputs": [],
      "source": [
        "# Initialize the model\n",
        "n_vars = 37\n",
        "atm_gru_model = GRUClassifier(n_vars, 256, 2, 256, 1).to(device)\n",
        "print(atm_gru_model)\n",
        "\n",
        "# Draw the model\n",
        "model_graph = draw_graph(atm_gru_model, input_size=(1, 256, n_vars),\n",
        "                         device=device,\n",
        "                         expand_nested=True)\n",
        "model_graph.visual_graph\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ts7cN7ByjGLI"
      },
      "source": [
        "Perfect! Let's train the model now. The process is the same as before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9nw8L2-5jGLJ"
      },
      "outputs": [],
      "source": [
        "# Set up optimizer and loss\n",
        "learning_rate = 0.001\n",
        "optimizer = optim.RMSprop(atm_gru_model.parameters(), lr=learning_rate)\n",
        "loss_fn = nn.BCEWithLogitsLoss(pos_weight=pos_weight).to(device)\n",
        "\n",
        "# Set global run parameters\n",
        "best_vloss = 10000000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tf4rsUHmjGLJ"
      },
      "outputs": [],
      "source": [
        "# Create the data loaders\n",
        "train_loader, val_loader = create_train_val_dataloaders(x_train, y_train.reshape(-1,1),\n",
        "                                                       batch_size=batch_size)\n",
        "dataloaders = {\n",
        "    \"train\": train_loader,\n",
        "    \"validation\": val_loader\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wu_KftImjGLJ"
      },
      "outputs": [],
      "source": [
        "# Set run parameters\n",
        "n_epochs = 200\n",
        "liveloss = PlotLosses()\n",
        "\n",
        "# Train!\n",
        "for epoch in range(n_epochs):\n",
        "   # Run the epoch\n",
        "  logs = {}\n",
        "\n",
        "  # Run a train epoch, and then a validation epoch.\n",
        "  for phase in ['train', 'validation']:\n",
        "      if phase == 'train':\n",
        "          atm_gru_model.train()\n",
        "      else:\n",
        "          atm_gru_model.eval()\n",
        "\n",
        "      running_loss = 0.0\n",
        "      running_corrects = 0\n",
        "\n",
        "      for i, data in enumerate(dataloaders[phase]):\n",
        "          inputs, labels = data\n",
        "          inputs = inputs.to(device)\n",
        "          labels = labels.to(device)\n",
        "\n",
        "          outputs = atm_gru_model(inputs).to(device)\n",
        "          loss = loss_fn(outputs, labels)\n",
        "\n",
        "          if phase == 'train':\n",
        "              optimizer.zero_grad()\n",
        "              loss.backward()\n",
        "              # Clip the gradient norm\n",
        "              nn.utils.clip_grad_norm_(atm_gru_model.parameters(), 0.1)\n",
        "              # Backpropagate\n",
        "              optimizer.step()\n",
        "\n",
        "          preds = softmax_func(outputs.detach().cpu().numpy())\n",
        "          preds = np.round(preds)\n",
        "          running_loss += loss.detach() * inputs.size(0)\n",
        "          running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "          if i % 10 == 9:\n",
        "            batch_loss = running_loss / (10 * (i+1))\n",
        "            print(f'{phase} batch {i+1} loss: {batch_loss:.3f}')\n",
        "            tb_x = epoch * len(dataloaders[phase]) + i + 1\n",
        "\n",
        "          # Delete the used VRAM\n",
        "          torch.cuda.empty_cache()\n",
        "\n",
        "      epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
        "      epoch_acc = running_corrects / len(dataloaders[phase].dataset)\n",
        "\n",
        "      prefix = ''\n",
        "      if phase == 'validation':\n",
        "          prefix = 'val_'\n",
        "\n",
        "          # Track best performance, and save the model's state\n",
        "          if epoch_loss < best_vloss:\n",
        "              best_vloss = epoch_loss\n",
        "              model_path = 'best_gru_model.ph'\n",
        "              print(f'New best model found. Saving it as {model_path}')\n",
        "              torch.save(atm_gru_model.state_dict(), model_path)\n",
        "\n",
        "      logs[prefix + 'log loss'] = epoch_loss.item()\n",
        "      logs[prefix + 'accuracy'] = epoch_acc.item()\n",
        "\n",
        "  liveloss.update(logs)\n",
        "  liveloss.send()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VCFaTnrIjGLK"
      },
      "outputs": [],
      "source": [
        "atm_gru_model.load_state_dict(torch.load('best_gru_model.ph'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6_24kZRNjGLK"
      },
      "outputs": [],
      "source": [
        "# Wrapper to save memory by not recomputing gradients.\n",
        "with torch.no_grad():\n",
        "    # Set the model in evaluation mode.\n",
        "    atm_gru_model.eval()\n",
        "\n",
        "    # Calculate running loss and accuracy\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    test_labels = np.array([])\n",
        "    test_probs = np.array([])\n",
        "    test_predictions = np.array([])\n",
        "\n",
        "    # Apply to the test set\n",
        "    for i, data in enumerate(test_loader):\n",
        "        inputs, labels = data\n",
        "        test_labels = np.append(test_labels, labels.cpu().numpy())\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = atm_gru_model(inputs)\n",
        "        test_probs = np.append(test_probs, outputs.cpu().numpy())\n",
        "        outputs = outputs.to(device)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        preds = softmax_func(outputs.cpu().numpy())\n",
        "        preds = np.round(preds)\n",
        "        test_predictions = np.append(test_predictions, preds)\n",
        "        running_loss += loss.detach() * inputs.size(0)\n",
        "        running_corrects += np.sum(preds.flatten() == labels.data.flatten().cpu().numpy())\n",
        "\n",
        "test_loss = running_loss / len(test_loader.dataset)\n",
        "test_acc = running_corrects / len(test_loader.dataset)\n",
        "\n",
        "print(f'The test set accuracy is {test_acc*100:.2f}%')\n",
        "print(f'The test set loss is {test_loss:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "6EybNNfijGLL"
      },
      "outputs": [],
      "source": [
        "# Calculate confusion matrix\n",
        "confusion_matrix_net = confusion_matrix(y_true = test_labels,\n",
        "                    y_pred = test_predictions)\n",
        "\n",
        "# Turn matrix to percentages\n",
        "confusion_matrix_net = confusion_matrix_net.astype('float') / confusion_matrix_net.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Turn to dataframe\n",
        "df_cm = pd.DataFrame(\n",
        "        confusion_matrix_net,\n",
        "        index=np.unique(test_labels),\n",
        "        columns=np.unique(test_labels),\n",
        ")\n",
        "\n",
        "# Parameters of the image\n",
        "figsize = (10,7)\n",
        "fontsize=14\n",
        "\n",
        "# Create image\n",
        "fig = plt.figure(figsize=figsize)\n",
        "heatmap = sns.heatmap(df_cm, annot=True, fmt='.2f')\n",
        "\n",
        "# Make it nicer\n",
        "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45,\n",
        "                             ha='right', fontsize=fontsize)\n",
        "\n",
        "# Add labels\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# Plot!\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "JV3Jzl55jGLL"
      },
      "outputs": [],
      "source": [
        "# Calculate the ROC curve points\n",
        "fpr, tpr, thresholds = roc_curve(test_labels, test_probs)\n",
        "\n",
        "# Save the AUC in a variable to display it. Round it first\n",
        "auc = np.round(roc_auc_score(y_true = test_labels,\n",
        "                             y_score = test_probs),\n",
        "              decimals = 3)\n",
        "\n",
        "# Create and show the plot\n",
        "plt.plot(fpr,tpr,label=\"ATM Failures - GRU, auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxyntjFs6tlu"
      },
      "source": [
        "We got an even better model! And it took only 200 epochs to get here. Try to see if you can reach better ones! Let's load the optimal parameters and measure performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4j-ndvT7pFL"
      },
      "source": [
        "Can you do better? Experiment with the parameters, set a [Learning Rate Scheduler](https://www.kaggle.com/code/isbhargav/guide-to-pytorch-learning-rate-scheduling) to train more slowly after epoch 200. Experiment! I got these results playing around with the parameters in a few hours. See what you can get!\n",
        "\n",
        "In any case, sequence-based models have evolved greatly beyond LSTM and GRU. In particular, one specific transform has been shown to be a significant advancement over these models: The Transformer."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
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  "nbformat_minor": 0
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