Bigdata_house.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNP4sjxh6Tze+nVjfvfZxG/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ahmad162539/ML-class-excercise-codes/blob/main/Bigdata_house.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "j-YgAXZGA0O3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "o135gjKD0OSt",
        "outputId": "e01cf55e-f308-4800-a22f-d34efcbcf754"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-8a8bee5597b6>:19: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  data = data.fillna(data.mean())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('/content/sample_data/Datasets/household_power_consumption.txt', delimiter=';', parse_dates={'DateTime': ['Date', 'Time']}, infer_datetime_format=True, low_memory=False, na_values=['?','nan'])\n",
        "\n",
        "# Print the first few rows of the dataset\n",
        "#print(data.head())\n",
        "\n",
        "# Check for missing values\n",
        "# print(data.isnull().sum())\n",
        "\n",
        "# Fill missing values with the mean of the column\n",
        "data = data.fillna(data.mean())\n",
        "\n",
        "# Resample the data to hourly frequency\n",
        "data = data.resample('H', on='DateTime').mean()\n",
        "\n",
        "# Plot the active power consumption\n",
        "plt.plot(data['Global_active_power'])\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Global active power (kW)')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Load the dataset\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('/content/sample_data/Datasets/household_power_consumption.txt', delimiter=';', parse_dates={'DateTime': ['Date', 'Time']}, infer_datetime_format=True, low_memory=False, na_values=['?','nan'])\n",
        "\n",
        "# Fill missing values with the mean of the column\n",
        "data = data.fillna(data.mean())\n",
        "\n",
        "# Resample the data to hourly frequency\n",
        "data = data.resample('H', on='DateTime').mean()\n",
        "\n",
        "\n",
        "# Convert the dataset to a PyTorch tensor\n",
        "X = torch.tensor(data.drop(\"target_variable\", axis=1).values, dtype=torch.float32)\n",
        "y = torch.tensor(data[\"target_variable\"].values, dtype=torch.float32)\n",
        "\n",
        "# Define the model\n",
        "class RegressionModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(RegressionModel, self).__init__()\n",
        "        self.linear = nn.Linear(X.shape[1], 1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = self.linear(x)\n",
        "        return out\n",
        "\n",
        "model = RegressionModel()\n",
        "\n",
        "# Define the loss function and optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.001)\n",
        "\n",
        "# Train the model\n",
        "num_epochs = 1000\n",
        "for epoch in range(num_epochs):\n",
        "    # Forward pass\n",
        "    outputs = model(X)\n",
        "    loss = criterion(outputs, y)\n",
        "    \n",
        "    # Backward and optimize\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    # Print progress\n",
        "    if (epoch+1) % 100 == 0:\n",
        "        print(\"Epoch [{}/{}], Loss: {:.4f}\".format(epoch+1, num_epochs, loss.item()))\n",
        "\n",
        "# Predict on new data\n",
        "new_data = torch.tensor([[1.0, 2.0, 3.0]])\n",
        "y_pred = model(new_data).item()\n",
        "print(\"Prediction: \", y_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "ci0X7aQaRPfE",
        "outputId": "97f12b7f-d99e-4daf-d80a-6bb8f6192562"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-653547b7ecba>:13: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  data = data.fillna(data.mean())\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-653547b7ecba>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Convert the dataset to a PyTorch tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"target_variable\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"target_variable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5397\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5398\u001b[0m         \"\"\"\n\u001b[0;32m-> 5399\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5400\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5401\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4505\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4545\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4546\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4547\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6934\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6935\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6936\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['target_variable'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "B1lRmzmXAzJ5"
      }
    }
  ]
}