task1(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxkgCNP-hN-A",
        "outputId": "c888c742-d91d-4e79-e421-402cee06d847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n",
            "Total dataset size:\n",
            "n_samples: 1288\n",
            "n_features: 1850\n",
            "n_classes: 7\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from sklearn.datasets import fetch_lfw_people\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)\n",
        "# Download the data, if not already on disk and load it as numpy arrays\n",
        "lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)\n",
        "lfw_torched = torch.from_numpy(lfw_people.data)\n",
        "\n",
        "# introspect the images arrays to find the shapes (for plotting)\n",
        "n_samples, h, w = lfw_people.images.shape\n",
        "# for machine learning we use the 2 data directly (as relative pixel\n",
        "# positions info is ignored by this model)\n",
        "X = lfw_torched.data\n",
        "\n",
        "n_features = X.shape[1]\n",
        "# the label to predict is the id of the person\n",
        "y = lfw_people.target\n",
        "target_names = lfw_people.target_names\n",
        "n_classes = target_names.shape[0]\n",
        "\n",
        "print(\"Total dataset size:\")\n",
        "print(\"n_samples: %d\" % n_samples)\n",
        "print(\"n_features: %d\" % n_features)\n",
        "print(\"n_classes: %d\" % n_classes)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "#X_train = X_train.clone().detach()\n",
        "y_train = torch.tensor(y_train)\n",
        "#X_test = X_test.clone().detach()\n",
        "y_test = torch.tensor(y_test)\n",
        "\n",
        "#print(type(X_train))\n",
        "#print(type(X_test))\n",
        "#print(type(y_train))\n",
        "#print(type(y_test))\n",
        "\n",
        "# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled\n",
        "# dataset): unsupervised feature extraction / dimensionality reduction\n",
        "n_components = 150\n",
        "\n",
        "# Center data\n",
        "mean = torch.mean(X_train, axis=0)\n",
        "X_train -= mean\n",
        "X_test -= mean\n",
        "#Eigen-decomposition\n",
        "U, S, V = torch.linalg.svd(X_train, full_matrices=False)\n",
        "components = V[:n_components]\n",
        "eigenfaces = components.reshape((n_components, h, w))\n",
        "\n",
        "#project into PCA subspace\n",
        "X_transformed = torch.matmul(X_train, components.T)\n",
        "print(X_transformed.shape)\n",
        "X_test_transformed = torch.matmul(X_test, components.T)\n",
        "print(X_test_transformed.shape)\n",
        "\n",
        "def plot_gallery(images, titles, h, w, n_row=3, n_col=4):\n",
        "    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))\n",
        "    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
        "    for i in range(n_row * n_col):\n",
        "        plt.subplot(n_row, n_col, i + 1)\n",
        "        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)\n",
        "        plt.title(titles[i], size=12)\n",
        "        plt.xticks(())\n",
        "        plt.yticks(())\n",
        "\n",
        "\n",
        "eigenface_titles = [\"eigenface %d\" % i for i in range(eigenfaces.shape[0])]\n",
        "plot_gallery(eigenfaces, eigenface_titles, h, w)\n",
        "#plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "_Mb0GlSKjgx9",
        "outputId": "bb12abba-8af5-4be7-c3f2-5ea5faba1fc4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([966, 150])\n",
            "torch.Size([322, 150])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "explained_variance = (S ** 2) / (n_samples - 1)\n",
        "total_var = explained_variance.sum()\n",
        "explained_variance_ratio = explained_variance / total_var\n",
        "ratio_cumsum = torch.cumsum(explained_variance_ratio, dim=0)\n",
        "print(ratio_cumsum.shape)\n",
        "eigenvalueCount = torch.arange(n_components)\n",
        "plt.plot(eigenvalueCount, ratio_cumsum[:n_components])\n",
        "plt.title('Compactness')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#build random forest\n",
        "estimator = RandomForestClassifier(n_estimators=150, max_depth=15, max_features=150)\n",
        "estimator.fit(X_transformed, y_train) #expects X as [n_samples, n_features]\n",
        "\n",
        "predictions = torch.tensor(estimator.predict(X_test_transformed))\n",
        "correct = predictions==y_test\n",
        "total_test = len(X_test_transformed)\n",
        "#print(\"Gnd Truth:\", y_test)\n",
        "print(\"Total Testing\", total_test)\n",
        "print(\"Predictions\", predictions)\n",
        "print(\"Which Correct:\",correct)\n",
        "print(\"Total Correct:\",torch.sum(correct))\n",
        "print(\"Accuracy:\",torch.sum(correct)/total_test)\n",
        "print(classification_report(y_test, predictions, target_names=target_names, zero_division=1))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "G_xEW0nDj0iT",
        "outputId": "b24ce3e5-f7ce-451e-9fd5-8b6290856e8f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([966])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Testing 322\n",
            "Predictions tensor([3, 3, 6, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 4, 1, 1, 3, 3,\n",
            "        1, 1, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 1, 3, 3, 3,\n",
            "        4, 3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 6, 1, 3, 1, 3, 5, 3, 4, 1, 3, 6, 1,\n",
            "        3, 3, 3, 2, 3, 6, 3, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 3, 1, 1, 3,\n",
            "        1, 1, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 6, 3, 3, 3, 1, 4, 1, 3,\n",
            "        1, 3, 3, 1, 3, 4, 5, 3, 1, 3, 6, 6, 3, 3, 3, 4, 3, 3, 1, 3, 3, 3, 3, 1,\n",
            "        3, 3, 3, 3, 6, 1, 4, 3, 1, 3, 3, 3, 6, 3, 3, 3, 6, 5, 5, 1, 3, 3, 5, 1,\n",
            "        3, 3, 1, 3, 3, 1, 5, 3, 3, 3, 3, 5, 3, 3, 3, 1, 3, 3, 3, 3, 1, 2, 6, 3,\n",
            "        2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 5, 1, 3, 2, 4, 3, 3, 5, 3, 3, 3, 4,\n",
            "        3, 2, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 1, 6, 3, 3, 3,\n",
            "        3, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 2, 3, 4, 3, 4, 3, 1, 3, 3, 3, 3, 3, 1,\n",
            "        3, 6, 6, 1, 6, 1, 6, 3, 3, 3, 6, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3,\n",
            "        3, 4, 3, 3, 5, 3, 3, 3, 4, 5, 3, 3, 3, 6, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3,\n",
            "        1, 3, 3, 3, 3, 1, 3, 3, 4, 3])\n",
            "Which Correct: tensor([ True,  True,  True,  True,  True,  True, False,  True,  True,  True,\n",
            "         True,  True,  True, False,  True,  True, False,  True,  True,  True,\n",
            "         True, False,  True, False, False,  True, False,  True,  True,  True,\n",
            "        False,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
            "         True,  True,  True,  True,  True, False, False,  True, False,  True,\n",
            "         True,  True, False,  True, False,  True,  True,  True, False,  True,\n",
            "        False,  True, False, False,  True,  True, False,  True,  True,  True,\n",
            "        False, False,  True,  True,  True,  True, False,  True, False, False,\n",
            "        False, False, False,  True,  True,  True,  True,  True,  True,  True,\n",
            "        False,  True,  True, False,  True, False,  True,  True, False,  True,\n",
            "         True, False,  True, False,  True,  True,  True,  True,  True,  True,\n",
            "        False,  True, False,  True,  True, False, False,  True,  True,  True,\n",
            "         True,  True,  True, False,  True,  True, False,  True,  True, False,\n",
            "         True,  True, False, False, False,  True,  True,  True,  True, False,\n",
            "        False, False, False,  True,  True, False, False,  True,  True,  True,\n",
            "        False, False,  True,  True,  True,  True,  True, False, False,  True,\n",
            "         True,  True,  True,  True,  True, False,  True,  True, False,  True,\n",
            "         True, False, False,  True,  True, False,  True, False, False, False,\n",
            "         True,  True,  True, False,  True,  True,  True,  True, False,  True,\n",
            "        False, False, False, False, False,  True,  True, False,  True, False,\n",
            "         True, False, False,  True, False, False, False,  True, False, False,\n",
            "         True,  True,  True,  True,  True, False,  True, False, False, False,\n",
            "         True,  True, False,  True, False,  True,  True,  True,  True,  True,\n",
            "         True,  True,  True, False,  True,  True,  True,  True,  True,  True,\n",
            "         True,  True, False, False,  True, False,  True,  True,  True, False,\n",
            "         True, False, False, False,  True,  True, False,  True, False, False,\n",
            "        False,  True, False,  True,  True, False, False,  True, False, False,\n",
            "        False, False,  True,  True,  True,  True, False,  True,  True,  True,\n",
            "         True, False,  True,  True, False, False,  True,  True,  True,  True,\n",
            "        False,  True,  True, False,  True, False,  True, False,  True, False,\n",
            "         True,  True, False, False,  True,  True, False,  True,  True,  True,\n",
            "         True,  True,  True, False,  True,  True, False,  True,  True,  True,\n",
            "        False, False])\n",
            "Total Correct: tensor(205)\n",
            "Accuracy: tensor(0.6366)\n",
            "                   precision    recall  f1-score   support\n",
            "\n",
            "     Ariel Sharon       1.00      0.00      0.00        13\n",
            "     Colin Powell       0.75      0.63      0.68        60\n",
            "  Donald Rumsfeld       0.62      0.19      0.29        27\n",
            "    George W Bush       0.63      0.91      0.75       146\n",
            "Gerhard Schroeder       0.50      0.40      0.44        25\n",
            "      Hugo Chavez       0.58      0.47      0.52        15\n",
            "       Tony Blair       0.57      0.33      0.42        36\n",
            "\n",
            "         accuracy                           0.64       322\n",
            "        macro avg       0.67      0.42      0.44       322\n",
            "     weighted avg       0.65      0.64      0.60       322\n",
            "\n"
          ]
        }
      ]
    }
  ]
}