UAS_4212101091.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f4ca2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skimage.feature import hog\n",
    "from sklearn import datasets\n",
    "from mlxtend.data import loadlocal_mnist\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "from mlxtend.plotting import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b4487a",
   "metadata": {},
   "source": [
    "1.  Load Image Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce3457dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images, train_labels = loadlocal_mnist(images_path='train-images.idx3-ubyte',\n",
    "                                             labels_path='train-labels.idx1-ubyte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5101cca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images, test_labels = loadlocal_mnist(images_path='t10k-images.idx3-ubyte',\n",
    "                                            labels_path='t10k-labels.idx1-ubyte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08f587a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23e8ba26e50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[5].reshape(28,28), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "763affb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16fc0c3",
   "metadata": {},
   "source": [
    "Extract HOG feature from image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "beb739a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_reshaped = train_images.reshape(-1, 28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "edcaefcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_hog = []\n",
    "for img in X_train_reshaped:\n",
    "    features, _ = hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualize=True)\n",
    "    X_train_hog.append(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82e0af88",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_hog = np.array(X_train_hog)\n",
    "y_train = np.array(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f919e874",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_reshaped = test_images.reshape(-1, 28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5b4ede5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_hog = []\n",
    "for img in X_test_reshaped:\n",
    "    features, _ = hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualize=True)\n",
    "    X_test_hog.append(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3524a03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_hog = np.array(X_test_hog)\n",
    "y_test = np.array(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34912479",
   "metadata": {},
   "source": [
    "Training Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24d7c3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC()\n",
    "svm.fit(X_train_hog, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b63a8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm.predict(X_test_hog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f8ad18",
   "metadata": {},
   "source": [
    "Predict Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "209ff94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x300 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predict(images, targets, sample_size=7, title_text='Digit {}' ):\n",
    "    nsamples = min(sample_size, len(images))\n",
    "    rand_idx = np.random.choice(len(images), nsamples)\n",
    "    images_and_labels = list(zip(images[rand_idx], targets[rand_idx]))\n",
    "\n",
    "    num_rows = 1\n",
    "    img = plt.figure(1, figsize=(15, 3), dpi=100)\n",
    "    for index, (image, label) in enumerate(images_and_labels):\n",
    "        plt.subplot(1, nsamples, index + 1)\n",
    "        plt.axis('off')\n",
    "        plt.imshow(image.reshape(28, 28), cmap='gray')\n",
    "        plt.title(title_text.format(label))\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "predict(test_images, y_pred, sample_size=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0552c21f",
   "metadata": {},
   "source": [
    "Evaluation Metrics A.Confussion metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9df4bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "798f7ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 973,    0,    1,    0,    0,    0,    5,    1,    0,    0],\n",
       "       [   1, 1124,    6,    0,    2,    0,    1,    1,    0,    0],\n",
       "       [   2,    0, 1011,    6,    2,    0,    1,    8,    2,    0],\n",
       "       [   2,    2,    8,  979,    0,    2,    0,    4,    6,    7],\n",
       "       [   4,    1,    0,    0,  950,    0,    9,    2,    2,   14],\n",
       "       [   3,    0,    0,   10,    0,  865,    7,    1,    5,    1],\n",
       "       [   6,    2,    1,    0,    2,    4,  940,    0,    2,    1],\n",
       "       [   0,    3,   13,    3,   11,    0,    0,  981,    4,   13],\n",
       "       [   6,    1,    4,   12,    2,    4,    1,    3,  934,    7],\n",
       "       [   2,    3,    0,    5,   11,    4,    0,    4,   13,  967]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat = metrics.confusion_matrix(y_test, y_pred)\n",
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "060d6019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_confusion_matrix(conf_mat=conf_mat, class_names=class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73449208",
   "metadata": {},
   "source": [
    "B. Precission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9166c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97397397, 0.98943662, 0.9683908 , 0.96453202, 0.96938776,\n",
       "       0.98407281, 0.97510373, 0.9761194 , 0.96487603, 0.95742574])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision = metrics.precision_score(y_test, y_pred, average=None)\n",
    "precision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fc7079",
   "metadata": {},
   "source": [
    "C. Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ca6a214f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9724"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  }
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