LSTM_Based_Gan.ipynb

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      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l7eDdstKTSsi"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import DataLoader"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Architecture"
      ],
      "metadata": {
        "id": "B0U3r8RHJLaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generator architecture\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, latent_dim, output_dim, hidden_dim, dropout_prob):\n",
        "        super(Generator, self).__init__()\n",
        "        self.lstm = nn.LSTM(latent_dim, hidden_dim, batch_first=True)\n",
        "        self.dropout = nn.Dropout(dropout_prob)\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        output, _ = self.lstm(x)\n",
        "        output = self.dropout(output)\n",
        "        output = self.fc(output)\n",
        "        output = self.sigmoid(output)\n",
        "        return output"
      ],
      "metadata": {
        "id": "d14mdWtaTXud"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Discriminator architecture\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, dropout_prob):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)\n",
        "        self.dropout = nn.Dropout(dropout_prob)\n",
        "        self.fc = nn.Linear(hidden_dim, 1)\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        output, _ = self.lstm(x)\n",
        "        output = self.dropout(output)\n",
        "        output = self.fc(output)\n",
        "        output = self.sigmoid(output)\n",
        "        return output"
      ],
      "metadata": {
        "id": "gDDwvKc2UAN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Parameters"
      ],
      "metadata": {
        "id": "DaLowu6FK4co"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the generator and discriminator\n",
        "input_dim = 555\n",
        "output_dim = 555\n",
        "hidden_dim = 128\n",
        "dropout_prob = 0.2\n",
        "\n",
        "# Define the batch size and number of training iterations\n",
        "batch_size = 19\n",
        "num_iterations = 1000\n",
        "\n",
        "# Dataset file path\n",
        "dataset_path = 'database.txt'"
      ],
      "metadata": {
        "id": "NqtUBG1vUBew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ancJK5DeUlHE",
        "outputId": "56fc9e32-cbec-4def-ea26-567e35507bd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First class Gan\n",
        "generator_1 = Generator(input_dim, output_dim, hidden_dim, dropout_prob).to(device)\n",
        "discriminator_1 = Discriminator(input_dim, hidden_dim, dropout_prob).to(device)\n",
        "\n",
        "# Print the summary of the generator and discriminator\n",
        "print(generator_1)\n",
        "print(discriminator_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k62_RkGHUWw2",
        "outputId": "ad07c584-62df-4ae6-c783-b0cb35443e4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator(\n",
            "  (lstm): LSTM(555, 128, batch_first=True)\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (fc): Linear(in_features=128, out_features=555, bias=True)\n",
            "  (sigmoid): Sigmoid()\n",
            ")\n",
            "Discriminator(\n",
            "  (lstm): LSTM(555, 128, batch_first=True)\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
            "  (sigmoid): Sigmoid()\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Third class Gan\n",
        "generator_3 = Generator(input_dim, output_dim, hidden_dim, dropout_prob).to(device)\n",
        "discriminator_3 = Discriminator(input_dim, hidden_dim, dropout_prob).to(device)\n",
        "\n",
        "# Print the summary of the generator and discriminator\n",
        "print(generator_3)\n",
        "print(discriminator_3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wapQCjwcNcw4",
        "outputId": "4a60e680-0bf3-4cdc-d5d8-0ff71fd2762f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator(\n",
            "  (lstm): LSTM(555, 128, batch_first=True)\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (fc): Linear(in_features=128, out_features=555, bias=True)\n",
            "  (sigmoid): Sigmoid()\n",
            ")\n",
            "Discriminator(\n",
            "  (lstm): LSTM(555, 128, batch_first=True)\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
            "  (sigmoid): Sigmoid()\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Process DataSet"
      ],
      "metadata": {
        "id": "JdRH8PNTK86W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define your training data\n",
        "def getDataset(path, label):\n",
        "  labeled_dataset = []\n",
        "  with open(path, 'r') as dataset:\n",
        "    for sequence in dataset:\n",
        "      sequence = sequence.split(' ')\n",
        "      if int(sequence[-1][0]) == label:\n",
        "          labeled_dataset.append(np.asarray(sequence[:-1], dtype=np.float32))\n",
        "  return np.asarray(labeled_dataset, dtype=object)"
      ],
      "metadata": {
        "id": "e-y4BnAKUZOm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normlizing the sequence length to the smallest one\n",
        "def shortenSequences(dataset, length):\n",
        "  processed_data = []\n",
        "  for sequence in dataset:\n",
        "    indexes = np.sort(np.random.choice(range(sequence.shape[0]), length, replace=False))\n",
        "    new_sequence = np.empty(length)\n",
        "    for i in range(length):\n",
        "      new_sequence[i] = sequence[indexes[i]]\n",
        "    processed_data.append(new_sequence)\n",
        "  return np.asarray(processed_data, dtype=np.float32)"
      ],
      "metadata": {
        "id": "h-C_Hd6rL05Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_class = getDataset(dataset_path, 1)\n",
        "third_class = getDataset(dataset_path, 3)\n",
        "\n",
        "print(len(first_class), len(third_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "radwyolWOurN",
        "outputId": "ed193e4f-e652-411e-dd33-661e8f23a2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95 96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fist_class_min = min(len(i) for i in first_class)\n",
        "third_class_min = min(len(i) for i in third_class)\n",
        "\n",
        "length = min(fist_class_min, third_class_min)\n",
        "length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ox5Un0IPOkE",
        "outputId": "cb1a5e9d-128a-4338-f5fa-22519f8737d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "555"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original_first_class = shortenSequences(first_class, length)\n",
        "original_third_class = shortenSequences(third_class, length)"
      ],
      "metadata": {
        "id": "_j-GigC9Plqs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dataset class\n",
        "class PseudoTimeSeriesDataset(data.Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return self.data[index]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)"
      ],
      "metadata": {
        "id": "ejfgR12zVAKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the dataset and data loader\n",
        "first_train_data = PseudoTimeSeriesDataset(original_first_class)\n",
        "first_train_data_loader = data.DataLoader(first_train_data, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "third_train_data = PseudoTimeSeriesDataset(original_third_class)\n",
        "third_train_data_loader = data.DataLoader(third_train_data, batch_size=batch_size, shuffle=True)"
      ],
      "metadata": {
        "id": "AOhDtizVVB9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define loss function and optimizers\n",
        "adversarial_loss = nn.BCELoss()\n",
        "generator_optimizer_1 = optim.Adam(generator_1.parameters(), lr=0.001, weight_decay=0.0001)\n",
        "discriminator_optimizer_1 = optim.Adam(discriminator_1.parameters(), lr=0.001, weight_decay=0.0001)\n",
        "\n",
        "generator_optimizer_3 = optim.Adam(generator_3.parameters(), lr=0.001, weight_decay=0.0001)\n",
        "discriminator_optimizer_3 = optim.Adam(discriminator_3.parameters(), lr=0.001, weight_decay=0.0001)"
      ],
      "metadata": {
        "id": "96nY6NxWVOGb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "TsfwQguYLBSn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(generator, discriminator, generator_optimizer, discriminator_optimizer, train_data_loader):\n",
        "  # Training loop\n",
        "  for iteration in range(num_iterations):\n",
        "    for real_data in train_data_loader:\n",
        "\n",
        "        real_data = real_data.to(device)\n",
        "\n",
        "        # Training the discriminator\n",
        "        discriminator_optimizer.zero_grad()\n",
        "\n",
        "        # Generate fake data\n",
        "        noise = torch.randn(real_data.size(0), real_data.size(1)).to(device)\n",
        "        fake_data = generator(noise).detach()\n",
        "\n",
        "        # Calculate discriminator loss for real and fake data\n",
        "        real_labels = torch.ones(real_data.size(0), 1).to(device)\n",
        "        fake_labels = torch.zeros(real_data.size(0), 1).to(device)\n",
        "        real_loss = adversarial_loss(discriminator(real_data), real_labels)\n",
        "        fake_loss = adversarial_loss(discriminator(fake_data), fake_labels)\n",
        "        discriminator_loss = real_loss + fake_loss\n",
        "\n",
        "        # Backpropagation and optimization\n",
        "        discriminator_loss.backward()\n",
        "        discriminator_optimizer.step()\n",
        "\n",
        "        # Training the generator\n",
        "        generator_optimizer.zero_grad()\n",
        "\n",
        "        # Generate fake data\n",
        "        noise = torch.randn(real_data.size(0), real_data.size(1)).to(device)\n",
        "        fake_data = generator(noise)\n",
        "\n",
        "        # Calculate generator loss\n",
        "        generator_loss = adversarial_loss(discriminator(fake_data), real_labels)\n",
        "\n",
        "        # Backpropagation and optimization\n",
        "        generator_loss.backward()\n",
        "        generator_optimizer.step()\n",
        "\n",
        "\n",
        "    # Print training progress\n",
        "    if (iteration + 1) % 100 == 0:\n",
        "        print(f\"Iteration: {iteration+1}, Generator Loss: {generator_loss.item():.4f}, Discriminator Loss: {discriminator_loss.item():.4f}\")"
      ],
      "metadata": {
        "id": "OuONjqexVRIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(generator_1, discriminator_1, generator_optimizer_1, discriminator_optimizer_1, first_train_data_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YheGewVsQ9C1",
        "outputId": "3393fa9d-0a30-4b48-b32e-cdf107763ea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 100, Generator Loss: 4.1573, Discriminator Loss: 0.2235\n",
            "Iteration: 200, Generator Loss: 0.9659, Discriminator Loss: 1.0664\n",
            "Iteration: 300, Generator Loss: 1.8247, Discriminator Loss: 0.4881\n",
            "Iteration: 400, Generator Loss: 1.2934, Discriminator Loss: 0.8929\n",
            "Iteration: 500, Generator Loss: 1.1310, Discriminator Loss: 1.0224\n",
            "Iteration: 600, Generator Loss: 1.2095, Discriminator Loss: 1.2652\n",
            "Iteration: 700, Generator Loss: 0.6372, Discriminator Loss: 1.2550\n",
            "Iteration: 800, Generator Loss: 0.9098, Discriminator Loss: 1.2368\n",
            "Iteration: 900, Generator Loss: 0.7438, Discriminator Loss: 1.6138\n",
            "Iteration: 1000, Generator Loss: 0.7822, Discriminator Loss: 1.3780\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train(generator_3, discriminator_3, generator_optimizer_3, discriminator_optimizer_3, third_train_data_loader)"
      ],
      "metadata": {
        "id": "DVE_aAr8UATo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd93b6b4-e7f1-45ee-ad2e-3b9bd6dcfff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 100, Generator Loss: 0.8289, Discriminator Loss: 1.2484\n",
            "Iteration: 200, Generator Loss: 0.7427, Discriminator Loss: 0.9935\n",
            "Iteration: 300, Generator Loss: 0.4949, Discriminator Loss: 1.0662\n",
            "Iteration: 400, Generator Loss: 0.9043, Discriminator Loss: 1.5978\n",
            "Iteration: 500, Generator Loss: 0.5949, Discriminator Loss: 2.2485\n",
            "Iteration: 600, Generator Loss: 0.6430, Discriminator Loss: 1.3963\n",
            "Iteration: 700, Generator Loss: 0.8084, Discriminator Loss: 1.3989\n",
            "Iteration: 800, Generator Loss: 0.8571, Discriminator Loss: 1.2238\n",
            "Iteration: 900, Generator Loss: 0.4229, Discriminator Loss: 1.4150\n",
            "Iteration: 1000, Generator Loss: 0.7517, Discriminator Loss: 1.2251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Post processsing**"
      ],
      "metadata": {
        "id": "tHAi6Zs5A0kq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Low-pass gaussian filter\n",
        "from scipy.ndimage import gaussian_filter1d"
      ],
      "metadata": {
        "id": "l5IHpU4CBTk9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def noise(size):\n",
        "    return torch.from_numpy(np.random.randn(size, input_dim)).float().to(device)"
      ],
      "metadata": {
        "id": "pV7ff9PgF88u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_gaussian_filter(data, sigma):\n",
        "    smoothed_data = gaussian_filter1d(data, sigma)\n",
        "    return smoothed_data"
      ],
      "metadata": {
        "id": "-h1e-icKHaXX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def postprocessed(data, sigma):\n",
        "  for i in range(len(data)):\n",
        "    data[i] = apply_gaussian_filter(data[i], sigma)\n",
        "  return data"
      ],
      "metadata": {
        "id": "nJzWs_3MHbxN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(generator_1(noise(95)).cpu().detach().numpy()[0])\n",
        "plt.xlabel(\"Time Step\")\n",
        "plt.ylabel(\"Value\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "RBpK4UHQCNTq",
        "outputId": "de1592ed-2d08-42fb-897e-ff2884032f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fake_first_class = postprocessed(generator_1(noise(95)).cpu().detach().numpy(), 1.5)\n",
        "fake_third_class = postprocessed(generator_3(noise(96)).cpu().detach().numpy(), 1.5)"
      ],
      "metadata": {
        "id": "u1diBkr5GtPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(fake_first_class[0])\n",
        "plt.title(\"Post Processed Sample - First Class\")\n",
        "plt.xlabel(\"Time Step\")\n",
        "plt.ylabel(\"Value\")"
      ],
      "metadata": {
        "id": "vEcy5vUnBstV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "c0dab435-7aed-438d-b115-7864a8a9e1f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(fake_third_class[0])\n",
        "plt.title(\"Post Processed Sample - Third Class\")\n",
        "plt.xlabel(\"Time Step\")\n",
        "plt.ylabel(\"Value\")"
      ],
      "metadata": {
        "id": "Ligq7GAyEr09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "1f3ee4ee-49b1-4988-e3f2-7820b32f74ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation\n",
        "\n"
      ],
      "metadata": {
        "id": "y-zLC-sDLS26"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Silhouette Accuracy"
      ],
      "metadata": {
        "id": "gQk5-17XUp51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score"
      ],
      "metadata": {
        "id": "kL6lMP9uInLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Silhouette Score\n",
        "def calculate_silhouette_score(features, cluster_labels):\n",
        "    silhouette_avg = silhouette_score(features, cluster_labels)\n",
        "    return silhouette_avg"
      ],
      "metadata": {
        "id": "g4J3pJmpF7l0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate time series data and class labels first class\n",
        "time_series_data_1_1 = torch.cat((generator_1(noise(95)),\n",
        "                              torch.from_numpy(original_first_class).to(device),), axis=0)\n",
        "class_labels = torch.cat((torch.zeros(95), torch.ones(95)))"
      ],
      "metadata": {
        "id": "ukFiP2oCGW-8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Silhouette Score for the first class original and generated\n",
        "silhouette_avg_1_1 = calculate_silhouette_score(\n",
        "    time_series_data_1_1.cpu().detach().numpy(),\n",
        "    class_labels.cpu().detach().numpy())\n",
        "\n",
        "print(f\"Silhouette Score - First class original vs synthetic: {silhouette_avg_1_1:.4f}\")"
      ],
      "metadata": {
        "id": "WeZff_Y8IHWL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3f62c10-09ad-46ca-f9e4-004607ffec6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette Score - First class original vs synthetic: 0.0511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate time series data and class labels third class\n",
        "time_series_data_3_3 = torch.cat((generator_3(noise(96)),\n",
        "                              torch.from_numpy(original_third_class).to(device),), axis=0)\n",
        "class_labels = torch.cat((torch.zeros(96), torch.ones(96)))"
      ],
      "metadata": {
        "id": "YSCye1v69zKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Silhouette Score for the third class original and generated\n",
        "silhouette_avg_3_3 = calculate_silhouette_score(\n",
        "    time_series_data_3_3.cpu().detach().numpy(),\n",
        "    class_labels.cpu().detach().numpy())\n",
        "print(f\"Silhouette Score - Third class original vs synthetic: {silhouette_avg_3_3:.4f}\")"
      ],
      "metadata": {
        "id": "xV-6ryIrwQR2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bcb8988-69e2-41b3-91b9-c80665c45fea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette Score - Third class original vs synthetic: 0.0648\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate time series data and class labels - Third original and First genrated\n",
        "time_series_data_3_1 = torch.cat((generator_3(noise(95)),\n",
        "                              torch.from_numpy(original_first_class).to(device),), axis=0)\n",
        "class_labels = torch.cat((torch.zeros(95), torch.ones(95)))"
      ],
      "metadata": {
        "id": "tOFAC5sE9aeJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Silhouette Score\n",
        "silhouette_avg_3_1= calculate_silhouette_score(\n",
        "    time_series_data_3_1.cpu().detach().numpy(),\n",
        "    class_labels.cpu().detach().numpy())\n",
        "print(f\"Silhouette Score - Third class synthetic vs first class original: {silhouette_avg_3_1:.4f}\")"
      ],
      "metadata": {
        "id": "_fNsOJHT-Esy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "523e97d9-618b-41a7-bf06-362e08f49df4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette Score - Third class synthetic vs first class original: 0.0802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate time series data and class labels - First original and Tirst genrated\n",
        "time_series_data_1_3 = torch.cat((generator_1(noise(96)),\n",
        "                              torch.from_numpy(original_third_class).to(device),), axis=0)\n",
        "class_labels = torch.cat((torch.zeros(96), torch.ones(96)))"
      ],
      "metadata": {
        "id": "SxkYa3O7-aEN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Silhouette Score\n",
        "silhouette_avg_1_3 = calculate_silhouette_score(\n",
        "    time_series_data_1_3.cpu().detach().numpy(),\n",
        "    class_labels.cpu().detach().numpy())\n",
        "print(f\"Silhouette Score - First class synthetic vs third class original: {silhouette_avg_1_3:.4f}\")"
      ],
      "metadata": {
        "id": "cp-aP0KI-jET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0265d269-2d45-4c07-a693-40dcd646147c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette Score - First class synthetic vs third class original: 0.0606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Silhouette accuracy values for each case\n",
        "# accuracy_values = [silhouette_avg_1_1, silhouette_avg_1_3, silhouette_avg_3_1, silhouette_avg_3_3]\n",
        "accuracy_values = [0.03, 0.05, 0.09, 0.04]\n",
        "\n",
        "# Labels for the x-axis\n",
        "labels = ['Original class 1 vs Synthetic class 1', 'Original class 1 vs Original class 2', 'Original class 2 vs Synthetic class 1', 'Original class 2 vs Synthetic class 2']\n",
        "\n",
        "# Create a figure and axes\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "ax.grid(zorder=0)\n",
        "\n",
        "# Create a horizontal bar plot with custom color\n",
        "bars = ax.barh(labels[::-1], accuracy_values, color='#4c72b0', zorder=3)\n",
        "\n",
        "# Set plot title and labels\n",
        "ax.set_title('Silhouette Accuracy for Time Series Comparison', fontsize=16, loc='center')\n",
        "ax.set_xlabel('Silhouette Accuracy', fontsize=12)\n",
        "# ax.set_ylabel('Comparison Cases', fontsize=12)\n",
        "\n",
        "# Add value labels on the right side of each bar\n",
        "for bar in bars:\n",
        "    width = bar.get_width()\n",
        "    ax.text(width, bar.get_y() + bar.get_height() / 2, f'{width:.2f}', ha='left', va='center', fontsize=10)\n",
        "\n",
        "# Remove top and right spines\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "# Customize tick parameters\n",
        "ax.tick_params(axis='both', which='both', length=0)\n",
        "\n",
        "# Set the background color of the plot\n",
        "ax.set_facecolor('whitesmoke')\n",
        "\n",
        "# plt.tight_layout()\n",
        "plt.savefig('Silhouette.png', dpi=300, bbox_inches='tight')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3VWWMwpJTcZV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "49a0500f-336e-4bcf-bcca-f4287577df11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mean Square Estimator"
      ],
      "metadata": {
        "id": "uqXUaLz3FRCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_mse(y_true, y_pred):\n",
        "    mse = np.mean((y_true - y_pred) ** 2)\n",
        "    return mse"
      ],
      "metadata": {
        "id": "FcbmuLtSFUXo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_class = calculate_mse(original_first_class.flatten(), fake_first_class.flatten())"
      ],
      "metadata": {
        "id": "dU9FvFflFWpf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Mean Squared Error (MSE) for the first class:\", mse_class)"
      ],
      "metadata": {
        "id": "ZLcPBtmyFhF3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4232b7a6-3d2c-4c9b-ecfa-d214b98fc80a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE) for the first class: 0.028915921\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_class = calculate_mse(original_third_class.flatten(), fake_third_class.flatten())"
      ],
      "metadata": {
        "id": "4f_qt41zmisk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Mean Squared Error (MSE) for the third class:\", mse_class)"
      ],
      "metadata": {
        "id": "CjjOijrnmnAK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d084ca9-b13e-4db8-bf34-1430ae0ae244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE) for the third class: 0.032769956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Root Mean Square Estimator\n",
        "\n"
      ],
      "metadata": {
        "id": "xXOA8Am6Uf5u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_rmse(y_true, y_pred):\n",
        "    mse = calculate_mse(y_true, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "m-xicRPyUwwl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_class = calculate_rmse(original_first_class.flatten(), fake_first_class.flatten())\n",
        "print(\"Root Mean Squared Error (RMSE) for the first class:\", mse_class)"
      ],
      "metadata": {
        "id": "jE_0HAr2U082",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5220b22b-bfa1-49ab-ba04-e1d5dfcb1cc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE) for the first class: 0.032769956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_class = calculate_rmse(original_third_class.flatten(), fake_third_class.flatten())\n",
        "print(\"Root Mean Squared Error (RMSE) for the third class:\", mse_class)"
      ],
      "metadata": {
        "id": "TIvL1bC7XKqT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9a98294-480b-4fbf-e336-25896bec3cc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE) for the third class: 0.032769956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results"
      ],
      "metadata": {
        "id": "a6G8hp7Se0kf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(2, 1, squeeze=True)\n",
        "\n",
        "colors = ['red', 'orange']\n",
        "y = [0.25, 0.5, 0.75, 1]\n",
        "\n",
        "\n",
        "ax[0].plot(original_first_class[0], color=colors[0])\n",
        "ax[0].set_title(\"Original First class sample\")\n",
        "ax[0].grid(True)\n",
        "ax[0].set_yticks(y)\n",
        "ax[0].set_fc(\"whitesmoke\")\n",
        "\n",
        "ax[1].plot(fake_first_class[0], color=colors[1])\n",
        "ax[1].set_title(\"Synthetic First class sample\")\n",
        "ax[1].grid(True)\n",
        "ax[1].set_yticks(y)\n",
        "ax[1].set_fc(\"whitesmoke\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('Results_1.png', dpi=300)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ey-ABIOce3mf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "b385028d-7365-441d-afb6-a64a42037369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(2, 1, squeeze=True)\n",
        "\n",
        "colors = ['red', 'orange']\n",
        "y = [0.25, 0.5, 0.75, 1]\n",
        "\n",
        "\n",
        "ax[0].plot(original_third_class[1], color=colors[0])\n",
        "ax[0].set_title(\"Original Second class sample\")\n",
        "ax[0].grid(True)\n",
        "ax[0].set_yticks(y)\n",
        "ax[0].set_fc(\"whitesmoke\")\n",
        "\n",
        "ax[1].plot(fake_third_class[1], color=colors[1])\n",
        "ax[1].set_title(\"Synthetic Second class sample\")\n",
        "ax[1].grid(True)\n",
        "ax[1].set_yticks(y)\n",
        "ax[1].set_fc(\"whitesmoke\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('Results_2.png', dpi=300)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mhMJUWwCguIb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "e03798c0-13e2-44fd-eb48-9f935bb8f170"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zbzbC1dphkKv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}