notebooks/1_EDA_sprint_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><font size=\"6\">Хакатон от Моторики</font>\n",
    "\n",
    "[Третий этап](https://www.kaggle.com/competitions/motorica-advanced-gesture-classification)\n",
    "\n",
    "Команда 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задача:**  Построить классификационную модель, которая по показаниям оптомиографических датчиков будет определять жест оператора протеза кисти во время записи данных, что отражено в значении целевой переменной. \n",
    "\n",
    "Имеется три оператора, выполнявшие некоторую последовательность жестов. В результате наблюдений получены показания датчиков в виде непрерывного временного ряда. \n",
    "\n",
    "Список файлов, в котором символ * обозначает индекс оператора:\n",
    "* X\\_train\\_*.npy - обучающая выборка, имеющая размерности \"наблюдения, датчики, время\";\n",
    "* X\\_test\\_ dataset\\_*.pkl - тестовая выборка, имеющая размерности \"наблюдения, датчики, время\";\n",
    "* sample_submission.csv - некоторое решение задачи, из которого можно взять формат загрузки данных на Kaggle;\n",
    "* y\\_train\\_*.npy - целевая переменная для обучающей выборки, которая содержит следующие классы:\n",
    "\n",
    "| Номер <br> класса | Описание жеста |\n",
    "| :---: | :--- |\n",
    "| \"-1\"  |  потенциально \"битые\" данные |\n",
    "| \"0\"  |  жест \"open\" - разгиб всех пальцев |\n",
    "| \"1\"  |  жест \"пистолет\" |\n",
    "| \"2\"  |  сгиб большого пальца |\n",
    "| \"3\"  |  жест \"ok\" |\n",
    "| \"4\"  |  жест \"grab\" |\n",
    "\n",
    "* functions.py - библиотека функций, применяемых в данном ноутбуке."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Установка версий программных пакетов\n",
    "#!pip install -r ../requirements.txt -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "p3tvY4ssJ6D0"
   },
   "outputs": [],
   "source": [
    "# импортируем библиотеки\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "from zipfile import ZipFile\n",
    "\n",
    "# отображать по умолчанию длину датафрейма\n",
    "pd.set_option(\"display.max_rows\", 9, \"display.max_columns\", 9)\n",
    "\n",
    "# Логгирование процесса\n",
    "from comet_ml import Experiment\n",
    "\n",
    "# библиотека взаимодействия с интерпретатором\n",
    "import sys, os, json\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "# отображение элементов массива не в экспоненциальном формате\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbd10c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Библиотека вызова функций, специально разработанных для данного ноутбука\n",
    "sys.path.insert(1, '../')\n",
    "from utils import figures\n",
    "from utils.functions import config_reader\n",
    "\n",
    "# Импортируем константы из файла config\n",
    "path_to_config = '../config/data_config.json'\n",
    "config = config_reader(path_to_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# константы и глобальные параметры данных\n",
    "PATH = config['PATH']\n",
    "GEST = config['GEST']          # количество основных жестов (кроме 'битых' данных)\n",
    "Pilots = config['Pilots']     # количество пилотов (независимых рядов данных)\n",
    "SENS = config['SENS']         # количество датчиков\n",
    "\n",
    "X_train = ['' for p in range(Pilots)]  # список объектов X_train\n",
    "y_train = ['' for p in range(Pilots)]  # список объектов y_train\n",
    "X_test = ['' for p in range(Pilots)]   # список объектов X_test\n",
    "\n",
    "t_train = ['' for p in range(Pilots)]   # список длин X_train и y_train (кол-во временных тиков)\n",
    "t_test = [0 for p in range(Pilots)]     # список с общим количеством временных тиков в X_test\n",
    "s_test = ['' for p in range(Pilots)]    # список с количеством событий в X_test\n",
    "tt_test = [[] for p in range(Pilots)]   # список списков с длинами событий в X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# experiment = Experiment(\n",
    "#     api_key = config.api_key, \n",
    "#     project_name =config.project_name,\n",
    "#     workspace = config.workspace,\n",
    "# );\n",
    "# experiment.set_name('EDA') # имя эксперимента"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57ecd646-f2a3-4e7d-b1f9-6f70e6452515",
   "metadata": {
    "id": "57ecd646-f2a3-4e7d-b1f9-6f70e6452515"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: {'path_X_train': 'X_train_1.npy',\n",
       "  'path_y_train': 'y_train_1.npy',\n",
       "  'path_X_test_dataset': 'X_test_dataset_1.pkl'},\n",
       " 2: {'path_X_train': 'X_train_2.npy',\n",
       "  'path_y_train': 'y_train_2.npy',\n",
       "  'path_X_test_dataset': 'X_test_dataset_2.pkl'},\n",
       " 3: {'path_X_train': 'X_train_3.npy',\n",
       "  'path_y_train': 'y_train_3.npy',\n",
       "  'path_X_test_dataset': 'X_test_dataset_3.pkl'}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionnary for data aggreagation.\n",
    "mounts = config.mounts.toDict()\n",
    "for i in mounts.keys():\n",
    "    mounts[int(i)] = mounts.pop(i)\n",
    "\n",
    "mounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f458b898",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f458b898",
    "outputId": "ec93976a-02be-4061-fa57-04f2ddd5e515"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество наблюдений пилота №1: 899\n",
      "Количество наблюдений пилота №2: 855\n",
      "Количество наблюдений пилота №3: 861\n"
     ]
    }
   ],
   "source": [
    "# Чтение данных из архива (без распаковки архива)\n",
    "path_to_zip = os.path.join(PATH, 'motorica-advanced-gesture-classification.zip')\n",
    "\n",
    "for mount_name, mount in mounts.items():  # ключи-int; значения - массивы\n",
    "    mount['X_train'] = np.load(path_to_zip)[mounts[mount_name]['path_X_train']]\n",
    "    mount['y_train'] = np.load(path_to_zip)[mounts[mount_name]['path_y_train']]\n",
    "    \n",
    "for i in range(config.Pilots):\n",
    "    with ZipFile(path_to_zip) as myzip:\n",
    "        with myzip.open(mounts[i+1]['path_X_test_dataset']) as myfile:\n",
    "            mounts[i+1]['X_test_dataset'] = pickle.load(myfile)\n",
    "    print(f\"Количество наблюдений пилота №{i+1}: {len(mounts[i+1]['X_test_dataset'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "длина обучающей выборки пилота №1 = 24030\n",
      "длина обучающей выборки пилота №2 = 23202\n",
      "длина обучающей выборки пилота №3 = 23177\n"
     ]
    }
   ],
   "source": [
    "# загрузка обучающей выборки и меток классов, тестовой выборки\n",
    "X_test_raw = ['' for p in range(config.Pilots)]\n",
    "\n",
    "for p in range(config.Pilots):\n",
    "    X_train[p] = mounts[p+1]['X_train']\n",
    "    t_train[p] = mounts[p+1]['X_train'].shape[0]\n",
    "    \n",
    "    y_train[p] = mounts[p+1]['y_train']\n",
    "    \n",
    "    if y_train[p].size == t_train[p]:\n",
    "        print(f'длина обучающей выборки пилота №{p+1} = {t_train[p]}')\n",
    "    else:\n",
    "        print(f'длина обучающей выборки и меток классов пилота №{p+1} не совпадают!')\n",
    "    \n",
    "    #with open(os.path.join(PATH, 'X_test_dataset_' + f'{p+1}' + '.pkl'), 'rb') as file:\n",
    "    X_test_raw[p] = config.mounts[p+1]['X_test_dataset'][p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тип данных X_train: <class 'numpy.ndarray'>\n",
      "Размерность файла X_train_1: (24030, 50) - время, датчики\n",
      "X_train первого пилота: \n",
      " [[2711   16 1803 ...    8   14    7]\n",
      " [2712   10 1802 ...    6   12    6]\n",
      " [2711   13 1803 ...    5   16    8]\n",
      " ...\n",
      " [2707   16 1851 ...    6   13    6]\n",
      " [2707   18 1850 ...    8   12    6]\n",
      " [2710   16 1851 ...    9   11    8]]\n"
     ]
    }
   ],
   "source": [
    "# смотрим на исходные данные X_train\n",
    "print(f\"Тип данных X_train: {type(mounts[1]['X_train'])}\")\n",
    "print(f\"Размерность файла X_train_1: {mounts[1]['X_train'].shape} - время, датчики\")\n",
    "print(f\"X_train первого пилота: \\n {mounts[1]['X_train']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тип данных y_train: <class 'numpy.ndarray'>\n",
      "Размерность файла y_train_1: (24030,) - временной период\n",
      "y_train первого пилота: \n",
      " [0 0 0 ... 3 3 3]\n"
     ]
    }
   ],
   "source": [
    "# смотрим на исходные данные y_train\n",
    "print(f\"Тип данных y_train: {type(mounts[1]['y_train'])}\")\n",
    "print(f\"Размерность файла y_train_1: {mounts[1]['y_train'].shape} - временной период\")\n",
    "print(f\"y_train первого пилота: \\n {mounts[1]['y_train']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590a9dc6-53aa-4db8-a1d5-866cdf81bc2e",
   "metadata": {},
   "source": [
    "# Обзор файла `X_test_dataset`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тип данных X_test: <class 'list'>\n",
      "Тип данных X_test для пилота 1 : <class 'numpy.ndarray'>\n",
      "Размерность X_test для пилота 1: (50, 256) - количество датчиков, периоды времени\n",
      "X_test первого пилота, наблюдение '0', начальное и последнее значение: [1370 1417]\n"
     ]
    }
   ],
   "source": [
    "# смотрим на исходные данные X_test\n",
    "print(f\"Тип данных X_test: {type(mount['X_test_dataset'])}\")\n",
    "print(f\"Тип данных X_test для пилота 1 : {type(mount['X_test_dataset'][1])}\")\n",
    "print(f\"Размерность X_test для пилота 1: {mount['X_test_dataset'][1].shape} - количество датчиков, периоды времени\")\n",
    "print(f\"X_test первого пилота, наблюдение '0', начальное и последнее значение: {mount['X_test_dataset'][1][0][[0,-1]]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e9a118d-8959-42c1-9b64-8eb1b50d5de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject 1\n",
      "\tshape (50, 105) appears 303 times.\n",
      "\tshape (50, 181) appears 299 times.\n",
      "\tshape (50, 256) appears 297 times.\n",
      "subject 2\n",
      "\tshape (50, 105) appears 286 times.\n",
      "\tshape (50, 181) appears 285 times.\n",
      "\tshape (50, 256) appears 284 times.\n",
      "subject 3\n",
      "\tshape (50, 105) appears 291 times.\n",
      "\tshape (50, 181) appears 282 times.\n",
      "\tshape (50, 256) appears 288 times.\n"
     ]
    }
   ],
   "source": [
    "for mount_name, mount in mounts.items():\n",
    "    X_test_dataset = mount['X_test_dataset']\n",
    "    print(f\"subject {mount_name}\")\n",
    "    shapes, counts = np.unique(\n",
    "        [arr.shape for arr in X_test_dataset],\n",
    "        return_counts=True,\n",
    "        axis=0\n",
    "    )\n",
    "    for shape, count in zip(shapes, counts):\n",
    "        print(f\"\\tshape {tuple(shape)} appears {count} times.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Отобразим три различных наблюдения первого пилота*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figures.get_signals_plot(data=mount['X_test_dataset'], mounts=mounts, test_id=[3,1,8], plot_counter='1-1')\n",
    "#experiment.log_figure(figure_name='fig_1-1') # логгируем график "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active sensors of pilot 1: [0, 2, 5, 8, 12, 15, 17, 19, 21, 24, 27, 29, 30, 33, 36, 38]\n",
      "Passive sensors of pilot 1: [1, 3, 4, 6, 7, 9, 10, 11, 13, 14, 16, 18, 20, 22, 23, 25, 26, 28, 31, 32, 34, 35, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]\n"
     ]
    }
   ],
   "source": [
    "active_sensors, passive_sensor = figures.get_sensor_list(Pilot_id=1, mounts=mounts, print_active=True, level_boundary=config['level_boundary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures.get_all_sensors_plot(Pilot_id=1, timesteps=[0,107], mounts=mounts, plot_counter='1-2')\n",
    "#experiment.log_image('../logs_and_figures/fig_1-2.png') # логгируем картинку"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src = \"../logs_and_figures/fig_1-2.png\"> </center>\n",
    "\n",
    "На рис.2 изображается сигнал датчиков во времени. Относительные амплитуды некоторых датчиков имеют значительный перепад. Отметим, что датчики можно разделить по средней величине сигнала на 2 группы:\n",
    "* активные (0, 2, 5, 8, 12, 15, 17, 19, 21, 24, 27, 29, 30, 33, 36, 38), величина сигнала которых превышает 200;\n",
    "* пассивные (все остальные), средняя величина менее 200.\n",
    "\n",
    "Отобразим обе группы датчиков раздельно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures.get_active_passive_sensors_plot(Pilot_id=1, timesteps=[0,107], mounts=mounts, level_boundary=config['level_boundary'], plot_counter='1-3')\n",
    "#experiment.log_image('../logs_and_figures/fig_1-3.png') # логгируем картинку"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src = \"../logs_and_figures/fig_1-3.png\"> </center>\n",
    "\n",
    "На рис.3 изображены диаграммы активных (слева) и пассивных (справа) датчиков. Отметим, что в районе 50 временного промежутка изменение сигнала достаточно чётко просматривается, несмотря на его относительно небольшой перепад."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Поиск периода времени, в котором изменился класс жеста"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выберем произвольно: пилота №3; проанализируем показания датчиков 0 и 2 во временной период c 100 по 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures.get_signal_and_train_plots(Pilot_id=3, timesteps=[100,300], sensors=[0,2], mounts=mounts, plot_counter='1-4')\n",
    "#experiment.log_image('../logs_and_figures/fig_1-4.png'); # логгируем картинку"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src = \"../logs_and_figures/fig_1-4.png\"> </center>\n",
    "\n",
    "На рис.4 изображена диаграмма изменения сигнала датчиков (сверху), на которой наблюдается скачкообразные изменения сигнала датчиков. На диаграмме снизу показано два изменения сигнала манипулятора:   \n",
    "- в районе 175 временного промежутка класс жеста изменяется с 0 до 2;\n",
    "- в районе 255 временного промежутка класс жеста изменяется с 2 до 0;\n",
    "\n",
    "Сравнивая обе диаграммы отметим отсутствие четкого характера изменения сигнала при возвращении к 0 классу жеста, а также запаздывание сигналов датчиков относительно манипулятора из-за задержки реакции оператора.\n",
    "\n",
    "Проверим производную сигнала датчиков и его нормализованное значение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures.get_signal_derivative_and_normalized_plot(Pilot_id=1, timesteps=[100,450], sensors=[0,2], mounts=mounts, plot_counter='1-5')\n",
    "#experiment.log_image('../logs_and_figures/fig_1-5.png'); # логгируем картинку "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src = \"../logs_and_figures/fig_1-5.png\"> </center>\n",
    "\n",
    "На рис.5 изображена диаграмма производной сигналов датчиков, квадрат производной сигналов и их нормализованные значения. Анализируя диаграммы совместно отметим, что начало жеста невозможно определить по величине сигнала. Кроме того, характерный паттерн изменения сигнала отсутствует."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#experiment.end();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Характерные уровни датчиков\n",
    "\n",
    "Выделим из данных X_train участки, где жест не меняется. Для этого при разделении на эпохи зададим следующие параметры:\n",
    "\n",
    "    epochs = mne.Epochs(\n",
    "        ...,\n",
    "        tmin=-1 * 1.5,\n",
    "        tmax=1 * 0,\n",
    "        ...\n",
    "    )\n",
    "\n",
    "Таким образом, получим сэмплы длиной 46 временных тиков, которые заканчиваются в момент времени, когда подается команда манипулятором.\n",
    "Поскольку наблюдения были построены таким образом, что команда манипулятора подавалась с периодичностью 77 временных тиков, мы \n",
    "можем считать, что с 31 по 77 временной тик (так мы выделили сэмплы) переход к новому жесту уже практически завершился \n",
    "или находится в завершающей фазе и данные y_train соответствуют фактическому состоянию кисти пилота.\n",
    "\n",
    "Наблюдения для каждого пилота были разбиты на три участка, условно обозначенные:\n",
    "\n",
    "b - первая треть наблюдения по времени,\n",
    "p - вторая треть наблюдения,\n",
    "e - финальная треть наблюдения, включенного в X_train.\n",
    "\n",
    "На полученных данных построим боксплоты датчиков в разрезе жестов. Рассмотрим показания 2-го и 3-го датчиков первого пилота.\n",
    "Мы видим, что для каждого жеста есть характерные уровни показаний датчиков. Для некоторых жестов интервалы характерных значений \n",
    "хорошо дифференцируются, для остальных - интервалы частично перекрываются.\n",
    "\n",
    "![boxplot1](../logs_and_figures/fig_1-6.png \"Рис.6 - показания датчиков №2,3 первого пилота\")\n",
    "#### <center>Рис.6 - показания датчиков №2,3 первого пилота"
   ]
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   "source": [
    "Обратим внимание на еще одну особенность - постепенное \"сползание\" характерных уровней датчиков по времени. Т.е., к примеру,\n",
    "интервал показаний датчика в положении \"open\" в первой трети наблюдения (красные бары) заметно отличается от интервала показаний того\n",
    "же датчика в последней трети наблюдения (фиолетовые бары). Для выявления особенностей проведения наблюдений, которые приводят к \n",
    "такому эффекту, (это могут быть нагревание кожи, смещение датчиков или другие) необходимы дополнительные данные.\n",
    "\n",
    "Так же можно обратить внимание, что так называемые датчики \"засветки\", ведут себя по разному:\n",
    "\n",
    "показания датчика 46 чувствительны к выполняемому жесту и существенно \"плывут\" со временем,\n",
    "\n",
    "датчик 47 практически нечувствителен к жестам, но со временем есть небольшое \"сползание\" уровня сигнала,\n",
    "\n",
    "а датчик 48 реагирует на жесты \"граб\" и \"ок\", но его показания практически не меняются со временем.\n",
    "\n",
    "![boxplot2](../logs_and_figures/fig_1-7.png)\n",
    "#### <center>Рис.7 - показания датчиков №47,48 первого пилота\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "В папке boxplots для ознакомления выложены боксплоты по всем датчикам для трех пилотов.\n",
    "\n",
    "# Промежуточные выводы\n",
    "\n",
    "В результате анализа данных выявлена сложность определения временного периода, в котором происходит изменение класса жеста. Поэтому применим нейронные сети для определения начала жеста. "
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