basic_stat_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Основы статистики. Часть II\n",
    "\n",
    "#### конспект лекций\n",
    "\n",
    "Автор лекций: **святой Анатолий Карпов**\n",
    "\n",
    "Конспектировал: **отрок Михаил Курочкин**\n",
    "  - telegram: @mikhail_kurochkin\n",
    "  - instagram: [mikhail_k17](https://www.instagram.com/mikhail_k17/) *если хотите вообще от души поблагодарить - подписка/лайк :)*\n",
    "\n",
    "\n",
    "<img src='img/Saint_Karpov.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Содержание\n",
    "* ~такой текст~, значит, что это ещё не законспектированно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Часть 1 Анализ номинативных данных\n",
    "\n",
    " - [~Постановка задачи~](#Постановка-задачи)\n",
    " - [~Расстояние Пирсона~](#Расстояние-Пирсона)\n",
    " - [Распределение Хи-квадрат Пирсона](#Распределение-Хи-квадрат-Пирсона)\n",
    " - [Расчет p-уровня значимости](#Расчет-p-уровня-значимости)\n",
    " - [~Анализ таблиц сопряженности~](#Анализ-таблиц-сопряженности)\n",
    " - [~Точный критерий Фишера~](#Точный-критерий-Фишера)\n",
    " - [](#)\n",
    " - [](#)\n",
    " - [](#)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Часть 2 Логистическая регрессия и непараметрические методы \n",
    "\n",
    " - [Логистическая регрессия](#Логистическая-регрессия)\n",
    "     - [Постановка задачи](#Постановка-задачи)\n",
    " - [Модель без предикторов. Intercept only model](#Модель-без-предикторов.-Intercept-only-model)\n",
    " - [Модель с одним номинативным предиктором](#Модель-с-одним-номинативным-предиктором)\n",
    " - [~Модель с двумя номинативными предикторами~](#Модель-с-двумя-номинативными-предикторами)\n",
    " - [~Взаимодействие номинативных предикторов~](#Взаимодействие-номинативных-предикторов)\n",
    " - [~Когда нужно использовать непараметрические методы и почему?~](#Когда-нужно-использовать-непараметрические-методы-и-почему?)\n",
    " - [~Непараметрические методы. Продолжение.~](#Непараметрические-методы.-Продолжение.)\n",
    " - [](#)\n",
    " - [](#)\n",
    " - [](#)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Часть 3 Кластерный анализ и метод главных компонент\n",
    "\n",
    " - [Кластерный анализ методом **k-средних**](#Кластерный-анализ-методом-k-средних)\n",
    " - [Может ли кластерный анализ \"ошибаться\"?](#Может-ли-кластерный-анализ-ошибаться?)\n",
    " - [Как определить оптимальное число кластеров?](#Как-определить-оптимальное-число-кластеров?)\n",
    " - [~**Иерархическая кластеризация**~](#Иерархическая-кластеризация)\n",
    " - [~Введение в метод анализа главных компонент~](#Введение-в-метод-анализа-главных-компонент)\n",
    " - [](#)\n",
    " - [](#)\n",
    " - [](#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# Часть 1 Анализ номинативных данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Постановка задачи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расстояние Пирсона"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Распределение Хи-квадрат Пирсона\n",
    "\n",
    "Распределе́ние $ \\chi^2 $ (хи-квадра́т) с $k$ степеня́ми свободы — это распределение суммы квадратов $k$ независимых **стандартных нормальных случайных величин**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,8)\n",
    "\n",
    "x = np.linspace(0, 10, 300)\n",
    "# степеня свободы\n",
    "df_set = {1, 2, 3, 4, 7, 10}\n",
    "\n",
    "for df in df_set:\n",
    "    y = chi2.pdf(x, df=df)\n",
    "    plt.plot(x, y, label=f'df={df}')\n",
    "\n",
    "plt.title('графики хи-квадрат распределения с различными степенями свобод')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расчет p-уровня значимости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1512 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 21)\n",
    "\n",
    "p = 0.95\n",
    "x1 = np.linspace(0, 10, 300)\n",
    "x2 = np.linspace(0, 20, 300)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
    "axs =  axs.flat\n",
    "\n",
    "for i, df in enumerate(df_set):\n",
    "    chi_value = chi2.isf(1-p, df=df)\n",
    "    y = chi2.pdf(x, df=df)\n",
    "    x = x1 if df <= 4 else x2\n",
    "    \n",
    "    axs[i].set_title(f'Хи-квадрат степень свободы {df}')\n",
    "    axs[i].plot(x, y, color='k')\n",
    "    left_x, right_x = x <= chi_value, x >= chi_value\n",
    "    axs[i].fill_between(x[left_x], y[left_x], color='yellow')\n",
    "    axs[i].fill_between(x[right_x], y[right_x], color='blue')\n",
    "\n",
    "\n",
    "fig.suptitle('Расчёт для каждого распределения хи-квадрат 95% p-уровня значимости')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Примеры.1.4\n",
    "Какая доля наблюдений лежит в диапазоне от 2 до 4 у распределения хи-квадрат с двумя степенями свободы?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2325\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "\n",
    "print(f'{chi2.cdf(4, df=2) - chi2.cdf(2, df=2):.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь рассчитаем p-уровень значимости для нашего примера с игральной костью. Напомню, что мы получили следующие значения наблюдаемых частот (от единички до шестерки):\n",
    "\n",
    "10, 10, 10, 5, 10, 15\n",
    "\n",
    "Проверьте нулевую гипотезу о том, что эмпирическое распределение частот не отличается от равномерного. В поле для ответа введите получившийся p-уровень значимости. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4159\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "import numpy as np\n",
    "\n",
    "observed = np.array([10, 10, 10, 5, 10, 15])\n",
    "\n",
    "def chi_value(observed):\n",
    "    # e - expected\n",
    "    e = np.mean(observed)\n",
    "    return sum([((o - e)**2)/e for o in observed])\n",
    "\n",
    "p = chi2.sf(chi_value(observed), df=(observed.size-1))\n",
    "print(f'{p:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Недавно я пришел на лекцию в Институт биоинформатики и обнаружил, что в группе различное число студентов женского и мужского пола: 15 юношей и 9 девушек. Возможно, кого-то это и могло бы оставить равнодушным, а я вот сразу взял лист бумаги и ручку, чтобы проверить гипотезу о том, что распределение частот девушек и юношей не отличается от равномерного в генеральной совокупности биоинформатиков!\n",
    "\n",
    "Давайте сверим наши результаты. Введите в поле для ответа получившийся у вас p-уровень значимости."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2207\n"
     ]
    }
   ],
   "source": [
    "observed = np.array([15, 9])\n",
    "p = chi2.sf(chi_value(observed), df=observed.size-1)\n",
    "print(f'{p:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В 2013 году Эдвард Сноуден передал СМИ секретную информацию АНБ, касающуюся слежки американских спецслужб за информационными коммуникациями между гражданами. Однако ﻿его поступок вызвал неоднозначную реакцию в обществе. Исследовательский центр USA TODAY провел опрос 1500 граждан США с целью выяснить, воспринимают ли они поступок Сноудена как положительный или отрицательный. 53% опрошенных респондентов оценили разоблачение положительно.\n",
    "\n",
    "При помощи теста хи-квадрат проверьте нулевую гипотезу о том, что в генеральной совокупности распределение отношения к поступку Сноудена является равномерным, то есть 50 на 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0201\n"
     ]
    }
   ],
   "source": [
    "n=1500\n",
    "observed = np.array([0.53*n, 0.47*n], dtype='int32')\n",
    "p = chi2.sf(chi_value(observed), df=observed.size-1)\n",
    "print(f'{p:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ таблиц сопряженности"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Точный критерий Фишера"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# Часть 2 Логистическая регрессия и непараметрические методы "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Логистическая регрессия\n",
    "\n",
    "### Постановка задачи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def odds(p):\n",
    "    '''Формула выпадения шансов от вероятности'''\n",
    "    return p/(1-p)\n",
    "\n",
    "def logit(x):\n",
    "    return np.exp(x)/(1 + np.exp(x))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, figsize=(20, 10))\n",
    "\n",
    "p1 = np.linspace(0, 1, 10000)\n",
    "x1 = np.log(odds(p1))\n",
    "x2 = np.linspace(-9, 9, 10000)\n",
    "p2 = logit(x2)\n",
    "\n",
    "ax1.plot(x1, p1, label='x = ln(odds); odds=p/(1-p))')\n",
    "ax2.plot(x2, p2, label='p = exp(x)/(1 + exp(x))')\n",
    "\n",
    "\n",
    "for ax in (ax1, ax2):\n",
    "    # создадим оси    \n",
    "    ax.axvline(0, color='k')\n",
    "    ax.axhline(0, color='k')\n",
    "    # подпишем оси\n",
    "    ax.text(9, 0.03, 'x', size='xx-large')\n",
    "    ax.text(0.03, 0.7, 'probality', size='x-large', rotation=-90)\n",
    "    ax.axhline(0.5, color='r')\n",
    "    ax.legend()\n",
    "    \n",
    "fig.suptitle('logit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модель без предикторов. Intercept only model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('data/titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nSurvived = df.groupby('Survived')['Survived'].count()\n",
    "nSurvived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "odds_1 0.6230\n",
      "odds_2 0.6230\n",
      "probability of Survival 0.3838\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y = df.Survived\n",
    "# фиктиный предиктор (чтобы сделать модель без предиктора)\n",
    "X = np.zeros((y.size, 1))\n",
    "\n",
    "model = LogisticRegression()\n",
    "clf = model.fit(X, y)\n",
    "# шансы полученные из модели и вычисленные нами\n",
    "odds_1 = np.exp(np.asscalar(clf.intercept_))\n",
    "odds_2 = nSurvived[1]/nSurvived[0]\n",
    "print(f'''\n",
    "odds_1 {odds_1:.4f}\n",
    "odds_2 {odds_2:.4f}\n",
    "probability of Survival {(odds_1/(odds_1 + 1)):.4f}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модель с одним номинативным предиктором"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таблица сопряжённости пола и выживаемости:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Survived              \n",
       "0             81   468\n",
       "1            233   109"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SexSurvived = pd.crosstab(df.Survived, df.Sex)\n",
    "SexSurvived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "coef:\t\t2.5137\n",
      "intercept:\t-1.4571\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y = df.Survived\n",
    "# так как там были значения типа string, то надо перевести в номинативный тип\n",
    "X, _ = pd.factorize(df.Sex)\n",
    "# convert 1d array to column vector\n",
    "X = X.reshape((-1, 1))\n",
    "\n",
    "# penalty - это регулиризация, которая нас, в данный момент, не касается\n",
    "model = LogisticRegression(penalty='none')\n",
    "clf = model.fit(X, y)\n",
    "print(f'''\n",
    "coef:\\t\\t{np.asscalar(clf.coef_):.4f}\n",
    "intercept:\\t{np.asscalar(clf.intercept_):.4f}\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "log(odds_f) = 1.0566\n",
      "log(odds_m) = -1.4571\n",
      "log(odds_fm) = 2.5137\n",
      "log(odds_mf) = -2.5137\n",
      "\n"
     ]
    }
   ],
   "source": [
    "odds_f = SexSurvived['female'][1]/SexSurvived['female'][0]\n",
    "odds_m = SexSurvived['male'][1]/SexSurvived['male'][0]\n",
    "odds_fm = odds_f/odds_m\n",
    "odds_mf = odds_m/odds_f\n",
    "print(f'''\n",
    "log(odds_f) = {np.log(odds_f):.4f}\n",
    "log(odds_m) = {np.log(odds_m):.4f}\n",
    "log(odds_fm) = {np.log(odds_fm):.4f}\n",
    "log(odds_mf) = {np.log(odds_mf):.4f}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном случае значение **odds_m** равно значению **intercept** в моделе и значит, что у мужчин было меньше шансов спастись (сексизм, да?!) на титанике.\n",
    "А значение коэффициента (**coef**) равно отношению шансов спастись женщин, по отношению к мужчин **odds_fm** ( дофига больше шансов было у них:-( )  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модель с двумя номинативными предикторами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Взаимодействие номинативных предикторов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Когда нужно использовать непараметрические методы и почему?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Непараметрические методы. Продолжение."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# Часть 3 Кластерный анализ и метод главных компонент"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Кластерный анализ методом k-средних\n",
    "\n",
    "[Сайт для визуализации k-means](https://www.naftaliharris.com/blog/visualizing-k-means-clustering/)\n",
    "\n",
    "**кратко о k-means**\n",
    " - кластерный анализ это метод обучения **без учителя**, в отличие от регрессии\n",
    " - количество кластеров задаётся исследователем\n",
    " - кластеры задаются с помощью центроидов, которые стремяться в центр каждого кластера, и каждая точка кластера находится наиболее близко к своему центроиду.\n",
    " - центроид кластера - аналог центра масс в физике. Или же по аналогии, можно сказать, что центроиды это геометрический центр кластера.\n",
    "\n",
    "**идея метода**\n",
    " - решаем на сколько кластеров будем делить наблюдения\n",
    " - случайно выбираем начальные позиции центроидов классов\n",
    " - выводим центроиды на наилучшие позиции\n",
    " - **Profit**\n",
    " \n",
    " <img src='img/k-means.gif' width=300/>\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df['target'] = iris.target\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - petal - лепесток\n",
    " - sepal - чашелистик (один из зеленых листочков, образующих чашечку цветка)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='petal width (cm)'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='sepal length (cm)', y='petal width (cm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно предположить, о наличие трёх кластеров тут, попробуем определить их методом k-means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  petal width (cm)  cluster\n",
       "0                5.1               0.2        1\n",
       "1                4.9               0.2        1\n",
       "2                4.7               0.2        1\n",
       "3                4.6               0.2        1\n",
       "4                5.0               0.2        1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# определим метод\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "# вырежем данные, которые нам нужны\n",
    "data = df.loc[:, ['sepal length (cm)','petal width (cm)']]\n",
    "# построим модель\n",
    "model = kmeans.fit(data)\n",
    "# получим кластеры\n",
    "data['cluster'] = model.labels_\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='petal width (cm)'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='sepal length (cm)', y='petal width (cm)', c='cluster', colormap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь сравним с реальными видами ирисами, и увидем, что кластеры были разделенны довольно корректно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='petal width (cm)'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='sepal length (cm)', y='petal width (cm)', c='target', colormap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Может-ли-кластерный-анализ-ошибаться?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Как определить оптимальное число кластеров?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146.0\n"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "Data = {'x': [-3,1,2,3,5,6,7],\n",
    "        'y': [3,4,6,8,2,11,1]\n",
    "       }\n",
    "  \n",
    "df = DataFrame(Data,columns=['x','y'])\n",
    "  \n",
    "kmeans = KMeans(1)\n",
    "kmeans.fit(df)\n",
    "print(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Иерархическая кластеризация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Введение в метод анализа главных компонент"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}