ml1lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bach Chorales Harmony\n",
    "\n",
    "Учитывая музыкальный поток, задача анализа гармонии музыки состоит в том, чтобы связать метку аккорда с каждой точкой времени. Такие ярлыки показывают основную гармонию, указывая фундаментальную ноту (корень) и режим, используя имена аккордов, такие как C minor.\n",
    "\n",
    "Набор данных состоит из 60 хоралов (5665 событий) J.S. Бах (1675-1750). Каждое событие каждого хорала помечено, используя 1 из 101 аккордовых меток.\n",
    "Количество атрибутов: 17 (имя последовательности, номер события, наличие метки (x12), аккорд).\n",
    "\n",
    "Информация о высотных классах была извлечена из загруженных источников MIDI от (JSB Chorales) [http://www.jsbchorales.net]. Информация о метре были рассчитаны через программу Meter, которая является частью Melisma музыкальный анализатор (Melisma) [http://www.link.cs.cmu.edu/music-analysis/]. Рукописные ярлыки вручную аннотируются экспертом-человеком.\n",
    "\n",
    "## Описание данных\n",
    "\n",
    "   * ***Choral ID***: соответствует именам файлов из (Bach Central) [http://www.bachcentral.com];\n",
    "   * ***Event number***:  индекс (начиная с 1) события внутри хорала;\n",
    "   * ***Pitch classes(C..B) (x12)***: YES / NO в зависимости от того, присутствует ли данный высотный класс [https://ru.wikipedia.org/wiki/Высотный_класс];\n",
    "   * ***Pitch class Bass***: высотный класс басовой ноты;\n",
    "   * ***Meter***: целые числа от 1 до 5, меньшие числа обозначают менее акцентированные события, более высокие числа обозначают более акцентированные события;\n",
    "   * ***Chord label***: аккорд, резонирующий во время данного события."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подключаем используемые в работе библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy  as np\n",
    "import matplotlib.pyplot  as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Считываем данные из датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('source/jsbach_chorals_harmony.data', sep = ',', engine = 'python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определение типа данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим часть данных в виде таблице для визуального контроля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>Choral ID</th>\n",
       "      <th>Event number</th>\n",
       "      <th>Pitch class C</th>\n",
       "      <th>Pitch class C#</th>\n",
       "      <th>Pitch class D</th>\n",
       "      <th>Pitch class D#</th>\n",
       "      <th>Pitch class E</th>\n",
       "      <th>Pitch class F</th>\n",
       "      <th>Pitch class F#</th>\n",
       "      <th>Pitch class G</th>\n",
       "      <th>Pitch class G#</th>\n",
       "      <th>Pitch class A</th>\n",
       "      <th>Pitch class A#</th>\n",
       "      <th>Pitch class B</th>\n",
       "      <th>Pitch class Bass</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Chord label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>3</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>4</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>5</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>6</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>D_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>7</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>D_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>8</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>9</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>10</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>5</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>11</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>1</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>12</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>13</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>14</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>15</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>16</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>17</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>18</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>5</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>19</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>2</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>20</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>1</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>21</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>22</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>C_M7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>23</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>1</td>\n",
       "      <td>C_M7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>24</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>25</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>26</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>27</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>28</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>D_m7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>29</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>D_m7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>30</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>D_m7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5635</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>80</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5636</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>81</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5637</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>82</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5638</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>83</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5639</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>84</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5640</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>85</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5641</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>86</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>4</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5642</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>87</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5643</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>88</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>D_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5644</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>89</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>D_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5645</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>90</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>4</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5646</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>91</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F#</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5647</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>92</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5648</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>93</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>3</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5649</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>94</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>D_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5650</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>95</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5651</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>96</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bb</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5652</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>97</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5653</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>98</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5654</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>99</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5655</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>100</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>BbM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5656</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>101</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5657</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>102</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5658</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>103</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5659</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>104</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5660</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>105</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>4</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5661</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>106</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>3</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5662</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>107</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5663</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>108</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>109</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5665 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Choral ID  Event number Pitch class C Pitch class C# Pitch class D  \\\n",
       "0     000106b_             1           YES             NO            NO   \n",
       "1     000106b_             2           YES             NO            NO   \n",
       "2     000106b_             3           YES             NO            NO   \n",
       "3     000106b_             4           YES             NO            NO   \n",
       "4     000106b_             5           YES             NO            NO   \n",
       "5     000106b_             6            NO             NO           YES   \n",
       "6     000106b_             7            NO             NO           YES   \n",
       "7     000106b_             8           YES             NO            NO   \n",
       "8     000106b_             9           YES             NO            NO   \n",
       "9     000106b_            10            NO             NO           YES   \n",
       "10    000106b_            11            NO             NO           YES   \n",
       "11    000106b_            12           YES             NO           YES   \n",
       "12    000106b_            13           YES             NO           YES   \n",
       "13    000106b_            14            NO             NO           YES   \n",
       "14    000106b_            15            NO             NO           YES   \n",
       "15    000106b_            16           YES             NO            NO   \n",
       "16    000106b_            17           YES             NO            NO   \n",
       "17    000106b_            18            NO             NO           YES   \n",
       "18    000106b_            19            NO             NO           YES   \n",
       "19    000106b_            20            NO             NO           YES   \n",
       "20    000106b_            21            NO             NO            NO   \n",
       "21    000106b_            22           YES             NO            NO   \n",
       "22    000106b_            23            NO             NO            NO   \n",
       "23    000106b_            24           YES             NO            NO   \n",
       "24    000106b_            25            NO             NO           YES   \n",
       "25    000106b_            26           YES             NO            NO   \n",
       "26    000106b_            27           YES             NO            NO   \n",
       "27    000106b_            28           YES             NO           YES   \n",
       "28    000106b_            29            NO             NO           YES   \n",
       "29    000106b_            30            NO             NO           YES   \n",
       "...        ...           ...           ...            ...           ...   \n",
       "5635  015505b_            80           YES             NO            NO   \n",
       "5636  015505b_            81           YES             NO            NO   \n",
       "5637  015505b_            82           YES             NO            NO   \n",
       "5638  015505b_            83           YES             NO            NO   \n",
       "5639  015505b_            84           YES             NO            NO   \n",
       "5640  015505b_            85           YES             NO            NO   \n",
       "5641  015505b_            86            NO             NO           YES   \n",
       "5642  015505b_            87           YES             NO           YES   \n",
       "5643  015505b_            88            NO             NO           YES   \n",
       "5644  015505b_            89            NO             NO           YES   \n",
       "5645  015505b_            90           YES             NO            NO   \n",
       "5646  015505b_            91           YES             NO            NO   \n",
       "5647  015505b_            92            NO             NO           YES   \n",
       "5648  015505b_            93           YES             NO            NO   \n",
       "5649  015505b_            94            NO             NO           YES   \n",
       "5650  015505b_            95           YES             NO            NO   \n",
       "5651  015505b_            96           YES             NO            NO   \n",
       "5652  015505b_            97           YES             NO            NO   \n",
       "5653  015505b_            98           YES             NO            NO   \n",
       "5654  015505b_            99            NO             NO           YES   \n",
       "5655  015505b_           100            NO             NO           YES   \n",
       "5656  015505b_           101           YES             NO            NO   \n",
       "5657  015505b_           102            NO             NO           YES   \n",
       "5658  015505b_           103           YES             NO            NO   \n",
       "5659  015505b_           104           YES             NO            NO   \n",
       "5660  015505b_           105            NO             NO           YES   \n",
       "5661  015505b_           106            NO             NO           YES   \n",
       "5662  015505b_           107           YES             NO            NO   \n",
       "5663  015505b_           108           YES             NO            NO   \n",
       "5664  015505b_           109            NO             NO            NO   \n",
       "\n",
       "     Pitch class D# Pitch class E Pitch class F Pitch class F# Pitch class G  \\\n",
       "0                NO            NO           YES             NO            NO   \n",
       "1                NO           YES            NO             NO           YES   \n",
       "2                NO           YES            NO             NO           YES   \n",
       "3                NO            NO           YES             NO            NO   \n",
       "4                NO            NO           YES             NO            NO   \n",
       "5                NO            NO           YES             NO            NO   \n",
       "6                NO            NO           YES             NO            NO   \n",
       "7                NO            NO           YES             NO            NO   \n",
       "8                NO            NO           YES             NO            NO   \n",
       "9                NO            NO           YES             NO            NO   \n",
       "10               NO            NO           YES             NO            NO   \n",
       "11               NO            NO           YES             NO            NO   \n",
       "12               NO            NO           YES             NO           YES   \n",
       "13               NO            NO           YES             NO            NO   \n",
       "14               NO           YES           YES             NO           YES   \n",
       "15               NO            NO           YES             NO            NO   \n",
       "16               NO            NO           YES             NO            NO   \n",
       "17               NO            NO           YES             NO            NO   \n",
       "18               NO            NO           YES             NO           YES   \n",
       "19               NO            NO           YES             NO            NO   \n",
       "20               NO           YES            NO             NO           YES   \n",
       "21               NO           YES            NO             NO           YES   \n",
       "22               NO           YES            NO             NO           YES   \n",
       "23               NO            NO           YES             NO            NO   \n",
       "24               NO            NO           YES             NO           YES   \n",
       "25               NO           YES            NO             NO           YES   \n",
       "26               NO           YES            NO             NO           YES   \n",
       "27               NO            NO           YES             NO            NO   \n",
       "28               NO           YES           YES             NO            NO   \n",
       "29               NO           YES           YES             NO            NO   \n",
       "...             ...           ...           ...            ...           ...   \n",
       "5635             NO            NO           YES             NO            NO   \n",
       "5636             NO            NO           YES             NO           YES   \n",
       "5637             NO            NO           YES             NO            NO   \n",
       "5638             NO            NO           YES             NO           YES   \n",
       "5639             NO            NO           YES             NO            NO   \n",
       "5640             NO           YES            NO             NO           YES   \n",
       "5641             NO            NO           YES             NO            NO   \n",
       "5642             NO            NO           YES             NO            NO   \n",
       "5643             NO            NO           YES             NO            NO   \n",
       "5644             NO            NO           YES             NO            NO   \n",
       "5645             NO           YES            NO             NO           YES   \n",
       "5646             NO           YES            NO            YES            NO   \n",
       "5647             NO            NO            NO             NO           YES   \n",
       "5648             NO           YES            NO             NO           YES   \n",
       "5649             NO            NO            NO            YES            NO   \n",
       "5650             NO            NO           YES             NO            NO   \n",
       "5651             NO            NO           YES             NO            NO   \n",
       "5652             NO           YES            NO             NO           YES   \n",
       "5653             NO           YES            NO             NO            NO   \n",
       "5654             NO            NO           YES             NO            NO   \n",
       "5655             NO           YES            NO             NO           YES   \n",
       "5656             NO            NO           YES             NO            NO   \n",
       "5657             NO            NO           YES             NO            NO   \n",
       "5658             NO           YES            NO             NO           YES   \n",
       "5659             NO           YES           YES             NO           YES   \n",
       "5660             NO            NO            NO             NO           YES   \n",
       "5661             NO            NO            NO             NO           YES   \n",
       "5662             NO           YES            NO             NO           YES   \n",
       "5663             NO           YES            NO             NO           YES   \n",
       "5664             NO            NO           YES             NO            NO   \n",
       "\n",
       "     Pitch class G# Pitch class A Pitch class A# Pitch class B  \\\n",
       "0                NO           YES             NO            NO   \n",
       "1                NO            NO             NO            NO   \n",
       "2                NO            NO             NO            NO   \n",
       "3                NO           YES             NO            NO   \n",
       "4                NO           YES             NO            NO   \n",
       "5                NO           YES             NO            NO   \n",
       "6                NO           YES             NO            NO   \n",
       "7                NO           YES             NO            NO   \n",
       "8                NO           YES             NO            NO   \n",
       "9                NO            NO            YES            NO   \n",
       "10               NO            NO            YES            NO   \n",
       "11               NO           YES            YES            NO   \n",
       "12               NO            NO            YES            NO   \n",
       "13               NO            NO            YES            NO   \n",
       "14               NO            NO            YES            NO   \n",
       "15               NO           YES             NO            NO   \n",
       "16               NO           YES             NO            NO   \n",
       "17               NO            NO            YES            NO   \n",
       "18               NO            NO            YES            NO   \n",
       "19               NO           YES            YES            NO   \n",
       "20               NO           YES            YES            NO   \n",
       "21               NO            NO            YES            NO   \n",
       "22               NO            NO            YES            NO   \n",
       "23               NO           YES             NO            NO   \n",
       "24               NO            NO             NO           YES   \n",
       "25               NO            NO             NO            NO   \n",
       "26               NO            NO             NO            NO   \n",
       "27               NO           YES             NO            NO   \n",
       "28               NO           YES             NO           YES   \n",
       "29               NO           YES             NO            NO   \n",
       "...             ...           ...            ...           ...   \n",
       "5635             NO           YES             NO            NO   \n",
       "5636             NO            NO            YES            NO   \n",
       "5637             NO           YES             NO            NO   \n",
       "5638             NO            NO            YES            NO   \n",
       "5639             NO           YES             NO            NO   \n",
       "5640             NO           YES             NO            NO   \n",
       "5641             NO            NO            YES            NO   \n",
       "5642             NO            NO             NO            NO   \n",
       "5643             NO           YES             NO            NO   \n",
       "5644             NO            NO            YES            NO   \n",
       "5645             NO            NO             NO            NO   \n",
       "5646             NO           YES             NO            NO   \n",
       "5647             NO            NO            YES            NO   \n",
       "5648             NO            NO            YES            NO   \n",
       "5649             NO           YES             NO            NO   \n",
       "5650             NO           YES             NO            NO   \n",
       "5651             NO           YES            YES            NO   \n",
       "5652             NO            NO             NO            NO   \n",
       "5653             NO           YES             NO            NO   \n",
       "5654             NO            NO            YES            NO   \n",
       "5655             NO            NO            YES            NO   \n",
       "5656             NO           YES             NO            NO   \n",
       "5657             NO           YES            YES            NO   \n",
       "5658             NO            NO             NO            NO   \n",
       "5659             NO            NO             NO            NO   \n",
       "5660             NO            NO            YES            NO   \n",
       "5661             NO           YES             NO            NO   \n",
       "5662             NO            NO             NO            NO   \n",
       "5663             NO            NO            YES            NO   \n",
       "5664             NO           YES             NO            NO   \n",
       "\n",
       "     Pitch class Bass  Meter Chord label  \n",
       "0                   F      3         F_M  \n",
       "1                   E      5         C_M  \n",
       "2                   E      2         C_M  \n",
       "3                   F      3         F_M  \n",
       "4                   F      2         F_M  \n",
       "5                   D      4         D_m  \n",
       "6                   D      2         D_m  \n",
       "7                   A      3         F_M  \n",
       "8                   A      2         F_M  \n",
       "9                  Bb      5         BbM  \n",
       "10                 Bb      1         BbM  \n",
       "11                  C      2         BbM  \n",
       "12                  C      1         BbM  \n",
       "13                  D      3         BbM  \n",
       "14                  E      2         BbM  \n",
       "15                  F      4         F_M  \n",
       "16                  F      3         F_M  \n",
       "17                 Bb      5         BbM  \n",
       "18                 Bb      2         BbM  \n",
       "19                 Bb      1         BbM  \n",
       "20                  A      3        C_M7  \n",
       "21                  G      2        C_M7  \n",
       "22                  G      1        C_M7  \n",
       "23                  A      4         F_M  \n",
       "24                  B      2         F_M  \n",
       "25                  C      3         C_M  \n",
       "26                  C      2         C_M  \n",
       "27                  F      5        D_m7  \n",
       "28                  F      2        D_m7  \n",
       "29                  F      1        D_m7  \n",
       "...               ...    ...         ...  \n",
       "5635                F      5         F_M  \n",
       "5636                F      3         F_M  \n",
       "5637                F      4         F_M  \n",
       "5638                G      3         F_M  \n",
       "5639                A      5         F_M  \n",
       "5640                A      3         F_M  \n",
       "5641               Bb      4         BbM  \n",
       "5642                C      3         BbM  \n",
       "5643                D      5         D_m  \n",
       "5644                D      3         D_m  \n",
       "5645                E      4         C_M  \n",
       "5646               F#      3         C_M  \n",
       "5647                G      5         G_m  \n",
       "5648                G      3         G_m  \n",
       "5649                D      4         D_M  \n",
       "5650                A      5         F_M  \n",
       "5651               Bb      3         F_M  \n",
       "5652                C      4         C_M  \n",
       "5653                C      3         C_M  \n",
       "5654                D      5         BbM  \n",
       "5655                E      3         BbM  \n",
       "5656                F      4         F_M  \n",
       "5657                F      3         F_M  \n",
       "5658                E      5         C_M  \n",
       "5659                F      3         C_M  \n",
       "5660                G      4         G_m  \n",
       "5661                G      3         G_m  \n",
       "5662                C      5         C_M  \n",
       "5663                C      3         C_M  \n",
       "5664                F      4         F_M  \n",
       "\n",
       "[5665 rows x 17 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пробник от таблицы выше, верхушка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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>Choral ID</th>\n",
       "      <th>Event number</th>\n",
       "      <th>Pitch class C</th>\n",
       "      <th>Pitch class C#</th>\n",
       "      <th>Pitch class D</th>\n",
       "      <th>Pitch class D#</th>\n",
       "      <th>Pitch class E</th>\n",
       "      <th>Pitch class F</th>\n",
       "      <th>Pitch class F#</th>\n",
       "      <th>Pitch class G</th>\n",
       "      <th>Pitch class G#</th>\n",
       "      <th>Pitch class A</th>\n",
       "      <th>Pitch class A#</th>\n",
       "      <th>Pitch class B</th>\n",
       "      <th>Pitch class Bass</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Chord label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>3</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>4</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>5</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Choral ID  Event number Pitch class C Pitch class C# Pitch class D  \\\n",
       "0  000106b_             1           YES             NO            NO   \n",
       "1  000106b_             2           YES             NO            NO   \n",
       "2  000106b_             3           YES             NO            NO   \n",
       "3  000106b_             4           YES             NO            NO   \n",
       "4  000106b_             5           YES             NO            NO   \n",
       "\n",
       "  Pitch class D# Pitch class E Pitch class F Pitch class F# Pitch class G  \\\n",
       "0             NO            NO           YES             NO            NO   \n",
       "1             NO           YES            NO             NO           YES   \n",
       "2             NO           YES            NO             NO           YES   \n",
       "3             NO            NO           YES             NO            NO   \n",
       "4             NO            NO           YES             NO            NO   \n",
       "\n",
       "  Pitch class G# Pitch class A Pitch class A# Pitch class B Pitch class Bass  \\\n",
       "0             NO           YES             NO            NO                F   \n",
       "1             NO            NO             NO            NO                E   \n",
       "2             NO            NO             NO            NO                E   \n",
       "3             NO           YES             NO            NO                F   \n",
       "4             NO           YES             NO            NO                F   \n",
       "\n",
       "   Meter Chord label  \n",
       "0      3         F_M  \n",
       "1      5         C_M  \n",
       "2      2         C_M  \n",
       "3      3         F_M  \n",
       "4      2         F_M  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пробник от таблицы выше, низушка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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>Choral ID</th>\n",
       "      <th>Event number</th>\n",
       "      <th>Pitch class C</th>\n",
       "      <th>Pitch class C#</th>\n",
       "      <th>Pitch class D</th>\n",
       "      <th>Pitch class D#</th>\n",
       "      <th>Pitch class E</th>\n",
       "      <th>Pitch class F</th>\n",
       "      <th>Pitch class F#</th>\n",
       "      <th>Pitch class G</th>\n",
       "      <th>Pitch class G#</th>\n",
       "      <th>Pitch class A</th>\n",
       "      <th>Pitch class A#</th>\n",
       "      <th>Pitch class B</th>\n",
       "      <th>Pitch class Bass</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Chord label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5660</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>105</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>4</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5661</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>106</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>G</td>\n",
       "      <td>3</td>\n",
       "      <td>G_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5662</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>107</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5663</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>108</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>015505b_</td>\n",
       "      <td>109</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Choral ID  Event number Pitch class C Pitch class C# Pitch class D  \\\n",
       "5660  015505b_           105            NO             NO           YES   \n",
       "5661  015505b_           106            NO             NO           YES   \n",
       "5662  015505b_           107           YES             NO            NO   \n",
       "5663  015505b_           108           YES             NO            NO   \n",
       "5664  015505b_           109            NO             NO            NO   \n",
       "\n",
       "     Pitch class D# Pitch class E Pitch class F Pitch class F# Pitch class G  \\\n",
       "5660             NO            NO            NO             NO           YES   \n",
       "5661             NO            NO            NO             NO           YES   \n",
       "5662             NO           YES            NO             NO           YES   \n",
       "5663             NO           YES            NO             NO           YES   \n",
       "5664             NO            NO           YES             NO            NO   \n",
       "\n",
       "     Pitch class G# Pitch class A Pitch class A# Pitch class B  \\\n",
       "5660             NO            NO            YES            NO   \n",
       "5661             NO           YES             NO            NO   \n",
       "5662             NO            NO             NO            NO   \n",
       "5663             NO            NO            YES            NO   \n",
       "5664             NO           YES             NO            NO   \n",
       "\n",
       "     Pitch class Bass  Meter Chord label  \n",
       "5660                G      4         G_m  \n",
       "5661                G      3         G_m  \n",
       "5662                C      5         C_M  \n",
       "5663                C      3         C_M  \n",
       "5664                F      4         F_M  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Контроль размеров таблицы и наименования признаков(столбцов)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5665, 17)\n",
      "Index(['Choral ID', 'Event number', 'Pitch class C', 'Pitch class C#',\n",
      "       'Pitch class D', 'Pitch class D#', 'Pitch class E', 'Pitch class F',\n",
      "       'Pitch class F#', 'Pitch class G', 'Pitch class G#', 'Pitch class A',\n",
      "       'Pitch class A#', 'Pitch class B', 'Pitch class Bass', 'Meter',\n",
      "       'Chord label'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Индексация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5665, step=1)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем элемент таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'YES'"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.at[2, 'Pitch class G']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем весь столбец"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         2\n",
       "2         3\n",
       "3         4\n",
       "4         5\n",
       "5         6\n",
       "6         7\n",
       "7         8\n",
       "8         9\n",
       "9        10\n",
       "10       11\n",
       "11       12\n",
       "12       13\n",
       "13       14\n",
       "14       15\n",
       "15       16\n",
       "16       17\n",
       "17       18\n",
       "18       19\n",
       "19       20\n",
       "20       21\n",
       "21       22\n",
       "22       23\n",
       "23       24\n",
       "24       25\n",
       "25       26\n",
       "26       27\n",
       "27       28\n",
       "28       29\n",
       "29       30\n",
       "       ... \n",
       "5635     80\n",
       "5636     81\n",
       "5637     82\n",
       "5638     83\n",
       "5639     84\n",
       "5640     85\n",
       "5641     86\n",
       "5642     87\n",
       "5643     88\n",
       "5644     89\n",
       "5645     90\n",
       "5646     91\n",
       "5647     92\n",
       "5648     93\n",
       "5649     94\n",
       "5650     95\n",
       "5651     96\n",
       "5652     97\n",
       "5653     98\n",
       "5654     99\n",
       "5655    100\n",
       "5656    101\n",
       "5657    102\n",
       "5658    103\n",
       "5659    104\n",
       "5660    105\n",
       "5661    106\n",
       "5662    107\n",
       "5663    108\n",
       "5664    109\n",
       "Name: Event number, Length: 5665, dtype: int64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Event number']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем элемент из столбца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Event number'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем тип данных в столбце"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int64'"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Event number'].dtype.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем тип данных в столбце"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'object'"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pitch class C'].dtype.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем тип данных конкретного элемента"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['Pitch class C'][3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип данных столбца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['Pitch class C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заголовок таблицы по нескольким выбранным столбцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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>Choral ID</th>\n",
       "      <th>Chord label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>C_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000106b_</td>\n",
       "      <td>F_M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Choral ID Chord label\n",
       "0  000106b_         F_M\n",
       "1  000106b_         C_M\n",
       "2  000106b_         C_M\n",
       "3  000106b_         F_M\n",
       "4  000106b_         F_M"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Choral ID', 'Chord label']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основные сведения о числовых признаках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "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>Event number</th>\n",
       "      <th>Meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.374404</td>\n",
       "      <td>3.134863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>37.268208</td>\n",
       "      <td>1.109710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>207.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Event number        Meter\n",
       "count   5665.000000  5665.000000\n",
       "mean      53.374404     3.134863\n",
       "std       37.268208     1.109710\n",
       "min        1.000000     1.000000\n",
       "25%       24.000000     2.000000\n",
       "50%       48.000000     3.000000\n",
       "75%       75.000000     4.000000\n",
       "max      207.000000     5.000000"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим числовые зависимости всего от всего"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(data, alpha = .01, figsize = (10, 10))\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Корреляция"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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>Event number</th>\n",
       "      <th>Meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Event number</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meter</th>\n",
       "      <td>-0.076262</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Event number     Meter\n",
       "Event number      1.000000 -0.076262\n",
       "Meter            -0.076262  1.000000"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Одна из числовых зависимостей с настраиваемыми параметрами графики "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Event number'], data['Meter'], 'o', alpha = 0.05)\n",
    "plt.xlabel('Event number')\n",
    "plt.ylabel('Meter')\n",
    "plt.title('Event number vs Meter')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Принадлежность элементов определённому классу признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "5       False\n",
       "6       False\n",
       "7       False\n",
       "8       False\n",
       "9       False\n",
       "10      False\n",
       "11      False\n",
       "12      False\n",
       "13      False\n",
       "14      False\n",
       "15      False\n",
       "16      False\n",
       "17      False\n",
       "18      False\n",
       "19      False\n",
       "20      False\n",
       "21      False\n",
       "22      False\n",
       "23      False\n",
       "24      False\n",
       "25      False\n",
       "26      False\n",
       "27      False\n",
       "28      False\n",
       "29      False\n",
       "        ...  \n",
       "5635    False\n",
       "5636    False\n",
       "5637    False\n",
       "5638    False\n",
       "5639    False\n",
       "5640    False\n",
       "5641    False\n",
       "5642    False\n",
       "5643    False\n",
       "5644    False\n",
       "5645    False\n",
       "5646     True\n",
       "5647    False\n",
       "5648    False\n",
       "5649     True\n",
       "5650    False\n",
       "5651    False\n",
       "5652    False\n",
       "5653    False\n",
       "5654    False\n",
       "5655    False\n",
       "5656    False\n",
       "5657    False\n",
       "5658    False\n",
       "5659    False\n",
       "5660    False\n",
       "5661    False\n",
       "5662    False\n",
       "5663    False\n",
       "5664    False\n",
       "Name: Pitch class F#, Length: 5665, dtype: bool"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pitch class F#'] == 'YES'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Диаграмма распределения бинарного признака в зависимости от двух числовых"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdb5c588>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "plt.scatter(data[data['Pitch class F#'] == 'YES']['Meter'],\n",
    "            data[data['Pitch class F#'] == 'YES']['Event number'],\n",
    "            alpha = 0.05,\n",
    "            label = 'YES',\n",
    "            color = 'b')\n",
    "\n",
    "plt.scatter(data[data['Pitch class F#'] != 'YES']['Meter'],\n",
    "            data[data['Pitch class F#'] != 'YES']['Event number'],\n",
    "            alpha = 0.05,\n",
    "            label = 'NO',\n",
    "            color = 'r')\n",
    "\n",
    "plt.xlabel('Meter')\n",
    "\n",
    "plt.ylabel('Event number')\n",
    "plt.yticks(np.arange(0, 210, step=50))\n",
    "plt.ylim(0, 210)\n",
    "plt.legend()\n",
    "#plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "То же, но со случайным разбросом одного из признаков, \"размытие\" зависимости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xe0f81d0>"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "education_num_rnd = data['Meter'] + np.random.rand(data.shape[0]) - .5\n",
    "\n",
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "plt.scatter(education_num_rnd[data['Pitch class F#'] == 'YES'],\n",
    "            data[data['Pitch class F#'] == 'YES']['Event number'],\n",
    "            alpha = 0.15,\n",
    "            label = 'YES',\n",
    "            color = 'b')\n",
    "\n",
    "plt.scatter(education_num_rnd[data['Pitch class F#'] != 'YES'],\n",
    "            data[data['Pitch class F#'] != 'YES']['Event number'],\n",
    "            alpha = 0.15,\n",
    "            label = 'NO',\n",
    "            color = 'r')\n",
    "\n",
    "plt.xlabel('Meter')\n",
    "\n",
    "plt.ylabel('Event number')\n",
    "plt.yticks(np.arange(0, 207, step=50))\n",
    "plt.ylim(0, 207)\n",
    "plt.legend()\n",
    "#plt.grid()\n",
    "#plt.title('ъ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основные сведения по данным, из таблицы видно, что пропусков в данных нет, уже хорошо, большинство признаков бинарные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "scrolled": false
   },
   "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>Choral ID</th>\n",
       "      <th>Pitch class C</th>\n",
       "      <th>Pitch class C#</th>\n",
       "      <th>Pitch class D</th>\n",
       "      <th>Pitch class D#</th>\n",
       "      <th>Pitch class E</th>\n",
       "      <th>Pitch class F</th>\n",
       "      <th>Pitch class F#</th>\n",
       "      <th>Pitch class G</th>\n",
       "      <th>Pitch class G#</th>\n",
       "      <th>Pitch class A</th>\n",
       "      <th>Pitch class A#</th>\n",
       "      <th>Pitch class B</th>\n",
       "      <th>Pitch class Bass</th>\n",
       "      <th>Chord label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "      <td>5665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>002908ch</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>D</td>\n",
       "      <td>D_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>206</td>\n",
       "      <td>3875</td>\n",
       "      <td>4711</td>\n",
       "      <td>3300</td>\n",
       "      <td>4956</td>\n",
       "      <td>3540</td>\n",
       "      <td>4381</td>\n",
       "      <td>4253</td>\n",
       "      <td>3523</td>\n",
       "      <td>5006</td>\n",
       "      <td>3290</td>\n",
       "      <td>4644</td>\n",
       "      <td>3874</td>\n",
       "      <td>689</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Choral ID Pitch class C Pitch class C# Pitch class D Pitch class D#  \\\n",
       "count       5665          5665           5665          5665           5665   \n",
       "unique        62             2              2             2              2   \n",
       "top     002908ch            NO             NO            NO             NO   \n",
       "freq         206          3875           4711          3300           4956   \n",
       "\n",
       "       Pitch class E Pitch class F Pitch class F# Pitch class G  \\\n",
       "count           5665          5665           5665          5665   \n",
       "unique             2             2              2             2   \n",
       "top               NO            NO             NO            NO   \n",
       "freq            3540          4381           4253          3523   \n",
       "\n",
       "       Pitch class G# Pitch class A Pitch class A# Pitch class B  \\\n",
       "count            5665          5665           5665          5665   \n",
       "unique              2             2              2             2   \n",
       "top                NO            NO             NO            NO   \n",
       "freq             5006          3290           4644          3874   \n",
       "\n",
       "       Pitch class Bass Chord label  \n",
       "count              5665        5665  \n",
       "unique               16         102  \n",
       "top                   D         D_M  \n",
       "freq                689         503  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = ['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Слияние двух таблиц выше, максимальная полнота сведений о данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Choral ID</th>\n",
       "      <th>Event number</th>\n",
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       "      <th>Pitch class C#</th>\n",
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       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>002908ch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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       "      <td>3523</td>\n",
       "      <td>5006</td>\n",
       "      <td>3290</td>\n",
       "      <td>4644</td>\n",
       "      <td>3874</td>\n",
       "      <td>689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>503</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.134863</td>\n",
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       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.109710</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Choral ID  Event number Pitch class C Pitch class C# Pitch class D  \\\n",
       "count       5665   5665.000000          5665           5665          5665   \n",
       "unique        62           NaN             2              2             2   \n",
       "top     002908ch           NaN            NO             NO            NO   \n",
       "freq         206           NaN          3875           4711          3300   \n",
       "mean         NaN     53.374404           NaN            NaN           NaN   \n",
       "std          NaN     37.268208           NaN            NaN           NaN   \n",
       "min          NaN      1.000000           NaN            NaN           NaN   \n",
       "25%          NaN     24.000000           NaN            NaN           NaN   \n",
       "50%          NaN     48.000000           NaN            NaN           NaN   \n",
       "75%          NaN     75.000000           NaN            NaN           NaN   \n",
       "max          NaN    207.000000           NaN            NaN           NaN   \n",
       "\n",
       "       Pitch class D# Pitch class E Pitch class F Pitch class F#  \\\n",
       "count            5665          5665          5665           5665   \n",
       "unique              2             2             2              2   \n",
       "top                NO            NO            NO             NO   \n",
       "freq             4956          3540          4381           4253   \n",
       "mean              NaN           NaN           NaN            NaN   \n",
       "std               NaN           NaN           NaN            NaN   \n",
       "min               NaN           NaN           NaN            NaN   \n",
       "25%               NaN           NaN           NaN            NaN   \n",
       "50%               NaN           NaN           NaN            NaN   \n",
       "75%               NaN           NaN           NaN            NaN   \n",
       "max               NaN           NaN           NaN            NaN   \n",
       "\n",
       "       Pitch class G Pitch class G# Pitch class A Pitch class A#  \\\n",
       "count           5665           5665          5665           5665   \n",
       "unique             2              2             2              2   \n",
       "top               NO             NO            NO             NO   \n",
       "freq            3523           5006          3290           4644   \n",
       "mean             NaN            NaN           NaN            NaN   \n",
       "std              NaN            NaN           NaN            NaN   \n",
       "min              NaN            NaN           NaN            NaN   \n",
       "25%              NaN            NaN           NaN            NaN   \n",
       "50%              NaN            NaN           NaN            NaN   \n",
       "75%              NaN            NaN           NaN            NaN   \n",
       "max              NaN            NaN           NaN            NaN   \n",
       "\n",
       "       Pitch class B Pitch class Bass        Meter Chord label  \n",
       "count           5665             5665  5665.000000        5665  \n",
       "unique             2               16          NaN         102  \n",
       "top               NO                D          NaN         D_M  \n",
       "freq            3874              689          NaN         503  \n",
       "mean             NaN              NaN     3.134863         NaN  \n",
       "std              NaN              NaN     1.109710         NaN  \n",
       "min              NaN              NaN     1.000000         NaN  \n",
       "25%              NaN              NaN     2.000000         NaN  \n",
       "50%              NaN              NaN     3.000000         NaN  \n",
       "75%              NaN              NaN     4.000000         NaN  \n",
       "max              NaN              NaN     5.000000         NaN  "
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Классы в бинарном признаке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' NO', 'YES'], dtype=object)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pitch class F#'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Классы в категориальном признаке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'E', 'D', 'A', 'Bb', 'C', 'G', 'B', 'F#', 'C#', 'Eb', 'D#',\n",
       "       'G#', 'A#', 'Ab', 'Db'], dtype=object)"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pitch class Bass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных\n",
    "\n",
    "   Выбросим признак Choral ID, поскольку от названия произведения мало что зависит, также мало что зависит от порядкового номера события, значит Event Number выбрасываем туда-же. \n",
    "   Задача состоит в предсказании резонирующего в данный момент аккорда по встречающимся или не встречающимся высотным классам основного исполнения и баса. Большинство признаков бинарных, остаётся только придумать, что делать с категориальным признаком Pitch Class Bass, например векторизовать его."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Choral ID', axis=1)\n",
    "data = data.drop('Event number', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поскольку столбец Chord label содержит ответ, уберём из таблицы и его"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Chord label']\n",
    "data = data.drop('Chord label', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pitch class C Pitch class C# Pitch class D Pitch class D# Pitch class E  \\\n",
       "0           YES             NO            NO             NO            NO   \n",
       "1           YES             NO            NO             NO           YES   \n",
       "2           YES             NO            NO             NO           YES   \n",
       "3           YES             NO            NO             NO            NO   \n",
       "4           YES             NO            NO             NO            NO   \n",
       "\n",
       "  Pitch class F Pitch class F# Pitch class G Pitch class G# Pitch class A  \\\n",
       "0           YES             NO            NO             NO           YES   \n",
       "1            NO             NO           YES             NO            NO   \n",
       "2            NO             NO           YES             NO            NO   \n",
       "3           YES             NO            NO             NO           YES   \n",
       "4           YES             NO            NO             NO           YES   \n",
       "\n",
       "  Pitch class A# Pitch class B Pitch class Bass  Meter  \n",
       "0             NO            NO                F      3  \n",
       "1             NO            NO                E      5  \n",
       "2             NO            NO                E      2  \n",
       "3             NO            NO                F      3  \n",
       "4             NO            NO                F      2  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'E', 'D', 'A', 'Bb', 'C', 'G', 'B', 'F#', 'C#', 'Eb', 'D#',\n",
       "       'G#', 'A#', 'Ab', 'Db'], dtype=object)"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pitch class Bass'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Имеются повторяющиеся с точки зрения музыки, но разные по названию ноты (классы), приведём их к стандарту как в заголовке таблицы для основного звучания(C, C# = Db, D, D# = Eb, E, F, F#, G, G# = Ab, A, A# = Bb, B)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Bb' : 'A#', 'Db' : 'C#', 'Eb': 'D#', 'Ab' : 'G#'}\n",
    "data = data.replace({'Pitch class Bass': d})\n",
    "#data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разобьём таблицу на части по типу признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pitch class C', 'Pitch class C#', 'Pitch class D', 'Pitch class D#', 'Pitch class E', 'Pitch class F', 'Pitch class F#', 'Pitch class G', 'Pitch class G#', 'Pitch class A', 'Pitch class A#', 'Pitch class B', 'Pitch class Bass']\n",
      "['Meter']\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = [c for c in data.columns if data[c].dtype.name == 'object']\n",
    "numerical_columns   = [c for c in data.columns if data[c].dtype.name != 'object']\n",
    "print(categorical_columns)\n",
    "print(numerical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pitch class C', 'Pitch class C#', 'Pitch class D', 'Pitch class D#', 'Pitch class E', 'Pitch class F', 'Pitch class F#', 'Pitch class G', 'Pitch class G#', 'Pitch class A', 'Pitch class A#', 'Pitch class B'] ['Pitch class Bass']\n"
     ]
    }
   ],
   "source": [
    "data_describe = data.describe(include = [object])\n",
    "binary_columns    = [c for c in categorical_columns if data_describe[c]['unique'] == 2]\n",
    "nonbinary_columns = [c for c in categorical_columns if data_describe[c]['unique'] > 2]\n",
    "print(binary_columns, nonbinary_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Бинарный признак делаем с циферками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.at[data['Pitch class C'] != 'YES', 'Pitch class C'] = 0\n",
    "data.at[data['Pitch class C'] == 'YES', 'Pitch class C'] = 1\n",
    "data.at[data['Pitch class C#'] != 'YES', 'Pitch class C#'] = 0\n",
    "data.at[data['Pitch class C#'] == 'YES', 'Pitch class C#'] = 1\n",
    "data.at[data['Pitch class D'] != 'YES', 'Pitch class D'] = 0\n",
    "data.at[data['Pitch class D'] == 'YES', 'Pitch class D'] = 1\n",
    "data.at[data['Pitch class D#'] != 'YES', 'Pitch class D#'] = 0\n",
    "data.at[data['Pitch class D#'] == 'YES', 'Pitch class D#'] = 1\n",
    "data.at[data['Pitch class E'] != 'YES', 'Pitch class E'] = 0\n",
    "data.at[data['Pitch class E'] == 'YES', 'Pitch class E'] = 1\n",
    "data.at[data['Pitch class F'] != 'YES', 'Pitch class F'] = 0\n",
    "data.at[data['Pitch class F'] == 'YES', 'Pitch class F'] = 1\n",
    "data.at[data['Pitch class F#'] != 'YES', 'Pitch class F#'] = 0\n",
    "data.at[data['Pitch class F#'] == 'YES', 'Pitch class F#'] = 1\n",
    "data.at[data['Pitch class G'] != 'YES', 'Pitch class G'] = 0\n",
    "data.at[data['Pitch class G'] == 'YES', 'Pitch class G'] = 1\n",
    "data.at[data['Pitch class G#'] != 'YES', 'Pitch class G#'] = 0\n",
    "data.at[data['Pitch class G#'] == 'YES', 'Pitch class G#'] = 1\n",
    "data.at[data['Pitch class A'] != 'YES', 'Pitch class A'] = 0\n",
    "data.at[data['Pitch class A'] == 'YES', 'Pitch class A'] = 1\n",
    "data.at[data['Pitch class A#'] != 'YES', 'Pitch class A#'] = 0\n",
    "data.at[data['Pitch class A#'] == 'YES', 'Pitch class A#'] = 1\n",
    "data.at[data['Pitch class B'] != 'YES', 'Pitch class B'] = 0\n",
    "data.at[data['Pitch class B'] == 'YES', 'Pitch class B'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Векторизуем единственный оставшийся категориальный признак с помощью метода get_dummies из pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pitch class Bass_A', 'Pitch class Bass_A#', 'Pitch class Bass_B',\n",
      "       'Pitch class Bass_C', 'Pitch class Bass_C#', 'Pitch class Bass_D',\n",
      "       'Pitch class Bass_D#', 'Pitch class Bass_E', 'Pitch class Bass_F',\n",
      "       'Pitch class Bass_F#', 'Pitch class Bass_G', 'Pitch class Bass_G#'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data_nonbinary = pd.get_dummies(data[nonbinary_columns])\n",
    "print(data_nonbinary.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нормализация количественных признаков\n",
    "\n",
    "Многие алгоритмы машинного обучения чувствительны к масштабированию данных. К таким алгоритмам, например, относится метод ближайших соседей, машина опорных векторов и др.\n",
    "\n",
    "В этом случае количественные признаки полезно нормализовать. Это можно делать разными способами. Например, каждый количественный признак приведем к нулевому среднему и единичному среднеквадратичному отклонению:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5665.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.134863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.109710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Meter\n",
       "count  5665.000000\n",
       "mean      3.134863\n",
       "std       1.109710\n",
       "min       1.000000\n",
       "25%       2.000000\n",
       "50%       3.000000\n",
       "75%       4.000000\n",
       "max       5.000000"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = data[numerical_columns]\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_numerical = (data_numerical - data_numerical.mean(axis = 0))/data_numerical.std(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>Meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.665000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.053605e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.923803e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.022667e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.215302e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.796064e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.680743e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Meter\n",
       "count  5.665000e+03\n",
       "mean  -1.053605e-15\n",
       "std    1.000000e+00\n",
       "min   -1.923803e+00\n",
       "25%   -1.022667e+00\n",
       "50%   -1.215302e-01\n",
       "75%    7.796064e-01\n",
       "max    1.680743e+00"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Соединяем всё в одну таблицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5665, 25)\n"
     ]
    }
   ],
   "source": [
    "data = pd.concat((data_numerical, data_nonbinary, data[binary_columns]), axis = 1)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "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>Meter</th>\n",
       "      <th>Pitch class Bass_A</th>\n",
       "      <th>Pitch class Bass_A#</th>\n",
       "      <th>Pitch class Bass_B</th>\n",
       "      <th>Pitch class Bass_C</th>\n",
       "      <th>Pitch class Bass_C#</th>\n",
       "      <th>Pitch class Bass_D</th>\n",
       "      <th>Pitch class Bass_D#</th>\n",
       "      <th>Pitch class Bass_E</th>\n",
       "      <th>Pitch class Bass_F</th>\n",
       "      <th>...</th>\n",
       "      <th>Pitch class D</th>\n",
       "      <th>Pitch class D#</th>\n",
       "      <th>Pitch class E</th>\n",
       "      <th>Pitch class F</th>\n",
       "      <th>Pitch class F#</th>\n",
       "      <th>Pitch class G</th>\n",
       "      <th>Pitch class G#</th>\n",
       "      <th>Pitch class A</th>\n",
       "      <th>Pitch class A#</th>\n",
       "      <th>Pitch class B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.665000e+03</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "      <td>5665.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.053605e-15</td>\n",
       "      <td>0.121447</td>\n",
       "      <td>0.055075</td>\n",
       "      <td>0.079259</td>\n",
       "      <td>0.105914</td>\n",
       "      <td>0.038482</td>\n",
       "      <td>0.121624</td>\n",
       "      <td>0.044307</td>\n",
       "      <td>0.119682</td>\n",
       "      <td>0.081553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417476</td>\n",
       "      <td>0.125154</td>\n",
       "      <td>0.375110</td>\n",
       "      <td>0.226655</td>\n",
       "      <td>0.249250</td>\n",
       "      <td>0.378111</td>\n",
       "      <td>0.116328</td>\n",
       "      <td>0.419241</td>\n",
       "      <td>0.180229</td>\n",
       "      <td>0.316152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.326675</td>\n",
       "      <td>0.228147</td>\n",
       "      <td>0.270166</td>\n",
       "      <td>0.307754</td>\n",
       "      <td>0.192374</td>\n",
       "      <td>0.326880</td>\n",
       "      <td>0.205795</td>\n",
       "      <td>0.324618</td>\n",
       "      <td>0.273707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.493186</td>\n",
       "      <td>0.330923</td>\n",
       "      <td>0.484194</td>\n",
       "      <td>0.418704</td>\n",
       "      <td>0.432617</td>\n",
       "      <td>0.484958</td>\n",
       "      <td>0.320647</td>\n",
       "      <td>0.493478</td>\n",
       "      <td>0.384412</td>\n",
       "      <td>0.465014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.923803e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>-1.022667e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.215302e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.796064e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.680743e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Meter  Pitch class Bass_A  Pitch class Bass_A#  \\\n",
       "count  5.665000e+03         5665.000000          5665.000000   \n",
       "mean  -1.053605e-15            0.121447             0.055075   \n",
       "std    1.000000e+00            0.326675             0.228147   \n",
       "min   -1.923803e+00            0.000000             0.000000   \n",
       "25%   -1.022667e+00            0.000000             0.000000   \n",
       "50%   -1.215302e-01            0.000000             0.000000   \n",
       "75%    7.796064e-01            0.000000             0.000000   \n",
       "max    1.680743e+00            1.000000             1.000000   \n",
       "\n",
       "       Pitch class Bass_B  Pitch class Bass_C  Pitch class Bass_C#  \\\n",
       "count         5665.000000         5665.000000          5665.000000   \n",
       "mean             0.079259            0.105914             0.038482   \n",
       "std              0.270166            0.307754             0.192374   \n",
       "min              0.000000            0.000000             0.000000   \n",
       "25%              0.000000            0.000000             0.000000   \n",
       "50%              0.000000            0.000000             0.000000   \n",
       "75%              0.000000            0.000000             0.000000   \n",
       "max              1.000000            1.000000             1.000000   \n",
       "\n",
       "       Pitch class Bass_D  Pitch class Bass_D#  Pitch class Bass_E  \\\n",
       "count         5665.000000          5665.000000         5665.000000   \n",
       "mean             0.121624             0.044307            0.119682   \n",
       "std              0.326880             0.205795            0.324618   \n",
       "min              0.000000             0.000000            0.000000   \n",
       "25%              0.000000             0.000000            0.000000   \n",
       "50%              0.000000             0.000000            0.000000   \n",
       "75%              0.000000             0.000000            0.000000   \n",
       "max              1.000000             1.000000            1.000000   \n",
       "\n",
       "       Pitch class Bass_F      ...        Pitch class D  Pitch class D#  \\\n",
       "count         5665.000000      ...          5665.000000     5665.000000   \n",
       "mean             0.081553      ...             0.417476        0.125154   \n",
       "std              0.273707      ...             0.493186        0.330923   \n",
       "min              0.000000      ...             0.000000        0.000000   \n",
       "25%              0.000000      ...             0.000000        0.000000   \n",
       "50%              0.000000      ...             0.000000        0.000000   \n",
       "75%              0.000000      ...             1.000000        0.000000   \n",
       "max              1.000000      ...             1.000000        1.000000   \n",
       "\n",
       "       Pitch class E  Pitch class F  Pitch class F#  Pitch class G  \\\n",
       "count    5665.000000    5665.000000     5665.000000    5665.000000   \n",
       "mean        0.375110       0.226655        0.249250       0.378111   \n",
       "std         0.484194       0.418704        0.432617       0.484958   \n",
       "min         0.000000       0.000000        0.000000       0.000000   \n",
       "25%         0.000000       0.000000        0.000000       0.000000   \n",
       "50%         0.000000       0.000000        0.000000       0.000000   \n",
       "75%         1.000000       0.000000        0.000000       1.000000   \n",
       "max         1.000000       1.000000        1.000000       1.000000   \n",
       "\n",
       "       Pitch class G#  Pitch class A  Pitch class A#  Pitch class B  \n",
       "count     5665.000000    5665.000000     5665.000000    5665.000000  \n",
       "mean         0.116328       0.419241        0.180229       0.316152  \n",
       "std          0.320647       0.493478        0.384412       0.465014  \n",
       "min          0.000000       0.000000        0.000000       0.000000  \n",
       "25%          0.000000       0.000000        0.000000       0.000000  \n",
       "50%          0.000000       0.000000        0.000000       0.000000  \n",
       "75%          0.000000       1.000000        0.000000       1.000000  \n",
       "max          1.000000       1.000000        1.000000       1.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Всё в порядке, 25 и ожидалось"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Х и У"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data\n",
    "feature_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5665\n",
       "unique     102\n",
       "top        D_M\n",
       "freq       503\n",
       "Name: Chord label, dtype: object"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Meter', 'Pitch class Bass_A', 'Pitch class Bass_A#',\n",
       "       'Pitch class Bass_B', 'Pitch class Bass_C', 'Pitch class Bass_C#',\n",
       "       'Pitch class Bass_D', 'Pitch class Bass_D#', 'Pitch class Bass_E',\n",
       "       'Pitch class Bass_F', 'Pitch class Bass_F#', 'Pitch class Bass_G',\n",
       "       'Pitch class Bass_G#', 'Pitch class C', 'Pitch class C#',\n",
       "       'Pitch class D', 'Pitch class D#', 'Pitch class E', 'Pitch class F',\n",
       "       'Pitch class F#', 'Pitch class G', 'Pitch class G#', 'Pitch class A',\n",
       "       'Pitch class A#', 'Pitch class B'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5665, 25)\n",
      "(5665,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)\n",
    "N, d = X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3965 1700\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "N_train, _ = X_train.shape \n",
    "N_test,  _ = X_test.shape \n",
    "\n",
    "print(N_train, N_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5665"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3965+1700"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *k*NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 10)\n",
    "#knn.set_params(n_neighbors=10)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24741488020176544 0.3058823529411765\n"
     ]
    }
   ],
   "source": [
    "y_train_predict = knn.predict(X_train)\n",
    "y_test_predict = knn.predict(X_test)\n",
    "\n",
    "err_train = np.mean(y_train != y_train_predict)\n",
    "err_test  = np.mean(y_test  != y_test_predict)\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_test = 1 - knn.score(X_test, y_test) # Другой способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3058823529411765"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим, kNN на этом наборе данных имеет достаточно большую ошибку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0 ...  0  0  0]\n",
      " [ 0 74  2 ...  0  0  0]\n",
      " [ 0  1  2 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  0 ...  0 53  0]\n",
      " [ 0  0  0 ...  0  3  0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test, y_test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 0],\n",
       "       [1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix([0,1,0,1,0], [0,1,0,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\sklearn\\model_selection\\_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.27591424968474154 3\n"
     ]
    }
   ],
   "source": [
    "# Долго!\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "nnb = [1, 3, 5, 10, 15, 20, 25, 35, 45, 55]\n",
    "knn = KNeighborsClassifier()\n",
    "grid = GridSearchCV(knn, param_grid = {'n_neighbors': nnb}, cv=10)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "best_cv_err = 1 - grid.best_score_\n",
    "best_n_neighbors = grid.best_estimator_.n_neighbors\n",
    "print(best_cv_err, best_n_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18688524590163935 0.29470588235294115\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = best_n_neighbors).fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != knn.predict(X_train))\n",
    "err_test  = np.mean(y_test  != knn.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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