IG_segmentation.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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загружаем исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.loadtxt('train.csv', delimiter=',', skiprows=1)\n",
    "test = np.loadtxt('test.csv', delimiter=',', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [7., 0., 0., ..., 0., 0., 0.],\n",
       "       [6., 0., 0., ..., 0., 0., 0.],\n",
       "       [9., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28000, 784)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализируем исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(train_img[0:8], 1):\n",
    "    subplot = fig.add_subplot(1, 8, 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": 10,
   "metadata": {},
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 28, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sobel_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_g, train_theta = cv2.cartToPolar(train_sobel_x, train_sobel_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_g, test_theta = cv2.cartToPolar(test_sobel_x, test_sobel_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "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": 15,
   "metadata": {},
   "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": 16,
   "metadata": {},
   "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": 17,
   "metadata": {},
   "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": 18,
   "metadata": {},
   "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": 19,
   "metadata": {},
   "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": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rfc.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6452380952380953\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy: %s' % accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.43      0.40      0.41       816\n",
      "         1.0       0.97      0.96      0.97       909\n",
      "         2.0       0.48      0.47      0.47       846\n",
      "         3.0       0.65      0.69      0.67       937\n",
      "         4.0       0.71      0.70      0.71       839\n",
      "         5.0       0.63      0.65      0.64       702\n",
      "         6.0       0.48      0.47      0.47       785\n",
      "         7.0       0.86      0.79      0.83       893\n",
      "         8.0       0.51      0.57      0.54       835\n",
      "         9.0       0.67      0.69      0.68       838\n",
      "\n",
      "    accuracy                           0.65      8400\n",
      "   macro avg       0.64      0.64      0.64      8400\n",
      "weighted avg       0.65      0.65      0.65      8400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[324   5  67  27  36  18 131  12 164  32]\n",
      " [  1 874   2   0  21   0   0   5   1   5]\n",
      " [ 53   0 396 127   5 103  48  17  82  15]\n",
      " [ 23   0  89 648   0  85   9   3  72   8]\n",
      " [ 25  12  13   2 590   1  78  30   6  82]\n",
      " [ 13   0  83 109   0 456   6   6  26   3]\n",
      " [132   1  63  12  64   6 366  16  70  55]\n",
      " [  9   4  33   6  48  12  18 707   5  51]\n",
      " [132   0  48  45   8  43  44   5 480  30]\n",
      " [ 44   1  29  14  55   5  65  18  28 579]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Предсказания на тестовых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = rfc.predict(test_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализируем предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(test_img[0:20], 1):\n",
    "    subplot = fig.add_subplot(1, 20, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % test_pred[i - 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Готовим файл для отправки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('submit.csv', 'w') as dst:\n",
    "    dst.write('ImageId,Label\\n')\n",
    "    for i, p in enumerate(test_pred, 1):\n",
    "        if int(p) == 0:\n",
    "            sec_n = 0\n",
    "        else:\n",
    "            sec_n = int(p)\n",
    "        dst.write('%s,%s\\n' % (i, sec_n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}