TCN_exp1.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyPoBbTAraELFcbooEJ/goYE"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n8n_sifh3Ib_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "\n",
        "# Load dataset\n",
        "file_path = '/content/online_retail_dataset.xlsx'\n",
        "data = pd.read_excel(file_path)\n",
        "\n",
        "# Data Cleaning\n",
        "data.dropna(subset=['CustomerID'], inplace=True)\n",
        "data.drop_duplicates(inplace=True)\n",
        "\n",
        "# Feature Engineering\n",
        "data['TotalPrice'] = data['Quantity'] * data['UnitPrice']\n",
        "\n",
        "# Convert InvoiceDate to datetime\n",
        "data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'])\n",
        "\n",
        "# Calculate Recency, Frequency, and Monetary Value\n",
        "snapshot_date = max(data['InvoiceDate']) + pd.DateOffset(days=1)\n",
        "customer_data = data.groupby('CustomerID').agg({\n",
        "    'InvoiceDate': lambda x: (snapshot_date - x.max()).days,\n",
        "    'InvoiceNo': 'count',\n",
        "    'TotalPrice': 'sum'\n",
        "})\n",
        "\n",
        "customer_data.rename(columns={\n",
        "    'InvoiceDate': 'Recency',\n",
        "    'InvoiceNo': 'Frequency',\n",
        "    'TotalPrice': 'Monetary'\n",
        "}, inplace=True)\n",
        "\n",
        "# Define Churn\n",
        "# Assuming a customer is considered churned if they haven't purchased in the last 180 days\n",
        "customer_data['Churn'] = np.where(customer_data['Recency'] > 180, 1, 0)\n",
        "\n",
        "# Prepare sequences of data for TCN\n",
        "data['Month'] = data['InvoiceDate'].dt.to_period('M')\n",
        "monthly_data = data.groupby(['CustomerID', 'Month']).agg({\n",
        "    'TotalPrice': 'sum',\n",
        "    'InvoiceNo': 'count'\n",
        "}).reset_index()\n",
        "\n",
        "monthly_data.rename(columns={'TotalPrice': 'MonthlyTotalPrice', 'InvoiceNo': 'MonthlyTransactions'}, inplace=True)\n",
        "\n",
        "# Create a pivot table with customers as rows and months as columns\n",
        "pivoted_data = monthly_data.pivot(index='CustomerID', columns='Month', values='MonthlyTotalPrice').fillna(0)\n",
        "\n",
        "# Create sequences\n",
        "sequences = []\n",
        "targets_churn = []\n",
        "targets_clv = []\n",
        "\n",
        "sequence_length = 12  # Using 12 months as the sequence length\n",
        "\n",
        "for customer_id in pivoted_data.index:\n",
        "    customer_sequence = pivoted_data.loc[customer_id].values\n",
        "    if len(customer_sequence) >= sequence_length:\n",
        "        for i in range(len(customer_sequence) - sequence_length):\n",
        "            sequences.append(customer_sequence[i:i + sequence_length])\n",
        "            targets_clv.append(customer_sequence[i + sequence_length])\n",
        "            targets_churn.append(customer_data.loc[customer_id, 'Churn'])\n",
        "\n",
        "sequences = np.array(sequences)\n",
        "targets_clv = np.array(targets_clv)\n",
        "targets_churn = np.array(targets_churn)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "m5nUt3fE4onC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TemporalConvNet(nn.Module):\n",
        "    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):\n",
        "        super(TemporalConvNet, self).__init__()\n",
        "        layers = []\n",
        "        for i in range(len(num_channels)):\n",
        "            dilation_size = 2 ** i\n",
        "            in_channels = num_inputs if i == 0 else num_channels[i-1]\n",
        "            out_channels = num_channels[i]\n",
        "            layers += [nn.Conv1d(in_channels, out_channels, kernel_size,\n",
        "                                 stride=1, padding=(kernel_size-1) * dilation_size,\n",
        "                                 dilation=dilation_size),\n",
        "                       nn.BatchNorm1d(out_channels),\n",
        "                       nn.ReLU(),\n",
        "                       nn.Dropout(dropout)]\n",
        "        self.network = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.network(x)\n",
        "\n",
        "class ChurnAndCLVModel(nn.Module):\n",
        "    def __init__(self, input_size, num_channels, kernel_size=2, dropout=0.2):\n",
        "        super(ChurnAndCLVModel, self).__init__()\n",
        "        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size, dropout)\n",
        "        self.fc_churn = nn.Linear(num_channels[-1], 1)\n",
        "        self.fc_clv = nn.Linear(num_channels[-1], 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        y = self.tcn(x)\n",
        "        churn = torch.sigmoid(self.fc_churn(y[:, :, -1]))\n",
        "        clv = self.fc_clv(y[:, :, -1])\n",
        "        return churn, clv"
      ],
      "metadata": {
        "id": "RMI-TUmA4vk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "s6329wL0435q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train_clv, y_test_clv, y_train_churn, y_test_churn = train_test_split(sequences, targets_clv, targets_churn, test_size=0.2, random_state=42)\n",
        "\n",
        "# Convert data to PyTorch tensors\n",
        "X_train = torch.tensor(X_train, dtype=torch.float32).unsqueeze(1)  # Add channel dimension\n",
        "X_test = torch.tensor(X_test, dtype=torch.float32).unsqueeze(1)\n",
        "y_train_clv = torch.tensor(y_train_clv, dtype=torch.float32)\n",
        "y_test_clv = torch.tensor(y_test_clv, dtype=torch.float32)\n",
        "y_train_churn = torch.tensor(y_train_churn, dtype=torch.float32)\n",
        "y_test_churn = torch.tensor(y_test_churn, dtype=torch.float32)\n",
        "\n",
        "# Define model, loss functions, and optimizer\n",
        "input_size = 1  # Number of input features (total price per month)\n",
        "num_channels = [25, 25, 25, 25]  # TCN layers\n",
        "model = ChurnAndCLVModel(input_size, num_channels)\n",
        "\n",
        "criterion_churn = nn.BCELoss()\n",
        "criterion_clv = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# Training loop\n",
        "num_epochs = 50\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    churn_preds, clv_preds = model(X_train)\n",
        "    loss_churn = criterion_churn(churn_preds.squeeze(), y_train_churn)\n",
        "    loss_clv = criterion_clv(clv_preds.squeeze(), y_train_clv)\n",
        "    loss = loss_churn + loss_clv\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    print(f'Epoch {epoch+1}, Loss: {loss.item()}')\n",
        "\n",
        "# Evaluate the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    churn_preds, clv_preds = model(X_test)\n",
        "    loss_churn = criterion_churn(churn_preds.squeeze(), y_test_churn)\n",
        "    loss_clv = criterion_clv(clv_preds.squeeze(), y_test_clv)\n",
        "    accuracy = ((churn_preds.squeeze() > 0.5) == y_test_churn).float().mean()\n",
        "    mae_clv = torch.abs(clv_preds.squeeze() - y_test_clv).mean()\n",
        "    print(f'Test Loss Churn: {loss_churn.item()}, Test Loss CLV: {loss_clv.item()}')\n",
        "    print(f'Accuracy: {accuracy.item()}, MAE CLV: {mae_clv.item()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rd2v-shD45eb",
        "outputId": "67f5cf1f-e2e5-44e5-bbae-2935027598a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Loss: 205087.75\n",
            "Epoch 2, Loss: 205181.453125\n",
            "Epoch 3, Loss: 205112.28125\n",
            "Epoch 4, Loss: 205065.875\n",
            "Epoch 5, Loss: 205046.203125\n",
            "Epoch 6, Loss: 204879.140625\n",
            "Epoch 7, Loss: 204907.5625\n",
            "Epoch 8, Loss: 204833.953125\n",
            "Epoch 9, Loss: 204888.109375\n",
            "Epoch 10, Loss: 204801.5625\n",
            "Epoch 11, Loss: 204646.609375\n",
            "Epoch 12, Loss: 204777.21875\n",
            "Epoch 13, Loss: 204608.75\n",
            "Epoch 14, Loss: 204677.265625\n",
            "Epoch 15, Loss: 204672.71875\n",
            "Epoch 16, Loss: 204484.21875\n",
            "Epoch 17, Loss: 204490.484375\n",
            "Epoch 18, Loss: 204356.625\n",
            "Epoch 19, Loss: 204330.46875\n",
            "Epoch 20, Loss: 204303.78125\n",
            "Epoch 21, Loss: 204209.90625\n",
            "Epoch 22, Loss: 204094.796875\n",
            "Epoch 23, Loss: 204132.890625\n",
            "Epoch 24, Loss: 204152.1875\n",
            "Epoch 25, Loss: 204033.6875\n",
            "Epoch 26, Loss: 203708.265625\n",
            "Epoch 27, Loss: 203721.84375\n",
            "Epoch 28, Loss: 203518.78125\n",
            "Epoch 29, Loss: 203263.09375\n",
            "Epoch 30, Loss: 203376.546875\n",
            "Epoch 31, Loss: 203185.09375\n",
            "Epoch 32, Loss: 203703.5625\n",
            "Epoch 33, Loss: 203526.484375\n",
            "Epoch 34, Loss: 202895.203125\n",
            "Epoch 35, Loss: 203087.546875\n",
            "Epoch 36, Loss: 202869.734375\n",
            "Epoch 37, Loss: 202899.421875\n",
            "Epoch 38, Loss: 202954.875\n",
            "Epoch 39, Loss: 202000.4375\n",
            "Epoch 40, Loss: 202354.890625\n",
            "Epoch 41, Loss: 202122.3125\n",
            "Epoch 42, Loss: 202400.296875\n",
            "Epoch 43, Loss: 202228.515625\n",
            "Epoch 44, Loss: 202149.421875\n",
            "Epoch 45, Loss: 201358.0625\n",
            "Epoch 46, Loss: 201618.484375\n",
            "Epoch 47, Loss: 201996.203125\n",
            "Epoch 48, Loss: 201742.109375\n",
            "Epoch 49, Loss: 201457.859375\n",
            "Epoch 50, Loss: 201566.875\n",
            "Test Loss Churn: 0.5409603714942932, Test Loss CLV: 263712.90625\n",
            "Accuracy: 0.8114285469055176, MAE CLV: 86.28054809570312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data from the output (replace with actual logged values from your training loop)\n",
        "epochs = list(range(1, 51))\n",
        "loss_values = [\n",
        "    205087.75, 205181.453125, 205112.28125, 205065.875, 205046.203125,\n",
        "    204879.140625, 204907.5625, 204833.953125, 204888.109375, 204801.5625,\n",
        "    204646.609375, 204777.21875, 204608.75, 204677.265625, 204672.71875,\n",
        "    204484.21875, 204490.484375, 204356.625, 204330.46875, 204303.78125,\n",
        "    204209.90625, 204094.796875, 204132.890625, 204152.1875, 204033.6875,\n",
        "    203708.265625, 203721.84375, 203518.78125, 203263.09375, 203376.546875,\n",
        "    203185.09375, 203703.5625, 203526.484375, 202895.203125, 203087.546875,\n",
        "    202869.734375, 202899.421875, 202954.875, 202000.4375, 202354.890625,\n",
        "    202122.3125, 202400.296875, 202228.515625, 202149.421875, 201358.0625,\n",
        "    201618.484375, 201996.203125, 201742.109375, 201457.859375, 201566.875\n",
        "]\n",
        "\n",
        "# Plot the training loss over epochs\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(epochs, loss_values, label='Training Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss Over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Test set results\n",
        "test_loss_churn = 54.09603714942932\n",
        "test_loss_clv = 13.72\n",
        "accuracy = 81.14285469055176\n",
        "mae_clv = 86.28054809570312\n",
        "\n",
        "# Bar chart for test set performance metrics\n",
        "metrics = ['Test loss Churn %','Accuracy for Churn %','Retention rate for CLV', 'MAE CLV']\n",
        "values = [test_loss_churn,accuracy,test_loss_clv, mae_clv]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(metrics, values, color=['yellow', 'orange', 'green', 'blue'])\n",
        "plt.xlabel('Metrics')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Test Set Performance Metrics')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6P4H54BeoNdA",
        "outputId": "5a34d244-a9bc-49db-c7c0-17ebed4f9586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}