Formulacion_1_Base.ipynb

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   "source": [
    "<center><h1 style:\"text-align:center;\">Formulación 1</h1></center>\n",
    "<center><h3>Base Notebook</h3></center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Acceder al sistema operativo\n",
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "import random\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Model Parameters\n",
    "\n",
    "#### LSTM parameters\n",
    "\n",
    "- **n_signals:** number of signals fed into model for each event.\n",
    "    - For example: for input signals: [$I_{a}$, $I_{b}$, $I_{c}$] -> n_signals = 3).\n",
    "    <br><br>\n",
    "- **N:** window size. \n",
    "    - For 3840Hz sampling frequency and a 0.016ms window (containing one cycle of a 60Hz wave), N = 64.\n",
    "    \\begin{equation}\n",
    "    N = \\frac{fs}{f_{fundamental}} w\n",
    "    \\end{equation}\n",
    "    <br>\n",
    "- **hidden_dim:** The number of features in the hidden state $a^{\\langle t \\rangle}$ (also refered as $h^{\\langle t \\rangle}$)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "- **train/test/dev_batch_size**: mini-batch size.\n",
    "    - train: portion of dataset used to train the model (i.e. adjust the weigths and biases).\n",
    "    - test: portion of dataset used to estimate the performance of the model.\n",
    "    <br><br>\n",
    "    \n",
    "\n",
    "<figure> \n",
    "<center><img src=\"./images/DatabaseSchema.jpeg\" style=\"height:220px;width:330px;\"></center>\n",
    "<caption><center><font color='whitesmoke'><b>Figure 1.</b> Mini-batch dataset geometry </font></center></caption>\n",
    "</figure>\n",
    "\n",
    "\n",
    "- **target_size:** number of classes.\n",
    "    - target_size = 1:\n",
    "        - Binary Classification: One output treated as probability of belonging to one class or the other (E.g. No-Fault = 0 | Fault = 1)\n",
    "    - target_size > 1:\n",
    "        - Multiclass: Multiple exclusive classes. Class with higher score represents predicted class. The sum of scores (using softmax) adds up 1.<br> (E.g. [No-fault, Fault, Caps], an output of values [0.07, 0.91, 0.2] represents a Fault class prediction)\n",
    "        - Multilabel: Multiple classes that are not mutually exclusive. Each position of output vector is a probability (using sigmoid).\n",
    "<br> (E.g. [$\\phi_A$, $\\phi_B$, $\\phi_C$], an output of values [0.95, 0.90, 0.01 ] represents faulted phases *A* and *B*, i.e. biphasic *AB* fault )\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "# LSTM parameters\n",
    "hidden_dim = 20\n",
    "n_signals = 1\n",
    "N = 64\n",
    "\n",
    "# _batch_size => m in figure 1.\n",
    "train_batch_size = 64\n",
    "dev_batch_size = 16\n",
    "test_batch_size = 16\n",
    "\n",
    "# Classification type (binary)\n",
    "tagset_size = 1\n",
    "\n",
    "# Set\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(device)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Model Creation\n",
    "\n",
    "The fault detector model consists of a block of *N* LSTM cells (figure 2.) in series. This block processes one training sample at a time (moving window). For each moving window, each cell processes a sample of the signal, and will pass context information sequentially to the next cell. This information will be passed in the hidden state $a^{\\langle t \\rangle}$  and the cell state $c^{\\langle t \\rangle}$. \n",
    "\n",
    "\n",
    "Each LSTM's input $x^{\\langle t \\rangle}$ is combined with it's previous hidden state $a^{\\langle t - 1 \\rangle}$. \n",
    "Each LSTM's cell state $c^{\\langle t \\rangle}$. will be inputed to the next LSTM $_{t + 1}$ . At each step $c^{\\langle t \\rangle}$ is updated to add _(remember)_ or delete _(forget)_ information.\n",
    "\n",
    "At each time step, $a^{\\langle t \\rangle}$ can be passed through a sigmoid function to obatain an output $\\hat{y}^{\\langle t \\rangle}$. However, this yields a many-to-many model. Since fault detection is made after processing _N_ input samples inside a moving window, only one label is needed per window (no-fault/fault) making it possible to approach the problem as binary classification. \n",
    "In other words, for each moving window, the prediction will be made at the last LSTM block cell, i.e. $\\hat{y}^{\\langle N-1 \\rangle}$.\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "<figure> \n",
    "<center><img src=\"./images/LSTM_cell.png\" style=\"width:500;height:400px;\" ></center>\n",
    "<caption><center><b>Figure 2.</b>LSTM cell</center></caption>\n",
    "</figure>\n",
    "\n",
    "\n",
    "##### WRITE MORE LSTM INFORMATION\n",
    "\n",
    "The LSTM block will be fed an input vector of dimension (m, N, s), where:\n",
    "\n",
    "            - m: mini-batch size.\n",
    "            - N: moving window size.\n",
    "            - s: signals _(n_signals)_\n",
    "        \n",
    "Pytorch **nn.Module** class will be used to create the FaultDetector LSTM detector class following the architecture shown in figure 3. The FaultDetector class includes the LSTM, linear and sigmoid blocks shown in the flow diagram. The linear + sigmoid blocks work as the classifier part of the model. The hidden activation $a^{\\langle N-1 \\rangle}$ _(lstm_out[:, -1, :])_ is passed through the classifier block to obtain $\\hat{y}^{\\langle N-1 \\rangle}$, with dimensions (m, 1).\n",
    "\n",
    "\n",
    "<figure> \n",
    "<center><img src=\"./images/LSTM_binary.jpeg\" style=\"width:700;height:600px;\"></center>\n",
    "<caption><center><b>Figure 3.</b>LSTM detector flow diagram</center></caption>\n",
    "</figure>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.1. FaultDetector Class Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Creation\n",
    "\n",
    "\n",
    "class FaultDetector(nn.Module):\n",
    "    \"\"\"Some Information about FaultDetector\"\"\"\n",
    "\n",
    "    def __init__(self, N, hidden_dim, tagset_size):\n",
    "        super(FaultDetector, self).__init__()\n",
    "        self.lstm = nn.LSTM(N, hidden_dim, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, tagset_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        lstm_out, _ = self.lstm(x)\n",
    "        fc_layer = self.fc(lstm_out[:, -1, :])\n",
    "\n",
    "        return torch.sigmoid(fc_layer)\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2. Model Instance Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model structure: FaultDetector(\n",
      "  (lstm): LSTM(1, 20, batch_first=True)\n",
      "  (fc): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "1861\n"
     ]
    }
   ],
   "source": [
    "model = FaultDetector(n_signals, hidden_dim, tagset_size).to(device)\n",
    "loss_fn = nn.BCELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)\n",
    "# Learning rate decay (optional)\n",
    "\n",
    "decayRate = 0.96\n",
    "my_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(\n",
    "    optimizer=optimizer, gamma=decayRate\n",
    ")\n",
    "\n",
    "# Model Summary\n",
    "print(f\"Model structure: {model}\\n\")\n",
    "\n",
    "# Number of parameters\n",
    "model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
    "params = sum([np.prod(p.size()) for p in model_parameters])\n",
    "print(f\"Number of parameters: {params}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Dataset Creation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.1. Dataset Class Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.signalload import CSV_pandas_path\n",
    "from utils.auxfunctions import moving_window\n",
    "import numpy as np\n",
    "from itertools import repeat\n",
    "\n",
    "\n",
    "class Form1Dataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Some Information about Form1Dataset\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        dataset_dir,\n",
    "        signal_name,\n",
    "        max_window_idx=193,\n",
    "        window_length=64,\n",
    "        step=1,\n",
    "        test=False,\n",
    "    ):\n",
    "        super(Form1Dataset, self).__init__()\n",
    "        self.max_window_idx = max_window_idx\n",
    "        self.test = test\n",
    "        self.signal_name = signal_name\n",
    "\n",
    "        self.csv_list = glob.glob(f\"{dataset_dir}*.csv\")\n",
    "        self.csv_amount = len(self.csv_list)\n",
    "        self.windows_amount = max_window_idx * self.csv_amount\n",
    "        self.window_length = window_length\n",
    "        self.step = step\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "\n",
    "        # sample_settings\n",
    "        window_length = self.window_length\n",
    "        step = self.step\n",
    "\n",
    "        # Calculate window_idx and file name\n",
    "        window_idx = index % self.max_window_idx\n",
    "        csv_idx = index // self.max_window_idx\n",
    "        csv_full_path = self.csv_list[csv_idx]\n",
    "\n",
    "        # Load CSV, signal and create windows\n",
    "        csv_name = os.path.basename(csv_full_path)\n",
    "        signal, t, _ = CSV_pandas_path(csv_full_path).load_data(self.signal_name)\n",
    "        signal_windows, t_windows = list(\n",
    "            map(moving_window, [signal, t], repeat(window_length), repeat(step))\n",
    "        )\n",
    "\n",
    "        # select window\n",
    "\n",
    "        signal_window = signal_windows[window_idx]\n",
    "        signal_window = np.expand_dims(signal_window, axis=1)\n",
    "        t_window = t_windows[window_idx]\n",
    "\n",
    "        # create labels\n",
    "        if window_idx < 66:\n",
    "            label = np.array([0])\n",
    "        else:\n",
    "            label = np.array([1])\n",
    "\n",
    "        # For dataset visualization return following information\n",
    "        signal_window = torch.from_numpy(np.copy(signal_window)).float()\n",
    "        label = torch.from_numpy(label).float()\n",
    "        if self.test == True:\n",
    "            return signal_window, t_window, label, csv_name, index, window_idx\n",
    "\n",
    "        # For training only signals and labels are required\n",
    "        \n",
    "        return signal_window, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.csv_amount * self.max_window_idx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.2. Database Directory Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "copying 35 files...\n",
      "finished\n",
      "Available Relays (current):\n",
      "I: X0038A-X0043A\n",
      "I: X0038B-X0043B\n",
      "I: X0038C-X0043C\n",
      "I: X0048A-X0053A\n",
      "I: X0048B-X0053B\n",
      "I: X0048C-X0053C\n",
      "I: X0060A-X0065A\n",
      "I: X0060B-X0065B\n",
      "I: X0060C-X0065C\n",
      "I: X0066A-X0067A\n",
      "I: X0066B-X0067B\n",
      "I: X0066C-X0067C\n",
      "I: X0023A-R1A\n",
      "I: X0023B-R1B\n",
      "I: X0023C-R1C\n",
      "I: X0004A-R2A\n",
      "I: X0004B-R2B\n",
      "I: X0004C-R2C\n",
      "I: X0071A-R3A\n",
      "I: X0071B-R3B\n",
      "I: X0071C-R3C\n",
      "copying 35 files...\n",
      "finished\n",
      "Available Relays (current):\n",
      "copying 35 files...\n",
      "finished\n",
      "Available Relays (current):\n"
     ]
    }
   ],
   "source": [
    "# Creating Dataset Folders\n",
    "\n",
    "dataset_dir_full = \"C:/Users/aherrada/OneDrive - Universidad del Norte/Uninorte/DetectionDataBase/LSTM_form_1/fault_01/\"\n",
    "dataset_dir = \"C:/Users/aherrada/OneDrive - Universidad del Norte/Uninorte/DetectionDataBase/LSTM_form_1/R1/\"\n",
    "dataset_dir_R2 = \"C:/Users/aherrada/OneDrive - Universidad del Norte/Uninorte/DetectionDataBase/LSTM_form_1/R2/\"\n",
    "dataset_dir_R3 = \"C:/Users/aherrada/OneDrive - Universidad del Norte/Uninorte/DetectionDataBase/LSTM_form_1/R3/\"\n",
    "max_window_idx = 130\n",
    "n_events = 35\n",
    "\n",
    "\n",
    "def dataset_creator(source_path, target_path, signal_list=False):\n",
    "    csv_list = glob.glob(f\"{source_path}*.csv\")\n",
    "    if not os.path.isdir(target_path):\n",
    "        print(\"creating\")\n",
    "        os.makedirs(target_path)\n",
    "    else:\n",
    "        file_list = glob.glob(f\"{target_path}*.csv\")\n",
    "        for file in file_list:\n",
    "            os.remove(file)\n",
    "    csv_list = random.sample(csv_list, n_events)\n",
    "    print(f\"copying {len(csv_list)} files...\")\n",
    "    for csv in csv_list:\n",
    "        source_path = csv\n",
    "        target_path = target_path\n",
    "        shutil.copy(source_path, target_path)\n",
    "    print(\"finished\")\n",
    "    print(\"Available Relays (current):\")\n",
    "    if signal_list == True:\n",
    "        CSV_pandas_path(csv_list[0]).relay_list()\n",
    "\n",
    "\n",
    "# Crear los 3 Datasets para entrenar los relés individualmente\n",
    "dataset_creator(dataset_dir_full, dataset_dir, signal_list=True)\n",
    "dataset_creator(dataset_dir_full, dataset_dir_R2)\n",
    "dataset_creator(dataset_dir_full, dataset_dir_R3)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.3. Dataset and Dataloader Instances Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "signal_name = \"I: X0023A-R1A\"\n",
    "dataset = Form1Dataset(\n",
    "    dataset_dir, max_window_idx=max_window_idx, signal_name=signal_name\n",
    ")\n",
    "train_dataset, test_dataset = random_split(dataset, [0.95, 0.05])\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=True)\n",
    "# EL test set realmente es un dev set\n",
    "# Como regla general, si es utilizado dentro del loop es un dev set."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.1. Train / Test(dev) Loop Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Training / Test / Validation Loops\n",
    "\n",
    "# Training\n",
    "def train(dataloader, model, loss_fn, optimizer, return_loss = False):\n",
    "    size = len(dataloader.dataset)\n",
    "    model.train()\n",
    "    loss_list = []\n",
    "    for batch, (x, y) in enumerate(dataloader):\n",
    "        x, y = x.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(x)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if return_loss == True:\n",
    "            loss_list.append(loss.item())\n",
    "        if batch % 2 == 0:\n",
    "            loss, current = loss.item(), batch * len(x)\n",
    "            print(f\"loss: {loss:>f} [{current:>5d}/{size:>5d}]\")\n",
    "\n",
    "        if batch % 5 == 0:\n",
    "            my_lr_scheduler.step()\n",
    "    if return_loss == True:\n",
    "        return loss_list\n",
    "\n",
    "\n",
    "\n",
    "def test(dataloader, model, loss_fn, return_metrics=False):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for x, y in dataloader:\n",
    "            x, y = x.to(device), y.to(device)\n",
    "            pred = model(x)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (torch.round(pred) == y).type(torch.float).sum().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "    print(\n",
    "        f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\"\n",
    "    )\n",
    "    if return_metrics==True:\n",
    "        return correct*100, test_loss\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.2. Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "--------------------------------\n",
      "Test Error: \n",
      " Accuracy: 97.8%, Avg loss: 0.103665 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 1\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}\\n--------------------------------\")\n",
    "    train_loss = train(train_dataloader, model, loss_fn, optimizer, return_loss=True)\n",
    "    test(test_dataloader, model, loss_fn)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.3. Loss Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e1c5b2b3a0>,\n",
       " <matplotlib.lines.Line2D at 0x1e1c5b2b400>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mini_batch_list = [index for index, _ in enumerate(train_loss)]\n",
    "train_loss_ewm = pd.DataFrame(train_loss).ewm(com=0.95).mean()\n",
    "plt.plot(mini_batch_list, train_loss, mini_batch_list, train_loss_ewm)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.4. Saving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save the Model\n",
    "# torch.save(model.state_dict(), './models/LSTMHarmonic_weights_R1_996.pth')\n",
    "# model.load_state_dict(torch.load('./models/LSTMHarmonic_weights_989.pth'))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Results Visualization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1. Confussion Matrix Results Table Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data visualization (CPU)\n",
    "\n",
    "def confussion_matrix_labels(pred_label, true_label):\n",
    "    label = \"\"\n",
    "    if int(pred_label) == int(true_label):\n",
    "        label += \"True\"\n",
    "    else:\n",
    "        label += \"False\"\n",
    "    if pred_label == 1:\n",
    "        label += \" positive\"\n",
    "    else:\n",
    "        label += \" negative\"\n",
    "    return label\n",
    "\n",
    "\n",
    "def confussion_matrix(x, labels):\n",
    "    data = {\n",
    "        \"Pred probability\": torch.reshape(x, (-1,)).numpy(),\n",
    "        \"Pred label\": torch.reshape(torch.round(x), (-1,)).int().numpy(),\n",
    "        \"True label\": torch.reshape(labels, (-1,)).int().numpy(),\n",
    "    }\n",
    "    df = pd.DataFrame(data)\n",
    "    df[\"prueba\"] = confussion_matrix_pandas(df[\"Pred label\"], df[\"True label\"])\n",
    "    return df\n",
    "\n",
    "confussion_matrix_pandas = np.vectorize(confussion_matrix_labels)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6.2. Matlab Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pred probability  Pred label  True label         prueba\n",
      "0          0.970602           1           1  True positive\n",
      "1          0.888951           1           1  True positive\n",
      "2          0.978314           1           1  True positive\n",
      "3          0.030253           0           0  True negative\n",
      "4          0.149297           0           0  True negative\n",
      "5          0.977493           1           1  True positive\n",
      "6          0.870592           1           1  True positive\n",
      "7          0.025624           0           0  True negative\n",
      "8          0.887179           1           1  True positive\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_test = Form1Dataset(\n",
    "    dataset_dir, max_window_idx=max_window_idx, signal_name=signal_name, test=True\n",
    ")\n",
    "test_dataloader = DataLoader(dataset_test, batch_size=train_batch_size, shuffle=True)\n",
    "\n",
    "dataiter = iter(test_dataloader)\n",
    "signal_windows, t_windows, labels, *_ = next(dataiter)\n",
    "fs = 3840\n",
    "\n",
    "x = model(signal_windows[1:10].to(device))\n",
    "x = x.detach()\n",
    "\n",
    "# Crear Matriz de confusión\n",
    "df = confussion_matrix(x, labels[1:10])\n",
    "print(df)\n",
    "\n",
    "figure = plt.figure(figsize=(10, 10))\n",
    "cols, rows = 3, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.plot(t_windows[i, :], signal_windows[i, :, 0])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6.3. Plotly Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pred probability  Pred label  True label         prueba\n",
      "0          0.154364           0           0  True negative\n",
      "1          0.043757           0           0  True negative\n",
      "2          0.977443           1           1  True positive\n",
      "3          0.093179           0           0  True negative\n",
      "4          0.889391           1           1  True positive\n",
      "5          0.027588           0           0  True negative\n",
      "6          0.884128           1           1  True positive\n",
      "7          0.028989           0           0  True negative\n",
      "8          0.030503           0           0  True negative\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "black"
         },
         "name": "0",
         "type": "scatter",
         "x": [
          0.0205716,
          0.020832,
          0.0210924,
          0.0213528,
          0.0216132,
          0.0218736,
          0.022134,
          0.0223944,
          0.0226548,
          0.0229152,
          0.0231756,
          0.023436,
          0.0236964,
          0.0239568,
          0.0242172,
          0.0244776,
          0.024738,
          0.0249984,
          0.0252588,
          0.0255192,
          0.0257796,
          0.02604,
          0.0263004,
          0.0265608,
          0.0268212,
          0.0270816,
          0.027342,
          0.0276024,
          0.0278628,
          0.0281232,
          0.0283836,
          0.028644,
          0.0289044,
          0.0291648,
          0.0294252,
          0.0296856,
          0.029946,
          0.0302064,
          0.0304668,
          0.0307272,
          0.0309876,
          0.031248,
          0.0315084,
          0.0317688,
          0.0320292,
          0.0322896,
          0.03255,
          0.0328104,
          0.0330708,
          0.0333312,
          0.0335916,
          0.033852,
          0.0341124,
          0.0343728,
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          0.0348936,
          0.035154,
          0.0354144,
          0.0356748,
          0.0359352,
          0.0361956,
          0.036456,
          0.0367164,
          0.0369768
         ],
         "xaxis": "x",
         "y": [
          -21.417137145996094,
          -24.96830940246582,
          -28.405187606811523,
          -31.268943786621094,
          -33.90782165527344,
          -36.29541778564453,
          -38.55408477783203,
          -40.34768295288086,
          -41.71569061279297,
          -42.53754806518555,
          -43.106895446777344,
          -43.309783935546875,
          -43.000431060791016,
          -42.231563568115234,
          -41.02321243286133,
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          -37.65876007080078,
          -35.60103225708008,
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          -19.54364776611328,
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          -11.348267555236816,
          -7.210482120513916,
          -3.0092787742614746,
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           "sequentialminus": [
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             "#fdca26"
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             1,
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           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Figuras"
        },
        "width": 900,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.2888888888888889
         ]
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
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        },
        "xaxis3": {
         "anchor": "y3",
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        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
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        },
        "xaxis5": {
         "anchor": "y5",
         "domain": [
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        },
        "xaxis6": {
         "anchor": "y6",
         "domain": [
          0.7111111111111111,
          1
         ]
        },
        "xaxis7": {
         "anchor": "y7",
         "domain": [
          0,
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         ]
        },
        "xaxis8": {
         "anchor": "y8",
         "domain": [
          0.35555555555555557,
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         ]
        },
        "xaxis9": {
         "anchor": "y9",
         "domain": [
          0.7111111111111111,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.7333333333333333,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.7333333333333333,
          1
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0.7333333333333333,
          1
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0.36666666666666664,
          0.6333333333333333
         ]
        },
        "yaxis5": {
         "anchor": "x5",
         "domain": [
          0.36666666666666664,
          0.6333333333333333
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        },
        "yaxis6": {
         "anchor": "x6",
         "domain": [
          0.36666666666666664,
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        },
        "yaxis7": {
         "anchor": "x7",
         "domain": [
          0,
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        },
        "yaxis8": {
         "anchor": "x8",
         "domain": [
          0,
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        },
        "yaxis9": {
         "anchor": "x9",
         "domain": [
          0,
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       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fs = 3840\n",
    "dataset_test = Form1Dataset(\n",
    "    dataset_dir, max_window_idx=max_window_idx, signal_name=signal_name, test=True\n",
    ")\n",
    "train_dataloader = DataLoader(dataset_test, batch_size=train_batch_size, shuffle=True)\n",
    "\n",
    "dataiter = iter(train_dataloader)\n",
    "signal_windows, t_windows, labels, *_ = next(dataiter)\n",
    "\n",
    "x = model(signal_windows[1:10].to(device))\n",
    "x = x.detach()\n",
    "\n",
    "# Crear Matriz de confusión\n",
    "df = confussion_matrix(x, labels[1:10])\n",
    "print(df)\n",
    "\n",
    "# figure = plt.figure(figsize=(10, 10))\n",
    "cols, rows = 3, 3\n",
    "fig = make_subplots(rows=rows, cols=cols)\n",
    "\n",
    "for i in range(0, cols * rows):\n",
    "\n",
    "    col = i % cols\n",
    "    row = i // cols\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            name=i, x=t_windows[i, :], y=signal_windows[i, :, 0], marker_color=\"black\"\n",
    "        ),\n",
    "        row=row + 1,\n",
    "        col=col + 1,\n",
    "    )\n",
    "\n",
    "fig.update_layout(\n",
    "    height=700,\n",
    "    width=900,\n",
    "    title_text=\"Figuras\",\n",
    "    margin={\"b\": 20, \"t\": 40, \"l\": 20, \"r\": 30},\n",
    "    plot_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    # paper_bgcolor=\"whitesmoke\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo R2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model structure: FaultDetector(\n",
      "  (lstm): LSTM(1, 20, batch_first=True)\n",
      "  (fc): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Epoch 1\n",
      "--------------------------------\n",
      "loss: 0.699276 [    0/ 4096]\n",
      "loss: 0.640427 [  128/ 4096]\n",
      "loss: 0.623498 [  256/ 4096]\n",
      "loss: 0.601174 [  384/ 4096]\n",
      "loss: 0.574079 [  512/ 4096]\n",
      "loss: 0.513729 [  640/ 4096]\n",
      "loss: 0.487733 [  768/ 4096]\n",
      "loss: 0.436654 [  896/ 4096]\n",
      "loss: 0.429537 [ 1024/ 4096]\n",
      "loss: 0.396921 [ 1152/ 4096]\n",
      "loss: 0.314566 [ 1280/ 4096]\n",
      "loss: 0.296397 [ 1408/ 4096]\n",
      "loss: 0.255865 [ 1536/ 4096]\n",
      "loss: 0.307133 [ 1664/ 4096]\n",
      "loss: 0.269831 [ 1792/ 4096]\n",
      "loss: 0.185034 [ 1920/ 4096]\n",
      "loss: 0.232722 [ 2048/ 4096]\n",
      "loss: 0.238132 [ 2176/ 4096]\n",
      "loss: 0.142724 [ 2304/ 4096]\n",
      "loss: 0.171021 [ 2432/ 4096]\n",
      "loss: 0.277281 [ 2560/ 4096]\n",
      "loss: 0.168091 [ 2688/ 4096]\n",
      "loss: 0.189481 [ 2816/ 4096]\n",
      "loss: 0.121623 [ 2944/ 4096]\n",
      "loss: 0.151956 [ 3072/ 4096]\n",
      "loss: 0.083514 [ 3200/ 4096]\n",
      "loss: 0.090595 [ 3328/ 4096]\n",
      "loss: 0.132138 [ 3456/ 4096]\n",
      "loss: 0.148001 [ 3584/ 4096]\n",
      "loss: 0.100255 [ 3712/ 4096]\n",
      "loss: 0.068380 [ 3840/ 4096]\n",
      "loss: 0.171877 [ 3968/ 4096]\n",
      "Test Error: \n",
      " Accuracy: 98.3%, Avg loss: 0.093816 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load Dataset\n",
    "signal_name_R2 = \"I: X0004A-R2A\"\n",
    "dataset_R2 = Form1Dataset(\n",
    "    dataset_dir_R2, max_window_idx=max_window_idx, signal_name=signal_name_R2\n",
    ")\n",
    "train_dataset, test_dataset, dev_dataset = random_split(dataset_R2, [0.9, 0.09, 0.01])\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=True)\n",
    "# EL test set realmente es un dev set\n",
    "# Como regla general, si es utilizado dentro del loop es un dev set.\n",
    "dev_dataloader = DataLoader(dev_dataset, batch_size=dev_batch_size, shuffle=True)\n",
    "epochs = 1\n",
    "\n",
    "# Create Model\n",
    "\n",
    "model_R2 = FaultDetector(n_signals, hidden_dim, tagset_size).to(device)\n",
    "loss_fn = nn.BCELoss()\n",
    "optimizer = torch.optim.Adam(model_R2.parameters(), lr=1e-2)\n",
    "# Learning rate decay\n",
    "decayRate = 0.96\n",
    "my_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=decayRate)\n",
    "\n",
    "print(f\"Model structure: {model_R2}\\n\")\n",
    "\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}\\n--------------------------------\")\n",
    "    train_loss = train(train_dataloader, model_R2, loss_fn, optimizer, return_loss=True)\n",
    "    test(test_dataloader, model_R2, loss_fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e1c5e3a4f0>,\n",
       " <matplotlib.lines.Line2D at 0x1e1c5e3a550>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mini_batch_list = [index for index, _ in enumerate(train_loss)]\n",
    "train_loss_ewm = pd.DataFrame(train_loss).ewm(com=0.98).mean()\n",
    "plt.plot(mini_batch_list, train_loss, mini_batch_list, train_loss_ewm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save the Model_R2\n",
    "# torch.save(model_R2.state_dict(), './models/LSTMHarmonic_weights_R2_972.pth')\n",
    "# model_R2.load_state_dict(torch.load('./models/LSTMHarmonic_weights_989.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 64, 1])\n",
      "tensor([[0., 0., 1., 1., 0., 0., 1., 0., 1.]])\n",
      "tensor([[0., 0., 0., 1., 0., 0., 1., 0., 1.]], grad_fn=<ReshapeAliasBackward0>)\n",
      "tensor([[0.0243, 0.0284, 0.0497, 0.9431, 0.0319, 0.0409, 0.9745, 0.0578, 0.9702]],\n",
      "       grad_fn=<ReshapeAliasBackward0>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(dev_dataloader)\n",
    "signal, labels = next(dataiter)\n",
    "fs = 3840\n",
    "t = np.linspace(0, N / fs, N)\n",
    "print(signal.shape)\n",
    "print(torch.reshape(labels[1:10], (1, -1)))\n",
    "figure = plt.figure(figsize=(10, 10))\n",
    "cols, rows = 3, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.plot(t, signal[i, :, 0])\n",
    "x = model_R2(signal[1:10].to(device))\n",
    "print(torch.reshape(torch.round(x), (1, -1)))\n",
    "print(torch.reshape(x, (1, -1)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo R3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model structure: FaultDetector(\n",
      "  (lstm): LSTM(1, 20, batch_first=True)\n",
      "  (fc): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Epoch 1\n",
      "--------------------------------\n",
      "loss: 0.688448 [    0/ 4096]\n",
      "loss: 0.619775 [  128/ 4096]\n",
      "loss: 0.582305 [  256/ 4096]\n",
      "loss: 0.558069 [  384/ 4096]\n",
      "loss: 0.532146 [  512/ 4096]\n",
      "loss: 0.510895 [  640/ 4096]\n",
      "loss: 0.475235 [  768/ 4096]\n",
      "loss: 0.408887 [  896/ 4096]\n",
      "loss: 0.462808 [ 1024/ 4096]\n",
      "loss: 0.363916 [ 1152/ 4096]\n",
      "loss: 0.348708 [ 1280/ 4096]\n",
      "loss: 0.325589 [ 1408/ 4096]\n",
      "loss: 0.322745 [ 1536/ 4096]\n",
      "loss: 0.288809 [ 1664/ 4096]\n",
      "loss: 0.297721 [ 1792/ 4096]\n",
      "loss: 0.404995 [ 1920/ 4096]\n",
      "loss: 0.323360 [ 2048/ 4096]\n",
      "loss: 0.267944 [ 2176/ 4096]\n",
      "loss: 0.289457 [ 2304/ 4096]\n",
      "loss: 0.257210 [ 2432/ 4096]\n",
      "loss: 0.205581 [ 2560/ 4096]\n",
      "loss: 0.326841 [ 2688/ 4096]\n",
      "loss: 0.240391 [ 2816/ 4096]\n",
      "loss: 0.196868 [ 2944/ 4096]\n",
      "loss: 0.197302 [ 3072/ 4096]\n",
      "loss: 0.202544 [ 3200/ 4096]\n",
      "loss: 0.153139 [ 3328/ 4096]\n",
      "loss: 0.213567 [ 3456/ 4096]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epochs):\n\u001b[0;32m     28\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEpoch \u001b[39m\u001b[39m{\u001b[39;00mepoch\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m--------------------------------\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 29\u001b[0m     train_loss \u001b[39m=\u001b[39m train(train_dataloader, model_R3, loss_fn, optimizer, return_loss\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m     30\u001b[0m     test(test_dataloader, model_R3, loss_fn)\n",
      "Cell \u001b[1;32mIn[8], line 8\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(dataloader, model, loss_fn, optimizer, return_loss)\u001b[0m\n\u001b[0;32m      6\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m      7\u001b[0m loss_list \u001b[39m=\u001b[39m []\n\u001b[1;32m----> 8\u001b[0m \u001b[39mfor\u001b[39;00m batch, (x, y) \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(dataloader):\n\u001b[0;32m      9\u001b[0m     x, y \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mto(device), y\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m     11\u001b[0m     \u001b[39m# Compute prediction error\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:628\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    625\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    626\u001b[0m     \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    627\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()  \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 628\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[0;32m    629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m    630\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    631\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    632\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32mc:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:671\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    669\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    670\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 671\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    672\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m    673\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[1;32mc:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:58\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     56\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     57\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 58\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     59\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     60\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32mc:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:58\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     56\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     57\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 58\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     59\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     60\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32mc:\\Users\\aherrada\\Documents\\Git\\Pytorch\\env\\lib\\site-packages\\torch\\utils\\data\\dataset.py:295\u001b[0m, in \u001b[0;36mSubset.__getitem__\u001b[1;34m(self, idx)\u001b[0m\n\u001b[0;32m    293\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(idx, \u001b[39mlist\u001b[39m):\n\u001b[0;32m    294\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindices[i] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m idx]]\n\u001b[1;32m--> 295\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindices[idx]]\n",
      "Cell \u001b[1;32mIn[5], line 60\u001b[0m, in \u001b[0;36mForm1Dataset.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     57\u001b[0m     label \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m1\u001b[39m])\n\u001b[0;32m     59\u001b[0m \u001b[39m# For dataset visualization return following information\u001b[39;00m\n\u001b[1;32m---> 60\u001b[0m signal_window \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mfrom_numpy(signal_window)\u001b[39m.\u001b[39;49mfloat()\n\u001b[0;32m     61\u001b[0m label \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mfrom_numpy(label)\u001b[39m.\u001b[39mfloat()\n\u001b[0;32m     62\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtest \u001b[39m==\u001b[39m \u001b[39mTrue\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Load Dataset\n",
    "signal_name_R3 = \"I: X0071A-R3A\"\n",
    "dataset_R3 = Form1Dataset(\n",
    "    dataset_dir_R3, max_window_idx=max_window_idx, signal_name=signal_name_R3\n",
    ")\n",
    "train_dataset, test_dataset, dev_dataset = random_split(dataset_R3, [0.9, 0.09, 0.01])\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=True)\n",
    "# EL test set realmente es un dev set\n",
    "# Como regla general, si es utilizado dentro del loop es un dev set.\n",
    "dev_dataloader = DataLoader(dev_dataset, batch_size=dev_batch_size, shuffle=True)\n",
    "epochs = 1\n",
    "\n",
    "# Create Model\n",
    "\n",
    "model_R3 = FaultDetector(n_signals, hidden_dim, tagset_size).to(device)\n",
    "loss_fn = nn.BCELoss()\n",
    "optimizer = torch.optim.Adam(model_R3.parameters(), lr=1e-2)\n",
    "# Learning rate decay\n",
    "decayRate = 0.96\n",
    "my_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=decayRate)\n",
    "\n",
    "print(f\"Model structure: {model_R3}\\n\")\n",
    "\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}\\n--------------------------------\")\n",
    "    train_loss = train(train_dataloader, model_R3, loss_fn, optimizer, return_loss=True)\n",
    "    test(test_dataloader, model_R3, loss_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1790877729654312"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mini_batch_list = [index for index, _ in enumerate(train_loss)]\n",
    "train_loss_ewm = pd.DataFrame(train_loss).ewm(com=0.98).mean()\n",
    "plt.plot(mini_batch_list, train_loss, mini_batch_list, train_loss_ewm)\n",
    "train_loss[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save the Model_R2\n",
    "# torch.save(model_R3.state_dict(), './models/LSTMHarmonic_weights_R3_983.pth')\n",
    "# model_R2.load_state_dict(torch.load('./models/LSTMHarmonic_weights_989.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 64, 1])\n",
      "tensor([[1., 1., 0., 1., 1., 0., 1., 0., 1.]])\n",
      "tensor([[1., 1., 0., 1., 1., 0., 1., 0., 1.]], grad_fn=<ReshapeAliasBackward0>)\n",
      "tensor([[0.9562, 0.9559, 0.0382, 0.9664, 0.9712, 0.0191, 0.9659, 0.0283, 0.9659]],\n",
      "       grad_fn=<ReshapeAliasBackward0>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(dev_dataloader)\n",
    "signal, labels = next(dataiter)\n",
    "fs = 3840\n",
    "t = np.linspace(0, N / fs, N)\n",
    "print(signal.shape)\n",
    "print(torch.reshape(labels[1:10], (1, -1)))\n",
    "figure = plt.figure(figsize=(10, 10))\n",
    "cols, rows = 3, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.plot(t, signal[i, :, 0])\n",
    "x = model_R2(signal[1:10].to(device))\n",
    "print(torch.reshape(torch.round(x), (1, -1)))\n",
    "print(torch.reshape(x, (1, -1)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. References\n",
    "1. PyTorch Contributors: LSTM. [LSTM — PyTorch 1.13 documentation](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html).\n",
    "<!-- @online{LSTM,\n",
    "    author = \"{PyTorch Contributors}\",\n",
    "    title = \"LSTM\",\n",
    "    url  = \"https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html\",\n",
    "    addendum = \"(accessed: 10.02.2023)\",\n",
    "    keywords = \"LSTM pytorch\"\n",
    "} -->\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "edf36f54e3c0d12e79cbfbdb2eab1260e125462eb03956a03e5e853a13b289dd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}