CIFAR_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/introspective321/ImRCIFAR/blob/main/CIFAR_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "id": "38d4b01f-1dbe-4def-b99e-3ff469857f30"
    },
    {
      "cell_type": "code",
      "source": [
        "#Necessary Libraries\n",
        "!pip install numpy\n",
        "!pip install opencv-python\n",
        "!pip install matplotlib\n",
        "!pip install -U scikit-learn\n",
        "!pip install pandas\n",
        "!pip install seaborn\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage.feature import hog\n",
        "from skimage import exposure\n",
        "import pandas as pd"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Requirement already satisfied: numpy in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (1.21.6)\nRequirement already satisfied: opencv-python in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (4.9.0.80)\nRequirement already satisfied: numpy>=1.17.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from opencv-python) (1.21.6)\nRequirement already satisfied: matplotlib in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (3.7.5)\nRequirement already satisfied: contourpy>=1.0.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (1.0.5)\nRequirement already satisfied: cycler>=0.10 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (4.25.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: numpy<2,>=1.20 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (1.21.6)\nRequirement already satisfied: packaging>=20.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (23.0)\nRequirement already satisfied: pillow>=6.2.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (9.2.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (3.1.0)\nRequirement already satisfied: python-dateutil>=2.7 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: importlib-resources>=3.2.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib) (5.12.0)\nRequirement already satisfied: zipp>=3.1.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.12.0)\nRequirement already satisfied: six>=1.5 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\nRequirement already satisfied: scikit-learn in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (1.3.2)\nRequirement already satisfied: numpy<2.0,>=1.17.3 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from scikit-learn) (1.21.6)\nRequirement already satisfied: scipy>=1.5.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from scikit-learn) (1.10.1)\nRequirement already satisfied: joblib>=1.1.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from scikit-learn) (1.2.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from scikit-learn) (3.1.0)\nRequirement already satisfied: pandas in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (1.1.5)\nRequirement already satisfied: python-dateutil>=2.7.3 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from pandas) (2.8.2)\nRequirement already satisfied: pytz>=2017.2 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from pandas) (2022.5)\nRequirement already satisfied: numpy>=1.15.4 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from pandas) (1.21.6)\nRequirement already satisfied: six>=1.5 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\nRequirement already satisfied: seaborn in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (0.12.2)\nRequirement already satisfied: numpy!=1.24.0,>=1.17 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from seaborn) (1.21.6)\nRequirement already satisfied: pandas>=0.25 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from seaborn) (1.1.5)\nRequirement already satisfied: matplotlib!=3.6.1,>=3.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from seaborn) (3.7.5)\nRequirement already satisfied: contourpy>=1.0.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\nRequirement already satisfied: cycler>=0.10 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\nRequirement already satisfied: pillow>=6.2.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.2.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.1.0)\nRequirement already satisfied: python-dateutil>=2.7 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\nRequirement already satisfied: importlib-resources>=3.2.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (5.12.0)\nRequirement already satisfied: pytz>=2017.2 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from pandas>=0.25->seaborn) (2022.5)\nRequirement already satisfied: zipp>=3.1.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.1->seaborn) (3.12.0)\nRequirement already satisfied: six>=1.5 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1713596266271
        },
        "id": "9a64e139",
        "outputId": "54872120-3ae6-4333-d25a-30cef88620fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "9a64e139"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Dataset"
      ],
      "metadata": {
        "id": "c7d4b92a"
      },
      "id": "c7d4b92a"
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
        "!tar -xzvf cifar-10-python.tar.gz"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "--2024-04-20 00:58:40--  https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\nResolving www.cs.toronto.edu (www.cs.toronto.edu)... 128.100.3.30\nConnecting to www.cs.toronto.edu (www.cs.toronto.edu)|128.100.3.30|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 170498071 (163M) [application/x-gzip]\nSaving to: ‘cifar-10-python.tar.gz.1’\n\ncifar-10-python.tar 100%[===================>] 162.60M  65.8MB/s    in 2.5s    \n\n2024-04-20 00:58:43 (65.8 MB/s) - ‘cifar-10-python.tar.gz.1’ saved [170498071/170498071]\n\ncifar-10-batches-py/\ncifar-10-batches-py/data_batch_4\ncifar-10-batches-py/readme.html\ncifar-10-batches-py/test_batch\ncifar-10-batches-py/data_batch_3\ncifar-10-batches-py/batches.meta\ncifar-10-batches-py/data_batch_2\ncifar-10-batches-py/data_batch_5\ncifar-10-batches-py/data_batch_1\n"
        }
      ],
      "execution_count": 2,
      "metadata": {
        "id": "b10f8221",
        "outputId": "24dc1eb6-9f69-4c41-a31e-f50839226cdd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "b10f8221"
    },
    {
      "cell_type": "code",
      "source": [
        "def unpickle(file):\n",
        "    import pickle\n",
        "    with open(file, 'rb') as fo:\n",
        "        dict = pickle.load(fo, encoding='latin1')\n",
        "    return dict"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713596220533
        },
        "id": "d4ada8a8-dbef-4045-93df-eee75694a8a0"
      },
      "id": "d4ada8a8-dbef-4045-93df-eee75694a8a0"
    },
    {
      "cell_type": "code",
      "source": [
        "data_batch_1 = unpickle(r'cifar-10-batches-py/data_batch_1')\n",
        "print(type(data_batch_1))\n",
        "print(\"--------------------------\")\n",
        "print(data_batch_1.keys())\n",
        "print(\"--------------------------\")\n",
        "for item in data_batch_1:\n",
        "    print(item, type(data_batch_1[item]))\n",
        "print(\"--------------------------\")\n",
        "print(\"Labels:\", set(data_batch_1['labels']))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "<class 'dict'>\n--------------------------\ndict_keys(['batch_label', 'labels', 'data', 'filenames'])\n--------------------------\nbatch_label <class 'str'>\nlabels <class 'list'>\ndata <class 'numpy.ndarray'>\nfilenames <class 'list'>\n--------------------------\nLabels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1713596223415
        },
        "id": "bc600e16",
        "outputId": "dce8e771-0326-482d-a0e0-086d4dc4e1a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "bc600e16"
    },
    {
      "cell_type": "code",
      "source": [
        "batch_1 = unpickle('cifar-10-batches-py/data_batch_1')\n",
        "batch_2 = unpickle('cifar-10-batches-py/data_batch_2')\n",
        "batch_3 = unpickle('cifar-10-batches-py/data_batch_3')\n",
        "batch_4 = unpickle('cifar-10-batches-py/data_batch_4')\n",
        "batch_5 = unpickle('cifar-10-batches-py/data_batch_5')\n",
        "test_batch =unpickle('cifar-10-batches-py/test_batch')\n",
        "\n",
        "X_train_pixel = np.concatenate([batch_1['data'], batch_2['data'], batch_3['data'], batch_4['data'], batch_5['data']], axis=0)\n",
        "y_train_pixel = np.concatenate([batch_1['labels'], batch_2['labels'], batch_3['labels'], batch_4['labels'], batch_5['labels']], axis=0)\n",
        "\n",
        "X_test_pixel = test_batch['data']\n",
        "y_test_pixel = test_batch['labels']\n",
        "\n",
        "# Reshape the image data to its original dimensions\n",
        "X_train_pixel = X_train_pixel.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)\n",
        "X_test_pixel = X_test_pixel.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)\n",
        "\n",
        "print(f'Training Samples: {len(X_train_pixel)}')\n",
        "print(f'Testing Samples: {len(X_test_pixel)}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Training Samples: 50000\nTesting Samples: 10000\n"
        }
      ],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1713596269487
        },
        "id": "0a39010f",
        "outputId": "a4a795b4-b1a9-4292-80a4-b84351d099f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "0a39010f"
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_pixel[0].shape)\n",
        "print(X_test_pixel[0].shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(32, 32, 3)\n(32, 32, 3)\n"
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1712044000572
        },
        "id": "7a60c1b8",
        "outputId": "9d9a44a1-205e-44e3-b381-c7841b53a5fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "7a60c1b8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "id": "7dc656cc"
      },
      "id": "7dc656cc"
    },
    {
      "cell_type": "code",
      "source": [
        "def rgb2gray(image):\n",
        "    return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "# Converting to grayscale\n",
        "X_training_gray = [ rgb2gray(np.array(X_train_pixel[i])) for i in range(50000)]\n",
        "X_testing_gray  = [ rgb2gray(np.array(X_test_pixel[i])) for i in range(10000)]"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1712044001407
        },
        "id": "6ca2d8dd"
      },
      "id": "6ca2d8dd"
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "num_images = 10\n",
        "\n",
        "plt.figure(figsize=(15, 3))\n",
        "for i in range(num_images):\n",
        "    # RGB image\n",
        "    plt.subplot(2, num_images, i + 1)\n",
        "    plt.imshow(X_train_pixel[i])\n",
        "    plt.axis('off')\n",
        "\n",
        "    # grayscale image\n",
        "    plt.subplot(2, num_images, i + num_images + 1)\n",
        "    plt.imshow(X_training_gray[i], cmap='gray')\n",
        "    plt.axis('off')\n",
        "\n",
        "print(\"RGB along with corresponding grayscaled image\\n\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "RGB along with corresponding grayscaled image\n\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x300 with 20 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712044002088
        },
        "id": "92889b99",
        "outputId": "dd8cfde9-22ab-4833-cf4b-f2f06c03a987"
      },
      "id": "92889b99"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction (HoGs)"
      ],
      "metadata": {
        "id": "5b801710"
      },
      "id": "5b801710"
    },
    {
      "cell_type": "code",
      "source": [
        "orientations = 9\n",
        "pixels_per_cell = (8, 8)\n",
        "cells_per_block = (2, 2)\n",
        "\n",
        "def extract_hog_features(image):\n",
        "    # Compute HoG features\n",
        "    hog_features, hog_image = hog(image, orientations=orientations, pixels_per_cell=pixels_per_cell,\n",
        "                                   cells_per_block=cells_per_block, visualize=True)\n",
        "\n",
        "    # Rescale histogram for better visualization\n",
        "    hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))\n",
        "\n",
        "    return hog_features, hog_image_rescaled"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1712044002260
        },
        "id": "2dae60a7"
      },
      "id": "2dae60a7"
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_HoG = []\n",
        "X_train_HoG_rescaled = []\n",
        "y_training = np.array(y_train_pixel)\n",
        "\n",
        "for image in X_training_gray:\n",
        "    hog_features, hog_image = extract_hog_features(image)\n",
        "    X_train_HoG.append(hog_features)\n",
        "    X_train_HoG_rescaled.append(hog_image)\n",
        "\n",
        "X_training = np.array(X_train_HoG)\n",
        "X_test_HoG = []\n",
        "X_test_HoG_rescaled = []\n",
        "y_testing = np.array(y_test_pixel)\n",
        "\n",
        "for image in X_testing_gray:\n",
        "    hog_features, hog_image = extract_hog_features(image)\n",
        "    X_test_HoG.append(hog_features)\n",
        "    X_test_HoG_rescaled.append(hog_image)\n",
        "X_testing = np.array(X_test_HoG)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1712044239453
        },
        "id": "eef89aca"
      },
      "id": "eef89aca"
    },
    {
      "cell_type": "code",
      "source": [
        "np.savez_compressed('X_testing_HoG_.npz', X_testing)\n",
        "np.savez_compressed('X_training_HoG_.npz', X_training)\n",
        "np.savez_compressed('y_training_.npz', y_training)\n",
        "np.savez_compressed('y_testing_.npz', y_testing)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1712044246430
        },
        "id": "ffede463"
      },
      "id": "ffede463"
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data from the compressed files\n",
        "loaded_data_X_testing = np.load('X_testing_HoG_.npz')\n",
        "loaded_data_X_training = np.load('X_training_HoG_.npz')\n",
        "loaded_data_y_training = np.load('y_training_.npz')\n",
        "loaded_data_y_testing = np.load('y_testing_.npz')\n",
        "\n",
        "# Access the variables from the loaded data\n",
        "X_testing = loaded_data_X_testing['arr_0']\n",
        "X_training = loaded_data_X_training['arr_0']\n",
        "y_training = loaded_data_y_training['arr_0']\n",
        "y_testing = loaded_data_y_testing['arr_0']"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713596271902
        },
        "id": "93b0ce03-1efb-4f3a-af61-293a82b1cb01"
      },
      "id": "93b0ce03-1efb-4f3a-af61-293a82b1cb01"
    },
    {
      "cell_type": "code",
      "source": [
        "num_images = 10\n",
        "\n",
        "plt.figure(figsize=(16, 5))  # Adjust the figure size to accommodate three images per row\n",
        "\n",
        "for i in range(num_images):\n",
        "    # RGB image\n",
        "    plt.subplot(3, num_images, i + 1)\n",
        "    plt.imshow(X_train_pixel[i])\n",
        "    plt.title('RGB')\n",
        "    plt.axis('off')\n",
        "\n",
        "    # Grayscale image\n",
        "    plt.subplot(3, num_images, i + 1 + num_images)\n",
        "    plt.imshow(X_training_gray[i], cmap='gray')\n",
        "    plt.title('Grayscale')\n",
        "    plt.axis('off')\n",
        "\n",
        "    # HoG visualization\n",
        "    plt.subplot(3, num_images, i + 1 + 2 * num_images)\n",
        "    # Assuming X_train_HoG_rescaled contains the rescaled HoG images\n",
        "    plt.imshow(X_train_HoG_rescaled[i], cmap='gray')\n",
        "    plt.title('HoG')\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()  # Adjust spacing between subplots\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x500 with 30 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1712065248492
        },
        "id": "851f2d77",
        "outputId": "56e9a6d2-826c-477c-ca8d-961354cb1a46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "id": "851f2d77"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Statistics"
      ],
      "metadata": {
        "id": "ba146f9d"
      },
      "id": "ba146f9d"
    },
    {
      "cell_type": "code",
      "source": [
        "print( 'X_training shape is {}'.format( X_training.shape) )\n",
        "print( 'y_training shape is {}'.format( y_training.shape ) )\n",
        "print( 'X_testing shape is {}'.format( X_testing.shape ) )\n",
        "print( 'y_testing shape is {}'.format( y_testing.shape ) )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "X_training shape is (50000, 324)\ny_training shape is (50000,)\nX_testing shape is (10000, 324)\ny_testing shape is (10000,)\n"
        }
      ],
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1713266494485
        },
        "id": "150c9ac8",
        "outputId": "45dd21d9-a834-4812-b6f6-87e23e185219",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "150c9ac8"
    },
    {
      "cell_type": "code",
      "source": [
        "print( 'Overview of Training Data (Features)')\n",
        "pd.DataFrame( X_training ).describe()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Overview of Training Data (Features)\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "                0             1             2             3             4    \\\ncount  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \nmean       0.137601      0.082897      0.090118      0.102097      0.171012   \nstd        0.095472      0.082497      0.084742      0.087668      0.101492   \nmin        0.000000      0.000000      0.000000      0.000000      0.000000   \n25%        0.050749      0.014953      0.020542      0.028851      0.082237   \n50%        0.124834      0.057155      0.064265      0.079492      0.171004   \n75%        0.226851      0.126563      0.136626      0.156967      0.254369   \nmax        0.628524      0.512540      0.552542      0.525049      0.682448   \n\n                5             6             7             8             9    \\\ncount  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \nmean       0.089900      0.072338      0.067848      0.077589      0.149983   \nstd        0.084565      0.076714      0.075014      0.085352      0.098381   \nmin        0.000000      0.000000      0.000000      0.000000      0.000000   \n25%        0.020243      0.011889      0.008619      0.007922      0.060562   \n50%        0.065074      0.046007      0.041164      0.046652      0.145254   \n75%        0.137034      0.108178      0.101114      0.119561      0.240287   \nmax        0.499473      0.528356      0.547740      0.493267      0.579524   \n\n       ...           314           315           316           317  \\\ncount  ...  50000.000000  50000.000000  50000.000000  50000.000000   \nmean   ...      0.097615      0.137268      0.085924      0.094136   \nstd    ...      0.092334      0.090723      0.080054      0.082016   \nmin    ...      0.000000      0.000000      0.000000      0.000000   \n25%    ...      0.019398      0.059121      0.021773      0.028385   \n50%    ...      0.069572      0.122129      0.061525      0.070361   \n75%    ...      0.153765      0.215830      0.127786      0.139650   \nmax    ...      0.545577      0.635999      0.491866      0.471242   \n\n                318           319           320           321           322  \\\ncount  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \nmean       0.116418      0.186274      0.094934      0.077149      0.074033   \nstd        0.090754      0.098338      0.082151      0.076954      0.076180   \nmin        0.000000      0.000000      0.000000      0.000000      0.000000   \n25%        0.042047      0.103639      0.028978      0.018068      0.014950   \n50%        0.094241      0.191000      0.072809      0.051962      0.048510   \n75%        0.176147      0.263477      0.140446      0.111715      0.108195   \nmax        0.527723      0.707107      0.488977      0.527286      0.468730   \n\n                323  \ncount  50000.000000  \nmean       0.079453  \nstd        0.082591  \nmin        0.000000  \n25%        0.013872  \n50%        0.051005  \n75%        0.119398  \nmax        0.534006  \n\n[8 rows x 324 columns]",
            "text/html": "\n  <div id=\"df-2eab2b44-60f7-435c-85e0-617e8b29d85f\" class=\"colab-df-container\">\n    <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>314</th>\n      <th>315</th>\n      <th>316</th>\n      <th>317</th>\n      <th>318</th>\n      <th>319</th>\n      <th>320</th>\n      <th>321</th>\n      <th>322</th>\n      <th>323</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>...</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n      <td>50000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.137601</td>\n      <td>0.082897</td>\n      <td>0.090118</td>\n      <td>0.102097</td>\n      <td>0.171012</td>\n      <td>0.089900</td>\n      <td>0.072338</td>\n      <td>0.067848</td>\n      <td>0.077589</td>\n      <td>0.149983</td>\n      <td>...</td>\n      <td>0.097615</td>\n      <td>0.137268</td>\n      <td>0.085924</td>\n      <td>0.094136</td>\n      <td>0.116418</td>\n      <td>0.186274</td>\n      <td>0.094934</td>\n      <td>0.077149</td>\n      <td>0.074033</td>\n      <td>0.079453</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.095472</td>\n      <td>0.082497</td>\n      <td>0.084742</td>\n      <td>0.087668</td>\n      <td>0.101492</td>\n      <td>0.084565</td>\n      <td>0.076714</td>\n      <td>0.075014</td>\n      <td>0.085352</td>\n      <td>0.098381</td>\n      <td>...</td>\n      <td>0.092334</td>\n      <td>0.090723</td>\n      <td>0.080054</td>\n      <td>0.082016</td>\n      <td>0.090754</td>\n      <td>0.098338</td>\n      <td>0.082151</td>\n      <td>0.076954</td>\n      <td>0.076180</td>\n      <td>0.082591</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.050749</td>\n      <td>0.014953</td>\n      <td>0.020542</td>\n      <td>0.028851</td>\n      <td>0.082237</td>\n      <td>0.020243</td>\n      <td>0.011889</td>\n      <td>0.008619</td>\n      <td>0.007922</td>\n      <td>0.060562</td>\n      <td>...</td>\n      <td>0.019398</td>\n      <td>0.059121</td>\n      <td>0.021773</td>\n      <td>0.028385</td>\n      <td>0.042047</td>\n      <td>0.103639</td>\n      <td>0.028978</td>\n      <td>0.018068</td>\n      <td>0.014950</td>\n      <td>0.013872</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.124834</td>\n      <td>0.057155</td>\n      <td>0.064265</td>\n      <td>0.079492</td>\n      <td>0.171004</td>\n      <td>0.065074</td>\n      <td>0.046007</td>\n      <td>0.041164</td>\n      <td>0.046652</td>\n      <td>0.145254</td>\n      <td>...</td>\n      <td>0.069572</td>\n      <td>0.122129</td>\n      <td>0.061525</td>\n      <td>0.070361</td>\n      <td>0.094241</td>\n      <td>0.191000</td>\n      <td>0.072809</td>\n      <td>0.051962</td>\n      <td>0.048510</td>\n      <td>0.051005</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.226851</td>\n      <td>0.126563</td>\n      <td>0.136626</td>\n      <td>0.156967</td>\n      <td>0.254369</td>\n      <td>0.137034</td>\n      <td>0.108178</td>\n      <td>0.101114</td>\n      <td>0.119561</td>\n      <td>0.240287</td>\n      <td>...</td>\n      <td>0.153765</td>\n      <td>0.215830</td>\n      <td>0.127786</td>\n      <td>0.139650</td>\n      <td>0.176147</td>\n      <td>0.263477</td>\n      <td>0.140446</td>\n      <td>0.111715</td>\n      <td>0.108195</td>\n      <td>0.119398</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.628524</td>\n      <td>0.512540</td>\n      <td>0.552542</td>\n      <td>0.525049</td>\n      <td>0.682448</td>\n      <td>0.499473</td>\n      <td>0.528356</td>\n      <td>0.547740</td>\n      <td>0.493267</td>\n      <td>0.579524</td>\n      <td>...</td>\n      <td>0.545577</td>\n      <td>0.635999</td>\n      <td>0.491866</td>\n      <td>0.471242</td>\n      <td>0.527723</td>\n      <td>0.707107</td>\n      <td>0.488977</td>\n      <td>0.527286</td>\n      <td>0.468730</td>\n      <td>0.534006</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 324 columns</p>\n</div>\n    <div class=\"colab-df-buttons\">\n\n  <div class=\"colab-df-container\">\n    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2eab2b44-60f7-435c-85e0-617e8b29d85f')\"\n            title=\"Convert this dataframe to an interactive table.\"\n            style=\"display:none;\">\n\n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n  </svg>\n    </button>\n\n  <style>\n    .colab-df-container {\n      display:flex;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    .colab-df-buttons div {\n      margin-bottom: 4px;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n    <script>\n      const buttonEl =\n        document.querySelector('#df-2eab2b44-60f7-435c-85e0-617e8b29d85f button.colab-df-convert');\n      buttonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n      async function convertToInteractive(key) {\n        const element = document.querySelector('#df-2eab2b44-60f7-435c-85e0-617e8b29d85f');\n        const dataTable =\n          await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                    [key], {});\n        if (!dataTable) return;\n\n        const docLinkHtml = 'Like what you see? Visit the ' +\n          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n          + ' to learn more about interactive tables.';\n        element.innerHTML = '';\n        dataTable['output_type'] = 'display_data';\n        await google.colab.output.renderOutput(dataTable, element);\n        const docLink = document.createElement('div');\n        docLink.innerHTML = docLinkHtml;\n        element.appendChild(docLink);\n      }\n    </script>\n  </div>\n\n\n<div id=\"df-697bc2ef-4def-49e0-a764-9f3fffe12e63\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-697bc2ef-4def-49e0-a764-9f3fffe12e63')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n\n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-697bc2ef-4def-49e0-a764-9f3fffe12e63 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>\n    </div>\n  </div>\n",
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1713266495664
        },
        "id": "6ab2199c",
        "outputId": "ae09dfc4-8037-4e84-915f-32ff6aa928ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "id": "6ab2199c"
    },
    {
      "cell_type": "code",
      "source": [
        "print( 'Overview of Training Data (Target)')\n",
        "pd.DataFrame( y_training ).describe()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Overview of Training Data (Target)\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "                 0\ncount  50000.00000\nmean       4.50000\nstd        2.87231\nmin        0.00000\n25%        2.00000\n50%        4.50000\n75%        7.00000\nmax        9.00000",
            "text/html": "\n  <div id=\"df-cb9373ff-dd8b-475f-85cc-c6d88ae877aa\" class=\"colab-df-container\">\n    <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>50000.00000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>4.50000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.87231</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.00000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.50000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.00000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n    <div class=\"colab-df-buttons\">\n\n  <div class=\"colab-df-container\">\n    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb9373ff-dd8b-475f-85cc-c6d88ae877aa')\"\n            title=\"Convert this dataframe to an interactive table.\"\n            style=\"display:none;\">\n\n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n  </svg>\n    </button>\n\n  <style>\n    .colab-df-container {\n      display:flex;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    .colab-df-buttons div {\n      margin-bottom: 4px;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n    <script>\n      const buttonEl =\n        document.querySelector('#df-cb9373ff-dd8b-475f-85cc-c6d88ae877aa button.colab-df-convert');\n      buttonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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Visit the ' +\n          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n          + ' to learn more about interactive tables.';\n        element.innerHTML = '';\n        dataTable['output_type'] = 'display_data';\n        await google.colab.output.renderOutput(dataTable, element);\n        const docLink = document.createElement('div');\n        docLink.innerHTML = docLinkHtml;\n        element.appendChild(docLink);\n      }\n    </script>\n  </div>\n\n\n<div id=\"df-f8153d37-19ad-41c9-9516-841caa219ad2\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8153d37-19ad-41c9-9516-841caa219ad2')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n\n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n\n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-f8153d37-19ad-41c9-9516-841caa219ad2 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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            }
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1713266495955
        },
        "id": "423bd138",
        "outputId": "04f9cde9-2a47-4c56-95e0-a1bd6de1199e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "id": "423bd138"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-Means Clustering (Just for the record)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "id": "87f39ea1-f816-443f-a639-d802cde2e815"
    },
    {
      "cell_type": "code",
      "source": [
        "def computeCentroid(features):\n",
        "    \"\"\"\n",
        "    Compute centroid of a list of features.\n",
        "\n",
        "    Parameters:\n",
        "    features (numpy array): Array of features.\n",
        "\n",
        "    Returns:\n",
        "    numpy array: Centroid of the features.\n",
        "    \"\"\"\n",
        "    num_features = len(features)\n",
        "    if num_features == 0:\n",
        "        return None\n",
        "\n",
        "    # Compute mean of the features\n",
        "    centroid = np.mean(features, axis=0)\n",
        "\n",
        "    return centroid\n",
        "\n",
        "# Convert the image_np array to a list of 3-dimensional features\n",
        "features_list = X_training\n",
        "\n",
        "# Compute the centroid of the features\n",
        "centroid = computeCentroid(features_list)\n",
        "\n",
        "def mykmeans(X, k, max_iters=100):\n",
        "    # Step 1: Initialize cluster centers randomly\n",
        "    np.random.seed(42)  # For reproducibility\n",
        "    centroids = X[np.random.choice(X.shape[0], k, replace=False)]\n",
        "\n",
        "    for _ in range(max_iters):\n",
        "        # Step 2: Assign each data point to the nearest cluster center\n",
        "        distances = np.sqrt(((X - centroids[:, np.newaxis])**2).sum(axis=2))\n",
        "        labels = np.argmin(distances, axis=0)\n",
        "\n",
        "        # Step 3: Update cluster centers\n",
        "        new_centroids = np.array([X[labels == i].mean(axis=0) for i in range(k)])\n",
        "\n",
        "        # Check for convergence\n",
        "        if np.allclose(centroids, new_centroids):\n",
        "            break\n",
        "\n",
        "        centroids = new_centroids\n",
        "\n",
        "    return centroids\n",
        "\n",
        "# Define the function to compress the image using centroids\n",
        "def compress_image_with_centroids(image, centroids):\n",
        "    distances = np.sqrt(((image - centroids[:, np.newaxis])**2).sum(axis=2))\n",
        "    labels = np.argmin(distances, axis=0)\n",
        "    compressed_image = centroids[labels]\n",
        "    return compressed_image\n",
        "\n",
        " # Display compressed images\n",
        "    plt.figure(figsize=(16, 6))\n",
        "    for i in range(10):  # Display first 10 compressed images\n",
        "        plt.subplot(2, 5, i + 1)\n",
        "        plt.imshow(compressed_images[i].reshape(X_training_gray[0].shape).astype(np.uint8), cmap='gray')\n",
        "        plt.title('Compressed Image {}'.format(i + 1))\n",
        "        plt.axis('off')"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713596658455
        }
      },
      "id": "f9b73a7a-8c30-4bcc-bb07-45e03a7f08a6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Principal Component Analysis"
      ],
      "metadata": {
        "id": "908dee61"
      },
      "id": "908dee61"
    },
    {
      "cell_type": "code",
      "source": [
        "#Standardizing\n",
        "X_training_mean = np.mean(X_training, axis=0)  # Compute mean along each feature\n",
        "X_training_std = np.std(X_training, axis=0)    # Compute standard deviation along each feature\n",
        "X_training_scaled = (X_training - X_training_mean) / X_training_std\n",
        "\n",
        "# Standardize the testing data using mean and std computed from training data\n",
        "X_testing_scaled = (X_testing - X_training_mean) / X_training_std\n",
        "\n",
        "# After standardization\n",
        "print('X_training_scaled shape is {}'.format(X_training_scaled.shape))\n",
        "print('X_testing_scaled shape is {}'.format(X_testing_scaled.shape))\n",
        "\n",
        "print('X_training_scaled after standardization:')\n",
        "print(X_training_scaled)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "X_training_scaled shape is (50000, 324)\nX_testing_scaled shape is (10000, 324)\nX_training_scaled after standardization:\n[[ 1.30303129  1.52615303  2.23499125 ...  1.97256941  0.09999164\n   0.89396738]\n [ 0.44367771  0.85759425  1.04603395 ...  0.57544604  1.23669589\n  -0.65262633]\n [-1.42118567 -1.00485381 -0.99941108 ... -0.77265876 -0.0516504\n  -0.53133202]\n ...\n [-1.25918274 -1.00485381 -1.06345529 ...  2.59886055  2.61687397\n   0.66112957]\n [-1.25145768 -1.00485381 -1.0278732  ... -0.04087711  1.59214579\n   1.0408739 ]\n [-0.1189403   0.64937907 -0.15295917 ... -0.20600012  1.73986766\n  -0.73048979]]\n"
        }
      ],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1713462108804
        },
        "id": "384a4569",
        "outputId": "cf821530-ccc1-42f3-c1c4-0a43ce69fc2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "384a4569"
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the covariance matrix\n",
        "cov_matrix = np.cov(X_training_scaled, rowvar=False)\n",
        "\n",
        "# Print the covariance matrix\n",
        "print(\"Covariance matrix:\")\n",
        "print(cov_matrix)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Covariance matrix:\n[[1.00002    0.47450196 0.21931131 ... 0.07142396 0.11198432 0.14578755]\n [0.47450196 1.00002    0.48996317 ... 0.06218371 0.09634328 0.11329057]\n [0.21931131 0.48996317 1.00002    ... 0.04492696 0.06542496 0.06409345]\n ...\n [0.07142396 0.06218371 0.04492696 ... 1.00002    0.49712666 0.22231806]\n [0.11198432 0.09634328 0.06542496 ... 0.49712666 1.00002    0.49731331]\n [0.14578755 0.11329057 0.06409345 ... 0.22231806 0.49731331 1.00002   ]]\n"
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1713462122578
        },
        "id": "1e75846c",
        "outputId": "c2fc7a71-285d-4912-ec43-3bf215cbbf87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "1e75846c"
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the eigenvectors and eigenvalues of the covariance matrix\n",
        "eigenvalues, eigenvectors = np.linalg.eig(cov_matrix)\n",
        "\n",
        "# Adjusting the eigenvectors (loadings) that are largest in absolute value to be positive\n",
        "max_abs_idx = np.argmax(np.abs(eigenvectors), axis=0)\n",
        "signs = np.sign(eigenvectors[max_abs_idx, range(eigenvectors.shape[0])])\n",
        "eigenvectors = eigenvectors * signs[np.newaxis, :]\n",
        "eigenvectors = eigenvectors.T\n",
        "\n",
        "# Print the eigenvalues and eigenvectors\n",
        "print(\"Number of Eigenvalues:\")\n",
        "print(len(eigenvalues))\n",
        "print(\"Eigenvector Matrix Shape:\")\n",
        "print(eigenvectors.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Number of Eigenvalues:\n324\nEigenvector Matrix Shape:\n(324, 324)\n"
        }
      ],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1713462124685
        },
        "id": "3c3dfbfb",
        "outputId": "dd177b6d-ae48-4ca3-d468-7db9c6715996",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "3c3dfbfb"
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a list of (eigenvalue, eigenvector) tuples\n",
        "eig_pairs = [(np.abs(eigenvalues[i]), eigenvectors[i, :]) for i in range(len(eigenvalues))]\n",
        "\n",
        "# Sort the tuples from the highest to the lowest based on eigenvalues magnitude\n",
        "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
        "\n",
        "# For further usage\n",
        "eig_vals_sorted = np.array([x[0] for x in eig_pairs])\n",
        "eig_vecs_sorted = np.array([x[1] for x in eig_pairs])\n",
        "\n",
        "print(f'Number of Eigen-Pairs: {len(eig_pairs)}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Number of Eigen-Pairs: 324\n"
        }
      ],
      "execution_count": 19,
      "metadata": {
        "gather": {
          "logged": 1713462154359
        },
        "id": "8b05168d",
        "outputId": "58fba2da-7d74-4c9f-8341-205dc9e4edc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "8b05168d"
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate explained variance and cumulative explained variance\n",
        "eig_vals_total = np.sum(eig_vals_sorted)\n",
        "explained_variance = [(i / eig_vals_total) * 100 for i in eig_vals_sorted]\n",
        "explained_variance = np.round(explained_variance, 2)\n",
        "cum_explained_variance = np.cumsum(explained_variance)\n",
        "\n",
        "print('Explained variance and Cumulative Explained Variance have been calculated!\\n')\n",
        "\n",
        "\n",
        "# Plotting cumulative explained variance\n",
        "num_components = len(explained_variance)\n",
        "plt.plot(np.arange(1, num_components + 1), cum_explained_variance, '-o')\n",
        "\n",
        "# Set x-axis tick labels explicitly\n",
        "custom_ticks = [0, 50, 100, 150, 200, 250, 300]\n",
        "plt.xticks(custom_ticks)\n",
        "plt.xlabel('Number of components')\n",
        "plt.ylabel('Cumulative explained variance')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Explained variance and Cumulative Explained Variance have been calculated!\n\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1713462157225
        },
        "id": "cbe5bbc4",
        "outputId": "93568ddc-a2cb-4ee5-f671-3ca892991661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        }
      },
      "id": "cbe5bbc4"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Over two components\n",
        "k = 130\n",
        "W = eig_vecs_sorted[:k, :]  # Projection matrix\n",
        "\n",
        "X_proj = X_training_scaled.dot(W.T)\n",
        "\n",
        "print(f'Shape of Projected Data: {X_proj.shape}')\n",
        "\n",
        "plt.scatter(X_proj[:, 0], X_proj[:, 1], c=y_training)\n",
        "plt.xlabel('PC1')\n",
        "plt.ylabel('PC2')\n",
        "plt.title('2 components, captures {} of total variation'.format(cum_explained_variance[1]))\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Shape of Projected Data: (50000, 130)\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1713462163056
        },
        "id": "223ef168",
        "outputId": "be553a26-2abf-43eb-df46-fbe9634e0d09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "id": "223ef168"
    },
    {
      "cell_type": "code",
      "source": [
        "class MyPCA:\n",
        "\n",
        "    def __init__(self, n_components):\n",
        "        self.n_components = n_components\n",
        "\n",
        "    def fit(self, X):\n",
        "        # Standardize data\n",
        "        X = X.copy()\n",
        "        self.mean = np.mean(X, axis=0)\n",
        "        self.scale = np.std(X, axis=0)\n",
        "        X_std = (X - self.mean) / self.scale\n",
        "\n",
        "        # Eigendecomposition of covariance matrix\n",
        "        cov_mat = np.cov(X_std.T)\n",
        "        eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
        "\n",
        "        # Adjusting the eigenvectors that are largest in absolute value to be positive\n",
        "        max_abs_idx = np.argmax(np.abs(eig_vecs), axis=0)\n",
        "        signs = np.sign(eig_vecs[max_abs_idx, range(eig_vecs.shape[0])])\n",
        "        eig_vecs = eig_vecs * signs[np.newaxis, :]\n",
        "        eig_vecs = eig_vecs.T\n",
        "\n",
        "        eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[i, :]) for i in range(len(eig_vals))]\n",
        "        eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
        "        eig_vals_sorted = np.array([x[0] for x in eig_pairs])\n",
        "        eig_vecs_sorted = np.array([x[1] for x in eig_pairs])\n",
        "\n",
        "        self.components = eig_vecs_sorted[:self.n_components, :]\n",
        "\n",
        "        # Explained variance ratio\n",
        "        self.explained_variance_ratio = [i / np.sum(eig_vals) for i in eig_vals_sorted[:self.n_components]]\n",
        "\n",
        "        self.cum_explained_variance = np.cumsum(self.explained_variance_ratio)\n",
        "\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()\n",
        "        X_std = (X - self.mean) / self.scale\n",
        "        X_proj = X_std.dot(self.components.T)\n",
        "\n",
        "        return X_proj"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1713462185690
        },
        "id": "b1a71129"
      },
      "id": "b1a71129"
    },
    {
      "cell_type": "code",
      "source": [
        "my_pca = MyPCA(n_components=130).fit(X_training)\n",
        "\n",
        "#print('Components:\\n', my_pca.components)\n",
        "#print('Explained variance ratio:\\n', my_pca.explained_variance_ratio)\n",
        "#print('Cumulative explained variance:\\n', my_pca.cum_explained_variance)\n",
        "\n",
        "X_train_pca = my_pca.transform(X_training)\n",
        "X_test_pca = my_pca.transform(X_testing)\n",
        "print('Transformed train data shape:', X_train_pca.shape)\n",
        "print('Transformed test data shape:', X_test_pca.shape)\n",
        "\n",
        "np.savez_compressed('X_testing_pca.npz', X_test_pca)\n",
        "np.savez_compressed('X_training_pca.npz', X_train_pca)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Transformed train data shape: (50000, 130)\nTransformed test data shape: (10000, 130)\n"
        }
      ],
      "execution_count": 23,
      "metadata": {
        "gather": {
          "logged": 1713462319140
        },
        "id": "ada8d3ca",
        "outputId": "576a0a6e-ab93-4058-d0af-c59e1cd29519",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "ada8d3ca"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Feature and Target Variables directly from compressed files"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "45176834-0285-412c-92fd-c828dbc9f2dd"
      },
      "id": "45176834-0285-412c-92fd-c828dbc9f2dd"
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data from the compressed files\n",
        "loaded_data_X_testing_pca = np.load('X_testing_pca.npz')\n",
        "loaded_data_X_training_pca = np.load('X_training_pca.npz')\n",
        "loaded_data_y_training = np.load('y_training_.npz')\n",
        "loaded_data_y_testing = np.load('y_testing_.npz')\n",
        "\n",
        "# Access the variables from the loaded data\n",
        "X_test_pca = loaded_data_X_testing_pca['arr_0']\n",
        "X_train_pca = loaded_data_X_training_pca['arr_0']\n",
        "y_training = loaded_data_y_training['arr_0']\n",
        "y_testing = loaded_data_y_testing['arr_0']"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713585658445
        },
        "id": "3fde0071-b489-4370-ae5e-dd15a4e71653"
      },
      "id": "3fde0071-b489-4370-ae5e-dd15a4e71653"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MultiClass Single Layer Perceptron"
      ],
      "metadata": {
        "id": "8870d987"
      },
      "id": "8870d987"
    },
    {
      "cell_type": "code",
      "source": [
        "# Train perceptron\n",
        "def initialize_weights(num_features):\n",
        "    return np.random.uniform(-1, 1, size=num_features + 1)\n",
        "\n",
        "def perceptron_train(X_train, y_train, iterations, learning_rate, num_classes):\n",
        "    num_features = X_train.shape[1]\n",
        "    weights = []\n",
        "\n",
        "    for class_label in range(num_classes):\n",
        "        class_weights = initialize_weights(num_features)\n",
        "        for _ in range(iterations):\n",
        "            # Shuffle data for each epoch\n",
        "            shuffled_indices = np.random.permutation(len(X_train))\n",
        "            X_train_shuffled = X_train[shuffled_indices]\n",
        "            y_train_shuffled = y_train[shuffled_indices]\n",
        "            for features, label in zip(X_train_shuffled, y_train_shuffled):\n",
        "                features_with_bias = np.insert(features, 0, 1)\n",
        "                target = 1 if label == class_label else 0\n",
        "                prediction = 1 if np.dot(class_weights, features_with_bias) >= 0 else 0\n",
        "                error = target - prediction\n",
        "                class_weights += learning_rate * error * features_with_bias\n",
        "        weights.append(class_weights)\n",
        "\n",
        "    return weights\n",
        "\n",
        "# Training perceptron\n",
        "weights = perceptron_train(X_training, y_training, iterations=50, learning_rate=0.001, num_classes=10)\n"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712044755345
        },
        "id": "f5823c04"
      },
      "id": "f5823c04"
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "def predict(X_test, weights):\n",
        "    predictions = []\n",
        "    for sample in X_test:\n",
        "        scores = [np.dot(sample, class_weights[1:]) + class_weights[0] for class_weights in weights]\n",
        "        predicted_label = np.argmax(scores)\n",
        "        predictions.append(predicted_label)\n",
        "    return np.array(predictions)\n",
        "\n",
        "predicted_labels = predict(X_testing, weights)\n",
        "\n",
        "# Compute accuracy\n",
        "accuracy = np.mean(predicted_labels == y_testing)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Accuracy: 0.3568\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712044755572
        },
        "id": "208076a9",
        "outputId": "ee9b1fb6-ba34-4a9f-8c50-da69e9ec2476"
      },
      "id": "208076a9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Reg"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "1fec699d-35a6-45cc-aafd-eff83eed63ac"
      },
      "id": "1fec699d-35a6-45cc-aafd-eff83eed63ac"
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegMultiClass:\n",
        "    def __init__(self, lr=0.001, iter=1000):\n",
        "        self.lr = lr\n",
        "        self.iter = iter\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "        self.loss = []\n",
        "\n",
        "    # Sigmoid function\n",
        "    def sigmoid_(self, x):\n",
        "        return 1 / (1 + np.exp(-x))\n",
        "\n",
        "    # Binary Cross Entropy Loss function\n",
        "    # Binary Cross Entropy Loss function\n",
        "    # Inside the loss_compute method\n",
        "    def loss_compute(self, y_actual, y_pred):\n",
        "        eps = 1e-9\n",
        "        y_actual_one_hot = np.eye(y_pred.shape[1])[y_actual]  # One-hot encode y_actual\n",
        "        loss = -np.mean(y_actual_one_hot * np.log(y_pred + eps) + (1 - y_actual_one_hot) * np.log(1 - y_pred + eps))\n",
        "        return loss\n",
        "\n",
        "\n",
        "\n",
        "    # Feedforward function\n",
        "    def feedforward(self, X):\n",
        "        z = self.bias + np.dot(X, self.weights)\n",
        "        A_ = self.sigmoid_(z)\n",
        "        return A_\n",
        "\n",
        "    # Fit function\n",
        "    def fit(self, X, y):\n",
        "        num_samples, num_features = X.shape\n",
        "        num_classes = np.unique(y).size\n",
        "\n",
        "        self.weights = np.zeros((num_features, num_classes))\n",
        "        self.bias = np.zeros(num_classes)\n",
        "\n",
        "        # Gradient Descent\n",
        "        for _ in range(self.iter):\n",
        "            A_ = self.feedforward(X)  # Calculate predictions for all classes\n",
        "            self.loss.append(self.loss_compute(y, A_))  # Compute loss for all classes\n",
        "            dz = A_ - np.eye(num_classes)[y]  # derivative of sigmoid and BCE\n",
        "            # Gradients\n",
        "            dw = (1 / num_samples) * np.dot(X.T, dz)\n",
        "            db = (1 / num_samples) * np.sum(dz, axis=0)\n",
        "            # Updating parameters\n",
        "            self.weights -= self.lr * dw\n",
        "            self.bias -= self.lr * db\n",
        "\n",
        "    # Predict function\n",
        "    def predict(self, X):\n",
        "        y_h = self.bias + np.dot(X, self.weights)\n",
        "        y_pred_prob = self.sigmoid_(y_h)\n",
        "        y_pred_class = np.argmax(y_pred_prob, axis=1)\n",
        "        return y_pred_class"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713585569192
        },
        "id": "0a091b52-b862-41d5-b370-0bc559cae558"
      },
      "id": "0a091b52-b862-41d5-b370-0bc559cae558"
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_regressor = LogisticRegMultiClass(lr=0.0001, iter=1000)\n",
        "logistic_regressor.fit(X_train_pca, y_training)\n",
        "preds = logistic_regressor.predict(X_test_pca)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713465768744
        },
        "id": "1f4e1be0-5aec-4a50-99fc-29f201904f38"
      },
      "id": "1f4e1be0-5aec-4a50-99fc-29f201904f38"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy: {accuracy_score(y_testing, preds)*100}%')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Accuracy: 40.26%\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713466009748
        },
        "id": "3d95b66a-aec6-4923-b0d2-690e06ba362e",
        "outputId": "bade9526-c5a2-4060-d726-cec0e576e6f9"
      },
      "id": "3d95b66a-aec6-4923-b0d2-690e06ba362e"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-Nearest Neighbour"
      ],
      "metadata": {
        "id": "86d743a2"
      },
      "id": "86d743a2"
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating Euclidean distance\n",
        "def euclidean_dist(pointA, pointB):\n",
        "\n",
        "    distance = np.sqrt(np.sum(np.square(pointA - pointB))) #Vectorization\n",
        "    return distance\n",
        "\n",
        "#Calculating distance of test point from all the training points\n",
        "def distance_from_all_training_points(test_point, train_features):\n",
        "#Returns- dist_array- Array holding distance values for all training data points\n",
        "    dist_array = np.array([euclidean_dist(test_point, train_point) for train_point in train_features])\n",
        "    return dist_array\n",
        "\n",
        "#Implementing KNN Classification model\n",
        "def KNNClassifier(train_features, train_target, test_features, k):\n",
        "    predictions = np.array([])\n",
        "    train_target = train_target.reshape(-1,1)\n",
        "\n",
        "    for test_point in test_features:\n",
        "        dist_array = distance_from_all_training_points(test_point, train_features)\n",
        "        neighbors = np.concatenate((dist_array.reshape(-1, 1), train_target), axis=1)\n",
        "        neighbors_sorted = neighbors[neighbors[:, 0].argsort()]\n",
        "        k_neighbors = neighbors_sorted[:k]\n",
        "        nearest_classes = k_neighbors[:, 1].astype(int)\n",
        "        target_class = np.argmax(np.bincount(nearest_classes))\n",
        "        predictions = np.append(predictions, target_class)\n",
        "\n",
        "    return np.array(predictions)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1713585668244
        },
        "id": "e10a186e",
        "outputId": "6f18176d-d36a-442c-b76f-821ff92e5343"
      },
      "id": "e10a186e"
    },
    {
      "cell_type": "code",
      "source": [
        "#running inference on test data\n",
        "test_predictions = KNNClassifier(X_train_pca, y_training, X_test_pca, 3)\n",
        "test_predictions"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "array([4., 8., 1., ..., 2., 2., 7.])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713588681174
        }
      },
      "id": "84931f43-a3da-4af1-841e-f8d613e8f24a"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "#Model Evaluation\n",
        "def accuracy(y_test, y_preds):\n",
        "    total_correct = sum(1 for true, pred in zip(y_test, y_preds) if true == pred)\n",
        "    acc = total_correct / len(y_test)\n",
        "    return acc\n",
        "acc = accuracy(y_testing, test_predictions)\n",
        "print('Model accuracy (Scratch) = ', acc*100)\n",
        "print(\"Score:\\n\", classification_report(y_testing, test_predictions))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Model accuracy (Scratch) =  46.92\nScore:\n               precision    recall  f1-score   support\n\n           0       0.57      0.57      0.57      1000\n           1       0.58      0.69      0.63      1000\n           2       0.33      0.43      0.37      1000\n           3       0.32      0.23      0.27      1000\n           4       0.28      0.48      0.35      1000\n           5       0.49      0.25      0.33      1000\n           6       0.42      0.69      0.52      1000\n           7       0.73      0.40      0.52      1000\n           8       0.69      0.49      0.58      1000\n           9       0.71      0.45      0.55      1000\n\n    accuracy                           0.47     10000\n   macro avg       0.51      0.47      0.47     10000\nweighted avg       0.51      0.47      0.47     10000\n\n"
        }
      ],
      "execution_count": 9,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713588681635
        },
        "id": "d89099aa-14a6-4f1b-b402-f7ec0c6149c1",
        "outputId": "94ff711f-a4a5-4e82-9d69-9bfe4a935e1f"
      },
      "id": "d89099aa-14a6-4f1b-b402-f7ec0c6149c1"
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "k_values = list(range(2, 8))\n",
        "accuracy_list = []\n",
        "\n",
        "# Assuming KNNClassifier and accuracy functions are defined/imported correctly\n",
        "for k in k_values:\n",
        "    test_predictions = KNNClassifier(X_train_pca[:10000], y_training[:10000], X_test_pca[:2000], k)\n",
        "    accuracy_list.append(accuracy(y_testing[:2000], test_predictions))\n",
        "\n",
        "sns.barplot(x=k_values, y=accuracy_list)\n",
        "plt.xlabel('K Values')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy vs K Values')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713591615544
        },
        "id": "74432a0f-5996-4b05-b915-3f12dfe6c6a6"
      },
      "id": "74432a0f-5996-4b05-b915-3f12dfe6c6a6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes"
      ],
      "metadata": {
        "id": "9e5b4589"
      },
      "id": "9e5b4589"
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 10 classes and 130 features in pca reduced dataset. This accounts to more than 1300 likelihood probabilities. Hence, implementing this algorithm manually from scratch is cumbersome. All three Naive Bayes classifier types(Gaussian, Multinomial, Bernoulli) are implemented in the cells below using Sklearn."
      ],
      "metadata": {
        "id": "756d14b8"
      },
      "id": "756d14b8"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
        "print(X_train_pca.shape)\n",
        "print(X_test_pca.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(50000, 130)\n(10000, 130)\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079439818
        },
        "id": "fc044b1d",
        "outputId": "14337b4e-9610-43dc-c051-4268ed67f00b"
      },
      "id": "fc044b1d"
    },
    {
      "cell_type": "code",
      "source": [
        "# initializing the classifiers\n",
        "clf1 = GaussianNB()\n",
        "clf2 = MultinomialNB()\n",
        "clf3 = BernoulliNB()\n",
        "from sklearn.metrics import accuracy_score\n",
        "import time"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079439985
        },
        "id": "0269c900"
      },
      "id": "0269c900"
    },
    {
      "cell_type": "code",
      "source": [
        "#Implementing Gaussian Naive Bayes Classifier\n",
        "try:\n",
        "  start = time.time()\n",
        "  clf1.fit(X_train_pca,y_training)\n",
        "  end = time.time()\n",
        "  print(f'time taken : {end - start}')\n",
        "\n",
        "  y_pred1 = clf1.predict(X_test_pca)\n",
        "  print(y_pred1.shape)\n",
        "  print(accuracy_score(y_testing,y_pred1))\n",
        "\n",
        "except Exception as e:\n",
        "  print(f'Some error occurred : {e}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "time taken : 0.07718133926391602\n(10000,)\n0.4923\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079440153
        },
        "id": "1c64bae7",
        "outputId": "14967c20-0cf5-448b-9a24-e57e98dbf835"
      },
      "id": "1c64bae7"
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementing Multinomial Naive Bayes Classifier\n",
        "\n",
        "try:\n",
        "  start = time.time()\n",
        "  clf2.fit(X_train_pca,y_training)\n",
        "  end = time.time()\n",
        "  print(f'time taken : {end - start}')\n",
        "\n",
        "  y_pred2 = clf2.predict(X_test_pca)\n",
        "  print(y_pred2.shape)\n",
        "  print(accuracy_score(y_testing,y_pred2))\n",
        "\n",
        "except Exception as e:\n",
        "  print(f'Some error occurred : {e}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Some error occurred : Negative values in data passed to MultinomialNB (input X)\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079440359
        },
        "id": "58d57c96",
        "outputId": "b3b5c5f9-cc97-4989-f144-6d79a65cdb4e"
      },
      "id": "58d57c96"
    },
    {
      "cell_type": "code",
      "source": [
        "negatives = (X_train_pca < 0).sum().sum()\n",
        "\n",
        "print(f\"Total count of negative values : {negatives} Percentage : {(negatives / (50000*130))*100}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Total count of negative values : 3252025 Percentage : 50.03115384615384\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079440608
        },
        "id": "482fa831",
        "outputId": "9ebacd2e-d6b4-4c1f-a8f6-c2f1cae74ad0"
      },
      "id": "482fa831"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since a high fraction of values are negative, manipulating this dataset to handle negative values is not a good deal."
      ],
      "metadata": {
        "id": "f162d82f"
      },
      "id": "f162d82f"
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementing Bernoulli Naive Bayes Classifier\n",
        "\n",
        "try:\n",
        "  start = time.time()\n",
        "  clf3.fit(X_train_pca,y_training)\n",
        "  end = time.time()\n",
        "  print(f'time taken : {end - start}')\n",
        "\n",
        "  y_pred3 = clf3.predict(X_test_pca)\n",
        "  print(y_pred3.shape)\n",
        "  print(accuracy_score(y_testing,y_pred3))\n",
        "\n",
        "except Exception as e:\n",
        "  print(f'Some error occurred : {e}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "time taken : 0.145125150680542\n(10000,)\n0.3985\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1712079441029
        },
        "id": "bf9b7b9b",
        "outputId": "6c473edd-b413-48a3-aada-86b108dde98b"
      },
      "id": "bf9b7b9b"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Verdict on Naive Bayes**\n",
        "\n",
        "Given that the dataset is so feature and classes enriched, Multinomial NB could be an appropriate classifier but it does not take negative values which has resulted into an error as stated above. Moreover, there are 10 classes while Bernoulli is used for binary classification. Hence, Naive Bayes is not an ideal classifier for this dataset."
      ],
      "metadata": {
        "id": "677c6742"
      },
      "id": "677c6742"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ensemble Learning"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "d18de017-d97a-46fd-8f0f-008410e9c8c9"
      },
      "id": "d18de017-d97a-46fd-8f0f-008410e9c8c9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decision Tree"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "47ff3070-f423-43a9-8d34-97c8240f4c64"
      },
      "id": "47ff3070-f423-43a9-8d34-97c8240f4c64"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from collections import Counter\n",
        "import random\n",
        "import multiprocessing as mp\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import datasets\n",
        "\n",
        "def entropy(y):\n",
        "    hist = np.bincount(y)\n",
        "    ps = hist / len(y)\n",
        "    en = -np.sum([p*np.log2(p) for p in ps if p>0])\n",
        "    return en\n",
        "\n",
        "class Node:\n",
        "    def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):\n",
        "        self.feature = feature\n",
        "        self.threshold = threshold\n",
        "        self.left = left\n",
        "        self.right = right\n",
        "        self.value = value\n",
        "\n",
        "    def is_leaf_node(self):\n",
        "        return self.value is not None\n",
        "\n",
        "class DecisionTree:\n",
        "    def __init__(self, min_sample_split=2, max_depth=100, max_features=None):\n",
        "        self.min_sample_split = min_sample_split\n",
        "        self.max_depth = max_depth\n",
        "        self.max_features = max_features\n",
        "        self.root = None\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.max_features = X.shape[1] if self.max_features is None else min(self.max_features, X.shape[1])\n",
        "        self.root = self._grow_tree(X, y)\n",
        "\n",
        "    # Add your _grow_tree, _best_criteria, _split, _most_common_label, and prune methods here\n",
        "    def _grow_tree(self, X, y, depth=0):\n",
        "        n_samples, n_features = X.shape\n",
        "        n_labels = len(np.unique(y))\n",
        "\n",
        "        if depth >= self.max_depth or n_labels == 1 or n_samples < self.min_sample_split:\n",
        "            leaf_value = self._most_common_label(y)\n",
        "            return Node(value=leaf_value)\n",
        "\n",
        "        feat_idxs = np.random.choice(n_features, self.max_features, replace=False)\n",
        "\n",
        "        best_feat, best_thresh = self._best_criteria(X, y, self.max_features)\n",
        "        left_idxs, right_idxs = self._split(X[:, best_feat], best_thresh)\n",
        "        left = self._grow_tree(X[left_idxs, :], y[left_idxs], depth + 1)\n",
        "        right = self._grow_tree(X[right_idxs, :], y[right_idxs], depth + 1)\n",
        "        return Node(best_feat, best_thresh, left, right)\n",
        "\n",
        "    def _most_common_label(self, y):\n",
        "        counter = Counter(y)\n",
        "        most_common = counter.most_common(1)[0][0]\n",
        "        return most_common\n",
        "\n",
        "    def _best_criteria(self, X, y, max_features):\n",
        "        \"\"\"\n",
        "        Find the best split for a node.\n",
        "        \"\"\"\n",
        "        # Ensure that max_features is an integer\n",
        "        max_features = int(max_features)\n",
        "\n",
        "        # Randomly select a subset of the features without replacement\n",
        "        feat_idxs = random.sample(range(X.shape[1]), max_features)\n",
        "\n",
        "        best_feat, best_thresh = None, None\n",
        "        best_gain = -1\n",
        "        for feature in feat_idxs:\n",
        "            X_column = X[:, feature]\n",
        "            thresholds = np.unique(X_column)\n",
        "            for threshold in thresholds:\n",
        "                gain = self._information_gain(y, X_column, threshold)\n",
        "\n",
        "                if gain > best_gain:\n",
        "                    best_gain = gain\n",
        "                    best_feat = feature\n",
        "                    best_thresh = threshold\n",
        "\n",
        "        return best_feat, best_thresh\n",
        "\n",
        "    def _information_gain(self, y, X_column, split_thresh):\n",
        "        parent_entropy = entropy(y)\n",
        "\n",
        "        left_idxs, right_idxs = self._split(X_column, split_thresh)\n",
        "        if len(left_idxs) == 0 or len(right_idxs) == 0:\n",
        "            return 0\n",
        "\n",
        "        n = len(y)\n",
        "        n_l, n_r = len(left_idxs), len(right_idxs)\n",
        "        e_l, e_r = entropy(y[left_idxs]), entropy(y[right_idxs])\n",
        "        child_entropy = (n_l / n) * e_l + (n_r / n) * e_r\n",
        "\n",
        "        ig = parent_entropy - child_entropy\n",
        "        return ig\n",
        "\n",
        "    def _split(self, X_column, split_thresh):\n",
        "        left_idxs = np.argwhere(X_column <= split_thresh).flatten()\n",
        "        right_idxs = np.argwhere(X_column > split_thresh).flatten()\n",
        "        return left_idxs, right_idxs\n",
        "\n",
        "    def prune(self, X_val, y_val):\n",
        "\n",
        "        self.accuracy_with_child = np.sum(self.predict(X_val) == y_val) / len(y_val)\n",
        "        self._prune(self.root, X_val, y_val)\n",
        "\n",
        "    def _prune(self, node, X_val, y_val):\n",
        "        \"\"\"\n",
        "        Recursively prune the tree.\n",
        "        \"\"\"\n",
        "        if node is None or node.is_leaf_node():\n",
        "            return\n",
        "\n",
        "        if node.left:\n",
        "            if node.left.is_leaf_node():\n",
        "                self._try_prune(node, 'left', X_val, y_val)\n",
        "            else:\n",
        "                self._prune(node.left, X_val, y_val)\n",
        "\n",
        "        if node.right:\n",
        "            if node.right.is_leaf_node():\n",
        "                self._try_prune(node, 'right', X_val, y_val)\n",
        "            else:\n",
        "                self._prune(node.right, X_val, y_val)\n",
        "\n",
        "    def _try_prune(self, node, child_side, X_val, y_val):\n",
        "        \"\"\"\n",
        "        Try to prune a child of a node and check if it improves the accuracy.\n",
        "        \"\"\"\n",
        "        child_node = getattr(node, child_side)\n",
        "        setattr(node, child_side, None)\n",
        "\n",
        "        y_pred = self.predict(X_val)\n",
        "        accuracy_without_child = np.sum(y_val == y_pred) / len(y_val)\n",
        "\n",
        "        setattr(node, child_side, child_node)\n",
        "\n",
        "        if accuracy_without_child >= self.accuracy_with_child:\n",
        "            setattr(node, child_side, None)\n",
        "\n",
        "    def predict(self, X):\n",
        "        return np.array([self._traverse_tree(x, self.root) for x in X])\n",
        "\n",
        "    def _traverse_tree(self, x, node):\n",
        "        if node is None:\n",
        "            return None\n",
        "\n",
        "        if node.is_leaf_node():\n",
        "            return node.value\n",
        "\n",
        "        if x[node.feature] <= node.threshold:\n",
        "            return self._traverse_tree(x, node.left)\n",
        "\n",
        "        return self._traverse_tree(x, node.right)"
      ],
      "outputs": [],
      "execution_count": 55,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1712079441193
        },
        "id": "4c7a232c-e6ce-46d1-b7f8-8a0a47b56f1d"
      },
      "id": "4c7a232c-e6ce-46d1-b7f8-8a0a47b56f1d"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "dad74b1b-7d83-47b5-9ddc-3334f2afb885"
      },
      "id": "dad74b1b-7d83-47b5-9ddc-3334f2afb885"
    },
    {
      "cell_type": "code",
      "source": [
        "class RandomForestClassifer:\n",
        "    def __init__(self, n_estimator=100, min_sample_split=2, max_depth=10, max_features=None):\n",
        "        self.n_estimator = n_estimator\n",
        "        self.min_sample_split = min_sample_split\n",
        "        self.max_depth = max_depth\n",
        "        self.max_features = max_features\n",
        "        self.trees = []\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.trees = []\n",
        "\n",
        "        # Create a pool of processes\n",
        "        with mp.Pool(processes=mp.cpu_count()) as pool:\n",
        "\n",
        "            # Define the arguments for each process\n",
        "            args = [(X, y) for _ in range(self.n_estimator)]\n",
        "\n",
        "            # Use starmap to apply the function to each set of arguments\n",
        "            self.trees = pool.starmap(self._train_tree, args)\n",
        "\n",
        "    def _train_tree(self, X, y):\n",
        "        tree = DecisionTree(min_sample_split=self.min_sample_split, max_depth=self.max_depth, max_features=self.max_features)\n",
        "        X_sample, y_sample = bootstrap_sample(X, y)\n",
        "        tree.fit(X_sample, y_sample)\n",
        "        return tree\n",
        "\n",
        "    def predict(self, X):\n",
        "        tree_preds = np.array([tree.predict(X) for tree in self.trees])\n",
        "        tree_preds = np.swapaxes(tree_preds, 0, 1)\n",
        "        y_pred = [most_common_label(tree_pred) for tree_pred in tree_preds]\n",
        "        return np.array(y_pred)\n",
        "\n",
        "    def prune(self, X_val, y_val):\n",
        "        for tree in self.trees:\n",
        "            tree.prune(X_val, y_val)\n",
        "\n",
        "def bootstrap_sample(X,y):\n",
        "    n_samples = X.shape[0]\n",
        "    idxs = np.random.choice(n_samples , size=n_samples,replace = True)\n",
        "    return X[idxs],y[idxs]\n",
        "\n",
        "def most_common_label(y):\n",
        "    counter = Counter(y)\n",
        "    most_common = counter.most_common(1)[0][0]  # Get the label, not the count\n",
        "    return most_common\n",
        "\n",
        "def accuracy(y_true, y_pred):\n",
        "    accuracy = np.sum(y_true == y_pred) / len(y_true)\n",
        "    return accuracy"
      ],
      "outputs": [],
      "execution_count": 33,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1712079441416
        },
        "id": "ad0e2b07-da3a-4f19-b5f6-b2ba61a2545a"
      },
      "id": "ad0e2b07-da3a-4f19-b5f6-b2ba61a2545a"
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape the training and testing data\n",
        "X_train_hog_flat = X_training.reshape(len(X_training), -1)\n",
        "X_test_hog_flat = X_testing.reshape(len(X_testing), -1)"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713593939468
        }
      },
      "id": "e75baacd-d5aa-4d73-a150-98b0a1eb0049"
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=5, max_depth=5, max_features=10)\n",
        "rf_classifier.fit(X_train_hog_flat, y_training)\n",
        "\n",
        "# Evaluate model\n",
        "y_pred = rf_classifier.predict(X_test_hog_flat)\n",
        "acc = accuracy(y_testing, y_pred)\n",
        "print(f\"Accuracy: {acc*100} %\")\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Accuracy: 33.0 %\n"
        }
      ],
      "execution_count": 64,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "4b066ea9-38a2-42ad-b398-06093c80d755",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91ca2207-286b-4bb0-fd29-e4ca892548c1"
      },
      "id": "4b066ea9-38a2-42ad-b398-06093c80d755"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing how the accuracy varies with the three factors (max_depth, n_estimators, max_features) to find best parameters and visualizing the results. (SKlearn)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "id": "3d217eac-3c56-4050-b0be-7819ebecf0cc"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define parameter grid\n",
        "param_grid = {\n",
        "    'max_depth': [5, 10, 15],\n",
        "    'n_estimators': [3, 5, 10],\n",
        "    'max_features': [5, 10, 15]\n",
        "}\n",
        "\n",
        "# Initialize Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier()\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(rf_classifier, param_grid, cv=5, scoring='accuracy')\n",
        "\n",
        "# Fit the model (assuming X_train_hog_flat and y_training are defined)\n",
        "grid_search.fit(X_train_hog_flat, y_training)\n",
        "\n",
        "# Get the best parameters and the best score\n",
        "best_params = grid_search.best_params_\n",
        "best_score = grid_search.best_score_\n",
        "\n",
        "print(f\"Best parameters: {best_params}\")\n",
        "print(f\"Best accuracy score: {best_score}\")\n",
        "\n",
        "# Convert the grid search results into a DataFrame\n",
        "results = pd.DataFrame(grid_search.cv_results_)\n",
        "\n",
        "# Create a figure and axes for subplots\n",
        "fig, axs = plt.subplots(3, 1, figsize=(10, 15))\n",
        "\n",
        "# Plot the mean test score for each value of 'max_depth'\n",
        "axs[0].plot(results['param_max_depth'], results['mean_test_score'], marker='o')\n",
        "axs[0].set_title('max_depth vs mean_test_score')\n",
        "axs[0].set_xlabel('max_depth')\n",
        "axs[0].set_ylabel('mean_test_score')\n",
        "axs[0].grid(True)\n",
        "\n",
        "# Plot the mean test score for each value of 'n_estimators'\n",
        "axs[1].plot(results['param_n_estimators'], results['mean_test_score'], marker='o')\n",
        "axs[1].set_title('n_estimators vs mean_test_score')\n",
        "axs[1].set_xlabel('n_estimators')\n",
        "axs[1].set_ylabel('mean_test_score')\n",
        "axs[1].grid(True)\n",
        "\n",
        "# Plot the mean test score for each value of 'max_features'\n",
        "axs[2].plot(results['param_max_features'], results['mean_test_score'], marker='o')\n",
        "axs[2].set_title('max_features vs mean_test_score')\n",
        "axs[2].set_xlabel('max_features')\n",
        "axs[2].set_ylabel('mean_test_score')\n",
        "axs[2].grid(True)\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plots\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Best parameters: {'max_depth': 10, 'max_features': 15, 'n_estimators': 10}\nBest accuracy score: 0.40168\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x1500 with 3 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713594302162
        }
      },
      "id": "f1179197-cc1c-49c8-b4e0-89f12c275b94"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM Classifier"
      ],
      "metadata": {
        "id": "rUmJ6f7EiBzE"
      },
      "id": "rUmJ6f7EiBzE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sklearn"
      ],
      "metadata": {
        "id": "0b0gm6z5iGhU"
      },
      "id": "0b0gm6z5iGhU"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Reshape the data\n",
        "X_train_hog_flat = X_train_pca.reshape(len(X_train_pca), -1)\n",
        "X_test_hog_flat = X_test_pca.reshape(len(X_test_pca), -1)\n",
        "\n",
        "# Train SVM classifier\n",
        "svm_classifier = svm.SVC(kernel='rbf')  # You can change the kernel as needed\n",
        "svm_classifier.fit(X_train_hog_flat, y_training)\n",
        "\n",
        "# Evaluate model\n",
        "y_pred = svm_classifier.predict(X_test_hog_flat)\n",
        "acc = accuracy_score(y_testing, y_pred)\n",
        "print(\"Accuracy:\", acc)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Accuracy: 0.6189\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "id": "BDC1J-w5iQxd",
        "gather": {
          "logged": 1713389253397
        },
        "outputId": "11533ff0-ba09-4b0f-a2aa-0155159e264a"
      },
      "id": "BDC1J-w5iQxd"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scratch Implementation with Hyperparameters tuning\n"
      ],
      "metadata": {
        "id": "h1T8EGUkiTcs"
      },
      "id": "h1T8EGUkiTcs"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "class SVM(object):\n",
        "    def __init__(self, kernel, C, learning_rate, lambda_param, n_iters, gamma, degree):\n",
        "        self.kernel = kernel\n",
        "        self.C = C\n",
        "        self.lr = learning_rate\n",
        "        self.lambda_param = lambda_param\n",
        "        self.n_iters = n_iters\n",
        "        self.gamma = gamma\n",
        "        self.degree = degree\n",
        "        self.w = None\n",
        "        self.b = None\n",
        "\n",
        "    def linear_kernel(self, x1, x2):\n",
        "        return np.dot(x1, x2)\n",
        "\n",
        "    def polynomial_kernel(self, x, y):\n",
        "        return (np.dot(x, y) + 1) ** self.degree\n",
        "\n",
        "    def gaussian_kernel(self, x, y):\n",
        "        return np.exp(-self.gamma * np.linalg.norm(x - y) ** 2)\n",
        "\n",
        "    def kernel_function(self, x, y):\n",
        "        if self.kernel == 'linear':\n",
        "            return self.linear_kernel(x, y)\n",
        "        elif self.kernel == 'polynomial':\n",
        "            return self.polynomial_kernel(x, y)\n",
        "        elif self.kernel == 'gaussian':\n",
        "            return self.gaussian_kernel(x, y)\n",
        "        else:\n",
        "            raise ValueError(\"Unsupported kernel type\")\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        y_ = np.where(y <= 0, -1, 1)\n",
        "        self.w = np.zeros(n_features)\n",
        "        self.b = 0\n",
        "        self.alphas = np.zeros(n_samples)\n",
        "        self.sv = None\n",
        "        self.sv_y = None\n",
        "\n",
        "        for _ in range(self.n_iters):\n",
        "            for idx in range(n_samples):\n",
        "                x_i = X[idx]\n",
        "                y_i = y_[idx]\n",
        "                Xw_b_i = np.dot(x_i, self.w) - self.b\n",
        "\n",
        "                condition = y_i * Xw_b_i >= 1\n",
        "                if not condition:\n",
        "                    self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_i))\n",
        "                    self.b += self.lr * y_i\n",
        "                    self.alphas[idx] += self.lr\n",
        "            # Store support vectors and coefficients\n",
        "            idx_support = self.alphas > 0\n",
        "            self.sv = X[idx_support]\n",
        "            self.sv_y = y_[idx_support]\n",
        "\n",
        "\n",
        "    def predict(self, X):\n",
        "        return np.sign(self.project(X))\n",
        "\n",
        "    def project(self, X):\n",
        "        if self.w is not None:\n",
        "            return np.dot(X, self.w) + self.b\n",
        "        else:\n",
        "            y_predict = np.zeros(len(X))\n",
        "            for i in range(len(X)):\n",
        "                s = 0\n",
        "                for a, sv_y, sv in zip(self.alphas, self.sv_y, self.sv):\n",
        "                    if self.kernel == 'linear':\n",
        "                        s += a * sv_y * self.linear_kernel(X[i], sv)\n",
        "                    elif self.kernel == 'polynomial':\n",
        "                        s += a * sv_y * self.polynomial_kernel(X[i], sv)\n",
        "                    elif self.kernel == 'gaussian':\n",
        "                        s += a * sv_y * self.gaussian_kernel(X[i], sv)\n",
        "                y_predict[i] = s\n",
        "            return y_predict + self.b\n",
        "\n",
        "    def kernel_matrix(self, X1, X2):\n",
        "        n_samples1, _ = X1.shape\n",
        "        n_samples2, _ = X2.shape\n",
        "        kernel_matrix = np.zeros((n_samples1, n_samples2))\n",
        "        for i in range(n_samples1):\n",
        "            for j in range(n_samples2):\n",
        "                kernel_matrix[i, j] = self.kernel_function(X1[i], X2[j])\n",
        "        return kernel_matrix"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "id": "yjmi32LNiZjx",
        "gather": {
          "logged": 1713388268593
        }
      },
      "id": "yjmi32LNiZjx"
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your dataset\n",
        "# X_train, y_train, X_test, y_test = ...\n",
        "# Reshape the data\n",
        "X_train_hog_flat = X_train_pca.reshape(len(X_train_pca), -1)\n",
        "X_test_hog_flat = X_test_pca.reshape(len(X_test_pca), -1)\n",
        "\n",
        "\n",
        "# Tune parameters (replace this with your own tuning function)\n",
        "def tune_parameters(X, y):\n",
        "    param_grid = {'C': [0.1, 1, 10, 100],\n",
        "                  'gamma': [0.001, 0.01, 0.1, 1],\n",
        "                  'kernel': ['rbf']}\n",
        "    svm_grid = GridSearchCV(SVC(), param_grid, cv=5)\n",
        "    svm_grid.fit(X, y)\n",
        "    best_params = svm_grid.best_params_\n",
        "    return best_params\n",
        "\n",
        "# Tune parameters\n",
        "best_params = tune_parameters(X_train_hog_flat[:1000], y_training[:1000])\n",
        "print(\"Best parameters:\", best_params)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "ce84527a-ac45-41c2-ae38-481c878acda6"
      },
      "id": "ce84527a-ac45-41c2-ae38-481c878acda6"
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_hog_flat = X_train_pca.reshape(len(X_train_pca), -1)\n",
        "X_test_hog_flat = X_test_pca.reshape(len(X_test_pca), -1)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713388284527
        },
        "id": "bfd6b3a8-2be4-48ee-8e21-58b79a861ff9"
      },
      "id": "bfd6b3a8-2be4-48ee-8e21-58b79a861ff9"
    },
    {
      "cell_type": "code",
      "source": [
        "best_params['C'] = 1\n",
        "best_params['gamma'] = 0.01"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713388286090
        },
        "id": "7ba89b09-243f-444b-98ca-d2d4e3050bf8"
      },
      "id": "7ba89b09-243f-444b-98ca-d2d4e3050bf8"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Initialize and train the SVM model\n",
        "svm = SVM(kernel=best_params['kernel'], C=best_params['C'], learning_rate=0.01, lambda_param=0.01, n_iters=1000, gamma=best_params['gamma'], degree=3)\n",
        "svm.fit(X_train_hog_flat, y_training)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = svm.predict(X_test_hog_flat)\n",
        "acc = accuracy_score(y_testing, y_pred)\n",
        "print(\"Accuracy:\", acc)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Accuracy: 0.1\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1713388826277
        },
        "id": "025edf91-a9ba-48d1-9422-657f21225b3b",
        "outputId": "9bb48e6a-059b-4882-c276-9c9e76482051"
      },
      "id": "025edf91-a9ba-48d1-9422-657f21225b3b"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking the accuracy of the model on some other Synthtic Dataset with 50000 points and noise = 20 %. (For the above failure case analysis)"
      ],
      "metadata": {
        "id": "IbShGWW54sNO"
      },
      "id": "IbShGWW54sNO"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "class SVM(object):\n",
        "    def __init__(self, kernel='linear', C=1, learning_rate=0.001, lambda_param=0.01, n_iters=1000, gamma=1, degree=3):\n",
        "        self.kernel = kernel\n",
        "        self.C = C\n",
        "        self.lr = learning_rate\n",
        "        self.lambda_param = lambda_param\n",
        "        self.n_iters = n_iters\n",
        "        self.gamma = gamma\n",
        "        self.degree = degree\n",
        "        self.w = None\n",
        "        self.b = None\n",
        "\n",
        "    def linear_kernel(self, x1, x2):\n",
        "        return np.dot(x1, x2)\n",
        "\n",
        "    def polynomial_kernel(self, x, y):\n",
        "        return (np.dot(x, y) + 1) ** self.degree\n",
        "\n",
        "    def gaussian_kernel(self, x, y):\n",
        "        return np.exp(-self.gamma * np.linalg.norm(x - y) ** 2)\n",
        "\n",
        "    def kernel_function(self, x, y):\n",
        "        if self.kernel == 'linear':\n",
        "            return self.linear_kernel(x, y)\n",
        "        elif self.kernel == 'polynomial':\n",
        "            return self.polynomial_kernel(x, y)\n",
        "        elif self.kernel == 'gaussian':\n",
        "            return self.gaussian_kernel(x, y)\n",
        "        else:\n",
        "            raise ValueError(\"Unsupported kernel type\")\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        y_ = np.where(y <= 0, -1, 1)\n",
        "        self.w = np.zeros(n_features)\n",
        "        self.b = 0\n",
        "\n",
        "        for _ in range(self.n_iters):\n",
        "            for idx in range(n_samples):\n",
        "                x_i = X[idx]\n",
        "                y_i = y_[idx]\n",
        "                Xw_b_i = np.dot(x_i, self.w) - self.b\n",
        "\n",
        "                condition = y_i * Xw_b_i >= 1\n",
        "                if not condition:\n",
        "                    self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_i))\n",
        "                    self.b += self.lr * y_i\n",
        "\n",
        "    def predict(self, X):\n",
        "        return np.sign(self.project(X))\n",
        "\n",
        "    def project(self, X):\n",
        "        if self.w is not None:\n",
        "            return np.dot(X, self.w) + self.b\n",
        "        else:\n",
        "            y_predict = np.zeros(len(X))\n",
        "            for i in range(len(X)):\n",
        "                s = 0\n",
        "                for a, sv_y, sv in zip(self.alphas, self.sv_y, self.sv):\n",
        "                    if self.kernel == 'linear':\n",
        "                        s += a * sv_y * self.linear_kernel(X[i], sv)\n",
        "                    elif self.kernel == 'polynomial':\n",
        "                        s += a * sv_y * self.polynomial_kernel(X[i], sv)\n",
        "                    elif self.kernel == 'gaussian':\n",
        "                        s += a * sv_y * self.gaussian_kernel(X[i], sv)\n",
        "                y_predict[i] = s\n",
        "            return y_predict + self.b\n",
        "\n",
        "    def kernel_matrix(self, X1, X2):\n",
        "        n_samples1, _ = X1.shape\n",
        "        n_samples2, _ = X2.shape\n",
        "        kernel_matrix = np.zeros((n_samples1, n_samples2))\n",
        "        for i in range(n_samples1):\n",
        "            for j in range(n_samples2):\n",
        "                kernel_matrix[i, j] = self.kernel_function(X1[i], X2[j])\n",
        "        return kernel_matrix"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "id": "BvScuCmg-TlK"
      },
      "id": "BvScuCmg-TlK"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_moons\n",
        "\n",
        "# Generate synthetic dataset\n",
        "X, y = make_moons(n_samples=50000, noise=0.1, random_state=42)\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='red', label='Class 0')\n",
        "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='blue', label='Class 1')\n",
        "plt.title('Synthetic Dataset')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Print the shape of the data\n",
        "print(\"Shape of X:\", X.shape)\n",
        "print(\"Shape of y:\", y.shape)\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 800x600 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Shape of X: (50000, 2)\nShape of y: (50000,)\n"
        }
      ],
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "5AwYh_-V-bsS",
        "outputId": "b6f7b7dd-d177-47ce-fbe6-b98cd70c826a"
      },
      "id": "5AwYh_-V-bsS"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Perform hyperparameter tuning with GridSearchCV for each kernel\n",
        "param_grids = {\n",
        "    'linear': {'C': [0.1, 1, 10, 100], 'kernel': ['linear']},\n",
        "    'poly': {'C': [0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1], 'kernel': ['poly']},\n",
        "    'rbf': {'C': [0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1], 'kernel': ['rbf']}\n",
        "}\n",
        "\n",
        "best_accuracies = {}\n",
        "\n",
        "for kernel, param_grid in param_grids.items():\n",
        "    svm_grid = GridSearchCV(SVC(), param_grid, cv=5)\n",
        "    svm_grid.fit(X_train, y_train)\n",
        "    best_params = svm_grid.best_params_\n",
        "    best_accuracy = svm_grid.best_score_\n",
        "    best_accuracies[kernel] = (best_params, best_accuracy)\n",
        "\n",
        "# Print best parameters and accuracies for each kernel\n",
        "for kernel, (best_params, best_accuracy) in best_accuracies.items():\n",
        "    print(f\"Best parameters for {kernel} kernel:\", best_params)\n",
        "    print(f\"Best accuracy for {kernel} kernel:\", best_accuracy)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Best parameters for linear kernel: {'C': 1, 'kernel': 'linear'}\nBest accuracy for linear kernel: 0.8815250000000001\nBest parameters for poly kernel: {'C': 100, 'gamma': 1, 'kernel': 'poly'}\nBest accuracy for poly kernel: 0.9328749999999999\nBest parameters for rbf kernel: {'C': 10, 'gamma': 1, 'kernel': 'rbf'}\nBest accuracy for rbf kernel: 0.9996\n"
        }
      ],
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klgpPPPg4rgH",
        "outputId": "648eeb17-cb3e-4bf7-a832-a02b86b0c81f"
      },
      "id": "klgpPPPg4rgH"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multilayer Perceptron"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "a758cf9e-de03-452e-979b-e7aca4a38009"
      },
      "id": "a758cf9e-de03-452e-979b-e7aca4a38009"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision torchaudio"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "id": "8ab2c17e-ddc1-4fbe-9cff-42fb89071708"
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "#MLP model\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
        "        self.fc2 = nn.Linear(hidden_size, output_size)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Hyperparameters\n",
        "input_size = 130  # Input features\n",
        "hidden_size = 128  # Hidden layer\n",
        "output_size = 10  # NOutput Classes\n",
        "\n",
        "# Instance\n",
        "model = MLP(input_size, hidden_size, output_size)\n",
        "\n",
        "# CrossEntropyLoss\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Adam Optimizer\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n",
        "\n",
        "# NumPy arrays to PyTorch tensors\n",
        "X_train_tensor = torch.Tensor(X_train_pca)\n",
        "y_train_tensor = torch.Tensor(y_training).long()\n",
        "X_test_tensor = torch.Tensor(X_test_pca)\n",
        "y_test_tensor = torch.Tensor(y_testing).long()\n",
        "\n",
        "# DataLoader\n",
        "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# Training\n",
        "num_epochs = 20\n",
        "for epoch in range(num_epochs):\n",
        "    for inputs, labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
        "\n",
        "# Evaluate\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    outputs = model(X_test_tensor)\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "# Accuracy\n",
        "accuracy = (predicted == y_test_tensor).sum().item() / len(y_test_tensor)\n",
        "print(f'Test Accuracy: {accuracy:.4f}')\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Epoch [1/20], Loss: 1.5379\nEpoch [2/20], Loss: 1.3610\nEpoch [3/20], Loss: 1.0601\nEpoch [4/20], Loss: 1.3355\nEpoch [5/20], Loss: 1.3748\nEpoch [6/20], Loss: 1.3779\nEpoch [7/20], Loss: 1.5043\nEpoch [8/20], Loss: 0.9277\nEpoch [9/20], Loss: 1.2497\nEpoch [10/20], Loss: 1.0766\nEpoch [11/20], Loss: 0.7170\nEpoch [12/20], Loss: 1.0398\nEpoch [13/20], Loss: 1.3389\nEpoch [14/20], Loss: 1.6056\nEpoch [15/20], Loss: 1.1935\nEpoch [16/20], Loss: 0.8228\nEpoch [17/20], Loss: 1.4386\nEpoch [18/20], Loss: 0.6620\nEpoch [19/20], Loss: 0.9773\nEpoch [20/20], Loss: 0.8076\nTest Accuracy: 0.5855\n"
        }
      ],
      "execution_count": null,
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