002-digit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digit Recognizer\n",
    "https://www.kaggle.com/c/digit-recognizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загружаем исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = np.loadtxt('./data/digit/train.csv', delimiter=',', skiprows=1)\n",
    "test = np.loadtxt('./data/digit/test.csv', delimiter=',', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# сохраняем разметку в отдельную переменную\n",
    "train_label = train[:, 0]\n",
    "# приводим размерность к удобному для обаботки виду\n",
    "train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))\n",
    "test_img = np.resize(test, (test.shape[0], 28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализируем исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2QEAAACQS6kvPd1e9ejRw2XN73SbdKFWkvXr17ss6WIzxOPAAw8s+3vXrVvnsq1bt1Yy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\nAACAXGKyAwAAACCX/h86C7r3mMzwbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206692d2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(train_img[0:5], 1):\n",
    "    subplot = fig.add_subplot(1, 7, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % train_label[i - 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вычисляем X и Y составляющие градиента с помощью оператора Собеля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_sobel_x = np.zeros_like(train_img)\n",
    "train_sobel_y = np.zeros_like(train_img)\n",
    "for i in range(len(train_img)):\n",
    "    train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)\n",
    "    train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_sobel_x = np.zeros_like(test_img)\n",
    "test_sobel_y = np.zeros_like(test_img)\n",
    "for i in range(len(test_img)):\n",
    "    test_sobel_x[i] = cv2.Sobel(test_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)\n",
    "    test_sobel_y[i] = cv2.Sobel(test_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вычисляем угол и длину вектора градиента"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_g, train_theta = cv2.cartToPolar(train_sobel_x, train_sobel_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_g, test_theta = cv2.cartToPolar(test_sobel_x, test_sobel_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VFBERkX0yZswY1qxZQ1VVFcXFxVx33XXpu7K2Lw6kZoSY2VIzW7p58+Ym91tE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\nAAAAoAImTQAAAABQAZMmAAAAAKjg/wEil5I4yqZ7wAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20652ec3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(train_g[:5], 1):\n",
    "    subplot = fig.add_subplot(1, 7, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % train_label[i - 1]);\n",
    "    subplot = fig.add_subplot(3, 7, i)\n",
    "    plt.hist(train_theta[i - 1].flatten(),\n",
    "             bins=16, weights=train_g[i - 1].flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вычисляем гистограммы градиентов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Гистограммы вычисляются с учетом длины вектора градиента\n",
    "train_hist = np.zeros((len(train_img), 16))\n",
    "for i in range(len(train_img)):\n",
    "    hist, borders = np.histogram(train_theta[i],\n",
    "                                 bins=16,\n",
    "                                 range=(0., 2. * np.pi),\n",
    "                                 weights=train_g[i])\n",
    "    train_hist[i] = hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_hist = np.zeros((len(test_img), 16))\n",
    "for i in range(len(test_img)):\n",
    "    hist, borders = np.histogram(test_theta[i],\n",
    "                                 bins=16,\n",
    "                                 range=(0., 2. * np.pi),\n",
    "                                 weights=test_g[i])\n",
    "    test_hist[i] = hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нормируем вектор гистограммы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# По умолчанию используется L2 норма\n",
    "train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_hist = test_hist / np.linalg.norm(test_hist, axis=1)[:, None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разбиваем выборку на обучение и валидацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y_train, y_val, x_train, x_val = train_test_split(\n",
    "    train_label, train_hist, test_size=0.2,\n",
    "    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вычисляем центроиды каждого из 10 классов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "centroids = np.zeros((10, train_hist.shape[1]), dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    centroids[i] = np.mean(x_train[y_train == i], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Находим ближайший центройд для валидации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_val = np.zeros_like(y_val)\n",
    "for i in range(len(y_val)):\n",
    "    distances = np.linalg.norm(centroids - x_val[i], axis=1)\n",
    "    pred_val[i] = np.argmin(distances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оцениваем качество решение на валидационной выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.427023809524\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy: %s' % accuracy_score(y_val, pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.34      0.12      0.18       816\n",
      "        1.0       0.59      0.76      0.66       909\n",
      "        2.0       0.33      0.29      0.31       846\n",
      "        3.0       0.55      0.49      0.51       937\n",
      "        4.0       0.33      0.31      0.32       839\n",
      "        5.0       0.46      0.67      0.55       702\n",
      "        6.0       0.24      0.45      0.31       785\n",
      "        7.0       0.73      0.41      0.53       893\n",
      "        8.0       0.39      0.49      0.44       835\n",
      "        9.0       0.48      0.27      0.34       838\n",
      "\n",
      "avg / total       0.45      0.43      0.42      8400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_val, pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[102  24  82  30 106  18 221   7 179  47]\n",
      " [  0 692   2   0  44   1 139  17  13   1]\n",
      " [ 21   0 245 158  12 203  54  16 119  18]\n",
      " [ 14   0 113 456   3 235   7   2  98   9]\n",
      " [ 11 248   5   2 260   0 205  43  37  28]\n",
      " [  3   0  84  97   5 472   3   4  33   1]\n",
      " [ 53  26  66  11 116   4 355  13  76  65]\n",
      " [  4 113  38  16  66  41 189 368  15  43]\n",
      " [ 81   6  68  44  36  44 107   3 411  35]\n",
      " [ 11  73  31  20 149  11 222  30  65 226]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_val, pred_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Предсказания на тестовыйх данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_test = np.zeros(len(test_img), np.uint8)\n",
    "for i in range(len(test_img)):\n",
    "    distances = np.linalg.norm(centroids - test_hist[i], axis=1)\n",
    "    pred_test[i] = np.argmin(distances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализируем предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s6CJi1K5zdsysn6QDJD1dis4zs3lmNsXMts94bECr6CJiQh8RC7qIWNBFxCL1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\nAACF9P8AfHcA56p8Uo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206692d2a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(test_img[0:5], 1):\n",
    "    subplot = fig.add_subplot(1, 7, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % pred_test[i - 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Готовим файл для отправки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('submit.txt', 'w') as dst:\n",
    "    dst.write('ImageId,Label\\n')\n",
    "    for i, p in enumerate(pred_test, 1):\n",
    "        dst.write('%s,%s\\n' % (i, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your submission scored 0.41700"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Улучшим результаты предобработкой изображений "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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geYfMvC0zf1Fs3g6s6m2ZkqTZKBPuK4FHm7Yni7F23gJ8qdUdEXFpRGyNiK27d+8uX6Uk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\nuyTVkOEuSTX0/+vFKFaJNLMhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2061fdddb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(train[:, 1:])\n",
    "\n",
    "S = pca.explained_variance_ratio_\n",
    "S_cumsum = np.cumsum(S) / np.sum(S)\n",
    "plt.plot(S_cumsum, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_thr = 0.95 \n",
    "n_comp = np.argmax(np.where(S_cumsum > S_thr, 1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.09748938  0.07160266  0.06145903  0.05379302  0.04894262  0.04303214\n",
      "  0.03277051  0.02892103  0.02766902  0.02348871  0.02099325  0.02059001\n",
      "  0.01702553  0.01692787  0.01581126  0.0148324   0.01319688  0.01282727\n",
      "  0.01187976  0.01152755  0.01072191  0.01015199  0.00964902  0.00912846\n",
      "  0.00887641  0.00838766  0.00811856  0.00777406  0.00740635  0.00686661\n",
      "  0.00657982  0.00638799  0.00599367  0.00588913  0.00564335  0.00540965\n",
      "  0.00509215  0.00487501  0.00475554  0.00466535  0.00452939  0.00444951\n",
      "  0.00418233  0.00397389  0.00384322  0.00374591  0.00360729  0.00348217\n",
      "  0.0033532   0.00319592]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=50)\n",
    "pca.fit(train[:, 1:])\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca = np.dot(train[:, 1:], pca.components_.T)\n",
    "X_test_pca = np.dot(test, pca.components_.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(X_train_pca, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('submit_new.txt', 'w') as f:\n",
    "    f.write('ImageId,Label\\n')\n",
    "    for i, p in enumerate(predicted, 1):\n",
    "        f.write('%s,%s\\n' % (i, int(p)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результат на kaggle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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scrHBchdbuEyZAC+7CQhwn43ZC5fg42keMDa3EzIetl+4wdfNDUsdJ2Pq+AmY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\nZib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t07L/gC+DRHM820wA+uVFG/uPDi0ImRl8Uuo6dDr0tXaUsDblb1gcBlflwstMNm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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='1.PNG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}