main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W02_CY8HmbPF"
   },
   "source": [
    "# Решение\n",
    "Решение задачи на\n",
    "[GitHub](https://github.com/tikovka72/yandex-lyceum-selection-round)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t3nS-L9fdROX"
   },
   "source": [
    "## Подготовка\n",
    "Установить все нужные библиотеки для работы программ\n",
    "(После этого нужно будет перезагрузить ядро)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5UsQme-IX7IK"
   },
   "source": [
    "### Установка зависимостей"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Установка через pip"
   ],
   "metadata": {
    "id": "EoHgs_8NEf38"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HUBGF-Qz1sb_"
   },
   "outputs": [],
   "source": [
    "!pip3 install -U matplotlib==3.5.1 \\\n",
    "                 tensorflow==2.7.0 \\\n",
    "                 pandas==1.3.5 \\\n",
    "                 numpy==1.19.5"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Перезагрузка ядра"
   ],
   "metadata": {
    "id": "6_92EdlqEZKM"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Перезагрузка ядра после установки пакетов зависимостей\n",
    "need = input('Вы точно хотите перезагрузить ядро? (Y/n) ')\n",
    "while not need or need.lower() not in 'yn':\n",
    "    need = input('Вы точно хотите перезагрузить ядро? (Y/n) ')\n",
    "if need.lower() == 'y': \n",
    "    print('Перезагрузка...')\n",
    "    exit()\n",
    "else:\n",
    "    print('Отмена')"
   ],
   "metadata": {
    "id": "HZ8pAtVtCFCH"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Проверка"
   ],
   "metadata": {
    "id": "6G2M-1bAEjTT"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib, tensorflow, pandas, numpy\n",
    "\n",
    "all_right = True \n",
    "\n",
    "for package, right_version in ((matplotlib, '3.5.1'), \n",
    "                               (tensorflow, '2.7.0'), \n",
    "                               (pandas, '1.3.5'),\n",
    "                               (numpy, '1.19.5')): \n",
    "    if package.__version__ != right_version:\n",
    "        print(f'Не та версия {package.__package__}: '\n",
    "              f'{package.__version__} != {right_version}')\n",
    "        all_right = False\n",
    "if all_right: \n",
    "    print('Все готово к работе!')"
   ],
   "metadata": {
    "id": "MLIoBD-mBmIY"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2E_LmKOQU58H"
   },
   "source": [
    "### Определение переменных для дальнейшей работы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GL2Fafc6Uz_S"
   },
   "outputs": [],
   "source": [
    "# columns\n",
    "survived = 'survived'\n",
    "pclass = 'pclass'\n",
    "sex = 'sex'\n",
    "age = 'age'\n",
    "sibsp = 'sibsp'\n",
    "parch = 'parch'\n",
    "fare = 'fare'\n",
    "embarked = 'embarked'\n",
    "class_ = 'class'\n",
    "who = 'who'\n",
    "adult_male = 'adult_male'\n",
    "deck = 'deck'\n",
    "embark_town = 'embark_town'\n",
    "alive = 'alive'\n",
    "alone = 'alone'\n",
    "\n",
    "# values\n",
    "unknown = 'Unknown'\n",
    "u = 'U'\n",
    "\n",
    "# для диаграмм\n",
    "\n",
    "# размеры фигур для .set_size(inches) \n",
    "one_axes_size = 10, 5\n",
    "two_axes_size = 20, 5\n",
    "three_axes_size = 30, 5\n",
    "\n",
    "# 2 полотна по горизонтали на фигуре \n",
    "two_axes_in_row = 1, 2\n",
    "three_axes_in_row = 1, 3\n",
    "\n",
    "# цвета \n",
    "good_color = '#A6B401'\n",
    "second_good_color = '#C3F294'\n",
    "third_good_color = '#EFF67B'\n",
    "bad_color = '#D50102'\n",
    "neutral_color = '#B3E7DC'\n",
    "second_neutral_color = '#B2B1AA'\n",
    "\n",
    "# другие значения\n",
    "bar_width = 0.4\n",
    "slim_bar_width = 0.2\n",
    "\n",
    "marker_size_l = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FlmEgYwamey5"
   },
   "source": [
    "## 1. Скачать файл по ссылке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZWr2A3U-meQH",
    "outputId": "b6874c56-9381-494d-9bbb-7d28ee9fca76"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--2022-02-16 16:28:46--  https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 57018 (56K) [text/plain]\n",
      "Saving to: ‘titanic.csv’\n",
      "\n",
      "\rtitanic.csv           0%[                    ]       0  --.-KB/s               \rtitanic.csv         100%[===================>]  55.68K  --.-KB/s    in 0.01s   \n",
      "\n",
      "2022-02-16 16:28:47 (5.39 MB/s) - ‘titanic.csv’ saved [57018/57018]\n",
      "\n",
      "--2022-02-16 16:28:47--  http://titanic.csv/\n",
      "Resolving titanic.csv (titanic.csv)... failed: Name or service not known.\n",
      "wget: unable to resolve host address ‘titanic.csv’\n",
      "FINISHED --2022-02-16 16:28:47--\n",
      "Total wall clock time: 0.2s\n",
      "Downloaded: 1 files, 56K in 0.01s (5.39 MB/s)\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv' 'titanic.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5I9xQAoeqw1S"
   },
   "source": [
    "## 2. Изучение данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vP4kQGvSTFVZ"
   },
   "source": [
    "### Информация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QT8aaifpqtiG",
    "outputId": "cd5d5bd1-1848-4d55-bd24-de3914d96922"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   survived     891 non-null    int64  \n",
      " 1   pclass       891 non-null    int64  \n",
      " 2   sex          891 non-null    object \n",
      " 3   age          714 non-null    float64\n",
      " 4   sibsp        891 non-null    int64  \n",
      " 5   parch        891 non-null    int64  \n",
      " 6   fare         891 non-null    float64\n",
      " 7   embarked     889 non-null    object \n",
      " 8   class        891 non-null    object \n",
      " 9   who          891 non-null    object \n",
      " 10  adult_male   891 non-null    bool   \n",
      " 11  deck         203 non-null    object \n",
      " 12  embark_town  889 non-null    object \n",
      " 13  alive        891 non-null    object \n",
      " 14  alone        891 non-null    bool   \n",
      "dtypes: bool(2), float64(2), int64(4), object(7)\n",
      "memory usage: 92.4+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('titanic.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pI1xhv4GrIaM"
   },
   "source": [
    "* возраст (`age`, 177 пропусков)\n",
    "* порт посадки (`embarked`, 2 пропуска)\n",
    "* палуба (`deck`, 688 пропусков)\n",
    "* город посадки (`embark_town`, 2 пропуска)\n",
    "\n",
    "### Заполнение пропусков\n",
    "#### Возраст \n",
    "Принято решение написать небольшую модель,\n",
    "обучить ее на заполненных данных\n",
    "и предположить примерный возраст в пропущенных ячейках.\n",
    "\n",
    "#### Порт посадки\n",
    "Все пропуски = `U` (unknown).\n",
    "Данный столбец в дальнейшем не потребуется,\n",
    "два пропущенных значения я решил заменить на `U`\n",
    "вместо удаления двух строк,\n",
    "так как объем данных и так достаточно мал.\n",
    "\n",
    "#### Город посадки\n",
    "Все пропуски = `Unknown`.\n",
    "То же самое, что и с портом.\n",
    "\n",
    "#### Палуба\n",
    "Все пропуски = `U`.\n",
    "В данном столбце очень мало значений,\n",
    "нормально обучить сеть на таком количестве затруднительно,\n",
    "а моего опыта в обработке данных не хватает,\n",
    "чтобы вручную найти закономерности и связи с другими столбцами.\n",
    "Данный столбец не потребуется для дальнейшей работы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DW-NZg5CAYGD"
   },
   "source": [
    "#### Пропуски посадки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YzvqgLyowsSe",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def fill_na_embark(data_frame: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Функция для заполнения пропусков embarked и embark_town\n",
    "    \n",
    "    \"\"\"\n",
    "    data_frame[embarked] = data_frame[embarked].fillna(u)\n",
    "    data_frame[embark_town] = data_frame[embark_town].fillna(unknown)\n",
    "\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "W8Us5STAJlPI"
   },
   "outputs": [],
   "source": [
    "def for_print_embark(data_frame: pd.DataFrame,\n",
    "                     column_offset: int = 4) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Функция для отображения информации \n",
    "    об уникальных значениях в столбцах 'embarked' и 'embark_town'\n",
    "\n",
    "    \"\"\"\n",
    "    for_print = [('Порты:', 'Города:')]\n",
    "    max_len_port = len(for_print[0][0])\n",
    "    output_str = ''\n",
    "\n",
    "    unique_ports, unique_towns = (data_frame[embarked].unique(),\n",
    "                                  data_frame[embark_town].unique())\n",
    "    \n",
    "    for row_index in range(max(len(unique_ports), len(unique_towns))):\n",
    "        # Доводим оба столбца до одной длины\n",
    "        temp_port = unique_ports[row_index] \\\n",
    "            if row_index < len(unique_ports) else ''\n",
    "        temp_town = unique_towns[row_index] \\\n",
    "            if row_index < len(unique_towns) else ''\n",
    "\n",
    "        max_len_port = max(max_len_port, len(temp_port))\n",
    "\n",
    "        for_print.append((temp_port, temp_town))\n",
    "\n",
    "    for port, town in for_print: \n",
    "        # смотрим, сколько занимает длина строки порта \n",
    "        # и делаем на основе этой строки отступ для второй колонки\n",
    "        output_str += \\\n",
    "            f'{port}' \\\n",
    "            f'{\" \" * (max_len_port + column_offset - len(port))}' \\\n",
    "            f'{town}\\n'\n",
    "\n",
    "    return output_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_26PE-Zh633A",
    "outputId": "fccb8402-d712-4215-a1f1-8751e64d101a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Порты:    Города:\n",
      "S         Southampton\n",
      "C         Cherbourg\n",
      "Q         Queenstown\n",
      "U         Unknown\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = fill_na_embark(df)\n",
    "print(for_print_embark(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ckswBfxILhOS"
   },
   "source": [
    "#### Пропуски возраста"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kGDHBZ_fCBP3"
   },
   "source": [
    "##### Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "AHhu8-gKLgTW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow.keras.losses import mse\n",
    "from tensorflow.keras.metrics import mae\n",
    "from tensorflow.keras.activations import relu, sigmoid\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from typing import Iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "jW0OqdyZP6Zc"
   },
   "outputs": [],
   "source": [
    "# константы для нормализации\n",
    "X_COLUMNS = [pclass, sex, sibsp, parch, fare, class_, alive, alone]\n",
    "Y_COLUMNS = [age]\n",
    "AGE_NORMALIZER = 0.01\n",
    "FARE_NORMALIZER = 0.001\n",
    "SIBSP_NORMALIZER = 0.1\n",
    "PARCH_NORMALIZER = 0.1\n",
    "CLASSES = {'First': 0.1, 'Second': 0.5, 'Third': 0.9}\n",
    "P_CLASSES = {1: 0.1, 2: 0.5, 3: 0.9}\n",
    "\n",
    "\n",
    "def prepare_data(data_frame): \n",
    "    \"\"\"\n",
    "    Функция для подготовки данных \n",
    "\n",
    "    \"\"\"\n",
    "    data_frame_copy = data_frame.copy()\n",
    "    data_frame_copy[pclass] = data_frame_copy[pclass] \\\n",
    "        .apply(lambda text_pclass: P_CLASSES[text_pclass])\n",
    "\n",
    "    data_frame_copy[sex] = data_frame_copy[sex].apply(\n",
    "        lambda text_sex: int(text_sex == 'female'))\n",
    "    data_frame_copy[fare] *= FARE_NORMALIZER\n",
    "    data_frame_copy[sibsp] *= SIBSP_NORMALIZER\n",
    "    data_frame_copy[parch] *= PARCH_NORMALIZER\n",
    "    data_frame_copy[class_] = data_frame_copy[class_].apply(\n",
    "        lambda text_class: CLASSES[text_class])\n",
    "    data_frame_copy[alive] = data_frame_copy[alive] \\\n",
    "        .apply(lambda text_alive: int(text_alive == 'yes'))\n",
    "\n",
    "    data_frame_copy[alone] = data_frame_copy[alone].apply(int)\n",
    "\n",
    "    data_frame_copy[age] *= AGE_NORMALIZER\n",
    "\n",
    "    return data_frame_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "6Qw6lhChM2zc"
   },
   "outputs": [],
   "source": [
    "data = df[df.loc[:, age].notna()]\n",
    "prepared_data = prepare_data(data)\n",
    "x_data, y_data = (prepared_data[X_COLUMNS].to_numpy(),\n",
    "                  prepared_data[Y_COLUMNS].to_numpy())\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x_data,\n",
    "    y_data,\n",
    "    train_size=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uPWlwLMTCRcs"
   },
   "source": [
    "##### Аугментация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "jEU9zASB_p1N"
   },
   "outputs": [],
   "source": [
    "RANDOM_NUMBER_VARIANT = 1\n",
    "RANDOM_CHOICE_VARIANT = 2\n",
    "\n",
    "\n",
    "def data_augmentation(\n",
    "        x: np.array,\n",
    "        y: np.array,\n",
    "        count: int = 1,\n",
    "        x_transformations: Iterable = tuple()) -> (np.array, np.array):\n",
    "    \"\"\"\n",
    "    Функция для аугментации данных. !Возвращает только новые данные\n",
    "    x: входные данные\n",
    "    y: выходные данные\n",
    "    count: количество новых данных\n",
    "    x_transformations: список с инструкциями для преобразований,\n",
    "        где инструкции могут быть двух видов:\n",
    "        1. {\n",
    "            't_variant': 1       вид инструкции,\n",
    "                                 1 - выбор случайного числа из диапазона\n",
    "            'index': int         индекс столбца,\n",
    "                                 нод которым надо произвести\n",
    "                                 данную инструкцию\n",
    "            'min': float         минимальное возможное значение столбца\n",
    "            'max': float         максимальное возможное значение\n",
    "                                 для столбца\n",
    "            'max_plus': float    какое максимальное отклонение\n",
    "                                 в большую сторону\n",
    "                                 может быть для значения x,\n",
    "                                 то есть задает\n",
    "                                 диапазон [x, x + max_plus]\n",
    "                                 для нового значения\n",
    "            'max_minus': float   аналогично 'max_plus',\n",
    "                                 но задает [x - max_minus, x]\n",
    "                                 * 'max_minus' и 'max_plus'\n",
    "                                 * задают границы\n",
    "                                 * для определенного значения x,\n",
    "                                 * а 'max' и 'min'\n",
    "                                 * задают глобальные границы,\n",
    "                                 * за которые невозможно выйти\n",
    "                                 * ни одному x\n",
    "            'round': int         до какого знака\n",
    "                                 после запятой нужно округлить\n",
    "        }\n",
    "        2. {\n",
    "            't_variant': 2      вид инструкции,\n",
    "                                2 - выбор из набора вариантов\n",
    "            'index': int\n",
    "            'choice': list      массив из вариантов новых значений.\n",
    "                                если x нет в массиве, он не меняется\n",
    "            'max_plus': int     на сколько индексов можно уйти\n",
    "                                в правую сторону от значения x,\n",
    "                                необходимо для отсортированных массивов,\n",
    "                                к примеру когда есть несколько классов\n",
    "                                и изменять данные нужно только переходом\n",
    "                                в соседние классы\n",
    "            'max_minus': int    аналогично 'max_plus',\n",
    "                                только в левую сторону\n",
    "                                * для того, чтобы выбор был\n",
    "                                * из всего массива,\n",
    "                                * 'max_plus' и 'max_minus'\n",
    "                                * следует установить такими,\n",
    "                                * чтобы они были больше длины\n",
    "                                * массива 'choice'\n",
    "        }\n",
    "\n",
    "    \"\"\"\n",
    "    new_x, new_y = [0] * count, [0] * count\n",
    "    for new_index in range(count):\n",
    "        # выбираем случайный элемент из массива\n",
    "        old_index = np.random.choice(len(x), 1)\n",
    "        new_x_elem = np.array(x[old_index], copy=True)[0]\n",
    "\n",
    "        # применяем изменения\n",
    "        for transform in x_transformations:\n",
    "            if transform['t_variant'] == RANDOM_NUMBER_VARIANT:\n",
    "                minimum = max(\n",
    "                    transform['min'],\n",
    "                    new_x_elem[transform['index']] -\n",
    "                    transform['max_minus'])\n",
    "\n",
    "                maximum = min(\n",
    "                    transform['max'],\n",
    "                    new_x_elem[transform['index']] +\n",
    "                    transform['max_plus'])\n",
    "\n",
    "                new_value = np.random.uniform(minimum, maximum)\n",
    "                new_x_elem[transform['index']] = round(\n",
    "                    new_value, transform['round'])\n",
    "\n",
    "            elif transform['t_variant'] == RANDOM_CHOICE_VARIANT:\n",
    "                # если нет элемента в массиве\n",
    "                # с новыми выборами, пропускаем\n",
    "                if new_x_elem[transform['index']] \\\n",
    "                        not in transform['choice']:\n",
    "                    continue\n",
    "\n",
    "                index_before_transformation = transform['choice'] \\\n",
    "                    .index(new_x_elem[transform['index']])\n",
    "                minimum_index = max(\n",
    "                    0,\n",
    "                    index_before_transformation -\n",
    "                    transform['max_minus'])\n",
    "                maximum_index = min(\n",
    "                    len(transform['choice']),\n",
    "                    index_before_transformation +\n",
    "                    transform['max_plus'] + 1)\n",
    "\n",
    "                choice_from = \\\n",
    "                    transform['choice'][minimum_index:maximum_index]\n",
    "                new_value = np.random.choice(choice_from)\n",
    "                new_x_elem[transform['index']] = new_value\n",
    "                \n",
    "        new_x[new_index] = new_x_elem\n",
    "        new_y[new_index] = y[old_index]\n",
    "\n",
    "    # в y создается лишняя размерность, убираем ее\n",
    "    return np.array(new_x), np.array(new_y)[:, 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "uMz5ycu0BhHF"
   },
   "outputs": [],
   "source": [
    "new_x_train, new_y_train = data_augmentation(\n",
    "    x_train, y_train, count=300,\n",
    "    x_transformations=[\n",
    "        {  # sibsp (количество родственников) может меняться \n",
    "           # в большую или меньшую сторону на 1 \n",
    "            'index': 2,\n",
    "            't_variant': RANDOM_CHOICE_VARIANT,\n",
    "            'choice': [0.1 * i for i in range(8)],\n",
    "            'max_plus': 1,\n",
    "            'max_minus': 1,\n",
    "        },\n",
    "        {  # parch (количество детей/родителей) может меняться \n",
    "           # в меньшую сторону на 1 \n",
    "            'index': 3,\n",
    "            't_variant': RANDOM_CHOICE_VARIANT,\n",
    "            'choice': [0.1 * i for i in range(8)],\n",
    "            'max_plus': 0,\n",
    "            'max_minus': 1,\n",
    "        },\n",
    "        {  # fare (оплата билета) может варьироваться\n",
    "           # на $10 вниз или вверх\n",
    "           # 0.010, а не 10 потому что данные уже нормализованы \n",
    "           # до значений в пределах [0, 1]\n",
    "            'index': 4,\n",
    "            't_variant': RANDOM_NUMBER_VARIANT,\n",
    "            'min': 0,\n",
    "            'max': 1,\n",
    "            'max_plus': 0.010,\n",
    "            'max_minus': 0.010,\n",
    "            'round': 7\n",
    "        }\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "bpUkiUoQgYos"
   },
   "outputs": [],
   "source": [
    "# собираем сгенерированные данные с уже имеющимися \n",
    "total_x_train, total_y_train = (\n",
    "    np.concatenate((x_train, new_x_train)), \n",
    "    np.concatenate((y_train, new_y_train))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dic0sroF3Y3X",
    "outputId": "a9548c17-6d41-4ca0-a918-2939969b2922"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Всего данных для обучения(942, 8)\n"
     ]
    }
   ],
   "source": [
    "print(f'Всего данных для обучения{total_x_train.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "veZCDjVZCW4G"
   },
   "source": [
    "##### Создание и обучение модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "F9-MiMifki8e"
   },
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    \"\"\"\n",
    "    Функция для создания модели\n",
    "    \n",
    "    \"\"\"\n",
    "    input_layer = Input(len(X_COLUMNS))\n",
    "    layers_values = ((16, relu), \n",
    "                     (256, relu), \n",
    "                     (512, relu),\n",
    "                     (768, relu),\n",
    "                     (768, relu),\n",
    "                     (768, relu),\n",
    "                     (1024, relu),\n",
    "                     (4096, relu),\n",
    "                     (4096, relu),\n",
    "                     (1024, relu),\n",
    "                     (512, relu),\n",
    "                     (256, relu),\n",
    "                     (128, relu),\n",
    "                     (128, relu), \n",
    "                     (56, relu), \n",
    "                     (len(Y_COLUMNS), sigmoid))\n",
    "    last_layer = Dense(\n",
    "        layers_values[0][0],\n",
    "        layers_values[0][1])(input_layer)\n",
    "\n",
    "    for units, activation in layers_values[1:]:\n",
    "        last_layer = Dense(units, activation)(last_layer)\n",
    "\n",
    "    return Model(input_layer, last_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "JKJ8lWUFnfK-"
   },
   "outputs": [],
   "source": [
    "model = create_model()\n",
    "model.compile(optimizer=Adam(), loss=mse, metrics=[mae])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "97819104-09ed-4057-a237-5bb578b37d2d",
    "id": "6pdE9nOcJzNU"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/600\n",
      "236/236 [==============================] - 5s 17ms/step - loss: 0.0240 - mean_absolute_error: 0.1234\n",
      "Epoch 2/600\n",
      "236/236 [==============================] - 4s 16ms/step - loss: 0.0205 - mean_absolute_error: 0.1114\n",
      "Epoch 3/600\n",
      "236/236 [==============================] - 4s 16ms/step - loss: 0.0187 - mean_absolute_error: 0.1077\n",
      "Epoch 4/600\n",
      "236/236 [==============================] - 4s 16ms/step - loss: 0.0183 - mean_absolute_error: 0.1067\n",
      "Epoch 5/600\n",
      "236/236 [==============================] - 4s 16ms/step - loss: 0.0176 - mean_absolute_error: 0.1060\n",
      "...\n",
      "Epoch 595/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0091 - mean_absolute_error: 0.0695\n",
      "Epoch 596/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0091 - mean_absolute_error: 0.0701\n",
      "Epoch 597/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0093 - mean_absolute_error: 0.0714\n",
      "Epoch 598/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0094 - mean_absolute_error: 0.0713\n",
      "Epoch 599/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0093 - mean_absolute_error: 0.0712\n",
      "Epoch 600/600\n",
      "236/236 [==============================] - 4s 18ms/step - loss: 0.0091 - mean_absolute_error: 0.0701\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 4\n",
    "EPOCHS = 600\n",
    "history = model.fit(\n",
    "    total_x_train,\n",
    "    total_y_train,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS).history\n",
    "# убрал длинный вывод данных, оставил только 5 первых и последних эпох"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z1yrSCHCZLrV"
   },
   "source": [
    "##### Итоги обучения\n",
    "Обученную модель можно скачать с\n",
    "[Google Drive](https://drive.google.com/drive/folders/1-6LeK9Z8WKHAy0vx96Wee4NnRItxWEqt?usp=sharing)\n",
    "или использовать её внутри\n",
    "[docker контейнера](https://hub.docker.com/repository/docker/tikovka72/age-model).\n",
    "Развернуть контейнер можно с помощью\n",
    "[Инструкции](https://github.com/tikovka72/yandex-lyceum-selection-round#%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C-%D0%B4%D0%BB%D1%8F-%D0%BF%D1%80%D0%B5%D0%B4%D1%81%D0%BA%D0%B0%D0%B7%D0%B0%D0%BD%D0%B8%D1%8F-%D0%B2%D0%BE%D0%B7%D1%80%D0%B0%D1%81%D1%82%D0%B0)."
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "fig, (losses_axes, metrics_axes) = plt.subplots(*two_axes_in_row)\n",
    "fig.set_size_inches(two_axes_size)\n",
    "fig.suptitle('Итоги обучения')\n",
    "\n",
    "losses_axes.plot([x * 100 for x in history.get('loss')],\n",
    "                 color=good_color)\n",
    "\n",
    "losses_axes.set_title('Потери по эпохам')\n",
    "losses_axes.set_xlabel('Эпохи')\n",
    "losses_axes.set_ylabel('Потери, %')\n",
    "losses_axes.grid(True)\n",
    "\n",
    "metrics_axes.plot([(1 - x) * 100\n",
    "                   for x in history.get('mean_absolute_error')],\n",
    "                  color=good_color)\n",
    "\n",
    "metrics_axes.set_title('Точность по эпохам')\n",
    "metrics_axes.set_xlabel('Эпохи')\n",
    "metrics_axes.set_ylabel('Точность, %')\n",
    "metrics_axes.grid(True, color=neutral_color)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "id": "zVzDxQ18fJkq",
    "outputId": "44fbed00-7bf2-4b74-c167-454f8d204b9e",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    }
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCbuV78tYg9X",
    "outputId": "cb471e70-0dba-4af0-9a65-b782184b296d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя ошибка на тестовых данных (в годах): 7.7\n"
     ]
    }
   ],
   "source": [
    "mean_err = round(\n",
    "    model.evaluate(x_test, y_test, verbose=0)[1] / AGE_NORMALIZER, 1)\n",
    "print('Средняя ошибка на тестовых данных (в годах): '\n",
    "      f'{mean_err}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "6YgvSoM1LKJd",
    "outputId": "7fd3c584-dbe1-4b2d-ea80-272973507612"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "fig.suptitle('Отклонение от правильных результатов')\n",
    "\n",
    "predicts = model.predict(x_test)\n",
    "\n",
    "values = predicts / AGE_NORMALIZER - y_test / AGE_NORMALIZER\n",
    "good_values = values[np.absolute(values) <= 10]\n",
    "bad_values = values[np.absolute(values) > 10]\n",
    "\n",
    "axes.fill_between(\n",
    "    np.arange(-1, max(len(good_values), len(bad_values)) + 1),\n",
    "     -10, 10, color=neutral_color)\n",
    "\n",
    "axes.plot(good_values, \n",
    "          linewidth=0, \n",
    "          markersize=marker_size_l, \n",
    "          marker='o',\n",
    "          color=good_color, \n",
    "          label='Отклонения до 10 лет')\n",
    "axes.plot(bad_values,\n",
    "          linewidth=0,\n",
    "          markersize=marker_size_l,\n",
    "          marker='o',\n",
    "          color=bad_color,\n",
    "          label='Отклонения больше 10 лет')\n",
    "\n",
    "\n",
    "axes.set_xlabel('Люди')\n",
    "axes.set_ylabel('Разница')\n",
    "axes.set_yticks(range(-30, 31, 10))\n",
    "axes.set_xticks(range(0, max(len(good_values), len(bad_values)), 5))\n",
    "axes.grid(True, axis='y', color=neutral_color)\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QXi3p6XaBxzp"
   },
   "source": [
    "##### Сохранение модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ztySeAz3Zn3R",
    "outputId": "6c8d9898-c317-4032-ff20-c06e13011b24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /content/drive/MyDrive/yandex-lyceum-selection-round/models/age-model/assets\n"
     ]
    }
   ],
   "source": [
    "model_path = 'age-model'\n",
    "model.save(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OjacCTuuCcrA"
   },
   "source": [
    "##### Загрузка модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model(model_path)"
   ],
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "Il-SLFBXV33u"
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ypNYRnMIDOWX"
   },
   "source": [
    "##### Вставка возрастов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oGkcbS9sDTcX"
   },
   "outputs": [],
   "source": [
    "def age_reformer(row):\n",
    "    \"\"\"\n",
    "    Вставляет все пропуски в столбце возраст\n",
    "\n",
    "    \"\"\"\n",
    "    if row.isnull()[age]:\n",
    "        prepared_row = prepare_data(row.to_frame().transpose())\n",
    "        age_value = model.predict(np.asarray(\n",
    "            prepared_row[X_COLUMNS].to_numpy()).astype('float32'))[0][0]\n",
    "        row[age] = round(age_value / AGE_NORMALIZER)\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t2MWkSbnMGxg"
   },
   "outputs": [],
   "source": [
    "df = df.apply(age_reformer, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "APGfJjqvL2EZ"
   },
   "source": [
    "##### Решение конфликта `adult_male` и `age`\n",
    "В датасете присутствует столбец `adult_male`,\n",
    "который был рассчитан автором такой функцией:\n",
    "```\n",
    "df[\"who\"] = df[[\"age\", \"sex\"]].apply(woman_child_or_man, axis=1)\n",
    "df[\"adult_male\"] = df.who == \"man\"\n",
    "\n",
    "\n",
    "def woman_child_or_man(passenger):\n",
    "    age, sex = passenger\n",
    "    if age < 16:\n",
    "        return \"child\"\n",
    "    else:\n",
    "        return dict(male=\"man\", female=\"woman\")[sex]\n",
    "```\n",
    "Но так как `np.nan < 16 == False`,\n",
    "то все мужчины, у которых не обозначен возраст, считаются взрослыми.\n",
    "После восстановления возраста можно исправить столбец `adult_male`,\n",
    "помимо этого я хочу в столбце `who`\n",
    "разбить класс `child` на `boy` и `girl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DIpXdrDBaH0l"
   },
   "outputs": [],
   "source": [
    "def who_replace(row): \n",
    "    # Берем сначала по индексу возраст, потом пол \n",
    "    row[who] = (\n",
    "        ('man', 'woman'), ('boy', 'girl')\n",
    "    )[row[age] < 16][row[sex] == 'female']\n",
    "\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yiCm1gyeOLkV"
   },
   "outputs": [],
   "source": [
    "df = df.apply(who_replace, 1)\n",
    "df[adult_male] = df[who] == 'man'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bZ3y5vxCV76D"
   },
   "source": [
    "#### Пропуски палубы\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UN9wRIZPWBPx"
   },
   "outputs": [],
   "source": [
    "def fill_na_deck(data_frame: pd.DataFrame) -> pd.DataFrame:\n",
    "    data_frame[deck] = data_frame[deck].fillna(u)\n",
    "\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u0kZFtiiWTMs"
   },
   "outputs": [],
   "source": [
    "df = fill_na_deck(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0R5bDSwGe4Th"
   },
   "source": [
    "#### Сохранение заполненной таблицы\n",
    "Лежит на\n",
    "[GitHub](https://github.com/tikovka72/yandex-lyceum-selection-round/blob/main/csv/filled_titanic.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q7Pc-pEdfWmQ"
   },
   "outputs": [],
   "source": [
    "csv_final_path = '/csv/filled_titanic.csv'\n",
    "df.to_csv(csv_final_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ppn4Rs9efjhF"
   },
   "source": [
    "#### Загрузка заполненной таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TsxGS2FYfmiU"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_final_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-77Ks1P83cCK"
   },
   "source": [
    "## Ответы на вопросы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9tw8yTiO3yY5"
   },
   "source": [
    "### А. Кого больше среди пассажиров — мужчин или женщин?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aS5EY4JL3_69",
    "outputId": "64a7fbae-7b41-4c6f-8ae8-9637031c3afe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "man      529\n",
       "woman    262\n",
       "girl      52\n",
       "boy       48\n",
       "Name: who, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[who].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HzJoYDEn4zk7"
   },
   "source": [
    "| Возраст  | Мужчин | Женщин | \n",
    "|----------|:------:|:------:|\n",
    "| Взрослые |  529   |   262  |\n",
    "|   Дети   |   48   |    52  | \n",
    "|  Всего   |  577   |   314  | \n",
    "\n",
    "В целом **мужчин было больше** среди пассажиров,\n",
    "но если смотреть на детей,\n",
    "то в этом случае девочки преобладают,\n",
    "но ответ на вопрос - **больше было мужчин**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9cEXSDA7_eK3"
   },
   "source": [
    "### В. Каков процент взрослых мужчин от всех пассажиров?\n",
    "Взрослых мужчин от всех пассажиров: `59%`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U7hc96vq_lge",
    "outputId": "52467289-64ab-42a4-bb60-2a32e127f6ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59% взрослых мужчин\n"
     ]
    }
   ],
   "source": [
    "print(f'{round(df[df[adult_male]].shape[0] / df.shape[0] * 100)}% '\n",
    "      'взрослых мужчин')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3xZcAZX5Fwk5"
   },
   "source": [
    "### С. Сколько людей в каждом классе?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l2aQ-VsiFzjl",
    "outputId": "0b944211-fe13-4bd4-8abc-465b376a0708"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[class_].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZfJJFgEF36Q"
   },
   "source": [
    "| Класс  | Количество |\n",
    "|--------|:----------:|\n",
    "| Первый |     216    |\n",
    "| Второй |     184    | \n",
    "| Третий |     491    | \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-kMJ4-_Vth-"
   },
   "source": [
    "## 4. Выводы из результатов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O791bRZEnBOs"
   },
   "source": [
    "Из метода `describe` мы можем узнать,\n",
    "что среднеквадратичное отклонение\n",
    "в столбцах `age` и `fare` достаточно большое,\n",
    "что может говорить о большом разбросе данных в данных столбцах.\n",
    "Интересен столбец `sibsp` - количество родственников на борту,\n",
    "только на `75%` появляется значение, равное одному родственнику.\n",
    "Большинство пассажиров путешествовали в одиночестве.\n",
    "Также эти же столбцы интересны и в цене билета (`fare`):\n",
    "`50%` пассажиров заплатили за билет не больше  `$14` при том,\n",
    "что максимальная цена билета была,  `$512`.\n",
    "Из этого можно сделать вывод,\n",
    "что на борту было относительно мало богатых людей.\n",
    "(В этом и последующих пунктах за единицу измерения цены билета\n",
    "я буду использовать `$`,\n",
    "так как нигде точно не указано, в какой валюте этот столбец)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "jK2qN2HtVth_",
    "outputId": "8f2a7aea-baa2-4fb8-cf1e-428eb669c46d"
   },
   "outputs": [
    {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>mean</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.067531</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.779669</td>\n",
       "      <td>1.102743</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>222.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
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       "      <th>75%</th>\n",
       "      <td>667.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
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      "text/plain": [
       "       Unnamed: 0    survived      pclass  ...       sibsp       parch        fare\n",
       "count  891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
       "mean   445.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
       "std    257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
       "min      0.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
       "25%    222.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
       "50%    445.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
       "75%    667.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
       "max    890.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
       "\n",
       "[8 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PfYTWdxPVth_"
   },
   "source": [
    "#### Средний возраст выживших \n",
    "Средний возраст выживших: 28 лет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "asfvlXgmVth_",
    "outputId": "5875f105-d748-4bca-d4a7-52554f9a954a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средний возраст выживших: 28 лет\n"
     ]
    }
   ],
   "source": [
    "print('Средний возраст выживших: '\n",
    "      f'{round(df[df[survived] == 1][age].mean())} лет')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b6PMfkWJVtiA"
   },
   "source": [
    "#### Процент выживших по классам\n",
    "Процент выживших прямо зависит от класса, из чего можно сделать вывод,\n",
    "что безопасность пассажиров также зависит от него.\n",
    "В первом классе спаслось более половины пассажиров при том,\n",
    "что этот класс занимал `24%` всех пассажиров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "lnFIBEIkVtiA",
    "outputId": "c1bbfa34-ed0c-4a79-b2a2-b8f195eeb643"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "fig.suptitle('Процент выживших в каждом классе')\n",
    "# вычисляет сколько процентов выживших в каждом классе \n",
    "# делит, потом сортирует по возрастанию класса \n",
    "items = np.array(sorted((\n",
    "    df[df[survived] == 1][pclass].value_counts() / \n",
    "    df[pclass].value_counts() * 100).items(),\n",
    "                        key=lambda column: column[0]))\n",
    "\n",
    "axes.bar(items[:, 0], items[:, 1], width=0.4, color=good_color)\n",
    "\n",
    "axes.set_xticks(items[:, 0])\n",
    "axes.set_xticklabels(('Первый', 'Второй', 'Третий'))\n",
    "axes.set_yticks(items[:, 1])\n",
    "axes.set_ylabel('Выжившие в классе, %')\n",
    "axes.set_xlabel('Класс')\n",
    "axes.grid(True, axis='y', color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhWiquGDa85P"
   },
   "source": [
    "#### Выжившие по цене билета\n",
    "Как и ожидалось, количество выживших прямо зависит от цены билета:\n",
    "чем больше цена, тем больше относительное количество выживших.\n",
    "Переход в \"положительное\" количество выживших виден при цене билета,\n",
    "равном примерно `$ln(3.95) ~ $51`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "B--E6NWBTVh0",
    "outputId": "05fc9408-4538-4f03-9f00-f6031cd6c3d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "fig.suptitle('Выжившие относительно цены билета')\n",
    "\n",
    "bins = 30\n",
    "\n",
    "not_survived_fare = df[df[survived] == 0][fare]\\\n",
    "    .apply(np.log).replace(float('-inf'), 0)\n",
    "\n",
    "not_survived_fare.hist(\n",
    "    bins=bins, \n",
    "    ax=ax, \n",
    "    label='Не выжили', \n",
    "    color=bad_color)\n",
    "\n",
    "survived_fare = df[df[survived] == 1][fare]\\\n",
    "    .apply(np.log).replace(float('-inf'), 0)\n",
    "\n",
    "survived_fare.hist(\n",
    "    bins=bins, \n",
    "    ax=ax, \n",
    "    label='Выжили', \n",
    "    alpha=0.8,\n",
    "    color=good_color)\n",
    "\n",
    "ax.set_xlabel('Цена билета, $ln')\n",
    "ax.set_ylabel('Количество людей')\n",
    "ax.set_xticks(list(ax.get_xticks()[ax.get_xticks() != 4]) + [3.95])\n",
    "ax.legend()\n",
    "ax.grid(True, color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Owlbl-zGVtiB"
   },
   "source": [
    "### Проверка на нормальность"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pytQZHU5VtiB"
   },
   "source": [
    "#### Возраст\n",
    "В данных возраста всех пассажиров\n",
    "присутствует небольшая положительная асимметрия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "nfyarPvVVtiB",
    "outputId": "b4e57556-7d8b-40dd-ec76-efbd7f1769e3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ai+FU4PXA7ye5EtgTOG0625ckSZpvxjplCSytqrOSnAxQVZuS3DXdnVbVm4E3b9H8HeAJ092mJEnSfDVuD9kPk+wJFECSQ4FbJhaVJEnSgIzbQ/b7wNnAI5P8K7AX8JyJRSVJkjQg415leXGSXwMOBAJ8s6p+OtHIJEmSBmLcqyyXAA+hu6P+94Bd21WXZyU5bJIBSpIkLXTjnrJcT3dfsNCNIwuwT1XtNKnAJEmShmLcguzyqnrcaEOSSyYQjyRJ0uCMW5Dt0h4ofhNwXVXdQrviUpIkSdtn3ILsG8AbgV2AfZNcy89uEitJkqTtMO5Vls8YnW4D+T+d5HTgfVX11UkEJ0mSNATj9pDdS1V9OcnBwI7AjTMbkiRJ0rCMVZAl2Rl4A3BEa/oC8KdV9aMJxSVJkjQY4z466W+BnYFXtZ9d6B4ILkmSpO007inL/1BVvzMy/QdJLp1APJIkSYMzbg/ZDkl+ZfNEkl8GNk0mJEmSpGEZt4fs9cD5Sb5Dd5f+xcAJE4tKkiRpQMa97cU5SZYBFwFPA66pKm8MK0mSNAPGvcrya3R35t8P+GRro6p+5f7WkyRJ0raNe8rymIlGIUmSNGDjFmS3TTQKSZKkARu3IFsPXEc3oH+zAh4x4xFJkiQNzLgF2eVV9biJRiJJkjRQ4xZkuyU5DvgJ8D26As37kEmSJM2AcQuy84FnAw8GHgY8PMl/rarPTCwySZKkgRj3PmQnjk4nOQD4OGBBJkmStJ3GfXTSvVTVlcBvzHAskiRJgzStggygqtbPZCCSJElDNe2CTJIkSTPDgkySJKlnYxVkSXZL8o4kF7Wfv0iy26SDkyRJGoJxe8hOB24Fntt+bgU+MKmgJEmShmTc+5A9sqqePTL91iSXTiAeSZKkwRm3h+yOJE/aPJHkcOCOyYQkSZI0LOP2kP134Iw2bizARuDFkwpKkiRpSMa9U/+lwGOSPKRN3zrJoCRJkoZk3KssD07yCrpnWf5Zko8ledxkQ5MkSRqGcceQfRg4EFgDfAU4C3j/pIKSJEkaknELsgdU1SuBO6vqtKo6awrr3keS3Vsv2zeSXJHksCRLkpyb5Fvt9x7T3b4kSdJ8Mm5RtUuSZwGLkvxWkmcDD9mO/b4L+GxVHQQ8BrgCeANwXlU9CjivTUuSJC14415leT7wjPb72Nb2xenssF2peSTtKs2quhO4M8lxwJPbYmcAXwBeP519SJIkzSfjFmTvrqqLZ2if+wM3Ah9I8hhgLfBqYO+qWt+WuR7Ye4b2J0mSNKelqra9UHJxVR0yIztMVgIXAodX1Zok76J7FNMrq2r3keVuqqr7jCNLchJwEsBDly97/FmXXDgTYd3H7ZvuZJdFO05k2/PBkPPfnPvGWy6a8rpLdls5gYjubdJxDfm9B/Mfcv5Dzh3MfzbyP3KvFWuraqtfyOP2kC1qg+wz2lhVG6cRzzpgXVWtadMfoxsv9v0k+1TV+iT7ADdsbeWqOhU4FWDlypV1xNLl0whh2y7YsI5JbXs+GHL+m3NftWbfKa973NF3TyCie5t0XEN+78H8h5z/kHMH8+87/3ELsgPpTi2OFmQFPGKqO6yq65Ncm+TAqvomcBRwefs5AfiT9vsTU922JEnSfDRuQXZ5Vc3kjWBfCXwoyY7Ad4AT6a74PCvJS4CrgefO4P4kSZLmrHELshnVHsW0tXOoR81yKBqQVeeMd5eXxQetntZpQUmSpmvc+5AdNtEoJEmSBmzcguyTSXbfPJFkjyT/NJmQJEmShmXcgmyvqrp580RV3QT8wkQikiRJGphxC7K7ktwzqCbJw+muspQkSdJ2GndQ/xuBLyU5n+7WF0fQbs4q6WfGvXBg1PGzcO8ySdLcNlZBVlWfTXIIcGhrek1VbZhcWJIkScMx1n/nkwR4KnBIVX0KWJzkCRONTJIkaSDGPb/yXrpbXxzfpm8D/noiEUmSJA3MuGPInlhVhyS5BLqrLNtd9iVJkrSdxu0h+2mSHWhXVibZC3AksiRJ0gwYtyD7K+AfgV9IcgrwJeBtE4tKkiRpQMa9yvJDSdbSPWsywDOr6oqJRiYNxHRulSFJWljGKsiSLAFuAFaNtlXVxkkFJkmSNBTjDupfSzd+LMA+wPo2/YgJxSVJkjQY456y3H/z6ySXVNXjJheSJEnSsExp8Eq71YW3u5AkSZpB444h+2R7+UvAhycXjiRJ0vCMO4bsz+nuO7auqr47wXgkSZIGZ9yC7GubX7QrLgHwKktJkqTtN25BtgH4PnAH3ZWW4FWWkiRJM2LcQf0nAeuAvwAeVVX7V5XFmCRJ0gwYqyCrqvcDTwIeBPxrkhdMNCpJkqQBGfcqy2e1l1cBfwO8PsnrquoxkwpM0syZyuOZFh+0mlVr9p3yPo4/+u4pryNJ6ow7huwZW0yvnelAJEmShmrcO/WfOOlAJEmShmrcU5Znb629qo6d2XAkSZKGZ9xTlr8EvHSSgUiSJA3VuAXZbVV1/kQjkSRJGqhxL716TJKbk1yf5OIk706ydKKRSZIkDcS49yHbAVgCPBJ4HnA9cMYE45IkSRqMsW9OVFV3V9UPq+pbVXUK8NkJxiVJkjQY444hI8mxwJFt8vyqevdkQpIkSRqWsXrIkvwx8Grg8vbzqiRvm2RgkiRJQzFuD9nRwGOr6m6AJGcAlwB/OKnAJC18U3mk02Y+oknSQjSVb8PdR17vNsNxSJIkDda4PWR/DFyS5PNA6MaSnTyxqCRJkgZk3GdZrkryBeA/tqbXV9X127PjJDsAFwHXVdUxSfYHzgT2pHt4+Qur6s7t2YckSdJ8cL8FWZKjq+ocgKpaD5zd2ndN8u6qeuV27PvVwBXAQ9r024F3VNWZSf4GeAnwvu3Yvhaw6Yw9kiRprtrWv2rvTPK7ow1Jfgf4d+CG6e40yXK6CwXe36YDPAX4WFvkDOCZ092+JEnSfLKtU5ZHAue0AupM4L3AT4Ffr6pvb8d+3wm8Dti1Te8J3FxVm9r0OmDZdmxfkiRp3khV3f8Cya7AauAI4MVVdeZ27TA5Bnh6Vf2PJE8G/gB4MXBhVR3QllkBfKaqHr2V9U8CTgJ46PJljz/rkgu3J5yf6/ZNd7LLoh0nsu35YK7nv/GWiya27Qfs9Eju/vH2/H9jfptu/kt2WznldabzPk5nP1Mx1//2J23I+Q85dzD/2cj/yL1WrK2qrX6JbXNQf1XdluRpwOnAC5J8vKp+vB3xHA4cm+TpwE50Y8jeBeyeZFHrJVsOXPdz4jkVOBVg5cqVdcTS5dsRys93wYZ1TGrb88Fcz3/Vmn0ntu3FB63mR9941sS2P9dNN//jpnF/sOm8j9PZz1TM9b/9SRty/kPOHcy/7/zvdwxZktuS3ApsBH6LbtzXxpH2Kauqk6tqeVXtBzwf+FxVvQD4PPDbbbETgE9MZ/uSJEnzzf32kFXVrvc3f4a9HjgzyR/RPQXgtFnctyRJUm/Gfrj4JFTVF4AvtNffAZ7QZzySJEl98GZOkiRJPbMgkyRJ6pkFmSRJUs8syCRJknpmQSZJktQzCzJJkqSeWZBJkiT1zIJMkiSpZxZkkiRJPbMgkyRJ6pkFmSRJUs8syCRJknrW68PFJS0cq87x/3eSNF1+g0qSJPXMgkySJKlnFmSSJEk9syCTJEnqmQWZJElSzyzIJEmSemZBJkmS1DMLMkmSpJ55Y9gxTPWGl8cfffeEIpE0nRvQ+pmUNNfZQyZJktQzCzJJkqSeecpS0oI3ldOciw9aDUuXTzAaSbove8gkSZJ6ZkEmSZLUMwsySZKknlmQSZIk9cyCTJIkqWcWZJIkST2zIJMkSeqZ9yGbAB/tMjXTOV7SJM3Vz/BcjUvS9vNfQkmSpJ5ZkEmSJPVs1guyJCuSfD7J5Um+nuTVrX1JknOTfKv93mO2Y5MkSepDHz1km4DXVtXBwKHAy5McDLwBOK+qHgWc16YlSZIWvFkf1F9V64H17fVtSa4AlgHHAU9ui50BfAF4/WzHJ0kLyVQvBPDh6lI/eh1DlmQ/4HHAGmDvVqwBXA/s3VdckiRJsylV1c+Ok12A84FTqmp1kpuraveR+TdV1X3GkSU5CTgJ4KHLlz3+rEsunEh8t2+6k10W7QjAxlsumsg+Ri3ZbeXE9zEVo/lP2mwc36l4wE6P5O4ff7vvMHpj/tPLfzY+w7PxWXnATo9k9wcNcwjvbH7vzUXmP/n8j9xrxdqq2uqXRS/3IUvyQOAfgA9V1erW/P0k+1TV+iT7ADdsbd2qOhU4FWDlypV1xIS61i/YsI7N2161Zt+J7GPUcXPsXkGj+U/abBzfqVh80Gp+9I1n9R1Gb8x/evnPxmd4Nj4riw9azRHLnjnx/cxFs/m9NxeZf7/5z3pBliTAacAVVfWXI7POBk4A/qT9/sRsxyZJs8mbIkvarI8essOBFwJfS3Jpa/tDukLsrCQvAa4GnttDbJIkSbOuj6ssvwTk58w+ajZjkSRJmgvsL5ckSeqZBZkkSVLPLMgkSZJ6ZkEmSZLUMwsySZKknlmQSZIk9cyCTJIkqWcWZJIkST2zIJMkSepZLw8Xl6SFxudSStoefoNIkiT1zB4ySdK9TLW37/ij755QJNJw2EMmSZLUMwsySZKknlmQSZIk9cyCTJIkqWcWZJIkST3zKssBmcqVU4sPWs2qNft69ZQkSbPAHjJJkqSeWZBJkiT1zFOWc8R0HrsyV08n+ggZaVgW0veX1Bf/5ZQkSeqZPWTzmD1RkiQtDP6LLkmS1DN7yHS/7IWTNAmz8d0ynXFqjodTX/zXVpIkqWf2kEmSFqSp9nYtPmj1hCKRts0eMkmSpJ5ZkEmSJPXMgkySJKlnFmSSJEk9c1C/JEnbYa7ewkPziz1kkiRJPbOHTJIksfGWi1i1Zt8prWPP3cyxh0ySJKln9pBJkrQAeWPc+WXO9ZAleWqSbya5Mskb+o5HkiRp0uZUQZZkB+CvgacBBwPHJzm436gkSZIma66dsnwCcGVVfQcgyZnAccDlvUYlSZLuYzq3/JiNCwGmE9fyJ14zgUjGN6d6yIBlwLUj0+tamyRJ0oKVquo7hnsk+W3gqVX10jb9QuCJVfWKkWVOAk5qkwcC35xQOEuBDRPa9nww5PyHnDuYv/kPN/8h5w7mPxv5P7yq9trajLl2yvI6YMXI9PLWdo+qOhU4ddKBJLmoqlZOej9z1ZDzH3LuYP7mP9z8h5w7mH/f+c+1U5ZfBR6VZP8kOwLPB87uOSZJkqSJmlM9ZFW1KckrgH8CdgBOr6qv9xyWJEnSRM2pggygqj4NfLrvOJiF06Jz3JDzH3LuYP7mP1xDzh3Mv9f859SgfkmSpCGaa2PIJEmSBseCbAtDe3RTktOT3JDkspG2JUnOTfKt9nuPPmOcpCQrknw+yeVJvp7k1a19EMcgyU5JvpLk31r+b23t+ydZ0z4HH2kX2SxISXZIckmST7XpIeV+VZKvJbk0yUWtbRB/+wBJdk/ysSTfSHJFksOGkn+SA9v7vvnn1iSvGVD+v9e+8y5Lsqp9F/b62bcgGzHQRzd9EHjqFm1vAM6rqkcB57XphWoT8NqqOhg4FHh5e8+Hcgx+Ajylqh4DPBZ4apJDgbcD76iqA4CbgJf0F+LEvRq4YmR6SLkD/KeqeuzI5f5D+dsHeBfw2ao6CHgM3d/BIPKvqm+29/2xwOOBHwH/yADyT7IMeBWwsqoeTXcR4fPp+bNvQXZv9zy6qaruBDY/umnBqqovAhu3aD4OOKO9PgN45mzGNJuqan1VXdxe30b3hbyMgRyD6tzeJh/Yfgp4CvCx1r5g80+yHDgaeH+bDgPJ/X4M4m8/yW7AkcBpAFV1Z1XdzEDy38JRwLer6mqGk/8i4MFJFgGLgfX0/Nm3ILs3H93U2buq1rfX1wN79xnMbEmyH/A4YA0DOgbtlN2lwA3AucC3gZuralNbZCF/Dt4JvA7Y/HC9PRlO7tAV3/+cZG17CgoM529/f+BG4APtlPX7k+zMcPIf9XxgVXu94POvquuAPweuoSvEbgHW0vNn34JM96u6y3AX/KW4SXYB/gF4TVXdOjpvoR+DqrqrnbZYTtdLfFC/Ec2OJMcAN1TV2r5j6dGTquoQumEaL09y5OjMBf63vwg4BHhfVT0O+CFbnJ5b4PkD0MZJHQt8dMt5CzX/Ni7uOLqi/GHAztx36M6ssyC7t20+umkgvp9kH4D2+4ae45moJA+kK8Y+VFWrW/OgjgFAO13zeeAwYPfWlQ8L93NwOHBskqvohic8hW5M0RByB+7pKaCqbqAbP/QEhvO3vw5YV1Vr2vTH6Aq0oeS/2dOAi6vq+216CPn/OvDdqrqxqn4KrKb7Puj1s29Bdm8+uqlzNnBCe30C8IkeY5moNmboNOCKqvrLkVmDOAZJ9kqye3v9YOA36MbRfR747bbYgsy/qk6uquVVtR/dZ/1zVfUCBpA7QJKdk+y6+TXwn4HLGMjfflVdD1yb5MDWdBRwOQPJf8Tx/Ox0JQwj/2uAQ5Msbv8GbH7ve/3se2PYLSR5Ot24ks2Pbjql34gmK8kq4Ml0T7n/PvBm4OPAWcC+wNXAc6tqy4H/C0KSJwEXAF/jZ+OI/pBuHNmCPwZJfoVu8OoOdP9BO6uq/leSR9D1Gi0BLgH+S1X9pL9IJyvJk4E/qKpjhpJ7y/Mf2+Qi4MNVdUqSPRnA3z5AksfSXdCxI/Ad4ETa54Bh5L8zXXHyiKq6pbUN4v1vt/h5Ht2V9pcAL6UbM9bbZ9+CTJIkqWeespQkSeqZBZkkSVLPLMgkSZJ6ZkEmSZLUMwsySZKknlmQSZr3ktyV5NIk/5bk4iS/2ndMkjQV3vZC0ryX5Paq2qW9/k3gD6vq13oOS5LGZg+ZpIXmIcBN0D2JIcmfJbksydeSPK+1/0qSi9pDpb+a5KDWflWSP23LfiXJAa39GUnWtOX/JcnerX2XJB9oy/97kmcn+b3WW3dNkhvb6/f3dCwkzRP2kEma95LcRfe0hZ2AfYCnVNXaJM8GXkb34OCldI9He2JVrR9Z921034Unt+da/m27Y/2L6O5Sfkx7GPHNVVVJXgr8UlW9NsnbgQdV1Wvatvaoqs3F4IuBlVX1ilk5CJLmtUXbXkSS5rw7quqxAEkOA/4uyaOBJwGrquouuocmnw/8R+Ds9pi0v6Z7bNRvjGxr1cjvd7TXy4GPtIct7wh8t7X/Ot1zMAHYXIxJ0lR5ylLSglJVX6brDdtrG8t9uqr2B94HHDs6ayuv3w28p6p+GfhvdD1xkjRjLMgkLShtPNgOwA/oHhz/vCQ7JNkLOBL4SpLdkqSt8mPg0SObeN7I7y+317sB17XXJ4wsey7w8pF97zGTuUgaDk9ZSloIHpzk0vY6wAlVdVeSfwQOA/6NrrfrdVV1fZLfAv5Xq8luB04c2dYeSf4d+AlwfGt7C/DRJDcBnwP2b+1/BPx1ksuAu4C3Aqsnk6KkhcxB/ZLUtEH9K6tqQ9+xSBoWT1lKkiT1zB4ySZKkntlDJkmS1DMLMkmSpJ5ZkEmSJPXMgkySJKlnFmSSJEk9syCTJEnq2f8HMDyQy89U/1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "fig.suptitle('Возраст пассажиров')\n",
    "\n",
    "df[age].hist(bins=40, ax=ax, color=good_color)\n",
    "\n",
    "ax.set_xlabel('Возраст')\n",
    "ax.set_ylabel('Количество людей')\n",
    "ax.grid(True, color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFY4kzk6VtiC"
   },
   "source": [
    "Если смотреть на возрасты по классам,\n",
    "то данные распределены нормально только в первом и втором классах,\n",
    "в третьем классе присутствует положительная асимметрия,\n",
    "которая скорее всего и переходит в асимметрию во всей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "mEs4ps_PVtiC",
    "outputId": "6c25c44e-3427-4abe-f807-e33abc80508f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, all_axes = plt.subplots(*three_axes_in_row)\n",
    "fig.suptitle('Возраст пассажиров по классам')\n",
    "\n",
    "fig.set_size_inches(*three_axes_size)\n",
    "\n",
    "\n",
    "classes = ('Первый класс', 'Второй класс', 'Третий класс')\n",
    "for index, axes in enumerate(all_axes):\n",
    "    axes.set_title(classes[index])\n",
    "    df[df[pclass] == index + 1][age].hist(\n",
    "        bins=30, ax=ax, color=good_color)\n",
    "    axes.set_xlabel('Возраст')\n",
    "    axes.set_ylabel('Количество людей')\n",
    "    axes.grid(True, color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YqYdUeVAVtiC"
   },
   "source": [
    "#### Цена билета\n",
    "В этом столбце присутствует очень большая положительная асимметрия. \n",
    "Я попробовал взять логарифм от значений\n",
    "(прочитал, что в целом так правильно рассматривать денежные значения),\n",
    "чтобы получить\n",
    "[логнормальное распределение](https://en.wikipedia.org/wiki/Log-normal_distribution),\n",
    "это немного исправило ситуацию, но глобально ничего почти не изменилось.\n",
    "Подавляющее большинство взяло дешевые билеты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "wh6hXWdJVtiC",
    "outputId": "29e02c40-71f5-4c9c-d645-7fa8df85333c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axes, axes_ln) = plt.subplots(*two_axes_in_row)\n",
    "fig.set_size_inches(*two_axes_size)\n",
    "fig.suptitle('Количество билетов от цены')\n",
    "\n",
    "bins = 50\n",
    "\n",
    "df[fare].hist(bins=bins, ax=axes, color=good_color)\n",
    "axes.set_xlabel('Цена, $')\n",
    "axes.set_ylabel('Количество билетов')\n",
    "axes.grid(True, color=neutral_color)\n",
    "\n",
    "# Заменяем все значения:\n",
    "# логарифм от 0 равен -inf, \n",
    "# поэтому для построения данной гистограммы пришлось заменять -inf на 0\n",
    "df[fare].apply(np.log).replace(float('-inf'), 0).hist(\n",
    "    bins=bins, \n",
    "    ax=axes_ln,\n",
    "    color=good_color)\n",
    "axes_ln.set_xlabel('Цена, $ln')\n",
    "axes_ln.set_ylabel('Количество билетов')\n",
    "axes_ln.grid(True, color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mrlj5payKKka"
   },
   "source": [
    "### Корреляция между возрастом и ценой билета\n",
    "Как видно из графиков ниже, корреляции между этими двумя столбцами нет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "2o1efRaSVtiD",
    "outputId": "fc3602b5-a802-4537-9170-b49eda270167"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axes, axes_ln) = plt.subplots(*two_axes_in_row)\n",
    "fig.set_size_inches(*two_axes_size)\n",
    "fig.suptitle('Корреляция цены билета и возраста пассажира')\n",
    "# Корреляция между возрастом и ценой билета\n",
    "axes.plot(df[fare], df[age], linewidth=0, marker='o', color=good_color)\n",
    "axes.set_xlabel('Цена, $')\n",
    "axes.set_ylabel('Возраст')\n",
    "axes.grid(True, color=neutral_color)\n",
    "\n",
    "# корреляция между возрастом и логарифмом цены билеты\n",
    "axes_ln.plot(df[fare].apply(np.log).replace(float('-inf'), 0), df[age], \n",
    "             linewidth=0, \n",
    "             marker='o', \n",
    "             color=good_color)\n",
    "\n",
    "axes_ln.set_xlabel('Цена, $ln')\n",
    "axes_ln.set_ylabel('Возраст')\n",
    "axes_ln.grid(True, color=neutral_color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PKLHezsZctqN"
   },
   "source": [
    "## Новая таблица только с выжившими\n",
    "Тоже на\n",
    "[GitHub](https://github.com/tikovka72/yandex-lyceum-selection-round/blob/main/csv/alive_titanic.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y6fn8Fltcxuf",
    "outputId": "e1e97b5e-57ab-4c25-8966-d6d2234caf21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 342 entries, 1 to 889\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   survived     342 non-null    int64  \n",
      " 1   pclass       342 non-null    int64  \n",
      " 2   sex          342 non-null    object \n",
      " 3   age          342 non-null    float64\n",
      " 4   sibsp        342 non-null    int64  \n",
      " 5   parch        342 non-null    int64  \n",
      " 6   fare         342 non-null    float64\n",
      " 7   embarked     342 non-null    object \n",
      " 8   class        342 non-null    object \n",
      " 9   who          342 non-null    object \n",
      " 10  adult_male   342 non-null    bool   \n",
      " 11  deck         342 non-null    object \n",
      " 12  embark_town  342 non-null    object \n",
      " 13  alive        342 non-null    object \n",
      " 14  alone        342 non-null    bool   \n",
      "dtypes: bool(2), float64(2), int64(4), object(7)\n",
      "memory usage: 38.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_alive = df[df[survived] == 1]\n",
    "df_alive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zcgJXPpE1od1"
   },
   "outputs": [],
   "source": [
    "csv_alive_path = 'csv/alive_titanic.csv'\n",
    "df_alive.to_csv(csv_alive_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "36OlLZAIeDFo"
   },
   "source": [
    "### Изменение в проценте взрослых мужчин и не только\n",
    "В измененной таблице процент взрослых мужчин резко упал,\n",
    "что подтверждает работу правила \"Дамы и дети вперед\" уже в те годы.\n",
    "На самом деле больший процент спасенных пал на женщин,\n",
    "за ними — девочки,\n",
    "потом мальчики, заключили же эту последовательность мужчины.\n",
    "Можно сказать, что на борту были настоящие джентльмены."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uZs09KLZeJuI",
    "outputId": "fa543ba1-5a14-47dd-d3dd-3e3d4ce0060d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выживших взрослых мужчин в изначальной таблице: 59%\n",
      "Выживших взрослых мужчин в измененной таблице: 25%\n"
     ]
    }
   ],
   "source": [
    "adult_percent_all = round(\n",
    "    df[df[adult_male] == 1].shape[0] / df.shape[0] * 100)\n",
    "adult_percent_alive = round(\n",
    "    df_alive[df_alive[adult_male] == 1].shape[0] /\n",
    "    df_alive.shape[0] * 100)\n",
    "print('Выживших взрослых мужчин в изначальной таблице: '\n",
    "      f'{adult_percent_all}%')\n",
    "print('Выживших взрослых мужчин в измененной таблице: '\n",
    "      f'{adult_percent_alive}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "mnxXLxuaglDl",
    "outputId": "b45950e0-bb19-46b3-805d-7c45beaab626"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def translate_and_to_numpy(value_counts):\n",
    "    who_eng_rus_pairs = (('man', 'Мужчины'),\n",
    "                         ('woman', 'Женщины'),\n",
    "                         ('girl', 'Девочки'),\n",
    "                         ('boy', 'Мальчики'))\n",
    "    return np.array(sorted(\n",
    "        [(rus, value_counts[eng]) for eng, rus in who_eng_rus_pairs],\n",
    "        key=lambda x: x[0]\n",
    "    ))\n",
    "\n",
    "who_items_all = translate_and_to_numpy(\n",
    "    dict(df[who].value_counts()))\n",
    "\n",
    "who_items_alive = translate_and_to_numpy(\n",
    "    dict(df_alive[who].value_counts()))\n",
    "\n",
    "# строим \n",
    "fig, (axes_absolutely, axes_relatively) = plt.subplots(*two_axes_in_row)\n",
    "fig.set_size_inches(*two_axes_size)\n",
    "fig.suptitle('Спасенные по своему статусу')\n",
    "\n",
    "for item, label, color in (\n",
    "    (who_items_all, 'Всего на борту', second_neutral_color), \n",
    "    (who_items_alive, 'Выжило', good_color)): \n",
    "    bar = axes_absolutely.bar(np.arange(4),  \n",
    "                              item[:, 1].astype(int), \n",
    "                              width=bar_width, \n",
    "                              label=label, color=color)\n",
    "\n",
    "    axes_absolutely.bar_label(bar, labels=item[:, 1])\n",
    "\n",
    "\n",
    "axes_absolutely.set_title('Абсолютно')\n",
    "axes_absolutely.set_xlabel('Кто')\n",
    "axes_absolutely.set_ylabel('Количество людей')\n",
    "axes_absolutely.legend()\n",
    "\n",
    "alive_percentages = (who_items_alive[:, 1].astype(int) / \n",
    "                     who_items_all[:, 1].astype(int) * 100)\n",
    "axes_relatively.bar(np.arange(4), alive_percentages, \n",
    "                    width=bar_width, color=good_color)\n",
    "\n",
    "axes_relatively.set_title('Относительно')\n",
    "axes_relatively.set_yticks(alive_percentages)\n",
    "axes_relatively.set_xlabel('Кто')\n",
    "axes_relatively.set_ylabel('Спасенные, %')\n",
    "\n",
    "for axes in (axes_absolutely, axes_relatively): \n",
    "    axes.set_xticks(np.arange(4))\n",
    "    axes.set_xticklabels(who_items_all[:, 0])\n",
    "    axes.grid(True, axis='y', color=neutral_color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tf6axGU4er2h"
   },
   "source": [
    "### Изменение в соотношении классов\n",
    "Первый и третий класс поменялись местами,\n",
    "учитывая, что в третьем классе было существенно больше людей,\n",
    "этот факт подтверждает тезис о том,\n",
    "что в первом классе безопасность была куда выше, чем в третьем.\n",
    "Второй класс так и остался на третьем месте в этом рейтинге,\n",
    "но отрыв от лидеров сокращен.\n",
    "\n",
    "На результаты большое влияние оказала существенная разница\n",
    "в количестве людей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "yaWYGMVkz0qj",
    "outputId": "eb0ef268-f6ee-4fcf-e975-ecaa55b5a37f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axes_all, axes_alive) = plt.subplots(*two_axes_in_row)\n",
    "fig.set_size_inches(*two_axes_size)\n",
    "fig.suptitle('Количество людей по классам')\n",
    "\n",
    "classes_all = sorted(\n",
    "    df[pclass].value_counts().items(), key=lambda x: x[0])\n",
    "classes_alive = sorted(\n",
    "    df_alive[pclass].value_counts().items(), key=lambda x: x[0])\n",
    "\n",
    "label_texts = {1: 'Первый', 2: 'Второй', 3: 'Третий'}\n",
    "\n",
    "# [[x, y], [x, y], [x, y]] ->\n",
    "# -> [[x, x, x], [y, y, y]] ->\n",
    "# -> [x, x, x], [y, y, y]\n",
    "labels_all, x_all = zip(*classes_all)\n",
    "labels_all = [label_texts[i] for i in labels_all]\n",
    "\n",
    "labels_alive, x_alive = zip(*classes_alive)\n",
    "labels_alive = [label_texts[i] for i in labels_alive]\n",
    "\n",
    "axes_all.pie(x_all, labels=labels_all, autopct='%1.0f%%', \n",
    "             colors=(good_color, \n",
    "                     second_good_color, \n",
    "                     third_good_color))\n",
    "axes_all.set_title('Все пассажиры')\n",
    "\n",
    "axes_alive.pie(x_alive, labels=labels_alive, autopct='%1.0f%%', \n",
    "               colors=(good_color, \n",
    "                       second_good_color, \n",
    "                       third_good_color))\n",
    "axes_alive.set_title('Выжившие')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NWENoRyypBeV"
   },
   "source": [
    "### Изменение соотношений возрастов людей\n",
    "На удивление мое предположение о том,\n",
    "что люди 20-30 вырвутся вперед по числу спасшихся,\n",
    "не подтвердилось, соотношение возрастов осталось примерно таким же,\n",
    "как и до крушения лайнера."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "sxyN5BE7p1MW",
    "outputId": "d6b375e5-91c1-40b1-8c27-488dc8d83ac0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "fig.suptitle('Соотношение людей разных возрастов')\n",
    "\n",
    "bins = 40\n",
    "\n",
    "df[age].hist(bins=bins, ax=axes,\n",
    "             label='Все пассажиры', color=second_neutral_color)\n",
    "df_alive[age].hist(bins=bins, ax=axes,\n",
    "                   alpha=0.8, label='Выжившие', color=good_color)\n",
    "\n",
    "axes.set_xlabel('Возраст')\n",
    "axes.set_ylabel('Количество людей')\n",
    "\n",
    "axes.grid(True, color=neutral_color)\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eVkY5FsYxylt"
   },
   "source": [
    "### Изменение в количестве одиночек\n",
    "В живых осталось немного меньше одиноких людей, нежели людей,\n",
    "которые отправились в путешествие с семьей, при том,\n",
    "что изначально одиноких людей было на 35% больше.\n",
    "\n",
    "Сила семьи, какой еще вывод сделать."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "ofmwXUA0rhyr",
    "outputId": "a71189fa-70d3-48f5-84e4-815d8722db63"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "fig.set_size_inches(*one_axes_size)\n",
    "\n",
    "alone_all = np.array(tuple(df[alone].value_counts().items()))\n",
    "axes.bar(alone_all[:, 0], alone_all[:, 1], \n",
    "         width=slim_bar_width, label='Все', color=second_neutral_color)\n",
    "\n",
    "alone_alive = np.array(tuple(df_alive[alone].value_counts().items()))\n",
    "axes.bar(alone_alive[:, 0], alone_alive[:, 1], \n",
    "         width=slim_bar_width, label='Выжившие', color=good_color)\n",
    "\n",
    "axes.set_xticks((0, 1))\n",
    "axes.set_xticklabels(('С родственниками', 'Одинокие'),\n",
    "                     rotation='horizontal')\n",
    "axes.legend()\n",
    "axes.grid(True, axis='y', color=neutral_color)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of yandex-lyceum-selection-round.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}