database_reading.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"database_new.csv\", delimiter=\";\", index_col=0, names=['id', 'user_id', 'problem_id', 'category', 'result'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id  problem_id  category  result\nid                                       \n1      7661       10332        26       0\n2      7661        4504        74       0\n3      7661         620       115       1\n4      7661        4170        76       0\n5      7661        4359        24       1",
      "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>user_id</th>\n      <th>problem_id</th>\n      <th>category</th>\n      <th>result</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>7661</td>\n      <td>10332</td>\n      <td>26</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7661</td>\n      <td>4504</td>\n      <td>74</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7661</td>\n      <td>620</td>\n      <td>115</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7661</td>\n      <td>4170</td>\n      <td>76</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>7661</td>\n      <td>4359</td>\n      <td>24</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(27864481, 4)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(88905,)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.user_id.unique().shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(11133,)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.problem_id.unique().shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## сколько заданий решают школьники"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "count_by_user = df.groupby(\"user_id\").agg({\"problem_id\":\"count\"}).rename({\"problem_id\":\"count_solved_tasks\"}, axis=1)\n",
    "count_by_user = count_by_user[count_by_user.count_solved_tasks < 5000]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "         count_solved_tasks\nuser_id                    \n1                       137\n2                        96\n3                        64\n4                        87\n6                       458",
      "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>count_solved_tasks</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>137</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>64</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>458</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:ylabel='Count'>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.histplot(count_by_user)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## самые популярные задания"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "df_problems_grouped = pd.DataFrame(df.groupby(\"problem_id\").agg({\"problem_id\":\"count\"}).rename({\"problem_id\":\"count_solutions\"}, axis=1).sort_values(\"count_solutions\", ascending=False))\n",
    "MIN_COUNT_SOLUTIONS_ON_TASK = 750\n",
    "saldom_problems = df_problems_grouped[df_problems_grouped.count_solutions < MIN_COUNT_SOLUTIONS_ON_TASK]\n",
    "df_count_solutions = df_problems_grouped[df_problems_grouped.count_solutions > MIN_COUNT_SOLUTIONS_ON_TASK]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## задания которые будут использоваться"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "27221513"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count_solutions.count_solutions.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "(2829, 1)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count_solutions.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## задания, которые не будут использоваться"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "(8304, 1)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saldom_problems.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "642968"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saldom_problems.count_solutions.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## распределение числа решений задач"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Count solutions for tasks', ylabel='Count'>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Count solutions for tasks\")\n",
    "\n",
    "sns.histplot(df_count_solutions.values, bins=100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "2829"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df_count_solutions.index.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## задания которые будут использоваться"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "(2829,)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_tasks = df_count_solutions.index.values\n",
    "available_tasks.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "available_tasks = set(available_tasks)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# df[\"true_result\"] = df.apply(lambda x: x.problem_id in available_tasks and x.result == dct_problems_to_max_score[x.problem_id], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "df_grouped_by_problems = df.groupby(\"problem_id\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## определим максимальные баллы за каждое задание"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2829/2829 [00:04<00:00, 642.04it/s] \n"
     ]
    }
   ],
   "source": [
    "## максимальные баллы за каждое задание\n",
    "dct_problems_to_max_score = dict()\n",
    "\n",
    "for task_id in tqdm(available_tasks):\n",
    "    tmp = df_grouped_by_problems.get_group(task_id)\n",
    "    task_result_max = tmp.result.max()\n",
    "    if tmp.result.value_counts()[task_result_max] < 10:\n",
    "        task_result_max = 1\n",
    "    dct_problems_to_max_score[task_id] = task_result_max\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## процент правильности решений"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 27/500 [00:04<01:20,  5.86it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[1;32mC:\\Users\\843E~1\\AppData\\Local\\Temp/ipykernel_15560/1763205654.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mtask\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtqdm\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlist\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mavailable_tasks\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;36m500\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m     \u001B[0mgroup\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdf_grouped_by_problems\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_group\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtask\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 5\u001B[1;33m     \u001B[0mgroup\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"true_result\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mgroup\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mresult\u001B[0m \u001B[1;33m==\u001B[0m \u001B[0mdct_problems_to_max_score\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mproblem_id\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      6\u001B[0m     \u001B[0mresults_tasks\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mgroup\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtrue_result\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmean\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      7\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36mapply\u001B[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001B[0m\n\u001B[0;32m   8738\u001B[0m             \u001B[0mkwargs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   8739\u001B[0m         )\n\u001B[1;32m-> 8740\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mop\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   8741\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   8742\u001B[0m     def applymap(\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\apply.py\u001B[0m in \u001B[0;36mapply\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    686\u001B[0m             \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply_raw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    687\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 688\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply_standard\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    689\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    690\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0magg\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\apply.py\u001B[0m in \u001B[0;36mapply_standard\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    810\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    811\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mapply_standard\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 812\u001B[1;33m         \u001B[0mresults\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mres_index\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply_series_generator\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    813\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    814\u001B[0m         \u001B[1;31m# wrap results\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\apply.py\u001B[0m in \u001B[0;36mapply_series_generator\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    826\u001B[0m             \u001B[1;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mv\u001B[0m \u001B[1;32min\u001B[0m \u001B[0menumerate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mseries_gen\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    827\u001B[0m                 \u001B[1;31m# ignore SettingWithCopy here in case the user mutates\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 828\u001B[1;33m                 \u001B[0mresults\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mf\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mv\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    829\u001B[0m                 \u001B[1;32mif\u001B[0m \u001B[0misinstance\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mresults\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mABCSeries\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    830\u001B[0m                     \u001B[1;31m# If we have a view on v, we need to make a copy because\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\Users\\843E~1\\AppData\\Local\\Temp/ipykernel_15560/1763205654.py\u001B[0m in \u001B[0;36m<lambda>\u001B[1;34m(x)\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mtask\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtqdm\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlist\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mavailable_tasks\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;36m500\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m     \u001B[0mgroup\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdf_grouped_by_problems\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_group\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtask\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 5\u001B[1;33m     \u001B[0mgroup\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"true_result\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mgroup\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mresult\u001B[0m \u001B[1;33m==\u001B[0m \u001B[0mdct_problems_to_max_score\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mproblem_id\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      6\u001B[0m     \u001B[0mresults_tasks\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mgroup\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtrue_result\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmean\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      7\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\generic.py\u001B[0m in \u001B[0;36m__getattr__\u001B[1;34m(self, name)\u001B[0m\n\u001B[0;32m   5484\u001B[0m             \u001B[1;32mand\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_info_axis\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_can_hold_identifiers_and_holds_name\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   5485\u001B[0m         ):\n\u001B[1;32m-> 5486\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mname\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   5487\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mobject\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__getattribute__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   5488\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\series.py\u001B[0m in \u001B[0;36m__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m    940\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    941\u001B[0m         \u001B[1;32melif\u001B[0m \u001B[0mkey_is_scalar\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 942\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_value\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    943\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    944\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mis_hashable\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\series.py\u001B[0m in \u001B[0;36m_get_value\u001B[1;34m(self, label, takeable)\u001B[0m\n\u001B[0;32m   1049\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1050\u001B[0m         \u001B[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1051\u001B[1;33m         \u001B[0mloc\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlabel\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1052\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_values_for_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mloc\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlabel\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1053\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001B[0m in \u001B[0;36mget_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m   3357\u001B[0m                     \u001B[1;34m\"backfill or nearest lookups\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3358\u001B[0m                 )\n\u001B[1;32m-> 3359\u001B[1;33m             \u001B[0mcasted_key\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_maybe_cast_indexer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3360\u001B[0m             \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3361\u001B[0m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_engine\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_loc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcasted_key\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\users\\никита\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001B[0m in \u001B[0;36m_maybe_cast_indexer\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   5693\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mslice\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mstart_slice\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mend_slice\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mstep\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   5694\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 5695\u001B[1;33m     \u001B[1;32mdef\u001B[0m \u001B[0m_maybe_cast_indexer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   5696\u001B[0m         \"\"\"\n\u001B[0;32m   5697\u001B[0m         \u001B[0mIf\u001B[0m \u001B[0mwe\u001B[0m \u001B[0mhave\u001B[0m \u001B[0ma\u001B[0m \u001B[0mfloat\u001B[0m \u001B[0mkey\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mare\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0ma\u001B[0m \u001B[0mfloating\u001B[0m \u001B[0mindex\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mthen\u001B[0m \u001B[1;32mtry\u001B[0m \u001B[0mto\u001B[0m \u001B[0mcast\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "results_tasks = []\n",
    "\n",
    "for task in tqdm(list(available_tasks)[:500]):\n",
    "    group = df_grouped_by_problems.get_group(task)\n",
    "    group[\"true_result\"] = group.apply(lambda x: x.result == dct_problems_to_max_score[x.problem_id], axis=1)\n",
    "    results_tasks.append(group.true_result.mean())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Распределение процента решенных заданий ')"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "sns.histplot(results_tasks)\n",
    "plt.title(\"Распределение процента решенных заданий \")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## определим множество людей которые решали только те задачи которые мы рассматриваем"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "df_grouped_by_user = df.groupby(\"user_id\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 88905/88905 [00:41<00:00, 2168.40it/s]\n"
     ]
    }
   ],
   "source": [
    "all_count = 0\n",
    "count_errors = 0\n",
    "count_errors_2 = 0\n",
    "count_errors_3 = 0\n",
    "available_users = set()\n",
    "\n",
    "procents_correct_solutions_for_user = []\n",
    "\n",
    "for user in tqdm(df.user_id.unique()):\n",
    "    tmp = df_grouped_by_user.get_group(user)\n",
    "\n",
    "    procent_correct_solutions = (tmp.result > 0).mean()\n",
    "    procents_correct_solutions_for_user.append(procent_correct_solutions)\n",
    "\n",
    "    if procent_correct_solutions < 0.6:\n",
    "        count_errors_3 += 1\n",
    "        continue\n",
    "\n",
    "    count_solved_tasks = tmp.shape[0]\n",
    "    all_count += 1\n",
    "\n",
    "    if count_solved_tasks < 100 or count_solved_tasks > 10000:\n",
    "        count_errors_2 += 1\n",
    "        continue\n",
    "\n",
    "    if tmp.problem_id.isin(available_tasks).all():\n",
    "        # if not all([task in available_tasks for task in tmp.problem_id.values]):\n",
    "        available_users.add(user)\n",
    "    else:\n",
    "        count_errors += 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кол-во ошибок:  32633\n",
      "Кол-во ошибок по проценту правильных решений:  31145\n"
     ]
    }
   ],
   "source": [
    "print(\"Кол-во ошибок: \", count_errors + count_errors_2)\n",
    "print(\"Кол-во ошибок по проценту правильных решений: \", count_errors_3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "25127"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.user_id.unique()) - count_errors - count_errors_2 - count_errors_3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(procents_correct_solutions_for_user).set_title(\"Распределение процента правильных ответов\")\n",
    "None"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25127/25127 [00:01<00:00, 13518.24it/s]\n"
     ]
    }
   ],
   "source": [
    "count_moves = 0\n",
    "counts_solved_tasks = []\n",
    "\n",
    "for user in tqdm(available_users):\n",
    "    count_solved_tasks =  df_grouped_by_user.get_group(user).shape[0]\n",
    "    count_moves += count_solved_tasks\n",
    "    counts_solved_tasks.append(count_solved_tasks)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "8427903"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_moves"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## распределение кол-ва решений школьников для отфильтрованного датасета"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Count solved tasks', ylabel='Count students with this count of solved tasks'>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.ylabel(\"Count students with this count of solved tasks\")\n",
    "plt.xlabel(\"Count solved tasks\")\n",
    "\n",
    "sns.histplot(counts_solved_tasks)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## главная обработка данных"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "необходимо привести в формат\n",
    "[номер_задачи_1, номер_задачи_2, ..., номер_задачи_n]\n",
    "[правильность_ответа_1, правильность_ответа_2, ...? правильность_ответа_n]\n",
    "для каждого ученика (из 20к примерно)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "2829"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dct_problems_to_max_score)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "outputs": [],
   "source": [
    "def zero_runs(a):\n",
    "    iszero = np.concatenate(([0], np.equal(a, 0).view(np.int8), [0]))\n",
    "    absdiff = np.abs(np.diff(iszero))\n",
    "    ranges = np.where(absdiff == 1)[0].reshape(-1, 2)\n",
    "    max_len = -1\n",
    "    for i, j in ranges.tolist():\n",
    "        max_len = max(max_len, j - i)\n",
    "    return max_len"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [
    {
     "data": {
      "text/plain": "3"
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_runs([11, 1, 1, 1, 0, 0, 0, 1, 1, 2, 0])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25127/25127 [00:15<00:00, 1655.43it/s]\n"
     ]
    }
   ],
   "source": [
    "all_histories = []\n",
    "deltas = []\n",
    "zero_runs_value = []\n",
    "\n",
    "for user in tqdm(available_users):\n",
    "    user_history = df_grouped_by_user.get_group(user)\n",
    "\n",
    "\n",
    "    problems_history = user_history.problem_id.values\n",
    "    correctness_history = []\n",
    "    last_right_answer_ind = None\n",
    "    first_right_answer = None\n",
    "\n",
    "    for ind, score in enumerate(user_history.result.values):\n",
    "        is_correct = score == dct_problems_to_max_score[problems_history[ind]]\n",
    "\n",
    "        correctness_history.append(is_correct)\n",
    "\n",
    "        if first_right_answer is None and is_correct:\n",
    "            first_right_answer = ind\n",
    "\n",
    "        if score == dct_problems_to_max_score[problems_history[ind]]:\n",
    "            last_right_answer_ind = ind\n",
    "\n",
    "    zero_runs_value.append(zero_runs(correctness_history))\n",
    "    if zero_runs(correctness_history) > 9:\n",
    "        continue\n",
    "\n",
    "    correctness_history = correctness_history[first_right_answer:last_right_answer_ind]\n",
    "    center = len(correctness_history) // 2\n",
    "\n",
    "    second_part_percent = sum(correctness_history[center:]) / center\n",
    "    first_part_percent = sum(correctness_history[:center]) / center\n",
    "\n",
    "    deltas.append(second_part_percent - first_part_percent)\n",
    "    all_histories.append((problems_history, correctness_history))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [
    {
     "data": {
      "text/plain": "221.70256825543728"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([len(history) for i, history in all_histories])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print(list(map(int, all_histories[856][1])))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Распределение длины самого длиного промежутка из нулей')"
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "sns.distplot(zero_runs_value).set_title(\"Распределение длины самого длиного промежутка из нулей\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "sns.distplot(deltas).set_title(\"Распределение метрики: (частота ответов во второй половине) - (частота правильных ответов в первой половине)\")\n",
    "None"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.015716194700670028\n",
      "0.12122203239560382\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(deltas))\n",
    "print(np.std(deltas))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "          user_id  problem_id  category  result\nid                                             \n1            7661       10332        26       0\n2            7661        4504        74       0\n3            7661         620       115       1\n4            7661        4170        76       0\n5            7661        4359        24       1\n...           ...         ...       ...     ...\n27739862     7661       10150       158       2\n27739864     7661       10447       158       2\n27739865     7661       29253       158       2\n27739866     7661       47236       158       2\n27739867     7661        8549       158       2\n\n[1973 rows x 4 columns]",
      "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>user_id</th>\n      <th>problem_id</th>\n      <th>category</th>\n      <th>result</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>7661</td>\n      <td>10332</td>\n      <td>26</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7661</td>\n      <td>4504</td>\n      <td>74</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7661</td>\n      <td>620</td>\n      <td>115</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7661</td>\n      <td>4170</td>\n      <td>76</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>7661</td>\n      <td>4359</td>\n      <td>24</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>27739862</th>\n      <td>7661</td>\n      <td>10150</td>\n      <td>158</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>27739864</th>\n      <td>7661</td>\n      <td>10447</td>\n      <td>158</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>27739865</th>\n      <td>7661</td>\n      <td>29253</td>\n      <td>158</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>27739866</th>\n      <td>7661</td>\n      <td>47236</td>\n      <td>158</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>27739867</th>\n      <td>7661</td>\n      <td>8549</td>\n      <td>158</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>1973 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.user_id == 7661]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "[False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n True,\n True,\n False,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n False,\n True,\n True,\n True,\n True,\n False,\n True,\n True,\n True,\n False,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False]"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctness_history"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "137"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(all_histories[0][0]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40359/40359 [00:02<00:00, 18081.17it/s]\n"
     ]
    }
   ],
   "source": [
    "all_tasks_in_dataset = set()\n",
    "\n",
    "for i in tqdm(all_histories):\n",
    "    for j in i[0]:\n",
    "        all_tasks_in_dataset.add(j)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "2829"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_tasks_in_dataset)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "{numpy.int64}"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([type(i) for i in all_tasks_in_dataset])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "LabelEncoder()"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(list(all_tasks_in_dataset))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"new_label_encoder.pkl\", \"wb\") as f:\n",
    "    pickle.dump(le, f)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "array([    8,    22,    23, ..., 52204, 52205, 52206], dtype=int64)"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40359/40359 [00:03<00:00, 11956.90it/s]\n"
     ]
    }
   ],
   "source": [
    "for ind, i in enumerate(tqdm(all_histories)):\n",
    "    all_histories[ind] = (le.transform(all_histories[ind][0]), all_histories[ind][1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40359/40359 [00:20<00:00, 2010.44it/s]\n"
     ]
    }
   ],
   "source": [
    "all_histories_filtered_by_count_right_answers = []\n",
    "\n",
    "for ind, i in enumerate(tqdm(all_histories)):\n",
    "    if sum(i[1]) / len(i[1]) > 0.4:\n",
    "        all_histories_filtered_by_count_right_answers.append(all_histories[ind])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "22427"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_histories_filtered_by_count_right_answers)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "all_sum_count = 0\n",
    "\n",
    "for i in all_histories_filtered_by_count_right_answers:\n",
    "    all_sum_count += len(i[1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "7832325"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sum_count"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## сохраняемся"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"proccesed_dataset_new.pickle\", \"wb\") as f:\n",
    "    pickle.dump(all_histories_filtered_by_count_right_answers, f)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
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