13_zApr22Adv_Code_GBDT__1_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "xQIfyjyXk3a2"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZSYBQfVcmbRe",
    "outputId": "6175525b-a563-42e8-f69c-49f49a9ccfb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1h86M8si2YT-aI4Zec1MeMP_mPYsLPy5F\n",
      "To: /content/emg.rar\n",
      "100% 18.6M/18.6M [00:00<00:00, 56.2MB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown 1h86M8si2YT-aI4Zec1MeMP_mPYsLPy5F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_0wQvtI-mdSA",
    "outputId": "c39f4ef7-8330-416c-cb4e-14a3df04b14d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "UNRAR 5.50 freeware      Copyright (c) 1993-2017 Alexander Roshal\n",
      "\n",
      "\n",
      "Extracting from ./emg.rar\n",
      "\n",
      "Creating    ./EMG Physical Action Data Set                            OK\n",
      "Extracting  ./EMG Physical Action Data Set/readme.txt                    \b\b\b\b  0%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub1                       OK\n",
      "Creating    ./EMG Physical Action Data Set/sub1/Aggressive            OK\n",
      "Creating    ./EMG Physical Action Data Set/sub1/Aggressive/log        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Elbowing.log     \b\b\b\b  0%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/FrontKicking.log     \b\b\b\b  0%\b\b\b\b  1%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Hamering.log     \b\b\b\b  1%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Headering.log     \b\b\b\b  1%\b\b\b\b  2%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Kneeing.log     \b\b\b\b  2%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Pulling.log     \b\b\b\b  2%\b\b\b\b  3%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Punching.log     \b\b\b\b  3%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Pushing.log     \b\b\b\b  4%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/SideKicking.log     \b\b\b\b  4%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/log/Slapping.log     \b\b\b\b  5%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub1/Aggressive/txt        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Elbowing.txt     \b\b\b\b  5%\b\b\b\b  6%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Frontkicking.txt     \b\b\b\b  6%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Hamering.txt     \b\b\b\b  7%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Headering.txt     \b\b\b\b  7%\b\b\b\b  8%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Kneeing.txt     \b\b\b\b  8%\b\b\b\b  9%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Pulling.txt     \b\b\b\b  9%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Punching.txt     \b\b\b\b 10%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Pushing.txt     \b\b\b\b 10%\b\b\b\b 11%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Sidekicking.txt     \b\b\b\b 11%\b\b\b\b 12%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Aggressive/txt/Slapping.txt     \b\b\b\b 12%\b\b\b\b 13%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub1/Normal                OK\n",
      "Creating    ./EMG Physical Action Data Set/sub1/Normal/log            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Bowing.log     \b\b\b\b 13%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Clapping.log     \b\b\b\b 13%\b\b\b\b 14%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Handshaking.log     \b\b\b\b 14%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Hugging.log     \b\b\b\b 14%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Jumping.log     \b\b\b\b 15%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Running.log     \b\b\b\b 15%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Seating.log     \b\b\b\b 16%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Standing.log     \b\b\b\b 16%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Walking.log     \b\b\b\b 16%\b\b\b\b 17%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/log/Waving.log     \b\b\b\b 17%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub1/Normal/txt            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Bowing.txt     \b\b\b\b 17%\b\b\b\b 18%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Clapping.txt     \b\b\b\b 18%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Handshaking.txt     \b\b\b\b 19%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Hugging.txt     \b\b\b\b 19%\b\b\b\b 20%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Jumping.txt     \b\b\b\b 20%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Running.txt     \b\b\b\b 21%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Seating.txt     \b\b\b\b 21%\b\b\b\b 22%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Standing.txt     \b\b\b\b 22%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Walking.txt     \b\b\b\b 22%\b\b\b\b 23%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub1/Normal/txt/Waving.txt     \b\b\b\b 23%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub2                       OK\n",
      "Creating    ./EMG Physical Action Data Set/sub2/Aggressive            OK\n",
      "Creating    ./EMG Physical Action Data Set/sub2/Aggressive/log        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Elbowing.log     \b\b\b\b 24%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Frontkicking.log     \b\b\b\b 24%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Hamering.log     \b\b\b\b 25%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Headering.log     \b\b\b\b 25%\b\b\b\b 26%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Kneeing.log     \b\b\b\b 26%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Pulling.log     \b\b\b\b 26%\b\b\b\b 27%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Punching.log     \b\b\b\b 27%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Pushing.log     \b\b\b\b 28%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Sidekicking.log     \b\b\b\b 28%\b\b\b\b 29%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/log/Slapping.log     \b\b\b\b 29%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub2/Aggressive/txt        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Elbowing.txt     \b\b\b\b 29%\b\b\b\b 30%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Frontkicking.txt     \b\b\b\b 30%\b\b\b\b 31%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Hamering.txt     \b\b\b\b 31%\b\b\b\b 32%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Headering.txt     \b\b\b\b 32%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Kneeing.txt     \b\b\b\b 33%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Pulling.txt     \b\b\b\b 33%\b\b\b\b 34%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Punching.txt     \b\b\b\b 34%\b\b\b\b 35%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Pushing.txt     \b\b\b\b 35%\b\b\b\b 36%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Sidekicking.txt     \b\b\b\b 36%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Aggressive/txt/Slapping.txt     \b\b\b\b 37%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub2/Normal                OK\n",
      "Creating    ./EMG Physical Action Data Set/sub2/Normal/log            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Bowing.log     \b\b\b\b 37%\b\b\b\b 38%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Clapping.log     \b\b\b\b 38%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Handshaking.log     \b\b\b\b 38%\b\b\b\b 39%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Hugging.log     \b\b\b\b 39%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Jumping.log     \b\b\b\b 39%\b\b\b\b 40%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Running.log     \b\b\b\b 40%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Seating.log     \b\b\b\b 40%\b\b\b\b 41%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Standing.log     \b\b\b\b 41%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Walking.log     \b\b\b\b 41%\b\b\b\b 42%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Walking1v.log     \b\b\b\b 42%\b\b\b\b 43%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Walking3v.log     \b\b\b\b 43%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/log/Waving.log     \b\b\b\b 43%\b\b\b\b 44%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub2/Normal/txt            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Bowing.txt     \b\b\b\b 44%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Clapping.txt     \b\b\b\b 44%\b\b\b\b 45%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Handshaking.txt     \b\b\b\b 45%\b\b\b\b 46%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Hugging.txt     \b\b\b\b 46%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Jumping.txt     \b\b\b\b 46%\b\b\b\b 47%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Running.txt     \b\b\b\b 47%\b\b\b\b 48%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Seating.txt     \b\b\b\b 48%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Standing.txt     \b\b\b\b 48%\b\b\b\b 49%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Walking.txt     \b\b\b\b 49%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub2/Normal/txt/Waving.txt     \b\b\b\b 50%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub3                       OK\n",
      "Creating    ./EMG Physical Action Data Set/sub3/Aggressive            OK\n",
      "Creating    ./EMG Physical Action Data Set/sub3/Aggressive/log        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Elbowing.log     \b\b\b\b 50%\b\b\b\b 51%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/FrontKicking.log     \b\b\b\b 51%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Hamering.log     \b\b\b\b 51%\b\b\b\b 52%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Headering.log     \b\b\b\b 52%\b\b\b\b 53%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Kneeing.log     \b\b\b\b 53%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Pulling.log     \b\b\b\b 53%\b\b\b\b 54%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Punching.log     \b\b\b\b 54%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Pushing.log     \b\b\b\b 54%\b\b\b\b 55%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/SideKicking.log     \b\b\b\b 55%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/log/Slapping.log     \b\b\b\b 56%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub3/Aggressive/txt        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Elbowing.txt     \b\b\b\b 56%\b\b\b\b 57%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Frontkicking.txt     \b\b\b\b 57%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Hamering.txt     \b\b\b\b 58%\b\b\b\b 59%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Headering.txt     \b\b\b\b 59%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Kneeing.txt     \b\b\b\b 60%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Pulling.txt     \b\b\b\b 60%\b\b\b\b 61%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Punching.txt     \b\b\b\b 61%\b\b\b\b 62%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Pushing.txt     \b\b\b\b 62%\b\b\b\b 63%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Sidekicking.txt     \b\b\b\b 63%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Aggressive/txt/Slapping.txt     \b\b\b\b 63%\b\b\b\b 64%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub3/Normal                OK\n",
      "Creating    ./EMG Physical Action Data Set/sub3/Normal/log            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Bowing.log     \b\b\b\b 64%\b\b\b\b 65%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Clapping.log     \b\b\b\b 65%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Handshaking.log     \b\b\b\b 65%\b\b\b\b 66%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Hugging.log     \b\b\b\b 66%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Jumping.log     \b\b\b\b 66%\b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Running.log     \b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Seating.log     \b\b\b\b 67%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Standing.log     \b\b\b\b 68%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Walking.log     \b\b\b\b 68%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/log/Waving.log     \b\b\b\b 69%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub3/Normal/txt            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Bowing.txt     \b\b\b\b 69%\b\b\b\b 70%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Clapping.txt     \b\b\b\b 70%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Handshaking.txt     \b\b\b\b 70%\b\b\b\b 71%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Hugging.txt     \b\b\b\b 71%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Jumping.txt     \b\b\b\b 72%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Running.txt     \b\b\b\b 72%\b\b\b\b 73%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Seating.txt     \b\b\b\b 73%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Standing.txt     \b\b\b\b 74%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Walking.txt     \b\b\b\b 74%\b\b\b\b 75%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub3/Normal/txt/Waving.txt     \b\b\b\b 75%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub4                       OK\n",
      "Creating    ./EMG Physical Action Data Set/sub4/Aggressive            OK\n",
      "Creating    ./EMG Physical Action Data Set/sub4/Aggressive/log        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Elbowing.log     \b\b\b\b 75%\b\b\b\b 76%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/FrontKicking.log     \b\b\b\b 76%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Hamering.log     \b\b\b\b 76%\b\b\b\b 77%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Headering.log     \b\b\b\b 77%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Kneeing.log     \b\b\b\b 78%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Pulling.log     \b\b\b\b 78%\b\b\b\b 79%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Punching.log     \b\b\b\b 79%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Pushing.log     \b\b\b\b 79%\b\b\b\b 80%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/SideKicking.log     \b\b\b\b 80%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/log/Slapping.log     \b\b\b\b 81%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub4/Aggressive/txt        OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Elbowing.txt     \b\b\b\b 81%\b\b\b\b 82%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Frontkicking.txt     \b\b\b\b 82%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Hamering.txt     \b\b\b\b 83%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Headering.txt     \b\b\b\b 83%\b\b\b\b 84%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Kneeing.txt     \b\b\b\b 84%\b\b\b\b 85%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Pulling.txt     \b\b\b\b 85%\b\b\b\b 86%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Punching.txt     \b\b\b\b 86%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Pushing.txt     \b\b\b\b 87%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Sidekicking.txt     \b\b\b\b 87%\b\b\b\b 88%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Aggressive/txt/Slapping.txt     \b\b\b\b 88%\b\b\b\b 89%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub4/Normal                OK\n",
      "Creating    ./EMG Physical Action Data Set/sub4/Normal/log            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Bowing.log     \b\b\b\b 89%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Clapping.log     \b\b\b\b 89%\b\b\b\b 90%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Handshaking.log     \b\b\b\b 90%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Hugging.log     \b\b\b\b 90%\b\b\b\b 91%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Jumping.log     \b\b\b\b 91%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Running.log     \b\b\b\b 91%\b\b\b\b 92%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Seating.log     \b\b\b\b 92%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Standing.log     \b\b\b\b 92%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Walking.log     \b\b\b\b 93%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/log/Waving.log     \b\b\b\b 93%\b\b\b\b\b  OK \n",
      "Creating    ./EMG Physical Action Data Set/sub4/Normal/txt            OK\n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Bowing.txt     \b\b\b\b 94%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Clapping.txt     \b\b\b\b 94%\b\b\b\b 95%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Handshaking.txt     \b\b\b\b 95%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Hugging.txt     \b\b\b\b 95%\b\b\b\b 96%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Jumping.txt     \b\b\b\b 96%\b\b\b\b 97%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Running.txt     \b\b\b\b 97%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Seating.txt     \b\b\b\b 97%\b\b\b\b 98%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Standing.txt     \b\b\b\b 98%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Walking.txt     \b\b\b\b 98%\b\b\b\b 99%\b\b\b\b\b  OK \n",
      "Extracting  ./EMG Physical Action Data Set/sub4/Normal/txt/Waving.txt     \b\b\b\b 99%\b\b\b\b\b  OK \n",
      "All OK\n"
     ]
    }
   ],
   "source": [
    "# x is extract\n",
    "\n",
    "!unrar x \"./emg.rar\" \"./\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "cVHL7dZtmeqv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wauf-oFTmhMk",
    "outputId": "8768359a-7e96-4703-be9b-bd14fc153caa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elbowing.txt\t  Hamering.txt\t Kneeing.txt  Punching.txt  Sidekicking.txt\n",
      "Frontkicking.txt  Headering.txt  Pulling.txt  Pushing.txt   Slapping.txt\n"
     ]
    }
   ],
   "source": [
    "# list files\n",
    "!ls ./EMG\\ Physical\\ Action\\ Data\\ Set/sub1/Aggressive/txt/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "neHOQZp_mjtO",
    "outputId": "b41d83f8-0dd3-413c-930a-e0ee5ac32d4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
      "-814\t-49\t-25\t64\t-746\t174\t-1034\t-301\n",
      "-1344\t252\t-90\t90\t158\t107\t-998\t-506\n",
      "-1674\t498\t-129\t162\t988\t77\t-1136\t-602\n",
      "-1801\t537\t-86\t-120\t692\t67\t-1458\t-778\n",
      "-1735\t461\t-45\t-397\t320\t143\t-1781\t-1404\n",
      "-1674\t363\t8\t-82\t-295\t90\t-2096\t-2259\n",
      "-1581\t254\t69\t111\t-274\t58\t-2223\t-2456\n",
      "-1296\t108\t12\t-76\t-11\t42\t-2312\t-1387\n",
      "-1010\t-4\t-52\t-15\t-217\t-11\t-2577\t-130\n",
      "-740\t-75\t-21\t122\t-408\t-33\t-2707\t716\n",
      "-554\t-21\t18\t221\t-525\t-37\t-2573\t1085\n",
      "-444\t-12\t1\t65\t772\t-94\t-2385\t1056\n",
      "-272\t-99\t33\t-10\t2822\t-141\t-2223\t1270\n",
      "-218\t-130\t64\t38\t3851\t-143\t-2079\t1452\n",
      "-156\t-92\t61\t-21\t3725\t-126\t-2039\t1332\n",
      "-117\t-87\t62\t-23\t3428\t-105\t-2076\t1150\n",
      "-66\t-98\t36\t-72\t2968\t-125\t-2009\t710\n",
      "2\t-137\t23\t-18\t2125\t-116\t-1962\t389\n",
      "67\t-203\t15\t6\t2243\t-55\t-1962\t448\n",
      "103\t-231\t52\t12\t2378\t-22\t-2043\t731\n",
      "100\t-215\t93\t10\t2321\t-26\t-1781\t817\n",
      "55\t-122\t83\t4\t2813\t-63\t-1262\t621\n",
      "33\t-106\t14\t2\t3474\t-68\t-941\t535\n",
      "59\t-98\t-4\t18\t2711\t-55\t-774\t451\n",
      "90\t-145\t-35\t32\t1721\t-74\t-375\t408\n",
      "216\t-277\t-56\t35\t1474\t-66\t-158\t304\n",
      "281\t-297\t-47\t56\t-61\t-125\t-216\t230\n",
      "256\t-320\t-70\t6\t-2955\t-159\t-85\t263\n",
      "193\t-306\t-63\t-5\t-4000\t-149\t-359\t274\n",
      "175\t-203\t-33\t-51\t-4000\t-61\t-715\t335\n",
      "62\t114\t49\t-39\t-4000\t-12\t-726\t330\n",
      "-52\t215\t16\t-24\t-4000\t-13\t-840\t361\n",
      "68\t56\t-31\t-72\t-4000\t28\t-798\t419\n",
      "28\t87\t-15\t-46\t-4000\t51\t-499\t480\n",
      "-31\t66\t-30\t-39\t-4000\t101\t-94\t556\n",
      "-42\t21\t-23\t-19\t-4000\t76\t102\t592\n",
      "-74\t8\t-78\t0\t-4000\t66\t112\t621\n",
      "-60\t103\t-125\t50\t-4000\t82\t236\t566\n",
      "-47\t130\t-165\t81\t-4000\t94\t545\t515\n",
      "80\t18\t-187\t23\t-4000\t67\t767\t434\n",
      "159\t-7\t-162\t-39\t-4000\t-2\t946\t322\n",
      "27\t50\t-102\t-153\t-4000\t-27\t1214\t208\n",
      "13\t25\t8\t-261\t-4000\t-33\t1255\t85\n",
      "147\t-62\t71\t-259\t-4000\t-40\t1280\t30\n",
      "82\t-63\t140\t-92\t-4000\t-61\t1247\t-2\n",
      "82\t-100\t149\t23\t-4000\t-107\t1145\t-117\n",
      "134\t-116\t133\t27\t-4000\t-87\t846\t-195\n",
      "226\t-194\t132\t73\t-4000\t-100\t384\t-175\n",
      "269\t-211\t84\t38\t-4000\t-107\t181\t-197\n",
      "122\t-164\t72\t-103\t-4000\t-17\t150\t-190\n",
      "-7\t-85\t77\t-132\t-4000\t80\t790\t-117\n",
      "-229\t189\t53\t-71\t-4000\t179\t1591\t-94\n",
      "-385\t253\t2\t-30\t-4000\t124\t1735\t-279\n",
      "-953\t84\t-20\t50\t-4000\t141\t1333\t-265\n",
      "-1951\t-27\t-77\t38\t-4000\t249\t872\t68\n",
      "-2541\t-39\t-116\t17\t-4000\t202\t795\t341\n",
      "-3088\t-29\t-98\t-48\t-2175\t221\t755\t800\n",
      "-2987\t-151\t-103\t-51\t97\t195\t467\t825\n",
      "-2202\t-275\t-60\t-6\t1775\t98\t110\t83\n",
      "-1300\t-401\t-15\t-44\t2699\t-47\t-216\t-753\n",
      "-979\t-599\t22\t-50\t2451\t-138\t-615\t-1465\n",
      "-1212\t-635\t11\t-79\t2231\t-74\t-1076\t-1911\n",
      "-697\t-680\t-18\t1\t2622\t-49\t-1584\t-2283\n",
      "351\t-887\t12\t-4\t3737\t27\t-2079\t-2649\n",
      "2085\t-822\t31\t-31\t4000\t118\t-2378\t-2580\n",
      "3639\t320\t57\t15\t3660\t114\t-2549\t-1746\n",
      "4000\t1266\t38\t12\t2699\t225\t-2707\t-427\n",
      "4000\t1172\t-4\t88\t2759\t334\t-2705\t397\n",
      "4000\t827\t10\t92\t3998\t311\t-2739\t211\n",
      "4000\t567\t28\t85\t4000\t278\t-2512\t123\n",
      "4000\t672\t51\t22\t4000\t77\t-2448\t373\n",
      "4000\t687\t83\t-33\t4000\t-306\t-2337\t238\n",
      "2927\t602\t33\t-8\t4000\t-745\t-1979\t-227\n",
      "640\t456\t20\t-63\t2748\t-578\t-1782\t-619\n",
      "-1410\t321\t46\t-75\t526\t-147\t-1377\t-766\n",
      "-2713\t335\t34\t-13\t-632\t-195\t-1172\t-482\n",
      "-3872\t266\t10\t11\t59\t-98\t-1006\t422\n",
      "-4000\t210\t-52\t32\t2386\t-204\t-1142\t1235\n",
      "-4000\t139\t-32\t22\t4000\t-102\t-1470\t1111\n",
      "-4000\t132\t24\t12\t4000\t62\t-1377\t238\n",
      "-4000\t103\t15\t-2\t2557\t-74\t-1048\t-388\n",
      "-4000\t-112\t54\t-49\t1018\t35\t-506\t-673\n",
      "-4000\t-379\t43\t-39\t562\t-16\t31\t-1143\n",
      "-4000\t-683\t28\t-38\t1507\t50\t514\t-1856\n",
      "-4000\t-680\t55\t-78\t2125\t73\t822\t-2319\n",
      "-4000\t-440\t-9\t-57\t2449\t92\t860\t-1884\n",
      "-3034\t-566\t27\t-17\t3410\t129\t866\t-571\n",
      "566\t-665\t40\t-6\t4000\t85\t767\t617\n",
      "3818\t-582\t-24\t26\t4000\t118\t334\t785\n",
      "4000\t-480\t-12\t-52\t4000\t89\t33\t728\n",
      "4000\t-338\t-64\t-86\t4000\t178\t-195\t652\n",
      "4000\t-232\t-33\t-2\t4000\t262\t-571\t794\n",
      "4000\t12\t46\t69\t4000\t219\t-652\t853\n",
      "4000\t298\t11\t122\t3139\t164\t-725\t629\n",
      "4000\t542\t10\t100\t2125\t209\t-680\t825\n",
      "4000\t615\t27\t100\t1710\t241\t-646\t798\n",
      "4000\t526\t130\t33\t871\t195\t-524\t811\n",
      "4000\t733\t149\t-79\t-217\t105\t-167\t864\n",
      "1311\t1071\t81\t-124\t-1333\t-8\t-27\t827\n",
      "-2208\t1097\t62\t-121\t-2286\t81\t-43\t960\n",
      "-4000\t803\t-82\t-104\t-2368\t244\t-363\t1076\n",
      "-4000\t506\t-153\t-118\t-1589\t193\t-719\t1065\n",
      "-4000\t428\t-132\t-141\t-179\t183\t-1006\t1006\n",
      "-4000\t474\t-144\t-83\t1135\t141\t-1161\t1000\n",
      "-4000\t615\t-76\t-2\t2085\t-1\t-1196\t878\n",
      "-4000\t566\t-50\t10\t2710\t-86\t-1161\t800\n",
      "-4000\t316\t-42\t-10\t3102\t-224\t-737\t686\n",
      "-4000\t141\t-67\t15\t3410\t-147\t-340\t601\n",
      "-1687\t-354\t-104\t13\t3851\t-2\t69\t686\n",
      "1059\t-622\t-18\t-5\t4000\t56\t510\t391\n",
      "3395\t-497\t16\t-59\t4000\t163\t759\t-288\n",
      "4000\t-638\t-9\t-108\t4000\t167\t913\t-809\n",
      "4000\t-940\t-57\t37\t4000\t183\t1050\t-1254\n",
      "4000\t-728\t-38\t-9\t4000\t151\t1158\t-1738\n",
      "2744\t-746\t-94\t-255\t4000\t130\t1119\t-2254\n",
      "78\t-983\t-194\t-214\t4000\t140\t1018\t-2084\n",
      "-2309\t-735\t-159\t-299\t4000\t42\t1018\t-1245\n",
      "-3217\t-844\t-137\t-365\t4000\t-17\t1214\t-549\n",
      "-3360\t-453\t-184\t100\t4000\t63\t1548\t279\n",
      "-3478\t371\t-247\t634\t4000\t188\t1772\t457\n",
      "-2435\t579\t-118\t598\t4000\t130\t1996\t13\n",
      "-1790\t540\t20\t-2\t4000\t20\t2316\t-370\n",
      "-2436\t537\t16\t-28\t4000\t-3\t2450\t-781\n",
      "-1348\t687\t32\t489\t4000\t20\t2896\t-1190\n",
      "1116\t1012\t40\t524\t3359\t2\t3465\t-1339\n",
      "1535\t1518\t107\t206\t3020\t40\t3673\t-703\n",
      "2158\t1624\t251\t-263\t2822\t58\t3795\t138\n",
      "1897\t580\t497\t-180\t2163\t42\t3548\t290\n",
      "1391\t-497\t707\t431\t1604\t185\t3205\t153\n",
      "1873\t-687\t567\t524\t746\t357\t2760\t142\n",
      "1497\t-1285\t196\t154\t43\t441\t2321\t-187\n",
      "566\t-2821\t47\t45\t-313\t402\t2183\t-793\n",
      "-550\t-3424\t20\t124\t-1162\t329\t2162\t-1135\n",
      "-1883\t-2253\t-95\t-282\t-1463\t311\t2364\t-1115\n",
      "-3303\t-1281\t-513\t-906\t-1971\t188\t2505\t-494\n",
      "-3805\t-188\t-1020\t-1887\t-2389\t-31\t2493\t837\n",
      "-3750\t1482\t-954\t-2497\t-2509\t14\t2317\t1671\n",
      "-3372\t2937\t-402\t-698\t-2550\t180\t2058\t1598\n",
      "-2455\t3252\t580\t1306\t-2224\t200\t2049\t1272\n",
      "-897\t2299\t699\t1320\t-2110\t208\t2036\t330\n",
      "361\t2925\t140\t655\t-1821\t102\t1851\t-317\n",
      "1938\t3079\t103\t-62\t-1528\t81\t1864\t-415\n",
      "3180\t3271\t166\t-325\t-1593\t219\t1670\t-378\n",
      "3298\t3730\t400\t-100\t-1768\t257\t1309\t-118\n",
      "2809\t3062\t337\t69\t-1719\t520\t1262\t301\n",
      "2085\t1071\t272\t64\t-1589\t860\t1115\t728\n",
      "1311\t-2912\t270\t83\t-1711\t1005\t824\t760\n",
      "-571\t-4000\t295\t27\t-1951\t1279\t725\t695\n",
      "-1278\t-3355\t-28\t-44\t-2128\t1531\t561\t785\n",
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      "1384\t-503\t-119\t1\t1409\t-602\t-745\t186\n",
      "994\t-413\t-87\t-19\t1358\t-195\t-465\t105\n",
      "632\t-314\t-84\t-14\t1205\t319\t-542\t-39\n",
      "309\t-399\t-60\t-8\t1191\t221\t-412\t-35\n",
      "-187\t-310\t-36\t3\t1669\t290\t-71\t-7\n",
      "-573\t0\t-7\t-26\t2376\t368\t345\t-68\n",
      "-820\t189\t-13\t-39\t2759\t250\t588\t-107\n",
      "-972\t306\t-3\t-27\t2748\t156\t518\t-168\n",
      "-844\t311\t15\t-30\t2581\t-47\t200\t-187\n",
      "-706\t211\t15\t-11\t2710\t72\t-312\t-217\n",
      "-539\t174\t23\t-33\t3076\t233\t-745\t-265\n",
      "-417\t120\t36\t-43\t3359\t168\t-1136\t-271\n",
      "-368\t122\t39\t-34\t3615\t209\t-990\t-260\n",
      "-311\t113\t61\t-46\t3725\t232\t-1332\t-267\n",
      "-453\t94\t72\t-40\t3474\t245\t-1582\t-244\n",
      "-909\t18\t42\t-23\t3037\t150\t-759\t-140\n",
      "-1494\t-35\t29\t-21\t2842\t28\t124\t-73\n",
      "-1491\t16\t17\t-28\t3085\t-27\t897\t-36\n",
      "-1078\t-15\t44\t-45\t3363\t-85\t687\t-6\n",
      "-742\t-80\t51\t-75\t3349\t-54\t321\t49\n",
      "-100\t-144\t69\t-109\t2971\t-102\t363\t182\n",
      "704\t-275\t67\t-102\t2822\t-24\t131\t251\n",
      "1311\t-356\t20\t-47\t2895\t254\t-4\t269\n",
      "1745\t-326\t25\t8\t2748\t260\t66\t294\n",
      "1997\t-220\t5\t48\t2645\t112\t421\t276\n",
      "2013\t-118\t2\t-57\t2645\t32\t392\t263\n",
      "1767\t-24\t2\t-99\t2882\t-63\t143\t230\n",
      "1441\t124\t17\t-39\t2710\t-305\t19\t167\n",
      "865\t162\t29\t-16\t1903\t-763\t337\t76\n",
      "298\t179\t24\t-16\t1398\t-1005\t832\t-2\n",
      "-56\t223\t36\t-30\t1147\t-552\t954\t-100\n",
      "-388\t278\t38\t20\t1189\t-153\t991\t-190\n",
      "-566\t282\t93\t10\t1378\t-305\t945\t-301\n",
      "-820\t347\t93\t-13\t1336\t-159\t815\t-313\n",
      "-999\t420\t64\t-24\t1186\t-91\t551\t-297\n",
      "-999\t392\t38\t-51\t1409\t-90\t628\t-348\n",
      "-979\t335\t30\t-95\t1995\t-71\t653\t-297\n",
      "-858\t213\t-9\t-98\t2516\t-153\t419\t-305\n",
      "-649\t93\t-63\t-62\t2822\t-11\t384\t-297\n",
      "-417\t-15\t-52\t-90\t2968\t-87\t102\t-294\n",
      "-245\t-91\t-76\t-54\t2947\t-216\t-379\t-319\n",
      "-93\t-192\t-59\t-48\t2822\t-251\t-236\t-288\n",
      "-42\t-215\t-66\t-12\t2374\t-137\t329\t-276\n",
      "-58\t-136\t-37\t-5\t1771\t-140\t411\t-278\n",
      "-39\t-161\t62\t-4\t1409\t-32\t329\t-314\n",
      "6\t-173\t37\t18\t1017\t180\t702\t-333\n",
      "38\t-167\t7\t1\t519\t426\t1119\t-380\n",
      "73\t-210\t19\t23\t-147\t766\t1520\t-314\n",
      "12\t-161\t9\t10\t-1007\t365\t1591\t-195\n",
      "-104\t43\t2\t23\t-1980\t-6\t1451\t-179\n",
      "-84\t87\t21\t2\t-2395\t101\t1402\t-154\n",
      "41\t-53\t8\t16\t-1945\t-113\t1320\t-105\n",
      "137\t-155\t7\t59\t-1583\t-722\t1446\t-101\n",
      "56\t-214\t12\t49\t-1729\t-1237\t1540\t-98\n",
      "-48\t-230\t-15\t68\t-2134\t-1005\t1906\t-56\n",
      "-106\t-91\t15\t46\t-2574\t-327\t2118\t-137\n",
      "-89\t113\t14\t63\t-3508\t-15\t1858\t-230\n",
      "-70\t9\t18\t77\t-4000\t-54\t2309\t-155\n",
      "-75\t-98\t77\t37\t-4000\t26\t3205\t-81\n",
      "-13\t-8\t131\t20\t-3852\t185\t3144\t-158\n",
      "44\t103\t149\t44\t-4000\t392\t2452\t-151\n",
      "55\t187\t46\t63\t-4000\t376\t1591\t-151\n",
      "141\t174\t-14\t61\t-4000\t246\t535\t-195\n",
      "167\t233\t-55\t49\t-4000\t336\t-110\t-287\n",
      "126\t239\t-66\t-10\t-4000\t464\t260\t-350\n",
      "146\t99\t-36\t-20\t-4000\t685\t1467\t-301\n",
      "212\t-37\t7\t-2\t-4000\t611\t2309\t-294\n",
      "117\t-128\t88\t16\t-4000\t658\t2444\t-325\n",
      "22\t-143\t140\t42\t-4000\t744\t2322\t-256\n",
      "15\t-179\t130\t9\t-4000\t433\t2375\t-166\n",
      "59\t-220\t41\t19\t-4000\t144\t2444\t-200\n",
      "133\t-216\t10\t34\t-4000\t55\t2189\t-258\n",
      "77\t-192\t-38\t-25\t-4000\t-3\t1866\t-297\n",
      "32\t8\t-99\t-65\t-3855\t-535\t1778\t-248\n",
      "31\t146\t-197\t-45\t-3172\t-1062\t1864\t-216\n",
      "89\t30\t-277\t-46\t-2550\t-419\t1772\t-246\n",
      "130\t16\t-255\t-69\t-2126\t229\t2118\t-234\n",
      "32\t-1\t-269\t-74\t-1593\t156\t2450\t-158\n",
      "43\t3\t-238\t-81\t-1685\t534\t1599\t-112\n",
      "-9\t4\t-192\t-64\t-2224\t498\t337\t-132\n",
      "-172\t-18\t-148\t-23\t-2484\t429\t-350\t-93\n",
      "-447\t38\t-127\t10\t-2344\t319\t-1142\t-43\n",
      "-665\t122\t-39\t-3\t-1729\t-193\t-2015\t37\n",
      "-453\t282\t158\t-19\t-1685\t-347\t-2549\t296\n",
      "-307\t212\t299\t-17\t-1837\t-34\t-3455\t485\n",
      "-187\t-38\t272\t-1\t-1398\t167\t-4000\t387\n",
      "-191\t-275\t140\t4\t-864\t107\t-4000\t339\n",
      "-482\t-318\t50\t-5\t-658\t283\t-4000\t308\n",
      "-596\t-162\t8\t-12\t-406\t115\t-4000\t202\n",
      "-361\t-149\t-55\t4\t625\t85\t-4000\t155\n",
      "-26\t-224\t-91\t-16\t976\t-37\t-4000\t-5\n",
      "186\t-155\t-87\t-32\t635\t27\t-4000\t-338\n",
      "408\t72\t-97\t22\t635\t27\t-4000\t-338\n"
     ]
    }
   ],
   "source": [
    "!cat ./EMG\\ Physical\\ Action\\ Data\\ Set/sub1/Aggressive/txt/Slapping.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Ei1ASFBWmlyy"
   },
   "outputs": [],
   "source": [
    "\n",
    "actions = {}\n",
    "\n",
    "data_dirs = [\"./EMG Physical Action Data Set/sub1/Aggressive/txt\",\n",
    "             \"./EMG Physical Action Data Set/sub1/Normal/txt\"]\n",
    "\n",
    "ind = 0\n",
    "data = pd.DataFrame()\n",
    "\n",
    "for dirs in data_dirs :\n",
    "\n",
    "  for files in os.listdir(dirs):\n",
    "\n",
    "    with open(os.path.join(dirs, files), \"r\") as f:\n",
    "\n",
    "      temp = pd.read_csv(f.name,\n",
    "                        sep = \"\\t\",\n",
    "                        header = None, \n",
    "                        names = [\"ch\" + str(i) for i in range(1, 9)] # 8 input channels\n",
    "                        )\n",
    "      \n",
    "      # chunking using Max of every 10 sequential values.\n",
    "      temp_chunked = pd.DataFrame()\n",
    "\n",
    "      for i in range(0, len(temp), 10):\n",
    "        temp_chunked = temp_chunked.append(temp.iloc[i:i+10].max(), ignore_index = True)\n",
    "\n",
    "      labels = [files[:-4] for i in range(len(temp_chunked))] # remove the last 4 characters=\".txt\" from the filename\n",
    "      actions[files[:-4]] = ind\n",
    "\n",
    "      temp_chunked[\"Action\"] = labels\n",
    "\n",
    "      data = pd.concat([data, temp_chunked])\n",
    "\n",
    "      ind+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XvZtQGn52EEQ",
    "outputId": "8770568d-48d0-4f9d-ea68-e9e3288ee5c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Sidekicking': 0, 'Slapping': 1, 'Pulling': 2, 'Elbowing': 3, 'Pushing': 4, 'Hamering': 5, 'Frontkicking': 6, 'Punching': 7, 'Headering': 8, 'Kneeing': 9, 'Running': 10, 'Clapping': 11, 'Handshaking': 12, 'Hugging': 13, 'Bowing': 14, 'Jumping': 15, 'Seating': 16, 'Waving': 17, 'Standing': 18, 'Walking': 19}\n"
     ]
    }
   ],
   "source": [
    "print(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "yHcLJZBlmuM9",
    "outputId": "1e6346e0-8a85-4591-c4f8-3eba6998026f"
   },
   "outputs": [
    {
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      "text/plain": [
       "      ch1    ch2     ch3    ch4     ch5     ch6     ch7     ch8       Action\n",
       "0  -506.0 -391.0   -73.0  363.0   933.0  4000.0  4000.0  4000.0  Sidekicking\n",
       "1   -43.0  -84.0   218.0  341.0  1720.0  3333.0  3726.0 -2429.0  Sidekicking\n",
       "2  1246.0  119.0  2539.0  354.0  2960.0  1864.0 -4000.0 -4000.0  Sidekicking\n",
       "3   838.0  590.0  2551.0  716.0  4000.0  4000.0 -4000.0 -2254.0  Sidekicking\n",
       "4   430.0  190.0  -255.0  329.0  4000.0  3670.0 -4000.0  4000.0  Sidekicking"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wzS3EuzTnhZK",
    "outputId": "85e44110-9dd9-4cea-9f39-772f66787a41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19711, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VjHbvSn-mwfH",
    "outputId": "79b17aae-0964-4c15-fb9b-70025d95dbb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19711 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   ch1     19711 non-null  float64\n",
      " 1   ch2     19711 non-null  float64\n",
      " 2   ch3     19711 non-null  float64\n",
      " 3   ch4     19711 non-null  float64\n",
      " 4   ch5     19711 non-null  float64\n",
      " 5   ch6     19711 non-null  float64\n",
      " 6   ch7     19711 non-null  float64\n",
      " 7   ch8     19711 non-null  float64\n",
      " 8   Action  19711 non-null  object \n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Mvd1jm23m1Iy"
   },
   "outputs": [],
   "source": [
    "Y = data[\"Action\"]\n",
    "X = data.drop(columns = [\"Action\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kVnPWop4m3Cq",
    "outputId": "94d49c56-81d3-4b82-834b-fb38c7f16e4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sidekicking' 'Slapping' 'Pulling' 'Elbowing' 'Pushing' 'Hamering'\n",
      " 'Frontkicking' 'Punching' 'Headering' 'Kneeing' 'Running' 'Clapping'\n",
      " 'Handshaking' 'Hugging' 'Bowing' 'Jumping' 'Seating' 'Waving' 'Standing'\n",
      " 'Walking']\n"
     ]
    }
   ],
   "source": [
    "print(Y.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3TSbuSncm_iR",
    "outputId": "455b28f6-006c-4f17-b008-ce6a327cc5ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Y.map(actions)\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXSW4LZH2pf2",
    "outputId": "15c4c1a2-d2f5-4bbf-90de-66d3a9ea5d41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    1000\n",
       "8     1000\n",
       "17    1000\n",
       "16    1000\n",
       "15    1000\n",
       "5     1000\n",
       "11    1000\n",
       "9     1000\n",
       "10     997\n",
       "14     983\n",
       "0      983\n",
       "6      982\n",
       "1      979\n",
       "3      978\n",
       "13     976\n",
       "18     973\n",
       "4      968\n",
       "2      966\n",
       "7      964\n",
       "12     962\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "CRRDRW8anDMr",
    "outputId": "7a197631-5897-491c-816f-edb4b8c34d65"
   },
   "outputs": [
    {
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       "      <th>ch1</th>\n",
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       "      <td>43.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>218.0</td>\n",
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       "      <td>1720.0</td>\n",
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       "      <td>2539.0</td>\n",
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       "      <td>716.0</td>\n",
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       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>2254.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>430.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3670.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-996ca824-ec1e-46ee-91e0-0f59f90a35de');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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      ],
      "text/plain": [
       "      ch1    ch2     ch3    ch4     ch5     ch6     ch7     ch8\n",
       "0   506.0  391.0    73.0  363.0   933.0  4000.0  4000.0  4000.0\n",
       "1    43.0   84.0   218.0  341.0  1720.0  3333.0  3726.0  2429.0\n",
       "2  1246.0  119.0  2539.0  354.0  2960.0  1864.0  4000.0  4000.0\n",
       "3   838.0  590.0  2551.0  716.0  4000.0  4000.0  4000.0  2254.0\n",
       "4   430.0  190.0   255.0  329.0  4000.0  3670.0  4000.0  4000.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = abs(X)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "iBnE44aFnI0q"
   },
   "outputs": [],
   "source": [
    "# moving average. \n",
    "\n",
    "X = X.ewm(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DzNhnjLwnsy5",
    "outputId": "8428447b-e4f3-4e72-f7ea-3987b1794f7a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sizes of the sets created are:\n",
      "Training set: 15768\n",
      "Test set: 3943\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = np.array(X.values)\n",
    "Y = np.array(Y.values)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, shuffle = True, random_state=3)\n",
    "\n",
    "print(f\"Sizes of the sets created are:\\nTraining set: {X_train.shape[0]}\\nTest set: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y39mIiLjlK-c"
   },
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5MjVrkan2ED",
    "outputId": "a58bdfd5-27a3-4a27-efd9-5704d4b70028"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 72.3 Validation: 71.3\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(random_state=7, max_depth = 7, )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 5, scoring = 'accuracy', return_train_score = True, )\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "aKPUSJnhlV9i"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVKlmhXQmZaP"
   },
   "source": [
    "# RandomForest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "soYT4wGtmX5M",
    "outputId": "86bd84c8-6b88-4d80-fd4a-d0a2dc3b951c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 83.5 Validation: 82.39999999999999\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "tree_clf = RandomForestClassifier(random_state=7, max_depth = 7, n_estimators= 100 )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 5, scoring = 'accuracy', return_train_score = True)\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b85QV_rNnAuP"
   },
   "source": [
    "# GBDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nNXOrMRhmtDd",
    "outputId": "ad665085-0b6e-4def-942b-d012ef4d2cae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 100.0 Validation: 94.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "tree_clf = GradientBoostingClassifier(random_state=7, max_depth = 4, n_estimators= 150, learning_rate = 0.1 )\n",
    "cv_acc_results = cross_validate(tree_clf, X_train, Y_train, cv = 3, scoring = 'accuracy', return_train_score = True)\n",
    "\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "O8AxeWQkedFv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X7D1RXl9hsYV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ca1VJv3ihs9S"
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "SigfrFQVec-u"
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# library \n",
    "\n",
    "# pip install xgboost\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "iPytQVphenyF"
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "        'learning_rate': [0.1, 0.5, 0.8],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3, 4, 5]\n",
    "        }\n",
    "xgb = XGBClassifier(n_estimators=100, objective='multi:softmax', num_class=20, silent=True)\n",
    "\n",
    "# xgb = XGBClassifier(n_estimators=100, objective='multi:softmax', num_class=20, subsample=0.8, max_depth=4, learning_rate=0.2, colsample_bytree=0.8, silent=True)\n",
    "# xgb.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uMXFzdm2e1-B",
    "outputId": "54d3824a-bc2a-49bd-c805-7dc71e4d6e01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    }
   ],
   "source": [
    "random_search = RandomizedSearchCV(xgb, param_distributions=params, n_iter=10, scoring='accuracy', n_jobs=-1, cv=3, verbose=1, random_state=1001 )\n",
    "\n",
    "\n",
    "start = dt.datetime.now()\n",
    "random_search.fit(X_train, Y_train)\n",
    "end = dt.datetime.now()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gYYqi_zxf51m",
    "outputId": "09515896-e38e-4a60-a6b9-299495cf70fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:\n",
      "{'subsample': 1.0, 'max_depth': 5, 'learning_rate': 0.5, 'colsample_bytree': 1.0}\n"
     ]
    }
   ],
   "source": [
    "print('Best hyperparameters:')\n",
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y_O0TwH8f9Fo",
    "outputId": "348738d5-6117-4044-f889-f0468986473b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(colsample_bytree=1.0, learning_rate=0.5, max_depth=5,\n",
       "              num_class=20, objective='multi:softprob', silent=True,\n",
       "              subsample=1.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_xgb = XGBClassifier(n_estimators=100, objective='multi:softmax', num_class=20, subsample=1.0, max_depth=5, learning_rate=0.5, colsample_bytree=1.0, silent=True)\n",
    "best_xgb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HguMxpz2f9B2",
    "outputId": "5cf13b25-3334-494f-da3f-96df45716a7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for training : 0:05:03.943361\n",
      "Training accuracy:1.0\n",
      "Test Accuracy: 0.9718488460563023\n"
     ]
    }
   ],
   "source": [
    "print(f\"Time taken for training : {end - start}\\nTraining accuracy:{best_xgb.score(X_train, Y_train)}\\nTest Accuracy: {best_xgb.score(X_test, Y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F5XwDyl5iqBo",
    "outputId": "a3ab9bdc-cda5-4f94-dfed-992bb6f7d5a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 100.0 Validation: 96.2\n"
     ]
    }
   ],
   "source": [
    "cv_acc_results = cross_validate(best_xgb, X_train, Y_train, cv = 3, scoring = 'accuracy', return_train_score = True)\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "id": "4nwMkjuJf8_H",
    "outputId": "8b0bdafb-f46c-4997-a7eb-ded40f789e84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11401166 0.10362112 0.13853835 0.10378643 0.24054739 0.13755548\n",
      " 0.06701676 0.09492283]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(best_xgb.feature_importances_)\n",
    "\n",
    "plt.bar(range(len(best_xgb.feature_importances_)), best_xgb.feature_importances_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bhgkX_9Ag4Ou"
   },
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SnC0QDhMg38Y",
    "outputId": "fcbf2f4b-c357-4203-ee16-6773c241efeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "#Refer: https://lightgbm.readthedocs.io/en/latest/Parameters.html\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "gridParams = {\n",
    "    'learning_rate': [0.1, 0.5, 0.8],\n",
    "    'boosting_type' : ['gbdt'],\n",
    "    'objective' : ['multiclass'],\n",
    "    'max_depth' : [5,6,7,8],\n",
    "    'colsample_bytree' : [0.5,0.7],\n",
    "    'subsample' : [0.5,0.7],\n",
    "    'metric':['multi_error'],\n",
    "    }\n",
    "\n",
    "clf = lgb.LGBMClassifier(num_classes=20)\n",
    "grid = RandomizedSearchCV(clf, gridParams, verbose=1, cv=3, n_jobs = -1, n_iter=10)\n",
    "\n",
    "start = dt.datetime.now()\n",
    "grid.fit(X_train,Y_train)\n",
    "end = dt.datetime.now()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oUUdu7SDg359",
    "outputId": "7e566437-6ae0-4a55-a323-6b2703dc1361"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.5,\n",
       " 'random_state': 501,\n",
       " 'objective': 'multiclass',\n",
       " 'metric': 'multi_error',\n",
       " 'max_depth': 7,\n",
       " 'learning_rate': 0.5,\n",
       " 'colsample_bytree': 0.7,\n",
       " 'boosting_type': 'gbdt'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JnWBDOfXg33k",
    "outputId": "4450e034-d03b-4bf3-8014-19feffaab117"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_typ='gbdt', colsample_bytree=0.7, learning_rate=0.5,\n",
       "               max_depth=7, num_class=20, objective='multiclass',\n",
       "               random_state=501, subsample=0.5)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_lgbm = lgb.LGBMClassifier(boosting_typ = 'gbdt',\n",
    "                              objective = 'multiclass',\n",
    "                              num_class=20, \n",
    "                              colsample_bytree=0.7, \n",
    "                              subsample=0.5, \n",
    "                              max_depth=7, \n",
    "                              learning_rate=0.5, \n",
    "                              random_state = 501)\n",
    "best_lgbm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oOX0xBYvg30_",
    "outputId": "956b15b4-b830-49ce-81ed-8741ebff5f82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for training : 0:01:07.203982\n",
      "Training accuracy:1.0\n",
      "Test Accuracy: 0.9807253360385493\n"
     ]
    }
   ],
   "source": [
    "print(f\"Time taken for training : {end - start}\\nTraining accuracy:{best_lgbm.score(X_train, Y_train)}\\nTest Accuracy: {best_lgbm.score(X_test, Y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d5wncsfEhqXQ",
    "outputId": "35bff3cf-96f5-4a1e-c80f-44d723309f61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Accuracy Mean: Train: 100.0 Validation: 97.0\n"
     ]
    }
   ],
   "source": [
    "cv_acc_results = cross_validate(best_lgbm, X_train, Y_train, cv = 3, scoring = 'accuracy', return_train_score = True)\n",
    "print(f\"K-Fold Accuracy Mean: Train: {cv_acc_results['train_score'].mean().round(3)*100} Validation: {cv_acc_results['test_score'].mean().round(3)*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uuE26yG_hqVF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T6zFXGC0Nroj"
   },
   "source": [
    "# Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "3Zke9ZARhqSh"
   },
   "outputs": [],
   "source": [
    "# from mlxtend.classifier import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N20TQgqXNtAl"
   },
   "outputs": [],
   "source": [
    "# clf1 = KNeighborsClassifier(n_neighbors=1)\n",
    "# clf2 = RandomForestClassifier(random_state=1)\n",
    "# clf3 = DecisionTreeClassifier()\n",
    "\n",
    "# meta_clf = LogisticRegression()\n",
    "\n",
    "# s_clf = StackingClassifier(classifiers=[clf1, clf2, clf3], \n",
    "#                           meta_classifier=meta_clf,\n",
    "#                           use_probas=True)\n",
    "\n",
    "# s_clf.fit(X_train, y_train)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}