final_class.ipynb

{
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.sparse as sprse\n",
    "from sklearn.metrics import accuracy_score\n",
    "from traitlets.config.manager import BaseJSONConfigManager\n",
    "cm = BaseJSONConfigManager()\n",
    "cm.update('livereveal', {\n",
    "    'theme': 'sky',\n",
    "    'transition': 'zoom',\n",
    "    'start_slideshow_at': 'selected',\n",
    "    'scroll': True\n",
    "})\n",
    "from final_class_ import AggregationMethods as AM\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# <font color='black'> Методы и алгоритмы агрегации оценок исполнителей на краудсорсинговой платформе Toloka </font>\n",
    "\n",
    "# <font color='black'> Александр Широков </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## [Toloka](https://toloka.yandex.ru/)\n",
    "\n",
    "`Толока` — крупнейший источник размеченных людьми данных для задач машинного обучения. \n",
    "\n",
    "Для любых исследований и экспериментов, связанных с машинным обучением, необходимы большие объёмы качественных данных.\n",
    "\n",
    "Раньше многие компании собирали такие оценки исключительно с помощью специально обученных сотрудников — асессоров. Но со временем задач в области машинного обучения стало слишком много, а сами задачи в массе своей перестали требовать особых знаний и опыта. Так появился спрос на помощь «толпы» (crowd). Но самостоятельно найти большое количество случайных исполнителей и работать с ними не каждому под силу. Краудсорсинговые платформы решают эту проблему.\n",
    "\n",
    "Аналог `Amazon Mechanical Turk`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## [Toloka Aggregation Relevance 2](https://toloka.ai/ru/datasets?turbo=true)\n",
    "\n",
    "Исполнителю предлагался запрос и регион пользователя, который его задавал, скриншот документа и ссылка на него, возможность воспользоваться поисковыми системами и варианты ответов: \n",
    "\n",
    "Переформулировать\n",
    "\n",
    "- `Релевантен` - **1**\n",
    "- `Нерелевантен` - **0**\n",
    "- Не отображается (не записывалось в датасет (набор данных))\n",
    "\n",
    "[*Статья с Хабра*](https://habr.com/en/company/yandex/blog/458326/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worker_id</th>\n",
       "      <th>task_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>w851</td>\n",
       "      <td>t30685</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>w6991</td>\n",
       "      <td>t30008</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>w2596</td>\n",
       "      <td>t36316</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>w5507</td>\n",
       "      <td>t15145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>w2982</td>\n",
       "      <td>t44785</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  worker_id task_id  label\n",
       "0      w851  t30685      1\n",
       "1     w6991  t30008      0\n",
       "2     w2596  t36316      0\n",
       "3     w5507  t15145      1\n",
       "4     w2982  t44785      1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from final_class_ import AggregationMethods as AM\n",
    "toloka_2 = AM(J=2, print_preview=False)\n",
    "(\n",
    "    toloka_2.preprocess_crowd_dataset(return_input_data=True)\n",
    "    .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Оценка точности\n",
    "\n",
    "Основной показатель качества — точность агрегированных ответов, оцениваемая на основе контрольных заданий (`голденсетов`)  набор заданий, для которых известен правильный ответ. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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       "      <th>1</th>\n",
       "      <td>t33578</td>\n",
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       "      <th>2</th>\n",
       "      <td>t22462</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t52093</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "  task_id  label\n",
       "0  t30006      0\n",
       "1  t33578      0\n",
       "2  t22462      1\n",
       "3  t52093      0\n",
       "4  t26935      0"
      ]
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "(\n",
    "    toloka_2.preprocess_golden_dataset(return_input_data=True)\n",
    "    .head()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## [Toloka Aggregation Relevance 5](https://toloka.ai/ru/datasets?turbo=true)\n",
    "\n",
    "Этот (датасет) такой же, как предыдущий, только оценки здесь собирались не по бинарной, а по пятибалльной шкале в проекте «Релевантность (`5` градаций)»:\n",
    "\n",
    "- `Витальный` - **5**\n",
    "- `Полезный` - **4**\n",
    "- `+Релевантный` - **3**\n",
    "- `-Релевантный` - **2**\n",
    "- `Нерелевантный` - **1**\n",
    "\n",
    "Отличие: некоторые задания в (датасете) имеют не один, а `несколько` правильных ответов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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       "      <th>0</th>\n",
       "      <td>w360</td>\n",
       "      <td>t186849</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>w43</td>\n",
       "      <td>t174611</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>w1040</td>\n",
       "      <td>t198665</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>w618</td>\n",
       "      <td>t63171</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "  worker_id  task_id  label\n",
       "0      w360  t186849      4\n",
       "1       w43  t174611      5\n",
       "2     w1040  t198665      3\n",
       "3      w618   t63171      5\n",
       "4       w98  t141574      5"
      ]
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   ],
   "source": [
    "toloka_5 = AM(J=5)\n",
    "(\n",
    "    toloka_5.preprocess_crowd_dataset(return_input_data=True)\n",
    "    .head()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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     "slide_type": "subslide"
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       "      <td>t342646</td>\n",
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       "      <td>t189317</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>t48377</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t73830</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>t60415</td>\n",
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       "   task_id  label\n",
       "0  t342646      2\n",
       "1  t189317      4\n",
       "2   t48377      5\n",
       "3   t73830      3\n",
       "4   t60415      2"
      ]
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     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "(\n",
    "    toloka_5.preprocess_golden_dataset(return_input_data=True)\n",
    "    .head()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Постановка задачи\n",
    "\n",
    "Пусть имеется $N$ сайтов $x_1, \\ldots, x_n$, каждый из которых принадлежит одному из $J$ классов ${1, \\ldots, J}$. \n",
    "\n",
    "Имеется $K$ толокеров, каждый из которых некоторым (необязательно всем) сайтам поставил в соответствие определённый класс. На входе имеем матрицу меток:\n",
    "\n",
    "$$\\large Y \\in \\mathbb{R}^{N \\times K}$$\n",
    "\n",
    "$$\\large y_{nk} \\in \\{0, \\ldots, J\\}$$\n",
    "\n",
    "где класс $0$ означает, что соответствующий толокер не проставил метку для сайта $n$:\n",
    "\n",
    "**Цель**: имея ответы толокеров для каждого примера $i$ предсказать правильную метку $y_i$. Предполагается, что данная метка существует и единственна, то есть необходимо минимизировать некоторый эмпирический риск:\n",
    "\n",
    "$$\\large F = \\frac{1}{N}\\sum\\limits_{i=1}^N \\mathrm{I}(y_i \\neq \\hat{y}_i) \\to \\min$$\n",
    "\n",
    "где $\\hat{y}_i$ - предсказание метки, а $I(x)$ - индикаторная функция, возвращающая $1$, если метка истинная и $0$ - в противном случае."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Toloka Aggregation 2: `InFo`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                1. Количество сайтов: \u001b[1m475536\u001b[0;0m.\n",
      "                2. Всего размеченных сайтов: \u001b[1m99319\u001b[0;0m.\n",
      "                    - среднее количество толокеров на разметку сайта:  \u001b[1m4.79\u001b[0;0m;\n",
      "                    - std: \u001b[1m0.69\u001b[0;0m;\n",
      "                    - медиана:\u001b[1m 5.0\u001b[0;0m.\n",
      "                3. Количестов толокеров, выполнявших задание: \u001b[1m7138\u001b[0;0m\n",
      "                    - в среднем толокеры выполняли заданий: \u001b[1m66.62\u001b[0;0m;\n",
      "                    - std: \u001b[1m85.45\u001b[0;0m; \n",
      "                    - медиана: \u001b[1m40.0\u001b[0;0m. \n",
      "                4. Распределение классов на crowd датасете:\n",
      "                    1: \u001b[1m0.58\u001b[0;0m\n",
      "                    0: \u001b[1m0.42\u001b[0;0m\n",
      "                5. Количество сайтов golden labels: \u001b[1m10079\u001b[0;0m\n",
      "                6. Распределение классов на golden датасете:\n",
      "                    1: \u001b[1m0.4\u001b[0;0m\n",
      "                    0: \u001b[1m0.6\u001b[0;0m\n",
      "                7. Accuracy толокеров:\n",
      "                    - в среднем: \u001b[1m63.3\u001b[0;0m\n",
      "                    - медиана: \u001b[1m66.7\u001b[0;0m\n",
      "                8. Средняя accuracy размеченных меток(в сравнении с golden set): \u001b[1m69.94%\u001b[0;0m\n",
      "                \n"
     ]
    }
   ],
   "source": [
    "toloka_2.visual_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shiro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
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N6PG1iPhVSmmDfyqR3ZxiaMpG12jvbYqiNnuRXezUF3q75vYPZONGF/sU2UWQXyX7enr9exYRbwLaUkpLSywHWVnHS8DPuhNIRPwLWT3zT1NKD3Vn2YJ1vA14MnW8qQdk5R2w4Vfl6/Llti081imlJyNiEXBYRLyxPVHMe5hL1ux2otdr2sk+CH4lIhpSPgReXp7xRbLyjGvaG+Z1+28Anin1nkTEDmQfXu5IBUOZlelcsvKEr1SwD125HNgJeDql9EAX7e4kqzH+QkT8JKW0QRlFPhLN1imlp3vpeJb8fQdOp+/uPtyrNdJ5D/e64l7+vHTnAODawm8l8vY7AqtTPqJMSmldRMwFjoiIt6aU7srbbkn2oewBHLFDNchEWuqBvOdzWhntnouIY4HfAfdHxCVkX+WOIOvNOoIsSV4I3AvcA5yaJyz3k10c9XmyXpt39vJu9HrNbUqpmWz/NlAw/N2NKaXi3qV7yUbkGFNiuXeTjdF9Reo4FFhhu4vJEpK/kn1L8F7gM2RDqH2pRPsEPJRS6rDNIocA50bEdWTlG0+QXVz6AbKxbx8nu6Cq3SJgCvDDiGgfTeHWvMfxy2TH+c8RMYtXh0sr++9xH9W0/w/Zh5zLIuJ7ZF+jf5psyLF/TRsOobgfWY94Z72Vx5HtT5e90RFxe76eB8hqYw8nK5O6IKU0p6jtB9q3mVIqtc0u5R90p5XRLkXEMWQX7/0t/129h+xCt/Fkv6unkY18AT0/nr8lS7qviYgLyD4sfojsm6lyv63plj6okT6IbDjQuWQfkF8mO0c+S7YPjUXtP05Wt/6fbHhMTiMbreS6/GLNtWR3NtwZ+Gj+7YVUU0ykpX6SUmrK6yG/RvYPZjRZreVSsiTsb3m7V/KenO+QJSTDyXrIjiMr+ej1RHoT0OlFhkUWk1389wmyMpBmsou4phdf6BQRW+U/llN3eSVZoncIcDLZBaAvk9V/Tge+XVSH/EuyW18fRZacbkZWQ/1gSumWiPgQWeL6NbL69yvJRrH4exmx9In84tQD8rgmk91QZAlwVErpV91c3efIPshsbNSKW8g+iOxC9n7+FTg6pVSqTKo7x6tHUkp/jYi3kyV2/0I2usizZMd7DgU35unp8Uwp/TkiPgF8HfgvsvftBuBAsuEyNwX3k5VdfIzsQsxhZN9I/ZhsyLqyjll+AWr7Ofg1st/hO4D6MspspKoIP+BJGozyko/fAx9MKXWn3lhVkPeSTwLGF5dGSVK1eItwSYPVROAPJtGbjInAN02iJdUSe6QlSZKkCtgjLUmSJFXARFqSJEmqgIm0JEmSVIFNdvi7UaNGpTFjxlQ7DEmSJA1gt99+++qU0uhS8zbZRHrMmDHcdpt3C5UkSVLfiYhO74praYckSZJUARNpSZIkqQIm0pIkSVIFTKQlSZKkCphIS5IkSRUwkZYkSZIqYCItSZIkVWCTHUe6XGvXrmXlypW0tbVVOxQNIEOHDqWuro7Ro0dTV1dX7XAkSVIVDOhEeu3atTz55JPsvPPObLHFFkREtUPSAJBS4uWXX2bdunU8/PDDbL/99myzzTbVDkuSJPWzAZ1Ir1y5kp133pnXvva11Q5FA0hEMGzYMEaOHMnmm2/OE088YSItSdIgNKBrpNva2thiiy2qHYYGsC222IIXX3yx2mFIkqQqGNCJNGA5h/qU55ckSYPXgC7tkCRJUu1paGjodN7kyZOpr68HYN68ecyaNWv9vLlz5/Z5bN1hIi1JkqR+19JaekS1JaueZesVT6//ub3dyLph/RZbuQZlIt3Q0MD06dMZP348ADNnzqSpqalk23HjxjFjxowNlu1MV5+gihV+ompsbGTp0qU19ylLkiSpL8ydO5eb82S5KxMOOpgJBx3Mqccf3Q9Rdd+Ar5HuzP/8+GJuXvE0N694mmVrnqOlta3k47F1revb3bzi6U7btbS2sWTVs+vbtX+C6uxRuM7b7rmvR/uycOFCIoLvfOc7vfTudM8dd9zBYYcdxnbbbUddXR1vectbmDFjBq+88kqHtm1tbZx77rm8+c1vZvPNN2e77bbjE5/4BPfd1/E9ePzxxznjjDOor69n9OjRRASTJk0qGcPKlSs5/vjj2Weffdh2222pq6tj/PjxnHDCCTQ3N5dc5sYbb+SQQw5hm2224bWvfS377rsvl156aY/eC0mSNHgMyh5pgPvuunP9z0dOOpEjJ51Y1nLnzb6srHbtn6AGuj/+8Y98+MMfZptttuGUU05h9OjRXH/99UydOpUlS5ZwwQUXrG+bUuKwww7j2muv5fDDD+eLX/wiq1at4oc//CH7778/f/7zn9lzzz3Xt7///vs599xzef3rX8+73vUurr322k7jaGlp4R//+Acf/vCH2XXXXdliiy144IEHuOSSS/j1r3/NokWLNlj3L3/5Sz7zmc8wduxYTjvtNIYPH85VV13Fcccdx4oVKzj99NP75g2TJEkDxqBNpNU7TjnlFDbbbDNuueUWdtttNwBOPvlkPv/5z3PBBRdw7LHH8t73vheA3//+91x77bWcdNJJ/OQnP1m/jmOOOYa99tqLU045hRtuuGH99He+852sXLmS0aNHs3r1akaPHt1pHHvssQd//vOfO0w/8sgj2W+//Zg5cyY//OEPgaxX/JRTTuF1r3sdt99+OyNGjABgypQpHHrooUybNo2jjjpq/f5IkqTe1djYyGPrWmmcdm61Q+mRQVvaoZ5raWnhrrvu4v3vf3+HpLO9BGP27Nnrpy1YsACA448/foO2u+22G+973/uYP38+Dz/88PrpW221VZfJczl23XXX9bG2u/vuu1m9ejWHH374+iQasqHsjj32WNra2vjFL37Ro+1KkqTOLV26lMceWl52+/NmX1aT15LZIz2I/POf/+Tppzde2N9u2223ZbPNOv+s1X4jklJ3jmyftmjRom61v/XWW3nDG95QdozF2traeOaZZ2hra6O5uZlp06YBcOihh1YctyRJUikm0oPIww8/zNixY8tu/+CDDzJmzJhO52+//faMGjWKRYsW8cILL2xwF8n23udHHnlk/bS3vOUtQHaR3z777LN++vPPP8+tt97aoX0lmpqaNhhZZfvtt+e73/0uxxxzzPppe+yxB0OGDGHhwoWklDa4qUqpuCVJkkoxka4B+x14ELuNGN7n29lhhx24/vrru9W+KxHB1KlTOeOMMzjiiCM4++yzGTVqFDfccANnnXUWQ4cO5fnnn1/f/rOf/SznnHMO3/jGNxg+fDiHHHIIq1ev5qyzzmL16tUAG7SvxIQJE7j++ut54YUXWLJkCZdffjktLS28/PLLDB2ane4jR47kc5/7HBdeeCGTJk3iy1/+8vqLDS+88MJeiUOSJPWeGdNO54ot6zYYkrgWDMpEevr06dzx+Jpqh7HekZNO5D27bNvn26mrq+OQQw7p1XV+7Wtf4/nnn+e73/0u++23HwBbbrkl3/ve9zjjjDN4+eWX17cdOXIkN9xwA8ceeywnnXTS+ukHHnggX/3qVznnnHPYeuutexTPqFGj1u9jQ0MDxxxzDPvssw8rV67c4ALH888/n4jgkksuWT/k3ejRo7nooos4+uijexyHJEnqPY89tJwXavCGLIPyYsPx48ezy9jBNyLDK6+8whNPPFH2o9Q40MU222wzzjnnHFavXs0tt9zCzTffzJNPPsmnP/1pVq9ezZve9KYN2u+9997ceeedPPDAA9x000088MADLFy4cH3dcnH7ntppp5045JBDuPjii9dvA7IPFT/5yU9YuXIlf/rTn1i8eDErVqzgrW99a5/EIUmSBp5B2SNda1Y8uIzm1qfX32mxrzzyyCO9WiNdaPjw4UyYMGH96yuvvJKU0gYX+RUaP378Bvt77bXXsvXWW3PAAQeUHV+5XnjhBV555RXWrl3bYRSQkSNHbrDNa665BqDTuCVJUs9NnDiRZWueq3YYPTYoE+mZM2eybM1zZd+Epa+df/aZjKwb1ufDuvR2jXRnnnrqKU4//XRGjRrFF77whY22/8EPfsDdd9/NWWedxfDhldWKP/nkk2y//fYdpi9ZsoT58+czbty4jQ6l9+CDD/Ktb32LN77xjXzyk5+sKA5JkrRxU6ZMKesW4bVuUCbSTU1NtLS21Uwi3V/6okb6mmuu4dvf/jYf+tCH2GGHHXjooYe46KKLaGlp4eqrr2bUqFEbtD/00EPZbbfd2HPPPYkIrrvuOn73u9/x0Y9+lDPOOKPD+s855xzg1Yv//va3v62f9v73v5/3v//9APz3f/83119/PR/96EcZM2YMKSXuvvtufvazn9HW1sasWbM2WO9PfvIT/vCHP/C+972PUaNGcd9993HhhRcydOhQfv3rX7P55pv36vskSZIGnkGZSLc79fijS04/4tgT1t/ee9GC+Vx16cWdrqPwluEzpp3e6eDi+x140PrEfcWDyzj/7DMrjLqjlBIAQ4YM6bV1lmvMmDHU1dXxgx/8gKeeeopRo0Zx8MEHc+aZZ7LHHnt0aL///vvzq1/9ijlz5gDw5je/mVmzZvH5z3++ZPxf//rXN3h95513cued2e3dzzrrrPWJ9Mc+9jFWrFjBFVdcwcqVK3nllVfYeeed+eQnP8lXvvKV9UPvtdtzzz25/PLL+fa3v83atWvZYYcd+PSnP82ZZ57JTjvt1BtvjSRJ6kRzczMrHl+zyV+zNigT6blz524w1nCxPUdvtX4UjbWjt2JBF1eJFo62ccWWdZ1eUbrbiOHr2za3Ps3IXrzydO3atQAb3KWvv+y5555ce+21Zbf/+te/3iE57kr7h4SNOeSQQ7rV2/6+971v/ZjRkiSpf02dOpWW1rYNOiS70l9DBXfXoEykgbLrkevr66mvry+rbbljG44fP75X66Hb78K3995799o6JUmSakV/DRXcXYM2kR4IfvnLX3Lbbbdx/vnn8/a3v51999232iFJkiQNGibSm7CTTz6ZiOCII47ge9/7XrXDkSRJ6hP9NVRwd5lIb8JaWlqqHYIkSVKf66+hgrtrUN7ZUJIkSeopE2lJkiSpAgO+tCOlRERUOwwNUOUOzydJkl41ffp07nh8TbXD6LGyeqQjYmpE3BMRd0fELyOiLiLGRsStEdEcEb+KiNfkbTfPXzfn88cUrOe0fPr9ETGxYHp9Pq05Ir7WWzs3dOhQXn755d5andRBW1tbVW6EI0nSpmz8+PGb/M1YoIxEOiJ2Bk4B9k0p7QUMAY4CvgVMTymNB1qAE/JFTgBa8unT83ZExJ75cm8B6oEfRsSQiBgCzAI+AuwJfDpv22N1dXWsW7euN1YllbR27Vq22mqraochSZKqoNwa6aHAFhExFHgt8DjwQeDKfP5PgcPznw/LX5PPPziy2orDgMtTSi+mlB4EmoH98kdzSmlZSukl4PK8bY+NHj2aVatW8fzzz/sVvHpNSomXXnqJ1atX09LSwrbb1t4A8ZIk1bKZM2dy5ZwLqx1Gj220Rjql9GhEfAd4GHgBuA64HViTUmqvm1gB7Jz/vDPwSL7syxHxDLBdPn1RwaoLl3mkaPq7K9qbInV1dWy//fY88cQTvPjii72xSgmAIUOGsNVWW/GGN7yBzTffvNrhSJK0SWlqaqKltY0jJ51YVvtTvnEO79hxRN8GVYGNJtIRMZKsh3gssAb4NVlpRr+LiJOAkwDe8IY3lLXMNttswzbbbNOXYUmSJKkP7TJ2N8bX4C3CyyntOAR4MKW0KqXUBlwFHACMyEs9AHYBHs1/fhR4PUA+fxvgqcLpRct0Nr2DlNIFKaV9U0r7jh49uozQJUmSpL5RTiL9MDAhIl6b1zofDCwBFgBH5m2OA36f/3x1/pp8/o0pK1C+GjgqH9VjLLA7sBj4C7B7PgrIa8guSLy657smSZKkgeDKORcyc+bMaofRwUYT6ZTSrWQXDd4B/D1f5gLgq8CXI6KZrAb64nyRi4Ht8ulfBr6Wr+ce4AqyJHweMDml9EpeZz0FaALuBa7I20qSJEksvmkBTU1N1Q6jg7JuyJJSOgs4q2jyMrIRN4rbtgKf7GQ93wS+WWL6NcA15cQiSZIk1YIBf2dDSZIk1ZZx48bx2LrWaofRYybSkiRJ6lczZszg5hVPVzuMHiv3hiySJEmSCphIS5IkSRWwtEOSJEn9qqGhgZbWNs6bfVlZ7XfadQw7bVnXx1F1n4m0JEmSalrjtHN5zyZ6Z0NJkiRJRUykJUmSpAqYSEuSJKmmnXr80TQ0NFQ7jA5MpCVJkqQKmEhLkiRJFXDUDkmSJPWryZMns2TVs9UOo8dMpCVJktSv6uvr2dpbhEuSJEmDk4m0JEmS+tW8efNYtGB+tcPoMUs7JEmS1K9mzZpFS2sbEw46uKz2Rxx7AnuO3qqPo+o+E2lJkiTVtAkHHewtwiVJkqSBwkRakiRJNW3RgvnMmzev2mF0YGmHJEmSatpVl17Mgrph1NfXVzuUDdgjLUmSJFXARFqSJEmqgKUdkiRJ6ldz587lZu9sKEmSJA1OJtKSJElSBUykJUmS1K8aGxuZMe30aofRY9ZIS5IkqV8tXbqUlta2stufN/sy72woSZIkDRQm0pIkSVIFTKQlSZJU02ZMO53GxsZqh9GBNdKSJEmqaY89tJwX6oZVO4wO7JGWJEmSKmCPtCRJkvrVxIkTWbbmuWqH0WMm0pIkSepXU6ZM8RbhkiRJ0mBlIi1JkqR+1dzczIoHl1U7jB6ztEOSJEn9aurUqbS0tnHe7MvKar/fgQex24jhfRxV95lIS5IkqaYdOelEbxEuSZIkDRQm0pIkSappKx5cRnNzc7XD6MDSDkmSJNW0888+k5F1w5g7d261Q9mAPdKSJElSBUykJUmSpApY2iFJkqR+NX36dO54fE21w+gxE2lJkiT1q/Hjx7OyzluES5IkSYOSibQkSZL61cyZM7lyzoXVDqPHLO2QJElSv2pqaqKltY0jJ51YVvtTvnEO79hxRN8GVQETaUmSJNW0XcbuxnhvES5JkiQNDCbSkiRJqmlXzrmQmTNnVjuMDkykJUmSVNMW37SApqamaofRgYm0JEmSVAEvNpQkSVK/GjduHI+ta612GD1mIi1JkqR+NWPGDG5e4Z0NJUmSpEHJRFqSJEmqgKUdkiRJ6lcNDQ20tLZx3uzLymq/065j2GnLuj6OqvtMpCVJklTTGqedy3u8s6EkSZI0MJhIS5IkSRUwkZYkSVJNO/X4o2loaKh2GB2UlUhHxIiIuDIi7ouIeyNi/4jYNiKuj4gH8ueReduIiPMjojki/hYR7yhYz3F5+wci4riC6e+MiL/ny5wfEdH7uypJkiT1nnJ7pL8PzEspvQl4K3Av8DVgfkppd2B+/hrgI8Du+eMk4EcAEbEtcBbwbmA/4Kz25Dtvc2LBcvU92y1JkiSpb2101I6I2AZ4PzAJIKX0EvBSRBwGfCBv9lNgIfBV4DDg0pRSAhblvdk75m2vTyk9na/3eqA+IhYCW6eUFuXTLwUOB67tjR2UJElSbZk8eTJLVj1b7TB6rJzh78YCq4DZEfFW4HbgS8D2KaXH8zZPANvnP+8MPFKw/Ip8WlfTV5SYLkmSpAGovr6erQfJLcKHAu8AfpRSejvwHK+WcQCQ9z6n3g9vQxFxUkTcFhG3rVq1qq83J0mSJHWqnER6BbAipXRr/vpKssT6ybxkg/x5ZT7/UeD1Bcvvkk/ravouJaZ3kFK6IKW0b0pp39GjR5cRuiRJkmrNvHnzWLRgfrXD6LGNJtIppSeARyJij3zSwcAS4GqgfeSN44Df5z9fDRybj94xAXgmLwFpAj4cESPziww/DDTl89ZGxIR8tI5jC9YlSZKkAWbWrFlcdenFZbc/4tgTmDx5ch9GVJlybxH+ReAXEfEaYBlwPFkSfkVEnAA8BHwqb3sNcCjQDDyftyWl9HRE/Bfwl7zd2e0XHgInA3OALcguMvRCQ0mSJAEw4aCDa/IW4WUl0imlvwL7lph1cIm2CSj5kSGldAlwSYnptwF7lROLJEmSVAu8s6EkSZJq2qIF85k3b161w+ig3NIOSZIkqSquuvRiFtQNo76+tu7ZZ4+0JEmSVAETaUmSJKkClnZIkiSpX82dO5ebB8mdDSVJkiQVMZGWJEmSKmAiLUmSpH7V2NjIjGmnVzuMHrNGWpIkSf1q6dKltLS2ld3+vNmX1eSdDe2RliRJkipgIi1JkiRVwERakiRJNW3GtNNpbGysdhgdWCMtSZKkmvbYQ8t5oW5YtcPowB5pSZIkqQL2SEuSJKlfTZw4kWVrnqt2GD1mIi1JkqR+NWXKFG8RLkmSJA1WJtKSJEnqV83Nzax4cFm1w+gxSzskSZLUr6ZOnUpLaxvnzb6srPb7HXgQu40Y3sdRdZ+JtCRJkmrakZNO9BbhkiRJ0kBhIi1JkqSatuLBZTQ3N1c7jA4s7ZAkSVJNO//sMxlZN4y5c+dWO5QN2CMtSZIkVcBEWpIkSaqApR2SJEnqV9OnT+eOx9dUO4weM5GWJElSvxo/fjwr67xFuCRJkjQomUhLkiSpX82cOZMr51xY7TB6zNIOSZIk9aumpiZaWts4ctKJZbU/5Rvn8I4dR/RtUBUwkZYkSVJN22Xsboz3FuGSJEnSwGAiLUmSpJp25ZwLmTlzZrXD6MBEWpIkSTVt8U0LaGpqqnYYHZhIS5IkSRXwYkNJkiT1q3HjxvHYutZqh9FjJtKSJEnqVzNmzODmFd7ZUJIkSRqUTKQlSZKkCljaIUmSpH7V0NBAS2sb582+rKz2O+06hp22rOvjqLrPRFqSJEk1rXHaubzHOxtKkiRJA4OJtCRJklQBE2lJkiTVtFOPP5qGhoZqh9GBibQkSZJUARNpSZIkqQKO2iFJkqR+NXnyZJaserbaYfSYibQkSZL6VX19PVt7i3BJkiRpcDKRliRJUr+aN28eixbMr3YYPWZphyRJkvrVrFmzaGltY8JBB5fV/ohjT2DP0Vv1cVTdZyItSZKkmjbhoIO9RbgkSZI0UJhIS5IkqaYtWjCfefPmVTuMDiztkCRJUk276tKLWVA3jPr6+mqHsgF7pCVJkqQKmEhLkiRJFbC0Q5IkSf1q7ty53OydDSVJkqTByURakiRJqoCJtCRJkvpVY2MjM6adXu0weswaaUmSJPWrpUuX0tLaVnb782Zftmnf2TAihkTEnRHxh/z12Ii4NSKaI+JXEfGafPrm+evmfP6YgnWclk+/PyImFkyvz6c1R8TXenH/JEmSpD7RndKOLwH3Frz+FjA9pTQeaAFOyKefALTk06fn7YiIPYGjgLcA9cAP8+R8CDAL+AiwJ/DpvK0kSZJUs8pKpCNiF+CjwEX56wA+CFyZN/kpcHj+82H5a/L5B+ftDwMuTym9mFJ6EGgG9ssfzSmlZSmll4DL87aSJEkSM6adTmNjY7XD6KDcGukZwKnAVvnr7YA1KaWX89crgJ3zn3cGHgFIKb0cEc/k7XcGFhWss3CZR4qmv7v8XZAkSdJA9thDy3mhbli1w+hgoz3SEfExYGVK6fZ+iGdjsZwUEbdFxG2rVq2qdjiSJEkaxMrpkT4A+JeIOBSoA7YGvg+MiIihea/0LsCjeftHgdcDKyJiKLAN8FTB9HaFy3Q2fQMppQuACwD23XffVEbskiRJqjETJ05k2Zrnqh1Gj220RzqldFpKaZeU0hiyiwVvTCl9BlgAHJk3Ow74ff7z1flr8vk3ppRSPv2ofFSPscDuwGLgL8Du+Sggr8m3cXWv7J0kSZJqzpQpUzhy0onVDqPHejKO9FeByyPiHOBO4OJ8+sXAzyKiGXiaLDEmpXRPRFwBLAFeBianlF4BiIgpQBMwBLgkpXRPD+KSJEmS+ly3EumU0kJgYf7zMrIRN4rbtAKf7GT5bwLfLDH9GuCa7sQiSZKkTVNzczMrHl/DLmN3q3YoPeKdDSVJktSvpk6dSktrG+fNvqys9vsdeBC7jRjex1F1n4m0JEmSatqRk07ctG8RLkmSJOlVJtKSJEmqaSseXEZzc3O1w+jA0g5JkiTVtPPPPpORdcOYO3dutUPZgD3SkiRJUgVMpCVJkqQKWNohSZKkfjV9+nTueHxNtcPoMRNpSZIk9avx48ezsu7paofRY5Z2SJIkSRUwkZYkSVK/mjlzJlfOubDaYfSYpR2SJEnqV01NTbS0tnHkpBPLan/KN87hHTuO6NugKmAiLUmSpJq2y9jdGO8twiVJkqSBwURakiRJNe3KORcyc+bMaofRgYm0JEmSatrimxbQ1NRU7TA6MJGWJEmSKuDFhpIkSepX48aN47F1rdUOo8dMpCVJktSvZsyYwc0rvLOhJEmSNCiZSEuSJEkVsLRDkiRJ/aqhoYGW1jbOm31ZWe132nUMO21Z18dRdZ+JtCRJkmpa47RzeY93NpQkSZIGBhNpSZIkqQIm0pIkSapppx5/NA0NDdUOowMTaUmSJKkCJtKSJElSBRy1Q5IkSf1q8uTJLFn1bLXD6DETaUmSJPWr+vp6tvYW4ZIkSdLgZCItSZKkfjVv3jwWLZhf7TB6zNIOSZIk9atZs2bR0trGhIMOLqv9EceewJ6jt+rjqLrPRFqSJEk1bcJBB3uLcEmSJGmgMJGWJElSTVu0YD7z5s2rdhgdWNohSZKkmnbVpRezoG4Y9fX11Q5lA/ZIS5IkSRUwkZYkSZIqYGmHJEmS+tXcuXO52TsbSpIkSYOTibQkSZJUARNpSZIk9avGxkZmTDu92mH0mDXSkiRJ6ldLly6lpbWt7Pbnzb7MOxtKkiRJA4WJtCRJklQBE2lJkiTVtBnTTqexsbHaYXRgjbQkSZJq2mMPLeeFumHVDqMDe6QlSZKkCtgjLUmSpH41ceJElq15rtph9JiJtCRJkvrVlClTvEW4JEmSNFiZSEuSJKlfNTc3s+LBZdUOo8cs7ZAkSVK/mjp1Ki2tbZw3+7Ky2u934EHsNmJ4H0fVfSbSkiRJqmlHTjrRW4RLkiRJA4WJtCRJkmraigeX0dzcXO0wOrC0Q5IkSTXt/LPPZGTdMObOnVvtUDZgj7QkSZJUARNpSZIkqQKWdkiSJKlfTZ8+nTseX1PtMHrMRFqSJEn9avz48aysGwS3CI+I10fEgohYEhH3RMSX8unbRsT1EfFA/jwynx4RcX5ENEfE3yLiHQXrOi5v/0BEHFcw/Z0R8fd8mfMjIvpiZyVJkqTeUk6N9MvAv6eU9gQmAJMjYk/ga8D8lNLuwPz8NcBHgN3zx0nAjyBLvIGzgHcD+wFntSffeZsTC5ar7/muSZIkqRbNnDmTK+dcWO0wemyjiXRK6fGU0h35z88C9wI7A4cBP82b/RQ4PP/5MODSlFkEjIiIHYGJwPUppadTSi3A9UB9Pm/rlNKilFICLi1YlyRJkgaYpqYmFt+0oOz2p3zjHKZPn96HEVWmWzXSETEGeDtwK7B9SunxfNYTwPb5zzsDjxQstiKf1tX0FSWmS5IkSewydjfGb8q3CI+ILYHfAI0ppbWF8/Ke5NTLsZWK4aSIuC0iblu1alVfb06SJEnqVFmJdEQMI0uif5FSuiqf/GRelkH+vDKf/ijw+oLFd8mndTV9lxLTO0gpXZBS2jeltO/o0aPLCV2SJEmbuCvnXMjMmTOrHUYH5YzaEcDFwL0ppe8VzLoaaB954zjg9wXTj81H75gAPJOXgDQBH46IkflFhh8GmvJ5ayNiQr6tYwvWJUmSpEFu8U0LaGpqqnYYHZRTI30AcAzw94j4az7tdOB/gCsi4gTgIeBT+bxrgEOBZuB54HiAlNLTEfFfwF/ydmenlNoHEDwZmANsAVybPyRJkqSatdFEOqX0J6CzcZ0PLtE+AZM7WdclwCUlpt8G7LWxWCRJkrTpGzduHI+ta612GD3mnQ0lSZLUr2bMmMHNKwbBnQ0lSZIkdWQiLUmSJFXA0g5JkiT1q4aGBlpa2zhv9mVltd9p1zHstGVdH0fVfSbSkiRJqmmN087lPZvynQ0lSZIkvcpEWpIkSaqAibQkSZJq2qnHH01DQ0O1w+jARFqSJEmqgIm0JEmSVAFH7ZAkSVK/mjx5MktWPVvtMHrMRFqSJEn9qr6+nq29RbgkSZI0OJlIS5IkqV/NmzePRQvmVzuMHrO0Q5IkSf1q1qxZtLS2MeGgg8tqf8SxJ7Dn6K36OKruM5GWJElSTZtw0MHeIlySJEkaKEykJUmSVNMWLZjPvHnzqh1GB5Z2SJIkqaZddenFLKgbRn19fbVD2YA90pIkSVIFTKQlSZKkCljaIUmSpH41d+5cbvbOhpIkSdLgZCItSZIkVcBEWpIkSf2qsbGRGdNOr3YYPWaNtCRJkvrV0qVLaWltK7v9ebMv886GkiRJ0kBhIi1JkiRVwERakiRJNW3GtNNpbGysdhgdWCMtSZKkmvbYQ8t5oW5YtcPowB5pSZIkqQL2SEuSJKlfTZw4kWVrnqt2GD1mIi1JkqR+NWXKFG8RLkmSJA1WJtKSJEnqV83Nzax4cFm1w+gxSzskSZLUr6ZOnUpLaxvnzb6srPb7HXgQu40Y3sdRdZ+JtCRJkmrakZNO9BbhkiRJ0kBhIi1JkqSatuLBZTQ3N1c7jA4s7ZAkSd3W2NjI0qVLS86bOHEiU6ZMAbKLyqZOncrcuXP7MzwNMOeffSYj64bV3HlkIi1JksrWPvbvY+taaWltK9lm2Zrn1re75McX91tsUn8zkZYkSWVpaGhYP9JC47Rzy1rmvrvuZGTdsD6OTKoOE2lJkiT1q+nTp3PH42uqHUaPmUhLkiSpX40fP56Vdd4iXJIkSRqUTKQlSZLUr2bOnMmVcy6sdhg9ZmmHJEmS+lVTUxMtrW0cOenEstqf8o1zeMeOI/o2qAqYSEuSpD5TqwmQNi27jN2N8TV4i3ATaUmSVJbJkyezZNWz3VqmVhMgqTeYSEuSpLLU19ez9YpNf6QFbXqunHMhd4wYvv6OmbXCiw0lSVKfuXLOhcycObPaYWgTt/imBTQ1NVU7jA5MpCVJUlnmzZvHogXzu7VMrSZAUm+wtEOSJJVl1qxZtLS2MeGgg6sdijZx48aN47F1rdUOo8dMpCVJktSvZsyYwc0DoN7e0g5JkiSpAibSkiRJUgUs7ZAkSVK/amhooKW1jfNmX1ZW+512HcNOW9b1cVTdZyItSZL6TK0mQNq0NE47l/fU4I19TKQlSVKfqdUESOoNJtJSlcycObPTsVXHjRvHjBkz+jcgDWgNDQ1Mnz6d8ePHA55/qszcuXMHxEgLUm8xke6m7vzzaWhoALI/PFKhm1c8zbI1z9HS2lZy/mPrWtf/szr1+KMZWTfM80g99j8/vpjPNf4HQNnnnz2JkmpBrf4vNJHupnL/+QC0tLYxsm5Yf4WmTURzczMrHl/DkZNO5MhJJ1Y7HA0i99115/qfyzn/avUflzYtnkcayGomkY6IeuD7wBDgopTS/1Q5pJJMftRTU6dO7daVypJUKxobG3lsXSuN086tdihSTaiJRDoihgCzgA8BK4C/RMTVKaUl1Y1sQ+09ibuM3a3aoUiS1O+WLl3a6beyUndMnjyZJauerXYYPVYTiTSwH9CcUloGEBGXA4cBNZVI25MoSZLUc/X19Ww9AC5crZVEemfgkYLXK4B3VymWjTr1+KNLTj/i2BOYcNDBACxaMB+AxYsXr7/osKcmT57MrFmzemVdA9Wm8B4tXryY3fd5u+dRPxqM+1ysu+fdVZdevH653jr3NnWeR55HpXhebFxn71FX326U+l9YiyKlVO0YiIgjgfqU0r/mr48B3p1SmlLU7iTgpPzlHsD9/RpoZhSwugrb1eDg+aW+5PmlvuY5pr5UrfNr15TS6FIzaqVH+lHg9QWvd8mnbSCldAFwQX8FVUpE3JZS2reaMWjg8vxSX/L8Ul/zHFNfqsXza7NqB5D7C7B7RIyNiNcARwFXVzkmSZIkqVM10SOdUno5IqYATWTD312SUrqnymFJkiRJnaqJRBogpXQNcE214yhDVUtLNOB5fqkveX6pr3mOqS/V3PlVExcbSpIkSZuaWqmRliRJkjYpJtJliohLImJlRNxd7Vg08ETE6yNiQUQsiYh7IuJL1Y5JA0dE1EXE4oi4Kz+//rPaMWngiYghEXFnRPyh2rFoYImI5RHx94j4a0TcVu14ClnaUaaIeD+wDrg0pbRXtePRwBIROwI7ppTuiIitgNuBw1NKNXV3T22aIiKA4SmldRExDPgT8KWU0qIqh6YBJCK+DOwLbJ1S+li149HAERHLgX1TSjU3Rrk90mVKKf0R2PTvZamalFJ6PKV0R/7zs8C9ZHf8lHosZdblL4flD3tR1GsiYhfgo8BF1Y5F6k8m0lKNiYgxwNuBW6scigaQ/Gv3vwIrgetTSp5f6k0zgFOBf1Y5Dg1MCbguIm7P73JdM0ykpRoSEVsCvwEaU0prqx2PBo6U0isppbeR3Tl2v4iwRE29IiI+BqxMKd1e7Vg0YL03pfQO4CPA5LzctiaYSEs1Iq9d/Q3wi5TSVdWORwNTSmkNsACor3IoGjgOAP4lr2O9HPhgRPy8uiFpIEkpPZo/rwR+C+xX3YheZSIt1YD8YrCLgXtTSt+rdjwaWCJidESMyH/eAvgQcF9Vg9KAkVI6LaW0S0ppDHAUcGNK6bNVDksDREQMzy/CJyKGAx8GamYENRPpMkXEL4FbgD0iYkVEnFDtmDSgHAAcQ9aT89f8cWi1g9KAsSOwICL+BvyFrEbaIcokbQq2B/4UEXcBi4H/TSnNq3JM6zn8nSRJklQBe6QlSZKkCphIS5IkSRUwkZYkSZIqYCItSZIkVcBEWpIkSaqAibQkSVovIj4aEfsXvH5zRHymmjFJtcpEWqqiiPhARKSI+EqJeUMj4jf5/FnViE/SoLQn8OOIeGtEvBH4AbD/RpaRBiUTaakGRcRQ4JfAEcCPgSnVjUjSIPIT4BXgr8D9wFjg29UMSKpVQ6sdgKQNRcQQ4OfAkcCFwMnJOydJ6icppbUR8S7gLWR5wt0ppZeqHJZUk+yRlmpInkT/DPh/wCXA50sl0RGxT0T8NiKeiojWiFgSEafmy5da7/K8RKT4sbCgzZh82rSiZfeIiJfyeR8omL4wIpaX2FZn0/fNY14dES9GxP0RcUbe+17cdnxEzI6IFfm2H4uI30fEO/P5pfal+DEpbzutaPpLEfFwRFwQEduX2PbhEfHniHguItblPx9W6n3t5L0eGhFfzY9Ja36MfhsRexe1K/l+5/M+ULgPlbyPnR2HfN7ywmNfMP1fI+KOiHghIp6JiOsi4r0l2qWImFM07fCIaIuIX3d2Hha0nVR8PhXMaz9eYwqmvSkifhgR90TEsxHxfETcHhH/2tV2Sqz7/RFxfb5vL+T7ekIX7Rd2cm4tL2pX8nhFxAcLlincnzkRUfLDcVfHrZP2n8iXWZO/L/dHxPkR8Zp8/mb5+fHHiHii4Pz/UURsV7Su9edkSumVlNLfUkp3AGOj6G9A+z5s5LGw3P2QNlX2SEs1IiI2A+YAn86fT+wkid4XuAloA2YBTwANwLeAtwKdXRR0H/DNgtfTywxtBjCszLYlRcRHgauAZuC7wNNkNZdnA28DPlnQdl9gfr7Ni4G7gW2BA4H3ALcDxxSsfhTZvvw230a7m4vCmAqsBl4DvBs4ERgPfLBg2yeTvaf35bEBTAJ+FxGfTyldUMbu/gL4FHA98CNgB2AycEtEvC+ldGcZ6yipO+9jBev+FnAqsBg4HdgKOAlYEBGHpZSu2UhcvwL+Fzg6pfRKpXF04gPA+4E/AA8Cw8n29cKIGJ1S+u+NrSAiGsjOkSfI3rtngaOAiyJit5TSGZ0suprs3Gl3BrBFGdsbAnx/Y+16IiK+SXaslpD9DjwOjAM+AXwDeInsfP8P4DfA74HngHcBJwDvjYh3ltHbPIOOfwN+AtxQ8PoMst/FwvfqyW7vlLSpSSn58OGjSg+yBCGRJTA/zX9OwAe7WObPwMvAPgXTArgiX/bgEss8CtxYNG05sLDg9Zh8+WkF0z6WT/td/vyBgnk3Ag+V2NZCYHnB6zqy5OWPwNCitlML15vvx91Aa+H+FbTfrMS0DnEXzZ+Wzx9TNH0usK7g9UhgHVmSunXB9K2BpWSJ14iNHM8P5dv6FRAF09+aH7P/KyfugvNiUiXvY6njsJFjvwfwT+BPwGsKpu8ErMnbDymYnoA5BfvcSpbkvqbU9kpsf1JxvF0dL2B4qXMh38dngGEb2d4Q4KF8X3YqmP4ast+nV4DdO/ldW9bV+d3F8ZqSr/fqEvszB0idxNrpcStqt1++3huBuqJ50X7+5T9vUWL5E/LlP9XVOUkXfwMqiduHj4H2sLRDqg3/DhwLXAmsIusl27K4UUS8jqxX9uqU0t/ap6eUEq/2Nn+8xPpfA7zYnYDyr4a/R9br9LsSTZ4Eto+IzTeyqg8B2wOzgRERMar9AbT3cn44f34bWV3m7ML9a5dS+md39qHIyHy7O0XEJ4D3kvUaF8Y5HDg/pbS2YJtrgfOBLYFDNrKN9vf+m/kxaV/HXWSJ+3sjYnSF8XfnfWy3WWG7gvbFf/sPI0u4zksFvZMppcfy7e0KvL04oIg4kKyX82bgE6n7dbTblIjttcWNUkrPFWyzLi9J2Ba4juyDzps2sp13Am8ALsn3qX29LwHnkb0fpcp3tiD7kNAtEbEt8J9k5Vl3dNGu1LEp99uf9m+eTkspbRBjyhX8/EK+vSERMSLfzo1583d3Ed/G/gZIg56lHVJteB1ZL+ZngMPJEurvkX21Xmhs/nxPiXXcS9aruFuJeSOAtSWmd+VL+fY+TvZVcLE/kn01/u2I+C7ZV8bQMRF4c/58SRfbaq9V3j1/rrj8oQvFCc0fgeMLXnf13rZPK/XeFhpLdgzu7WQdh+dtVm1kPaV0531s9/outrWs4Ody9/22gulvJeuF3gLYjqy3srt+V06j/EPlNLKSmdeXaDJyI6uo9NjuQNaT3V3/RdYLfgZwchftOjs25Wxzd7L3/K6NNYyIT5F9WH87HX8/u3rvNvY3QBr0TKSl2vAH4LMpqy39TUT8EjgxIq5KKc3ryYrzXuyhwGMba1uwzA7A14EfppTuiewK/mIXktUtfzF/FCpMBCJ//g+y4bRKKTu2HvgsWS/6UGAvsiTn2oh4b+r9mt6+UMn7+CTZfhf7eS/E8zbgFrJh0X4DnENWotQdX6FjIngsG9bAA1xGVmJwAdkHoKfIyiYOJStr6fVvV/PkfXuy6xG6s9zewOeB/0gprYyIrpp/qMS07wLblLm59lKwruI5guxD+mKyxPgRsl72IcA8OnnvyvwbIA16JtJSbbgppfRywespZHWXF0XEXimlNfn0B/Pnt5RYx5vI/ikuK5q+T/58dzfi+W+yUpCzOmuQx3tURHyV7AKn9n/IxYnAA/nzcymlwouTSvlH/vy2bsRarj+nlJbnP8+LiDayi6g+SFbi0f6+vYXsYsdCe+bPxe9tsWVk78ObgeLSlPZ1PEhluvM+tmst1TYiissVCvd9adG8zvb9HuAjKaVnIrth0L9HxNyU0v+VGRvA7SmlhUWxvbfo9QiyJPpnKaUvFM3bWKlNu8L9K9bZ/k0gO5aLy9xGu++THauZG2vYybFpobxE+h/AR8i+GegqxmPIEueDUkrPF2xnY+UwG/0bIMnh76SalFJ6mmxUiZ0p+IecUlpJVo/aEBF7tU+PrNvrtPzlb4tWN4ms16qpzM2/CzgOOLMgge8q1odSSjemlG7IE4OWoiZNwErga3nt6AYiYouI2Cp/eRdZgva5iOiQ9MRGuve6qX3kha3z5+vJylO+WBAP+c9fJLsQ8Xq69rv8+bTCWPNj9S/An1JKlZR1QPfex+5qvyDuPyJi/Vf/EbEjWfnLQ3Qst7ktpfRM/vOpZMnjT3sQQ2favy3Y4NjnsZU7/N0dwMPA8XlPa/s6hpH18CeyWu9CJ5CV6fyuG7F+AjgIaEwptXVjuUpclj+fm9cyb6Dg/HuFbP82K5p3Zhfr7tbfAGkws0daqlEppf+NiEvIksrfpJTaE+QvkX3d/H95T+ATZD12E4HLUkrzASLifWRDY9WTXUC3osxNH0pWOnBhL+3HcxFxLFlCcn++T81kddtvIrt748fJRpFIEXE8WY/w4ohoH/5uBFkZyTyy2xVX4vCIWE32d+8tZEPSrQYW5HGuiYhTyYa/uzVeHSd5EtkweZ8vSBw729frI+IKstrxkRHxB14d/q4VOKXEYuMjor5oWvu3CHtHxN4ppb93533c+FvRIe77I+LbZAnxHyPiV7w6/N2WwGe6Kn9JKb0QEceQfcibQZaE9oqU0rMRcR3w2Yh4AfgL2cWPnyfr3d+uq+XzdbwSEVPIPmT+JSIuIBuF5f+R9Tyfm1J6ACAidiP74HQU2TCIB0TEAQWr2x4YHhGfBX5beCEk2e/h3JRSuR9aK5ZSWhzZkIVfBe7Ij9kTZDXNR5KN6rGG7HqLTwA3RsSlZDXSh1Pios4Cvfo3QBrQqj1siA8fg/nBq8NmfaWT+VuTDT22EnhdwfS3kiVUT5N9/XovWRJUOETZ/5BdHPYFCoZiK5i/nNLD3yXg/UVtJ9HF0FdFbRdSYhgssrrkn5MNxfcSWf3uzWR1mNsWtd0jb/tE3vaxfH/fUWK97XFP6ySeaQX7lciGoXskX/8eJdp/PI/rufxxM3B4N47pULLk5t782Dydx753J3Fv7DGnkvexs+NQ6tgXTD+RrOe5lezi1OuB95Vo1yGuovf6sI28R52eT5Qe/m4UcFF+HrQCf89jLfu8zNdzYL5Pa/P13Amc0Els5TzGFP0evwiML2N/5tDD4e8K2n+abJi+Z/Pz9T6yDzOFwxieSDbWdCvZWNMXkI16ssFxpAd/A7obtw8fA+XRPs6kJKnGtPeKp5QmVTeSwSOyuxNOSymN6aLNGLLe8LHp1bp7SYOQNdKSJElSBayRlqTa9fdqBzAILaXjBbvF1pHdCn5d34cjqZZZ2iFJkiRVwNIOSZIkqQIm0pIkSVIFTKQlSZKkCphIS5IkSRUwkZYkSZIqYCItSZIkVcBEWpIkSarA/weRLGHbKQC7qAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dat = toloka_2.preprocess_crowd_dataset()\n",
    "de = dat.groupby('task_id')['worker_id'].count()\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "sns.distplot(de.values, ax=ax, \n",
    "             hist_kws = {'color':'lightblue', \n",
    "                         'edgecolor':'black',\n",
    "                         'linewidth':2, \n",
    "                         'linestyle':'--', \n",
    "                         'alpha':0.7}, \n",
    "             color='blue', kde=False, label='J = 99139')\n",
    "ax.set_xlabel('Количество оценок на один сайт', fontsize=18)\n",
    "ax.set_title('AR2\\nMean = 4.79, Std = 0.79, Median = 5.0', fontsize=18)\n",
    "ax.legend(fontsize=18)\n",
    "ax.set_xticks(np.arange(1, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dat = toloka_5.preprocess_crowd_dataset()\n",
    "de = dat.groupby('task_id')['worker_id'].count()\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "pd.value_counts(de)[:3].plot.bar(color = 'lightblue', \n",
    "                                 linestyle='--', \n",
    "                                 edgecolor='black', \n",
    "                                 linewidth = 2, \n",
    "                                 alpha = 0.7, label='J=363814')\n",
    "ax.set_xlabel('Количество оценок на один сайт', fontsize=18)\n",
    "ax.set_title('AR5\\nMean = 3.0, Std = 0.75, Median = 3.0', fontsize=18)\n",
    "ax.legend(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shiro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dat = toloka_2.preprocess_crowd_dataset()\n",
    "de = dat.groupby('task_id')['worker_id'].count()\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "sns.distplot(de.values, ax=ax, \n",
    "             hist_kws = {'color':'lightblue', \n",
    "                         'edgecolor':'black',\n",
    "                         'linewidth':2, \n",
    "                         'linestyle':'--', \n",
    "                         'alpha':0.7}, \n",
    "             color='blue', kde=False, label='J = 99139')\n",
    "ax.set_xlabel('Количество оценок на один сайт', fontsize=18)\n",
    "ax.set_title('AR2\\nMean = 4.79, Std = 0.79, Median = 5.0', fontsize=18)\n",
    "ax.legend(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                1. Количество сайтов: \u001b[1m1091918\u001b[0;0m.\n",
      "                2. Всего размеченных сайтов: \u001b[1m363814\u001b[0;0m.\n",
      "                    - среднее количество толокеров на разметку сайта:  \u001b[1m3.0\u001b[0;0m;\n",
      "                    - std: \u001b[1m0.75\u001b[0;0m;\n",
      "                    - медиана:\u001b[1m 3.0\u001b[0;0m.\n",
      "                3. Количестов толокеров, выполнявших задание: \u001b[1m1273\u001b[0;0m\n",
      "                    - в среднем толокеры выполняли заданий: \u001b[1m857.75\u001b[0;0m;\n",
      "                    - std: \u001b[1m1684.48\u001b[0;0m; \n",
      "                    - медиана: \u001b[1m198.0\u001b[0;0m. \n",
      "                4. Распределение классов на crowd датасете:\n",
      "                    1: \u001b[1m0.05\u001b[0;0m\n",
      "                    2: \u001b[1m0.01\u001b[0;0m\n",
      "                    3: \u001b[1m0.36\u001b[0;0m\n",
      "                    4: \u001b[1m0.18\u001b[0;0m\n",
      "                    5: \u001b[1m0.39\u001b[0;0m\n",
      "                5. Количество сайтов golden labels: \u001b[1m33860\u001b[0;0m\n",
      "                6. Распределение классов на golden датасете:\n",
      "                    1: \u001b[1m0.12\u001b[0;0m\n",
      "                    2: \u001b[1m0.27\u001b[0;0m\n",
      "                    3: \u001b[1m0.27\u001b[0;0m\n",
      "                    4: \u001b[1m0.16\u001b[0;0m\n",
      "                    5: \u001b[1m0.19\u001b[0;0m\n",
      "                7. Accuracy толокеров:\n",
      "                    - в среднем: \u001b[1m77.11%\u001b[0;0m\n",
      "                    - медиана: \u001b[1m82.63%\u001b[0;0m\n",
      "                8. Средняя accuracy размеченных меток(в сравнении с golden set): \u001b[1m84.3%\u001b[0;0m\n",
      "                \n"
     ]
    }
   ],
   "source": [
    "toloka_5.visual_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dat = toloka_2.preprocess_crowd_dataset()\n",
    "d = dat.groupby('worker_id')['task_id'].count()\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "sns.distplot(d.values, ax=ax, \n",
    "             hist_kws = {'color':'lightblue', \n",
    "                         'edgecolor':'black',\n",
    "                         'linewidth':2, \n",
    "                         'linestyle':'--', \n",
    "                         'alpha':0.7}, \n",
    "             color='blue', kde=False, label='W = 7137')\n",
    "ax.set_xlabel('Количестов заданий, выполняемых толокеров', fontsize=18)\n",
    "ax.set_title('AR2\\nMean = 66.62, Std = 85.45, Median = 40.0', fontsize=18)\n",
    "ax.legend(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dat = toloka_5.preprocess_crowd_dataset()\n",
    "d = dat.groupby('worker_id')['task_id'].count()\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "sns.distplot(d.values, ax=ax, \n",
    "             hist_kws = {'color':'lightblue', \n",
    "                         'edgecolor':'black',\n",
    "                         'linewidth':2, \n",
    "                         'linestyle':'--', \n",
    "                         'alpha':0.7}, \n",
    "             color='blue', kde=False, label='W = 1273')\n",
    "ax.set_xlabel('Количестов заданий, выполняемых толокеров', fontsize=18)\n",
    "ax.set_title('AR5\\nMean = 857.75, Std = 1684.48, Median = 198.0', fontsize=18)\n",
    "ax.legend(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "toloka_estimates = toloka_2.preprocess_crowd_dataset()\n",
    "etoloka_estimates = toloka_2.preprocess_golden_dataset()\n",
    "merged = pd.merge(left=toloka_estimates, \n",
    "                  right=etoloka_estimates, \n",
    "                  on='task_id', \n",
    "                  suffixes=('_pred', '_true'))\n",
    "grouped = merged.groupby(by=['worker_id', 'task_id']).sum()\n",
    "acc = [accuracy_score(grouped.query(f\"worker_id=='{i}'\").label_true.values,\n",
    "                      grouped.query(f\"worker_id=='{i}'\").label_pred.values) \n",
    "       for i in pd.unique(np.array(list(grouped.index))[:, 0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=pd.DataFrame(np.array(acc)*100, columns=['Точность верных ответов работников']), \n",
    "            kind='box', color = 'lightblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shiro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "sns.distplot(np.array(acc) * 100, ax=ax, \n",
    "             hist_kws = {'color':'lightblue', \n",
    "                         'edgecolor':'black',\n",
    "                         'linewidth':2, \n",
    "                         'linestyle':'--', \n",
    "                         'alpha':0.7}, \n",
    "             color='blue', kde=False)\n",
    "ax.set_xlabel('Точность верных ответов работников', fontsize=18)\n",
    "ax.set_title('Mean = 69.94%', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ошибка комитета большинства\n",
    "\n",
    "Пусть $n$ - количество работников, которые дали ответ на определённое задание, $\\mu$ - `mean accuracy` $p = 1 - \\mu$ - вероятность ошибки.\n",
    "\n",
    "### Теорема 1. \n",
    "\n",
    "Математическое ожидание вероятности того, что как минимум $\\left[\\frac{n}{2}\\right]$ работников выдадут правильный результат при условии, что толокеры отвечают независимо:\n",
    "\n",
    "$$\\large \\mathrm{E}\\left(\\mathrm{P}_{\\frac{n}{2}}\\right) = \\sum\\limits_{k = \\left[\\frac{n}{2}\\right]}^n C_n^k \\mu_k (1-\\mu)^{n-k}$$\n",
    "\n",
    "Можно оценить нижнюю границу с помощью [Границы Чернова](https://neerc.ifmo.ru/wiki/index.php?title=%D0%93%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0_%D0%A7%D0%B5%D1%80%D0%BD%D0%BE%D0%B2%D0%B0).\n",
    "\n",
    "### Theorem 2. `Неравенство Хёфдинга`\n",
    "\n",
    "$$ \\large \\mathrm{E}\\left(\\mathrm{P}_{\\frac{n}{2}}\\right) = \\sum\\limits_{k = \\left[\\frac{n}{2}\\right]}^n C_n^k \\geqslant 1 - e^{-2n\\left(\\mu - \\frac{1}{2}\\right)^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ошибка комитета большинства\n",
    "\n",
    "Тогда из `Theorem 2`, полагая, что $1 - e^{-2n\\left(\\mu - \\frac{1}{2}\\right)^2} \\geqslant C$, где $C$ - нижняя граница, что означает, что `expected accuracy` не меньше $C$, можно вывести следующий теоретический результат:\n",
    "\n",
    "### Theorem 3.\n",
    "\n",
    "Для заданной необходимой точности $C$ и `mean accuracy` работнико $\\mu$, число работников равное:\n",
    "\n",
    "$$\\large n \\geqslant \\frac{-\\ln (1-C)}{2\\left(\\mu - \\frac{1}{2}\\right)^2}$$\n",
    "\n",
    "**гарантирует** (с точки зрения теории вероятностей), что ожидаемая `accuracy` будет не меньше необходимой точности $C$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "accuracy = np.arange(0.5, 0.99, 0.05)\n",
    "n = [int(- np.log(1 - C) / (2 * (0.6994 - 1 / 2) ** 2)) for C in np.arange(0.5, 0.99, 0.05)]\n",
    "dependec_workers_accuracy = pd.DataFrame.from_dict(dict(zip(accuracy, n)),  orient='index', columns=['n'])\n",
    "dependec_workers_accuracy.index.name = 'Accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Accuracy</th>\n",
       "      <th>0.50</th>\n",
       "      <th>0.55</th>\n",
       "      <th>0.60</th>\n",
       "      <th>0.65</th>\n",
       "      <th>0.70</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.80</th>\n",
       "      <th>0.85</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Accuracy  0.50  0.55  0.60  0.65  0.70  0.75  0.80  0.85  0.90  0.95\n",
       "n            8    10    11    13    15    17    20    23    28    37"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependec_workers_accuracy.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "[Proof: 3.2.1](https://arxiv.org/pdf/1207.0143v1.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Toloka Aggregation 5: `InFo`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                1. Количество сайтов: \u001b[1m1091918\u001b[0;0m.\n",
      "                2. Всего размеченных сайтов: \u001b[1m363814\u001b[0;0m.\n",
      "                    - среднее количество толокеров на разметку сайта:  \u001b[1m3.0\u001b[0;0m;\n",
      "                    - std: \u001b[1m0.75\u001b[0;0m;\n",
      "                    - медиана:\u001b[1m 3.0\u001b[0;0m.\n",
      "                3. Количестов толокеров, выполнявших задание: \u001b[1m1273\u001b[0;0m\n",
      "                    - в среднем толокеры выполняли заданий: \u001b[1m857.75\u001b[0;0m;\n",
      "                    - std: \u001b[1m1684.48\u001b[0;0m; \n",
      "                    - медиана: \u001b[1m198.0\u001b[0;0m. \n",
      "                4. Распределение классов на crowd датасете:\n",
      "                    1: \u001b[1m0.05\u001b[0;0m\n",
      "                    2: \u001b[1m0.01\u001b[0;0m\n",
      "                    3: \u001b[1m0.36\u001b[0;0m\n",
      "                    4: \u001b[1m0.18\u001b[0;0m\n",
      "                    5: \u001b[1m0.39\u001b[0;0m\n",
      "                5. Количество сайтов golden labels: \u001b[1m33860\u001b[0;0m\n",
      "                6. Распределение классов на golden датасете:\n",
      "                    1: \u001b[1m0.12\u001b[0;0m\n",
      "                    2: \u001b[1m0.27\u001b[0;0m\n",
      "                    3: \u001b[1m0.27\u001b[0;0m\n",
      "                    4: \u001b[1m0.16\u001b[0;0m\n",
      "                    5: \u001b[1m0.19\u001b[0;0m\n",
      "                7. Accuracy толокеров:\n",
      "                    - в среднем: \u001b[1m77.11%\u001b[0;0m\n",
      "                    - медиана: \u001b[1m82.63%\u001b[0;0m\n",
      "                8. Средняя accuracy размеченных меток(в сравнении с golden set): \u001b[1m84.3%\u001b[0;0m\n",
      "                \n"
     ]
    }
   ],
   "source": [
    "toloka_5.visual_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "accuracy = np.arange(0.5, 0.99, 0.05)\n",
    "n = [int(- np.log(1 - C) / (2 * (0.843 - 1 / 2) ** 2)) for C in np.arange(0.5, 0.99, 0.05)]\n",
    "dependec_workers_accuracy_5 = pd.DataFrame.from_dict(dict(zip(accuracy, n)),  orient='index', columns=['n'])\n",
    "dependec_workers_accuracy_5.index.name = 'Accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Accuracy</th>\n",
       "      <th>0.50</th>\n",
       "      <th>0.55</th>\n",
       "      <th>0.60</th>\n",
       "      <th>0.65</th>\n",
       "      <th>0.70</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.80</th>\n",
       "      <th>0.85</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Accuracy  0.50  0.55  0.60  0.65  0.70  0.75  0.80  0.85  0.90  0.95\n",
       "n            2     3     3     4     5     5     6     8     9    12"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependec_workers_accuracy_5.T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Aggregation Methods\n",
    "\n",
    "1. Voting Ensambles\n",
    "\n",
    "2. Averaging\n",
    "\n",
    "3. Improved Averaging\n",
    "\n",
    "4. EM\n",
    "\n",
    "5. Matrix Decompositions*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## I. Voting Ensembles\n",
    "\n",
    "### I.I Голосование по большинству (Majority vote: MV)\n",
    "\n",
    "$$\\large a(x) = \\operatorname{mode}\\left(b_1(x), \\ldots, b_n(x)\\right)$$\n",
    "\n",
    "### I.II Комитеты единогласия в бинарной задаче классификации (UC)\n",
    "\n",
    "$$\\large a(x) = \\operatorname{min}\\left(b_1(x), \\ldots, b_n(x)\\right)$$\n",
    "\n",
    "### I.III Обнаружение аномалий: `Mata Alghorithm` (AM)\n",
    "\n",
    "$$\\large a(x) = \\operatorname{max}\\left(b_1(x), \\ldots, b_n(x)\\right)$$\n",
    "\n",
    "А ещё может быть `treshold commitet`."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## II. Averaging\n",
    "\n",
    "### II.I Mean (Average)\n",
    "\n",
    "$$ \\large a(x) = \\frac{1}{n}\\left(b_1(x) + \\ldots + b_n(x) \\right)$$\n",
    "\n",
    "### II.II [Kolmogorov averaging](https://ru.wikipedia.org/wiki/%D0%A1%D1%80%D0%B5%D0%B4%D0%BD%D0%B5%D0%B5_%D0%9A%D0%BE%D0%BB%D0%BC%D0%BE%D0%B3%D0%BE%D1%80%D0%BE%D0%B2%D0%B0)\n",
    "\n",
    "$$ \\large M(x_1, \\ldots, x_n) = \\varphi^{-1}\\left(\\frac{1}{n} \\sum\\limits_{k=1}^n \\varphi(x_k)\\right) =  \\varphi^{-1}\\left(\\frac{\\varphi(x_1) + \\ldots + \\varphi(n)}{n}\\right)$$\n",
    "\n",
    "где $\\varphi$ - непрерывная строго монотонная функция, а $\\varphi^{-1}$ - функция, обратная к $\\varphi$. При $\\varphi(x) = x$ - среднее геометрическое, при $\\varphi(x) = \\ln x$.\n",
    "\n",
    "### II.III [Ручные методы ассамблирования](https://www.kaggle.com/c/amazon-employee-access-challenge) (`метод Ефимова`)\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## III.  Improved Averaging\n",
    "\n",
    "### III.I Weighted Averaging (MVA)\n",
    "\n",
    "$$ \\large a(x) = \\frac{\\sum\\limits_{i=1}^n w_i b_i(x)}{\\sum\\limits_{i=1}^n w_i}$$\n",
    "\n",
    "где $w_j$ - уровень надежности толокера, отражающий вероятность предоставить правильную метку для произволного примера.\n",
    "\n",
    "### III.II Голосование с весами (WAWA)\n",
    "\n",
    "$$ \\large a(x) = \\underset{j}{\\operatorname{argmax}}\\left[\\sum\\limits_{t: b_t(x) = j} w_t\\right]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### III.III Bayes Wote\n",
    "\n",
    "При известных $w_j$ - вероятностях предоставления правильного ответа, доказано, что оптимальным является байесовское голосование, где вероятность класса пропорциональна его правдоподобию:\n",
    "\n",
    "$$\\large \\mathbb{P}\\{y_i = c\\} = \\prod\\limits_{j=1}^K q_j^{I(y_{ij} = c)} \\left(\\frac{1-q_j}{J-1}\\right)^{I(y_{ij} \\neq c)}$$\n",
    "\n",
    "Оценка характеристики толокера становится основной задачей и вот почему: посмотрим на результаты работы рассмотренных алгоритмов в первой части.\n",
    "\n",
    "**Proof**:\n",
    "\n",
    "- [Proof 1](https://i.cs.hku.hk/~ckcheng/papers/edbt15-jury.pdf)\n",
    "- [Proof 2](https://arxiv.org/pdf/1207.0143v1.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Functions Toloka Aggregation 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 classes: Done Majority Vote\n",
      "2 classes: Done Average Vote\n",
      "2 classes: Done UC\n",
      "Done Alghorithm Maximise\n",
      "2 classes: Done MWA\n",
      "2 classes: Done Aggregation by Weights (WAWA) using W weights\n",
      "Iteration 40000\n",
      "Iteration 80000\n",
      "2 classes: Done WAWA + Bayes(W)\n",
      "2 classes: Done Aggregation by Weights (WAWA) using IDW weights\n",
      "2 classes: Done Aggregation by Weights (WAWA) using DWS weights\n",
      "Iteration 40000\n",
      "Iteration 80000\n",
      "2 classes: Done WAWA + Bayes(IDW)\n"
     ]
    }
   ],
   "source": [
    "MV_2 = toloka_2.MV()\n",
    "Average_2 = toloka_2.average()\n",
    "UC_2 = toloka_2.UC()\n",
    "AM_2 = toloka_2.AM()\n",
    "MWA, mwa_wr = toloka_2.MWA()\n",
    "WAWA, wawa_wr = toloka_2.WAWA()\n",
    "WAWA_Bayes_Wote = toloka_2.WAWA(bayes_likelyhood=True)\n",
    "WAWA_IDW, w_idw = toloka_2.WAWA(W='IDW')\n",
    "WAWA_DWS, w_dws = toloka_2.WAWA(W='DWS')\n",
    "WAWA_IDW, w_idw = toloka_2.WAWA(W='IDW', bayes_likelyhood=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MV</th>\n",
       "      <th>average</th>\n",
       "      <th>pred_UC(min)</th>\n",
       "      <th>pred_AM(max)</th>\n",
       "      <th>MWA</th>\n",
       "      <th>WAWA(W)</th>\n",
       "      <th>WAWA + Bayes(W)</th>\n",
       "      <th>WAWA(IDW)</th>\n",
       "      <th>WAWA(DWS)</th>\n",
       "      <th>WAWA + Bayes(IDW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <td>7.664925</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>0.628199</td>\n",
       "      <td>0.323794</td>\n",
       "      <td>8.817951</td>\n",
       "      <td>9.531563</td>\n",
       "      <td>320.653467</td>\n",
       "      <td>48.391266</td>\n",
       "      <td>48.219386</td>\n",
       "      <td>397.280352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Accuracy (%)</th>\n",
       "      <td>77.636670</td>\n",
       "      <td>76.763568</td>\n",
       "      <td>74.551047</td>\n",
       "      <td>34.527235</td>\n",
       "      <td>80.166683</td>\n",
       "      <td>80.176605</td>\n",
       "      <td>82.488342</td>\n",
       "      <td>82.458577</td>\n",
       "      <td>82.528029</td>\n",
       "      <td>83.143169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            MV    average  pred_UC(min)  pred_AM(max)  \\\n",
       "Time (s)              7.664925   0.238843      0.628199      0.323794   \n",
       "Golden Accuracy (%)  77.636670  76.763568     74.551047     34.527235   \n",
       "\n",
       "                           MWA    WAWA(W)  WAWA + Bayes(W)  WAWA(IDW)  \\\n",
       "Time (s)              8.817951   9.531563       320.653467  48.391266   \n",
       "Golden Accuracy (%)  80.166683  80.176605        82.488342  82.458577   \n",
       "\n",
       "                     WAWA(DWS)  WAWA + Bayes(IDW)  \n",
       "Time (s)             48.219386         397.280352  \n",
       "Golden Accuracy (%)  82.528029          83.143169  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(toloka_2.result_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Functions Toloka Aggregation 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 classes: Done Majority Vote\n",
      "5 classes: Done Average Vote\n",
      "5 classes: Done MWA\n",
      "5 classes: Done Aggregation by Weights (WAWA)\n"
     ]
    }
   ],
   "source": [
    "MV_5 = toloka_5.MV()\n",
    "Average_5 = toloka_5.average()\n",
    "MWA_5, mwa_wr_5 = toloka_5.MWA()\n",
    "WAWA_5, wawa_wr_5 = toloka_5.WAWA()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MV</th>\n",
       "      <th>pred_average</th>\n",
       "      <th>MWA</th>\n",
       "      <th>WAWA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <td>9.078467</td>\n",
       "      <td>2.558555</td>\n",
       "      <td>11.487422</td>\n",
       "      <td>10.544983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Accuracy (%)</th>\n",
       "      <td>90.135854</td>\n",
       "      <td>84.087419</td>\n",
       "      <td>90.281187</td>\n",
       "      <td>90.431187</td>\n",
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      "text/plain": [
       "                            MV  pred_average        MWA       WAWA\n",
       "Time (s)              9.078467      2.558555  11.487422  10.544983\n",
       "Golden Accuracy (%)  90.135854     84.087419  90.281187  90.431187"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(toloka_5.result_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV. Expectation Maximisation\n",
    "\n",
    "Задача ставится следуюшая:\n",
    "\n",
    "$$\\ln L(X_1, \\ldots, X_n \\vert  \\overset{\\to}{\\theta}) = \\sum\\limits_{i=1}^n \\log p(x_i \\vert \\overset{\\to}{\\theta}) \\to \\underset{\\theta}{\\max}$$\n",
    "\n",
    "Численное задача решать сложно. Поэтому примненяется EM-алгоритм. Вводятся латентные переменные: $Y_1, \\ldots, Y_n \\in \\{0, 1\\}$\n",
    "\n",
    "`EM-Алгоритм`:\n",
    "\n",
    "1. Подбираем начальные значения параметров $\\overset{\\to}{\\theta}^{(0)}$\n",
    "\n",
    "2. Expectation Step (E-step):\n",
    "\n",
    "$$\\mathbb{E}_Y \\left( \\overset{\\sim}{L}(x_1, \\ldots,x_n, Y_1, \\ldots, Y_n \\vert \\overset{\\to}{\\theta}^{(0)}\\right)$$\n",
    "\n",
    "3. Maximisation Step (M-step):\n",
    "\n",
    "$$\\max E_{Y}\\overset{\\sim}{L} \\to \\overset{\\to}{\\theta}^{(1)}$$\n",
    "\n",
    "4. Повторяем шаги $1-2$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Свойство EM-Алгоритма\n",
    "\n",
    "### Theorem 4. \n",
    "\n",
    "С каждым шагом процедуры `EM`-алгоритма логарифм правдоподобия **увеличивается**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Proof**:\n",
    "\n",
    "$$\\log L(x_1, \\ldots, x_n \\vert \\overset{\\to}{\\theta}) = \\sum\\limits_{i=1}^n \\log p(x_i \\vert \\theta) = \\sum\\limits_{i=1}^n \\log \\frac{P_{(X, Y)}(x_i, y_i \\vert \\theta)}{P_{(Y \\vert X)}(y_i \\vert x_i, \\theta)} = $$\n",
    "$$ = \\sum\\limits_{i=1}^n \\log P_{(X, Y)}(x_i, y_i \\vert \\theta) - \\sum\\limits_{i=1}^n \\log P_{(Y \\vert X)}(y_i \\vert x_i, \\theta)$$\n",
    "\n",
    "На каждом шаге мы переходим к оценке $\\theta^{(n+1)}$:\n",
    "$$\\theta^{(n)} \\to \\theta^{(n+1)} =  \\underset{\\theta}{\\operatorname{argmax}} E_{Y} \\log L(x_,1 \\ldots, x_n, Y_1, \\ldots, Y_n)$$\n",
    "$$\\log L(x_1, \\ldots, x_n \\vert \\theta) = E_Y \\log L(x_1, \\ldots, x_n, Y_1, \\ldots, Y_n) - E_{Y}\\sum\\limits_{i=1}^n \\log p_{Y \\vert x}(y_i \\vert x_i, \\theta)$$\n",
    "$$\\log L(x_1, \\ldots, x_n \\vert \\theta^{(n)}) - \\log L(x_1, \\ldots, x_n \\vert \\theta^{(n+1)}) = $$\n",
    "$$E_{Y}\\log L(x_1,\\ldots,x_n, Y_1, \\ldots, Y_n \\vert \\theta_n ) - E_y \\log L(x_1, \\ldots, x_n, y_1, \\ldots, y_n) - \\sum\\limits_{i=1}^n E_Y \\log \\frac{P_{y \\vert x}(Y_i \\vert x_i, \\theta^{(n)})}{P_{Y \\vert X}(y_i \\vert x_i, \\theta^{(n+1)})}$$\n",
    "\n",
    "Первая часть - меньше нуля, вторая часть - больше нуля, потому что есть метрика Кульбака-Лейблера:\n",
    "$$K(f, g) = \\int\\limits f(x) \\log \\frac{f(x)}{g(x)} dx $$\n",
    "$$K(f, g) \\geqslant 0, K(f, g) = 0 \\Leftrightarrow f = g$$\n",
    "\n",
    "Следовательно, с каждым шагом логарифм правдоподобия будет увеличиваться. $\\blacksquare$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.I [Dawid Scene](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.469.1377)\n",
    "\n",
    "Идея: будем вычислять для каждого толокера матрицу ошибок `IEE`:\n",
    "\n",
    "$$\\large \\pi_{qj}^{(k)}$$\n",
    "\n",
    "вероятность того, что толокер $k \\in {1, \\ldots, J}$ поставит метку $j \\in \\{1, \\ldots, J\\}$, если настоящей меткой является $q \\in \\{1, \\ldots, J\\}$.\n",
    "\n",
    "Выпишем правдоподобие выборки в предположении, что толокеры проставляют метки независимо друг от друга. Обозначим за $p_j$ - вероятности распределения классов, $n_{il}^{(k)}$ - количество раз, которое аннтотатор поставил метку $l$ примеру $i$, а $T_{ij}, j = 1, \\ldots, J$ - индикторная **латентная переменная**, $T_{iq}=1, T_{iq}=0, j \\neq q$. Тогда логарифм правдоподобия:\n",
    "\n",
    "$$\\large L \\sim \\prod\\limits_{j=1}^{J} \\left\\{p_j \\cdot \\prod\\limits_{k=1}^{K}\\prod\\limits_{l=1}^J \\left(\\pi_{jl}^{(k)}\\right)^{n_{il}^{(k)}}\\right\\}^{T_{ij}}$$\n",
    "\n",
    "**Факт**: аналитически было найдено, что выражение для матрицы ошибок, максимизирующее функцию правдоподбия, имеет следующий вид:\n",
    "$$\\large \\hat{\\pi}_{jl}^{(k)} = \\frac{\\sum\\limits_{i} T_{ij}n_{il}^{(k)}}{\\sum\\limits_i \\sum\\limits_j T_{ij} n_{il}^{(k)}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.I [Dawid Scene: EM]()\n",
    "\n",
    "1. Давайте зададим некоторые начальные значения латентных переменных $T_{ij}$, это можно сделать либо равномерно распределенным вектором из $0$ или $1$, либо по формуле:\n",
    "\n",
    "$$\\large T_{ij}^{(0)} = \\frac{\\sum\\limits_k n_{il}^{(k)}}{\\sum\\limits_k\\sum\\limits_l n_{il}^{(k)}}$$\n",
    "\n",
    "2. Высчитываем параметры матрицы ошибок `IEE` и распределение классов $p_j$ в предположении о распределении латентных переменных на предыдущем шаге `EM`-алгоритма:\n",
    "\n",
    "$$\\large \\hat{\\pi}_{jl}^{(k)} = \\frac{\\sum\\limits_{i} T_{ij}n_{il}^{(k)}}{\\sum\\limits_i \\sum\\limits_j T_{ij} n_{il}^{(k)}}$$\n",
    "\n",
    "$$\\large p_j = \\frac{\\sum\\limits_{i} T_{ij}}{I}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### IV.I [Dawid Scene: EM]()\n",
    "\n",
    "3. Высчитываем математические ожидание выборки с высчитанными на шаге $2$ параметрами `IIE` и `p` и получаем новое распределение латентных переменных:\n",
    "\n",
    "$$p(T_{ij} = 1  | \\text{data}) = \\frac{\\prod\\limits_{k=1}^K \\prod\\limits_{l=1}^J \\left(\\pi_{ql}^{(k)}\\right)^{n_{il}^{(k)}} \\cdot p_j}{\\sum\\limits_{q=1}^J\\prod\\limits_{k=1}^K \\prod\\limits_{l=1}^J \\left(\\pi_{ql}^{(k)}\\right)^{n_{il}^{(k)}} \\cdot p_q}$$\n",
    "\n",
    "4. Повторяем шаги $2-3$ до сходимости.\n",
    "\n",
    "Итого получаем:\n",
    "\n",
    "- вероятности $T_{ij}$ принадлежности каждого сайта определенному классу\n",
    "- `worker_reliability` - надежность работников (то, что мы хотели)\n",
    "    - сумма диагональных элементов / количество классов\n",
    "    - взвешенный `worker_reliability` - домножаем на $p_j$ - распределение классов    \n",
    "\n",
    "Недостатки:\n",
    "\n",
    "- не обязательно сходится к минимуму, предположение о независимости не всегда оправдано"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Dawid Scene: Toloka Aggregation 2\n",
    "\n",
    "Можно полученные веса `worker_reliability` использовать в различных взвешенных подходах, рассмотренных ранее.\n",
    "\n",
    "Улучшения: [Fast Dawid Scene](https://arxiv.org/abs/1803.02781)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------START ITERATION #0-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #0-----------------\n",
      "---------------START ITERATION #1-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #1-----------------\n",
      "---------------START ITERATION #2-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #2-----------------\n",
      "---------------START ITERATION #3-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #3-----------------\n",
      "---------------START ITERATION #4-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #4-----------------\n",
      "---------------START ITERATION #5-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #5-----------------\n",
      "---------------START ITERATION #6-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #6-----------------\n",
      "---------------START ITERATION #7-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #7-----------------\n",
      "---------------START ITERATION #8-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #8-----------------\n",
      "---------------START ITERATION #9-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #9-----------------\n",
      "EM Done\n"
     ]
    }
   ],
   "source": [
    "DS_2, worker_reliability_ds = toloka_2.DS(max_iter=10, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 classes: Done WAWR\n"
     ]
    }
   ],
   "source": [
    "WAWR, wr_ds = toloka_2.WAWR(worker_reliability_ds, \n",
    "                         bayes_likelyhood=False, \n",
    "                         name=' DS_Weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Dawid Scene: Toloka Aggregation 2\n",
    "\n",
    "Рассмотрим взвешенное аггрегирование с весами, данными работникам в результате работы алгоритма `DS`. Так же применим улучшающий `Bayes Wote`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 40000\n",
      "Iteration 80000\n",
      "2 classes: Done WAWA + Bayes DS_Weights_Bayes\n"
     ]
    }
   ],
   "source": [
    "WAWR_Bayes, wr_ds_bayes = toloka_2.WAWR(worker_reliability_ds, \n",
    "                         bayes_likelyhood=True, \n",
    "                         name=' DS_Weights_Bayes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <th>MV</th>\n",
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       "      <th>Time (s)</th>\n",
       "      <td>7.664925</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>0.628199</td>\n",
       "      <td>0.323794</td>\n",
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       "      <th>Golden Accuracy (%)</th>\n",
       "      <td>77.636670</td>\n",
       "      <td>76.763568</td>\n",
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       "      <td>34.527235</td>\n",
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       "      <td>78.073222</td>\n",
       "      <td>80.960413</td>\n",
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      "text/plain": [
       "                            MV    average  pred_UC(min)  pred_AM(max)  \\\n",
       "Time (s)              7.664925   0.238843      0.628199      0.323794   \n",
       "Golden Accuracy (%)  77.636670  76.763568     74.551047     34.527235   \n",
       "\n",
       "                           MWA    WAWA(W)  WAWA + Bayes(W)  WAWA(IDW)  \\\n",
       "Time (s)              8.817951   9.531563       320.653467  48.391266   \n",
       "Golden Accuracy (%)  80.166683  80.176605        82.488342  82.458577   \n",
       "\n",
       "                     WAWA(DWS)  WAWA + Bayes(IDW)  DawidScene       WAWR  \\\n",
       "Time (s)             48.219386         397.280352  345.720450   9.090810   \n",
       "Golden Accuracy (%)  82.528029          83.143169   81.069551  78.073222   \n",
       "\n",
       "                     WAWA + Bayes DS_Weights_Bayes  \n",
       "Time (s)                                302.585573  \n",
       "Golden Accuracy (%)                      80.960413  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(toloka_2.result_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Dawid Scene: Toloka Aggregation 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------START ITERATION #0-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #0-----------------\n",
      "---------------START ITERATION #1-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #1-----------------\n",
      "---------------START ITERATION #2-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #2-----------------\n",
      "---------------START ITERATION #3-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #3-----------------\n",
      "---------------START ITERATION #4-----------------\n",
      "start M step\n",
      "done M step\n",
      "start E step\n",
      "done E step\n",
      "---------------END ITERATION #4-----------------\n",
      "EM Done\n",
      "5 classes: Done DS\n"
     ]
    }
   ],
   "source": [
    "DS_5, worker_reliability_ds_5 = toloka_5.DS(max_iter=5, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 classes: Done WAWR\n"
     ]
    }
   ],
   "source": [
    "WAWR_5, wr_ds_5 = toloka_5.WAWR(worker_reliability_ds_5, \n",
    "                         bayes_likelyhood=False, \n",
    "                         name=' DS_Weights_Bayes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MV</th>\n",
       "      <th>pred_average</th>\n",
       "      <th>MWA</th>\n",
       "      <th>WAWA</th>\n",
       "      <th>DawidScene</th>\n",
       "      <th>WAWR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <td>9.078467</td>\n",
       "      <td>2.558555</td>\n",
       "      <td>11.487422</td>\n",
       "      <td>10.544983</td>\n",
       "      <td>442.434574</td>\n",
       "      <td>9.459579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Accuracy (%)</th>\n",
       "      <td>90.135854</td>\n",
       "      <td>84.087419</td>\n",
       "      <td>90.281187</td>\n",
       "      <td>90.431187</td>\n",
       "      <td>90.785588</td>\n",
       "      <td>90.295334</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                            MV  pred_average        MWA       WAWA  \\\n",
       "Time (s)              9.078467      2.558555  11.487422  10.544983   \n",
       "Golden Accuracy (%)  90.135854     84.087419  90.281187  90.431187   \n",
       "\n",
       "                     DawidScene       WAWR  \n",
       "Time (s)             442.434574   9.459579  \n",
       "Golden Accuracy (%)   90.785588  90.295334  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(toloka_5.result_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.II [GLAD - Generative Model of Labels, Abilities and Difficulties](http://papers.nips.cc/paper/3644-whose-vote-should-count-more-optimal-integration-of-labels-from-labelers-of-unknown-expertise.pdf)\n",
    "\n",
    "Допустим, у нас есть $n$ сайтов, каждый из которых является релевантным или нет (`1` или `0`). Мы бы хотели предсказывать `Z_j` - **правильную метку** для каждого сайта `j` при наличии голосов от `m` толокеров.\n",
    "\n",
    "Предположим, что $Z_j$ зависит от нескольких факторов, таких как:\n",
    "\n",
    "1. неодназначность изображения (1)\n",
    "\n",
    "2. опыт (надежность) работника (2)\n",
    "\n",
    "3. истинных значений `true label`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.GLAD - Generative Model of Labels, Abilities and Difficulties\n",
    "\n",
    "- **Сложность сайта** `j` будем моделировать с помощью параметра\n",
    "\n",
    "$$\\large \\frac{1}{\\beta_j} \\in [0, \\infty)$$\n",
    "\n",
    "$\\frac{1}{\\beta_j}  = \\infty$ означает, что непонятно, каким является сайт - релевантным или нерелевантным, и, следовательно, даже самые опытные толокеры имеют `50%` вероятность ошибиться / поставить правильную метку.\n",
    "\n",
    "$\\frac{1}{\\beta_j}  = 0$ означает, что даже самый неосевдомленный толокер способен правильно оценить данный сайт\n",
    "\n",
    "- **Надежность работника** моделируется параметром $\\alpha_i \\in (-\\infty, +\\infty)$. $\\alpha_i = \\infty$ означает, что работник всегда отвечает правильные ответы, а $\\alpha_i = -\\infty$ означает, что толокер всегда маркирует изображения неправильно (то есть он всегда может различать два класса, но всегда неправильно). В таком случае толокер называется `adversarial` - противоборствующий. Наконец, $\\alpha_i = 0$ означает, что толокер не может различать два класса - его метки не содержат информации об истинной метке изображения $Z_j$. Кстати, мы не обязательно требуем, чтобы толокеры были людьми, это могут быть и классификаторы (`Logistic Regression e.t.c`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.GLAD - Generative Model of Labels, Abilities and Difficulties\n",
    "\n",
    "\n",
    "Теперь идея следующая: `метки, которые выданы толокером` $i$ для `сайта ` $j$, которые мы будем называть `given labels`, обозначаются как $L_{ij}$ и будут генерироваться следующим образом:\n",
    "\n",
    "$$\\LARGE \\mathbb{P}\\left( L_{ij} = {Z_j} \\vert \\alpha_i, \\beta_j \\right) = \\frac{1}{1 + e^{-\\alpha_i \\cdot \\beta_j}}$$\n",
    "\n",
    "Таким образом в данной модели логарифм отношения вероятности принадлежности классам равна билинейной функции сложности картинки и надежности толокера:\n",
    "\n",
    "$$\\LARGE \\ln \\frac{\\mathbb{P}\\left( L_{ij} = {Z_j} \\right)}{1-\\mathbb{P}\\left( L_{ij} = {Z_j} \\right)} = \\alpha_i \\beta_j$$\n",
    "\n",
    "Итого, как решать задачу нахождения `правильных меток`. Неизвестными в данной модели являются `правильные метки` $Z_j$, надежность работников $\\alpha_i$ и сложность сайтов $\\frac{1}{\\beta_j}$.\n",
    "\n",
    "Наша цель - эффективно искать наиболее вероятные значения ненаблюдаемых переменных $Z, \\alpha, \\beta$ для данных. Будем использовать `Expectation Maximization` алгоритм:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IV.GLAD - Generative Model of Labels, Abilities and Difficulties: EM\n",
    "\n",
    "## `E-step`\n",
    "\n",
    "Давайте обозначим все значения для сайта $j$ за $\\large \\textbf{l}_j = \\{l_{ij'} = j' \\vert j \\}$. Кстати, каждый толокер не обязательно должен пометить все картинки, поэтому в данной формуле $i$ обозначает только те индексы толокеров, которые пометили данную картинку.\n",
    "\n",
    "Нам необходоим на данном шаге посчитать вероятности меток $z_j \\in \\{0, 1\\}$ для значения $\\alpha, \\beta$ с последнего шага `M`-алгоритма и переменных $l_j$:\n",
    "\n",
    "$$\\LARGE p(z_j \\vert l, \\alpha, \\beta) = p(z_j) \\cdot \\prod\\limits_{i=1} p(l_{ij} \\vert z_j, \\alpha_i, \\beta_j)$$\n",
    "\n",
    "## `M-step`\n",
    "\n",
    "Мы максимизируем $Q$:\n",
    "\n",
    "$$\\large Q(\\alpha, \\beta) = E(\\ln p(l, z \\vert \\alpha, \\beta)) = \\sum\\limits_j E[\\ln p(z_j)] + \\sum\\limits_{ij}E(\\ln p(l_{ij} \\vert z_j, \\alpha_i, \\beta_j)$$\n",
    "\n",
    "Сожаление: приходится максимизировать по $\\alpha$ и $\\beta$, аналитического максимума нет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test Realisation: GLAD\n",
    "\n",
    "Улучшение: [модель Андрея Мищенко]() - учитывается вероятность того, что пользователь не посмотрит на задание."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molding data\n",
      "Finished to Mold data\n",
      "start M step\n",
      "calculate\n",
      "done M step\n",
      "start E step\n",
      "done M step\n",
      "start M step\n",
      "calculate\n",
      "done M step\n",
      "start E step\n",
      "done M step\n",
      "start M step\n",
      "calculate\n",
      "done M step\n",
      "start E step\n",
      "done M step\n",
      "2 classes: Done GLAD\n"
     ]
    }
   ],
   "source": [
    "GLAD_2 = toloka_2.GLAD(max_iter=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MV</th>\n",
       "      <th>average</th>\n",
       "      <th>pred_UC(min)</th>\n",
       "      <th>pred_AM(max)</th>\n",
       "      <th>MWA</th>\n",
       "      <th>WAWA(W)</th>\n",
       "      <th>WAWA + Bayes(W)</th>\n",
       "      <th>WAWA(IDW)</th>\n",
       "      <th>WAWA(DWS)</th>\n",
       "      <th>WAWA + Bayes(IDW)</th>\n",
       "      <th>DawidScene</th>\n",
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       "      <th>GLAD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time (s)</th>\n",
       "      <td>7.664925</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>0.628199</td>\n",
       "      <td>0.323794</td>\n",
       "      <td>8.817951</td>\n",
       "      <td>9.531563</td>\n",
       "      <td>320.653467</td>\n",
       "      <td>48.391266</td>\n",
       "      <td>48.219386</td>\n",
       "      <td>397.280352</td>\n",
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       "      <td>9.090810</td>\n",
       "      <td>302.585573</td>\n",
       "      <td>380.537457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Accuracy (%)</th>\n",
       "      <td>77.636670</td>\n",
       "      <td>76.763568</td>\n",
       "      <td>74.551047</td>\n",
       "      <td>34.527235</td>\n",
       "      <td>80.166683</td>\n",
       "      <td>80.176605</td>\n",
       "      <td>82.488342</td>\n",
       "      <td>82.458577</td>\n",
       "      <td>82.528029</td>\n",
       "      <td>83.143169</td>\n",
       "      <td>81.069551</td>\n",
       "      <td>78.073222</td>\n",
       "      <td>80.960413</td>\n",
       "      <td>78.678440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            MV    average  pred_UC(min)  pred_AM(max)  \\\n",
       "Time (s)              7.664925   0.238843      0.628199      0.323794   \n",
       "Golden Accuracy (%)  77.636670  76.763568     74.551047     34.527235   \n",
       "\n",
       "                           MWA    WAWA(W)  WAWA + Bayes(W)  WAWA(IDW)  \\\n",
       "Time (s)              8.817951   9.531563       320.653467  48.391266   \n",
       "Golden Accuracy (%)  80.166683  80.176605        82.488342  82.458577   \n",
       "\n",
       "                     WAWA(DWS)  WAWA + Bayes(IDW)  DawidScene       WAWR  \\\n",
       "Time (s)             48.219386         397.280352  345.720450   9.090810   \n",
       "Golden Accuracy (%)  82.528029          83.143169   81.069551  78.073222   \n",
       "\n",
       "                     WAWA + Bayes DS_Weights_Bayes        GLAD  \n",
       "Time (s)                                302.585573  380.537457  \n",
       "Golden Accuracy (%)                      80.960413   78.678440  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(toloka_2.result_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## V. [Max Eigenvalue](http://www.arpitaghosh.com/papers/ecfinal-ratings.pdf)*\n",
    "\n",
    "**Идея**: рассмотрим матрицу оценок $L$ в задаче бинарной классификации с классами ${-1,1}$. Тогда собственный вектор, соответствующий максимальному собственному значению матрицы будет являться вектором предсказаний меток.\n",
    "\n",
    "Как?: можно посмотреть статью, я не до конца понял мотивацию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Final Result: Toloka Aggregation 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "result_toloka_2 = pd.DataFrame(toloka_2.result_dict).T\n",
    "result_toloka_5 = pd.DataFrame(toloka_5.result_dict).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <th>pred_UC(min)</th>\n",
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       "    <tr>\n",
       "      <th>pred_AM(max)</th>\n",
       "      <td>34.527235</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Golden Accuracy (%)\n",
       "WAWA + Bayes(IDW)                        83.143169\n",
       "WAWA(DWS)                                82.528029\n",
       "WAWA + Bayes(W)                          82.488342\n",
       "WAWA(IDW)                                82.458577\n",
       "DawidScene                               81.069551\n",
       "WAWA + Bayes DS_Weights_Bayes            80.960413\n",
       "WAWA(W)                                  80.176605\n",
       "MWA                                      80.166683\n",
       "GLAD                                     78.678440\n",
       "WAWR                                     78.073222\n",
       "MV                                       77.636670\n",
       "average                                  76.763568\n",
       "pred_UC(min)                             74.551047\n",
       "pred_AM(max)                             34.527235"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    result_toloka_2['Golden Accuracy (%)']\n",
    "    .sort_values(ascending=False)\n",
    "    .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Final Result: Toloka Aggregation 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "result_toloka_2['Time (s)'].plot.barh(color='darkblue', ax=ax)\n",
    "ax.set_title('Tuning Time: Toloka Aggregation 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Final Result: Toloka Aggregation 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Golden Accuracy (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DawidScene</th>\n",
       "      <td>90.785588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAWA</th>\n",
       "      <td>90.431187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAWR</th>\n",
       "      <td>90.295334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWA</th>\n",
       "      <td>90.281187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MV</th>\n",
       "      <td>90.135854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_average</th>\n",
       "      <td>84.087419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Golden Accuracy (%)\n",
       "DawidScene              90.785588\n",
       "WAWA                    90.431187\n",
       "WAWR                    90.295334\n",
       "MWA                     90.281187\n",
       "MV                      90.135854\n",
       "pred_average            84.087419"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    result_toloka_5['Golden Accuracy (%)']\n",
    "    .sort_values(ascending=False)\n",
    "    .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Final Result: Toloka Aggregation 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "result_toloka_5['Time (s)'].plot.barh(color='darkblue', ax=ax)\n",
    "ax.set_title('Tuning Time: Toloka Aggregation 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Save Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "toloka_2.predictions.astype(int).to_csv('predict/prediction_toloka_2.csv', index=False)\n",
    "toloka_5.predictions.astype(int).to_csv('predict/prediction_toloka_5.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Спасибо за внимание!"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}