{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f0ddabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e8e6ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "delim = ';'\n",
    "\n",
    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
    "\n",
    "Xpickle_file = './X.pickle'\n",
    "\n",
    "ypickle_file = './y.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39cf526d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten(npList):\n",
    "    temp        = npList['Force']\n",
    "    thresh      = 100\n",
    "    leeway      = 5\n",
    "    \n",
    "    temps_over_T = np.where(temp > thresh)[0]\n",
    "    return npList[max(temps_over_T[0]-leeway,0):temps_over_T[-1]+leeway]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b169e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pickles():\n",
    "    _p = open(Xpickle_file, 'rb')\n",
    "    X = pickle.load(_p)\n",
    "    _p.close()\n",
    "        \n",
    "    _p = open(ypickle_file, 'rb')\n",
    "    y = pickle.load(_p)\n",
    "    _p.close()\n",
    "    \n",
    "    return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a507393",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
    "        return load_pickles()\n",
    "    data = []\n",
    "    label = []\n",
    "    for user in range(0, user_count):\n",
    "        user_path = base_path + str(user) + '/split_letters_csv/'\n",
    "        for file in os.listdir(user_path):\n",
    "            file_name = user_path + file\n",
    "            letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
    "            data.append(pd.read_csv(file_name, delim))\n",
    "            label.append(letter)\n",
    "    return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str), np.asarray(file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d8da08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.8 s, sys: 161 ms, total: 2.96 s\n",
      "Wall time: 2.96 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "X, y = load_data()\n",
    "\n",
    "X.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "700db412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.12 s, sys: 20.1 ms, total: 3.14 s\n",
      "Wall time: 3.14 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<timed exec>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "XX = np.array(list(map(shorten, X)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cdb8f5",
   "metadata": {},
   "source": [
    "**How to fix this error**:\n",
    "```python\n",
    "<timed exec>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f411bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[           Millis   Acc1 X   Acc1 Y  Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "108  1.559648e+12  -8450.0  13086.0 -4548.0 -1992.0 -3305.0   981.0   384.0   \n",
      "109  1.559648e+12  -8006.0  12109.0 -4724.0 -2190.0 -3093.0   874.0   475.0   \n",
      "110  1.559648e+12  -7676.0  11804.0 -3880.0 -2407.0 -2849.0   879.0   561.0   \n",
      "111  1.559648e+12  -7604.0  11928.0 -4587.0 -2513.0 -2978.0  1004.0   580.0   \n",
      "112  1.559648e+12  -7434.0  12796.0 -3926.0 -2728.0 -2989.0  1030.0   614.0   \n",
      "..            ...      ...      ...     ...     ...     ...     ...     ...   \n",
      "175  1.559648e+12  -6502.0  17157.0 -5903.0 -1094.0 -3943.0   -69.0   306.0   \n",
      "176  1.559648e+12 -10394.0  20632.0 -5614.0   -29.0 -4922.0   584.0   590.0   \n",
      "177  1.559648e+12  -9342.0  21390.0 -5431.0   871.0 -5150.0  1533.0   612.0   \n",
      "178  1.559648e+12  -7897.0  20951.0 -5580.0   557.0 -4971.0  1307.0   562.0   \n",
      "179  1.559648e+12  -6954.0  19025.0 -4719.0   -98.0 -4448.0   807.0   588.0   \n",
      "\n",
      "     Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z  Force    Time  \n",
      "108  -144.0    87.0  -40.0  134.0  217.0    0.0  3371.0  \n",
      "109  -195.0   105.0  -40.0  132.0  216.0    0.0  3372.0  \n",
      "110   -96.0   228.0  -41.0  132.0  214.0    0.0  3373.0  \n",
      "111  -162.0   418.0  -38.0  136.0  213.0    0.0  3374.0  \n",
      "112  -230.0   685.0  -34.0  127.0  214.0    2.0  3375.0  \n",
      "..      ...     ...    ...    ...    ...    ...     ...  \n",
      "175  -554.0 -1416.0  -29.0  143.0  212.0  279.0  3438.0  \n",
      "176  -622.0 -1803.0  -32.0  140.0  214.0    0.0  3439.0  \n",
      "177 -1114.0 -2738.0  -33.0  140.0  213.0    0.0  3440.0  \n",
      "178 -1541.0 -3670.0  -35.0  138.0  214.0    0.0  3441.0  \n",
      "179 -1850.0 -4341.0  -39.0  139.0  211.0    0.0  3442.0  \n",
      "\n",
      "[72 rows x 15 columns]\n",
      "            Millis  Acc1 X   Acc1 Y  Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "96   1.559649e+12 -5389.0  13395.0 -5709.0 -1680.0 -3298.0  1510.0  -231.0   \n",
      "97   1.559649e+12 -5805.0  13311.0 -5048.0 -1834.0 -3264.0  1600.0  -301.0   \n",
      "98   1.559649e+12 -5836.0  13067.0 -5101.0 -1874.0 -3231.0  1683.0  -431.0   \n",
      "99   1.559649e+12 -6053.0  12917.0 -5352.0 -1939.0 -3152.0  1781.0  -577.0   \n",
      "100  1.559649e+12 -8495.0  18632.0 -7335.0 -2253.0 -3634.0  1754.0  -577.0   \n",
      "..            ...     ...      ...     ...     ...     ...     ...     ...   \n",
      "173  1.559649e+12 -4353.0  14977.0 -7064.0   233.0 -3720.0  1054.0   430.0   \n",
      "174  1.559649e+12 -6825.0  19690.0 -6418.0   992.0 -4942.0   908.0   633.0   \n",
      "175  1.559649e+12 -5829.0  20732.0 -7816.0   788.0 -5034.0   528.0   950.0   \n",
      "176  1.559649e+12 -4577.0  18516.0 -6998.0   641.0 -4826.0  1259.0  1307.0   \n",
      "177  1.559649e+12 -3997.0  15577.0 -6250.0   203.0 -4036.0  1648.0  1358.0   \n",
      "\n",
      "     Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z  Force     Time  \n",
      "96     70.0   368.0  -34.0  165.0  280.0    0.0 -24209.0  \n",
      "97    140.0   513.0  -36.0  167.0  285.0    0.0 -24208.0  \n",
      "98    287.0   650.0  -31.0  172.0  284.0    0.0 -24207.0  \n",
      "99    389.0   807.0  -32.0  172.0  285.0    0.0 -24206.0  \n",
      "100   421.0   860.0  -29.0  174.0  286.0   96.0 -24205.0  \n",
      "..      ...     ...    ...    ...    ...    ...      ...  \n",
      "173   502.0  -685.0  -24.0  174.0  282.0  356.0 -24132.0  \n",
      "174   501.0 -1240.0  -23.0  172.0  283.0    0.0 -24131.0  \n",
      "175   -36.0 -2019.0  -22.0  173.0  281.0    0.0 -24130.0  \n",
      "176  -702.0 -2660.0  -26.0  168.0  283.0    0.0 -24129.0  \n",
      "177 -1131.0 -3098.0  -33.0  167.0  284.0    0.0 -24128.0  \n",
      "\n",
      "[82 rows x 15 columns]\n",
      "            Millis   Acc1 X   Acc1 Y   Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "78   1.559649e+12  -4234.0  14086.0  -6199.0 -1178.0 -3513.0  1496.0    94.0   \n",
      "79   1.559649e+12  -5241.0  13339.0  -6165.0 -1220.0 -3334.0  1571.0    96.0   \n",
      "80   1.559649e+12  -6329.0  12705.0  -6123.0 -1712.0 -3166.0  1713.0    67.0   \n",
      "81   1.559649e+12  -5990.0  12080.0  -5961.0 -2181.0 -3019.0  1839.0   -11.0   \n",
      "82   1.559649e+12  -8149.0  15320.0  -7929.0 -2157.0 -3551.0  1274.0   -19.0   \n",
      "83   1.559649e+12  -9563.0  14871.0  -7387.0 -1216.0 -4017.0   585.0   282.0   \n",
      "84   1.559649e+12  -7243.0  13994.0  -6533.0 -2115.0 -3536.0  1820.0   389.0   \n",
      "85   1.559649e+12  -9009.0  13437.0  -4020.0 -1165.0 -3511.0  1806.0   202.0   \n",
      "86   1.559649e+12  -4764.0  15608.0  -6769.0 -1164.0 -3657.0  1097.0   -17.0   \n",
      "87   1.559649e+12  -8437.0  14828.0  -4650.0  -627.0 -3653.0  1893.0   -67.0   \n",
      "88   1.559649e+12  -4117.0  16647.0    -86.0  -322.0 -4078.0  2363.0  -336.0   \n",
      "89   1.559649e+12  -1677.0  15411.0 -12917.0 -1145.0 -4030.0  1242.0  -840.0   \n",
      "90   1.559649e+12  -6613.0  13620.0  -8036.0   372.0 -3686.0  1732.0 -1112.0   \n",
      "91   1.559649e+12  -4524.0  14625.0 -10169.0 -1525.0 -3505.0  1908.0 -1594.0   \n",
      "92   1.559649e+12 -12547.0   7829.0 -26995.0   114.0 -2795.0   -15.0 -1895.0   \n",
      "93   1.559649e+12  -2059.0  12855.0 -10219.0  -879.0 -3253.0   462.0 -1339.0   \n",
      "94   1.559649e+12 -11501.0   9966.0 -12259.0  -796.0 -3214.0  2506.0 -1056.0   \n",
      "95   1.559649e+12  -2784.0  13328.0  -9743.0 -3223.0 -3277.0  1210.0 -1007.0   \n",
      "96   1.559649e+12  -5287.0  12082.0  -9844.0 -2941.0 -3173.0  -162.0  -568.0   \n",
      "97   1.559649e+12  -2626.0  13573.0  -1014.0 -2232.0 -2969.0  -381.0   119.0   \n",
      "98   1.559649e+12 -13271.0  12439.0  -3350.0 -2057.0 -3235.0  1489.0   873.0   \n",
      "99   1.559649e+12  -8522.0  15439.0  -3818.0 -2097.0 -2572.0   918.0   971.0   \n",
      "100  1.559649e+12  -9228.0  12492.0  -6148.0 -1474.0 -2803.0  1199.0   758.0   \n",
      "101  1.559649e+12  -7956.0  13559.0  -6100.0 -1048.0 -3444.0  1749.0   661.0   \n",
      "102  1.559649e+12  -6170.0  14772.0  -2600.0 -1113.0 -3264.0   629.0   892.0   \n",
      "103  1.559649e+12 -10866.0  14377.0  -7069.0  -235.0 -3075.0   450.0  1119.0   \n",
      "104  1.559649e+12  -5418.0  11945.0  -4631.0  -176.0 -3487.0  1462.0  1303.0   \n",
      "105  1.559649e+12  -4795.0  20091.0  -1385.0  -530.0 -3298.0  1775.0  1154.0   \n",
      "106  1.559649e+12  -5469.0  16492.0  -2704.0  -829.0 -3483.0  2238.0   748.0   \n",
      "107  1.559649e+12  -5235.0  16217.0  -2117.0  -574.0 -4133.0  2111.0   341.0   \n",
      "108  1.559649e+12  -5353.0  15575.0  -7943.0  -740.0 -4818.0  2831.0  -336.0   \n",
      "109  1.559649e+12  -4429.0  14464.0  -8381.0  -285.0 -3997.0  3178.0  -986.0   \n",
      "110  1.559649e+12  -5635.0  16428.0  -7906.0  -183.0 -3966.0  2028.0 -1247.0   \n",
      "111  1.559649e+12  -5270.0  18141.0  -8642.0  -410.0 -4364.0   734.0  -999.0   \n",
      "112  1.559649e+12  -5463.0  17769.0  -7574.0  -319.0 -4323.0   270.0  -404.0   \n",
      "113  1.559649e+12  -4385.0  16043.0  -7985.0  -579.0 -4049.0  1057.0   -19.0   \n",
      "\n",
      "     Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z   Force     Time  \n",
      "78     28.0   227.0  -47.0  162.0  291.0     0.0 -23807.0  \n",
      "79     86.0   249.0  -43.0  162.0  292.0     0.0 -23806.0  \n",
      "80    192.0   273.0  -43.0  163.0  293.0     0.0 -23805.0  \n",
      "81    283.0   403.0  -45.0  159.0  291.0     4.0 -23804.0  \n",
      "82    405.0   599.0  -46.0  162.0  294.0    88.0 -23803.0  \n",
      "83    386.0   386.0  -40.0  157.0  293.0   403.0 -23802.0  \n",
      "84    102.0   253.0  -40.0  157.0  290.0   534.0 -23801.0  \n",
      "85    401.0   326.0  -37.0  158.0  295.0   734.0 -23800.0  \n",
      "86    405.0    63.0  -40.0  163.0  292.0   931.0 -23799.0  \n",
      "87    219.0   -30.0  -40.0  162.0  293.0  1133.0 -23798.0  \n",
      "88    450.0  -320.0  -41.0  158.0  292.0  1235.0 -23797.0  \n",
      "89    302.0  -616.0  -39.0  161.0  291.0  1317.0 -23796.0  \n",
      "90    412.0  -739.0  -37.0  162.0  291.0  1391.0 -23795.0  \n",
      "91     59.0  -726.0  -35.0  164.0  290.0  1434.0 -23794.0  \n",
      "92   -258.0  -256.0  -40.0  158.0  288.0  1499.0 -23793.0  \n",
      "93    -81.0  -403.0  -40.0  167.0  287.0  1438.0 -23792.0  \n",
      "94   -533.0  -350.0  -42.0  167.0  287.0  1423.0 -23791.0  \n",
      "95   -786.0    -7.0  -42.0  166.0  287.0  1396.0 -23790.0  \n",
      "96  -1011.0   731.0  -43.0  170.0  285.0  1407.0 -23789.0  \n",
      "97  -1137.0  1242.0  -44.0  173.0  283.0  1445.0 -23788.0  \n",
      "98   -721.0  1341.0  -43.0  171.0  283.0  1412.0 -23787.0  \n",
      "99   -323.0  1151.0  -44.0  167.0  285.0  1443.0 -23786.0  \n",
      "100   -53.0   829.0  -39.0  171.0  286.0  1461.0 -23785.0  \n",
      "101   509.0   591.0  -43.0  170.0  284.0  1475.0 -23784.0  \n",
      "102   948.0   463.0  -41.0  170.0  287.0  1504.0 -23783.0  \n",
      "103  1278.0   211.0  -40.0  170.0  290.0  1535.0 -23782.0  \n",
      "104  1341.0  -368.0  -37.0  167.0  290.0  1469.0 -23781.0  \n",
      "105   994.0  -645.0  -37.0  166.0  291.0  1255.0 -23780.0  \n",
      "106   477.0  -825.0  -38.0  163.0  291.0  1044.0 -23779.0  \n",
      "107   282.0  -999.0  -40.0  165.0  293.0   794.0 -23778.0  \n",
      "108  -219.0 -1111.0  -38.0  161.0  295.0   554.0 -23777.0  \n",
      "109  -725.0 -1306.0  -38.0  162.0  290.0   236.0 -23776.0  \n",
      "110  -846.0 -1565.0  -39.0  166.0  291.0    49.0 -23775.0  \n",
      "111  -817.0 -1770.0  -39.0  164.0  292.0     0.0 -23774.0  \n",
      "112 -1122.0 -2047.0  -47.0  167.0  289.0     0.0 -23773.0  \n",
      "113 -1537.0 -2300.0  -42.0  163.0  286.0     0.0 -23772.0\n",
      " ...\n",
      "            Millis  Acc1 X   Acc1 Y   Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "62   1.578922e+12 -3556.0  10369.0  -9027.0   124.0 -2479.0  3018.0  -732.0   \n",
      "63   1.578922e+12 -2139.0  10330.0  -9056.0   167.0 -2505.0  2840.0  -964.0   \n",
      "64   1.578922e+12   158.0  10001.0  -7716.0  -426.0 -2333.0  2881.0 -1217.0   \n",
      "65   1.578922e+12  2151.0   9763.0  -8134.0 -1003.0 -2275.0  2797.0 -1516.0   \n",
      "66   1.578922e+12  3380.0   9609.0  -7065.0 -1230.0 -2205.0  2915.0 -1823.0   \n",
      "..            ...     ...      ...      ...     ...     ...     ...     ...   \n",
      "168  1.578922e+12  1955.0  13799.0  -9059.0   472.0 -3471.0   667.0   397.0   \n",
      "169  1.578922e+12  1620.0  17206.0 -11487.0   554.0 -4276.0   455.0   945.0   \n",
      "170  1.578922e+12  1264.0  17143.0  -9569.0  1283.0 -4174.0  1073.0  1554.0   \n",
      "171  1.578922e+12  1504.0  14184.0  -7735.0  1579.0 -3424.0  2238.0  1805.0   \n",
      "172  1.578922e+12   731.0  12692.0  -6991.0   849.0 -3016.0  3073.0  1608.0   \n",
      "\n",
      "     Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z  Force     Time  \n",
      "62   1252.0   594.0   96.0   86.0 -279.0   16.0 -22621.0  \n",
      "63   1929.0   227.0  100.0   90.0 -283.0   17.0 -22620.0  \n",
      "64   2443.0    82.0   95.0   92.0 -291.0   19.0 -22619.0  \n",
      "65   2911.0   319.0   98.0   89.0 -296.0   16.0 -22618.0  \n",
      "66   3612.0   802.0   99.0   90.0 -308.0   35.0 -22617.0  \n",
      "..      ...     ...    ...    ...    ...    ...      ...  \n",
      "168    74.0   547.0  107.0   51.0 -316.0  209.0 -22515.0  \n",
      "169   -10.0   514.0  108.0   52.0 -314.0   14.0 -22514.0  \n",
      "170  -345.0   345.0  109.0   53.0 -314.0   12.0 -22513.0  \n",
      "171  -797.0    -2.0  114.0   54.0 -314.0   15.0 -22512.0  \n",
      "172 -1103.0  -371.0  111.0   56.0 -311.0   14.0 -22511.0  \n",
      "\n",
      "[111 rows x 15 columns]\n",
      "            Millis   Acc1 X   Acc1 Y   Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "68   1.578921e+12  -2459.0  10029.0  -8802.0   193.0 -2360.0  3947.0  -717.0   \n",
      "69   1.578921e+12   -986.0  11497.0  -9360.0  -569.0 -2724.0  3438.0 -1222.0   \n",
      "70   1.578921e+12   -294.0  11844.0  -9224.0 -1105.0 -2789.0  2963.0 -1539.0   \n",
      "71   1.578921e+12    412.0  11670.0  -9180.0  -985.0 -2839.0  2732.0 -1724.0   \n",
      "72   1.578921e+12    404.0  18906.0 -24605.0 -1346.0 -7292.0 -3929.0 -1462.0   \n",
      "73   1.578921e+12   1273.0  14727.0  -8358.0  2520.0 -3471.0   612.0  1096.0   \n",
      "74   1.578921e+12  -1606.0  16095.0  -8806.0  2291.0 -3768.0  5738.0   777.0   \n",
      "75   1.578921e+12   2200.0  15529.0  -8851.0 -1246.0 -3795.0  3399.0  -123.0   \n",
      "76   1.578921e+12  -4639.0  18057.0  -7071.0  1641.0 -4152.0  4231.0  -664.0   \n",
      "77   1.578921e+12  -3678.0  16124.0  -9967.0  1371.0 -4033.0  4060.0 -1501.0   \n",
      "78   1.578921e+12  12089.0  13788.0  -8969.0  2263.0 -3176.0  2566.0 -2255.0   \n",
      "79   1.578921e+12   9421.0  15524.0 -16051.0 -1903.0 -2211.0  1121.0 -2283.0   \n",
      "80   1.578921e+12  12094.0   5387.0 -17642.0 -2558.0 -2124.0  2806.0 -2398.0   \n",
      "81   1.578921e+12    849.0  10754.0 -11402.0   393.0 -2575.0   891.0 -2136.0   \n",
      "82   1.578921e+12   2689.0  12375.0  -7439.0   501.0 -2633.0   509.0 -1392.0   \n",
      "83   1.578921e+12   3755.0   9071.0 -12782.0 -1859.0 -2533.0  1656.0  -782.0   \n",
      "84   1.578921e+12   3416.0  10134.0 -11975.0  -692.0 -2545.0  1176.0  -152.0   \n",
      "85   1.578921e+12   1339.0  13550.0 -10101.0   323.0 -3247.0  3901.0    52.0   \n",
      "86   1.578921e+12   4496.0  14248.0  -9085.0  1489.0 -3259.0  2500.0  -341.0   \n",
      "87   1.578921e+12   2495.0  13108.0  -9057.0   618.0 -3307.0  1859.0  -293.0   \n",
      "88   1.578921e+12   5816.0  13270.0  -8591.0  -616.0 -3218.0  1768.0  -130.0   \n",
      "89   1.578921e+12  17165.0  14294.0  -8402.0 -1413.0 -2681.0  3190.0  -302.0   \n",
      "90   1.578921e+12  -9215.0  17010.0 -11918.0    21.0 -3770.0  3779.0  -773.0   \n",
      "91   1.578921e+12  -1794.0  12397.0  -7219.0  1365.0 -3222.0   769.0  -960.0   \n",
      "92   1.578921e+12    314.0  14171.0 -10170.0 -1130.0 -3166.0  2199.0  -616.0   \n",
      "93   1.578921e+12  -2098.0  12379.0 -10604.0  3009.0 -3077.0  1377.0  -603.0   \n",
      "94   1.578921e+12   2439.0  13613.0  -8419.0   524.0 -3338.0  1469.0  -228.0   \n",
      "95   1.578921e+12   2360.0  13564.0  -8551.0  1238.0 -3318.0  1855.0  -143.0   \n",
      "96   1.578921e+12   2786.0  13594.0  -8455.0  1613.0 -3360.0  1718.0   -18.0   \n",
      "97   1.578921e+12   3066.0  13638.0  -8537.0  1599.0 -3346.0  1931.0    41.0   \n",
      "98   1.578921e+12   3171.0  13810.0  -8545.0  1729.0 -3394.0  2102.0    96.0   \n",
      "99   1.578921e+12   3073.0  14261.0  -8310.0  1647.0 -3484.0  2409.0    36.0   \n",
      "100  1.578921e+12   2595.0  15529.0  -7686.0  1018.0 -3752.0  3021.0  -146.0   \n",
      "101  1.578921e+12   1440.0  16126.0  -6383.0   922.0 -3703.0  3792.0  -616.0   \n",
      "102  1.578921e+12    597.0  14001.0  -9246.0   563.0 -3431.0  1866.0 -1012.0   \n",
      "103  1.578921e+12   1990.0  17606.0 -12885.0  -397.0 -4420.0 -1524.0  -446.0   \n",
      "104  1.578921e+12   2875.0  16901.0 -12582.0    70.0 -4245.0 -2015.0  1237.0   \n",
      "105  1.578921e+12   1991.0  13404.0  -7506.0   982.0 -3171.0  1919.0  2412.0   \n",
      "106  1.578921e+12   1852.0  11687.0  -6112.0   878.0 -2715.0  4323.0  2051.0   \n",
      "107  1.578921e+12   2086.0  12069.0  -7541.0   142.0 -2885.0  3308.0  1222.0   \n",
      "\n",
      "     Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z   Force    Time  \n",
      "68     17.0   754.0   19.0   19.0 -420.0    13.0  2822.0  \n",
      "69    355.0   612.0   23.0   16.0 -422.0    26.0  2823.0  \n",
      "70    830.0   785.0   18.0   16.0 -427.0    24.0  2824.0  \n",
      "71   1086.0  1123.0   24.0   18.0 -430.0    39.0  2825.0  \n",
      "72   1312.0  1369.0   24.0   18.0 -437.0    39.0  2826.0  \n",
      "73    -45.0  1347.0   19.0   12.0 -440.0   847.0  2827.0  \n",
      "74   -166.0   513.0   19.0   16.0 -440.0   707.0  2828.0  \n",
      "75   -273.0   186.0   20.0   17.0 -440.0   927.0  2829.0  \n",
      "76    388.0   217.0   20.0   19.0 -440.0  1026.0  2830.0  \n",
      "77     75.0  -682.0   24.0   13.0 -439.0  1148.0  2831.0  \n",
      "78    207.0 -1712.0   28.0    7.0 -440.0  1299.0  2832.0  \n",
      "79    -77.0 -1758.0   29.0    3.0 -441.0  1391.0  2833.0  \n",
      "80    899.0 -1330.0   31.0   -3.0 -440.0  1462.0  2834.0  \n",
      "81   1407.0  -772.0   32.0   -8.0 -440.0  1512.0  2835.0  \n",
      "82    949.0  -535.0   35.0  -14.0 -442.0  1549.0  2836.0  \n",
      "83    384.0  -303.0   32.0  -16.0 -445.0  1568.0  2837.0  \n",
      "84    703.0   383.0   34.0  -20.0 -443.0  1610.0  2838.0  \n",
      "85    468.0   654.0   33.0  -20.0 -445.0  1607.0  2839.0  \n",
      "86    399.0   599.0   32.0  -23.0 -447.0  1807.0  2840.0  \n",
      "87    233.0   406.0   33.0  -22.0 -444.0  2033.0  2841.0  \n",
      "88    510.0   503.0   32.0  -24.0 -446.0  2194.0  2842.0  \n",
      "89    444.0  1602.0   32.0  -23.0 -447.0  2310.0  2843.0  \n",
      "90    309.0  2642.0   27.0  -31.0 -449.0  2420.0  2844.0  \n",
      "91    879.0  1890.0   23.0  -33.0 -453.0  2606.0  2845.0  \n",
      "92    375.0  1793.0   16.0  -37.0 -453.0  2588.0  2846.0  \n",
      "93    600.0  1407.0   17.0  -40.0 -452.0  2585.0  2847.0  \n",
      "94     -8.0   761.0   15.0  -44.0 -457.0  2581.0  2848.0  \n",
      "95     -5.0   632.0   14.0  -46.0 -457.0  2587.0  2849.0  \n",
      "96    -12.0   389.0   16.0  -49.0 -453.0  2581.0  2850.0  \n",
      "97    -66.0    70.0   15.0  -45.0 -454.0  2562.0  2851.0  \n",
      "98   -120.0  -233.0   18.0  -48.0 -457.0  2503.0  2852.0  \n",
      "99   -267.0  -566.0   16.0  -46.0 -452.0  2290.0  2853.0  \n",
      "100  -261.0  -806.0   14.0  -49.0 -454.0  1634.0  2854.0  \n",
      "101  -317.0 -1007.0   19.0  -47.0 -454.0  1058.0  2855.0  \n",
      "102  -286.0 -1069.0   18.0  -50.0 -454.0   607.0  2856.0  \n",
      "103  -647.0 -1070.0   15.0  -51.0 -453.0   141.0  2857.0  \n",
      "104 -1463.0  -801.0   16.0  -52.0 -450.0    17.0  2858.0  \n",
      "105 -2241.0  -730.0   16.0  -47.0 -447.0    16.0  2859.0  \n",
      "106 -2268.0  -943.0   22.0  -45.0 -441.0    16.0  2860.0  \n",
      "107 -1810.0 -1067.0   21.0  -45.0 -436.0    18.0  2861.0\n",
      "           Millis   Acc1 X   Acc1 Y   Acc1 Z  Acc2 X  Acc2 Y  Acc2 Z  Gyro X  \\\n",
      "47  1.578921e+12   2156.0  11546.0  -9231.0  -225.0 -2792.0  2972.0  -686.0   \n",
      "48  1.578921e+12   1035.0  11303.0  -9158.0  -142.0 -2704.0  2996.0  -901.0   \n",
      "49  1.578921e+12   -372.0  10214.0  -9463.0   166.0 -2467.0  2908.0 -1113.0   \n",
      "50  1.578921e+12  -1403.0   8318.0  -9028.0   742.0 -1999.0  2871.0 -1297.0   \n",
      "51  1.578921e+12 -12701.0  19449.0  -2454.0   353.0 -6589.0 -4941.0  -252.0   \n",
      "52  1.578921e+12   1298.0  14492.0  -9084.0  1665.0 -2989.0  2209.0  1204.0   \n",
      "53  1.578921e+12  -1662.0  15692.0  -9374.0  1105.0 -3394.0  5106.0   837.0   \n",
      "54  1.578921e+12    239.0  11960.0 -11137.0  -757.0 -3127.0  2985.0   201.0   \n",
      "55  1.578921e+12   -323.0  12752.0  -9910.0   -34.0 -3103.0  2121.0   131.0   \n",
      "56  1.578921e+12   -821.0  11602.0 -11810.0  -353.0 -3417.0  2421.0   130.0   \n",
      "57  1.578921e+12  -4458.0   7924.0 -12630.0   262.0 -3122.0  1466.0   300.0   \n",
      "58  1.578921e+12  -1662.0  17985.0  -8097.0  1140.0 -2951.0  1967.0   592.0   \n",
      "59  1.578921e+12   3903.0  18282.0  -7922.0 -1451.0 -3502.0  3005.0   406.0   \n",
      "60  1.578921e+12  -5712.0  13299.0  -9373.0 -2782.0 -3276.0  3776.0   -16.0   \n",
      "61  1.578921e+12   -659.0  11048.0 -11231.0  2755.0 -3368.0  1713.0    64.0   \n",
      "62  1.578921e+12  -2797.0  14340.0  -8083.0  1868.0 -3365.0  2688.0    50.0   \n",
      "63  1.578921e+12   5056.0  15836.0  -8875.0   734.0 -3282.0  3965.0  -485.0   \n",
      "64  1.578921e+12  -1068.0  17002.0  -3231.0 -1031.0 -3385.0  3047.0  -845.0   \n",
      "65  1.578921e+12  -3708.0  14562.0  -6881.0 -1366.0 -3298.0  2437.0 -1108.0   \n",
      "66  1.578921e+12   6534.0  12693.0 -13400.0   382.0 -3493.0  3895.0 -1595.0   \n",
      "67  1.578921e+12   1622.0   9202.0 -13743.0    -4.0 -3483.0  3294.0 -1803.0   \n",
      "68  1.578921e+12   1623.0  12551.0 -10288.0  -525.0 -3265.0  3141.0 -1981.0   \n",
      "69  1.578921e+12   7202.0  14860.0 -10191.0   286.0 -3710.0  3898.0 -2475.0   \n",
      "70  1.578921e+12  -8630.0   1331.0 -22144.0  -215.0 -1856.0 -1415.0 -1736.0   \n",
      "71  1.578921e+12  -9387.0  10187.0  -9311.0 -2549.0 -1866.0   306.0  -779.0   \n",
      "72  1.578921e+12  -5723.0  12301.0  -8579.0  -650.0 -2473.0  3278.0  -807.0   \n",
      "73  1.578921e+12  -6078.0  13007.0  -8988.0  -937.0 -2808.0  1805.0  -608.0   \n",
      "74  1.578921e+12  -7183.0  11660.0  -9794.0  1571.0 -3152.0  2773.0  -597.0   \n",
      "75  1.578921e+12  -9645.0  10989.0 -11352.0  1254.0 -2527.0  1585.0  -425.0   \n",
      "76  1.578921e+12  -2943.0   5962.0 -14323.0  1288.0 -1836.0   396.0   372.0   \n",
      "77  1.578921e+12    790.0   3977.0  -8445.0   746.0 -2101.0   984.0   970.0   \n",
      "78  1.578921e+12   7857.0  23657.0  -5774.0   469.0 -2770.0  2631.0   967.0   \n",
      "79  1.578921e+12   9327.0  17081.0  -8607.0 -1200.0 -3658.0  2407.0   755.0   \n",
      "80  1.578921e+12   2159.0  16886.0  -5210.0   111.0 -3943.0  2825.0   436.0   \n",
      "81  1.578921e+12   2988.0  16446.0  -6559.0  -957.0 -4076.0  2124.0   188.0   \n",
      "82  1.578921e+12   5641.0  14913.0  -9760.0  -780.0 -3796.0  2238.0    76.0   \n",
      "83  1.578921e+12   -698.0  11794.0 -11670.0 -3803.0 -4056.0  2378.0    32.0   \n",
      "84  1.578921e+12  -4934.0  16273.0 -10479.0 -4575.0 -4621.0  2111.0   216.0   \n",
      "85  1.578921e+12  -4505.0  18583.0 -12726.0  -154.0 -4598.0   776.0   843.0   \n",
      "86  1.578921e+12  -4417.0  15265.0 -11255.0  3278.0 -4078.0  1005.0  1492.0   \n",
      "87  1.578921e+12   2000.0  12847.0  -8800.0   283.0 -3445.0  2356.0  1680.0   \n",
      "88  1.578921e+12   1215.0  12439.0  -9199.0 -1526.0 -2938.0  2622.0  1571.0   \n",
      "\n",
      "    Gyro Y  Gyro Z  Mag X  Mag Y  Mag Z   Force     Time  \n",
      "47  -665.0   748.0  206.0 -203.0 -241.0    17.0  13577.0  \n",
      "48  -625.0   966.0  205.0 -209.0 -241.0    17.0  13578.0  \n",
      "49  -517.0  1013.0  208.0 -217.0 -239.0    18.0  13579.0  \n",
      "50  -353.0   821.0  206.0 -221.0 -238.0     5.0  13580.0  \n",
      "51  -493.0   434.0  205.0 -231.0 -237.0    21.0  13581.0  \n",
      "52 -1129.0  -159.0  210.0 -235.0 -239.0   656.0  13582.0  \n",
      "53 -1294.0  -578.0  213.0 -237.0 -238.0   729.0  13583.0  \n",
      "54 -1091.0  -518.0  212.0 -238.0 -239.0  1036.0  13584.0  \n",
      "55  -912.0  -419.0  215.0 -234.0 -236.0  1253.0  13585.0  \n",
      "56  -933.0  -373.0  217.0 -230.0 -236.0  1465.0  13586.0  \n",
      "57  -279.0  -621.0  218.0 -228.0 -233.0  1662.0  13587.0  \n",
      "58  -410.0 -1228.0  222.0 -228.0 -234.0  1707.0  13588.0  \n",
      "59  -452.0  -875.0  221.0 -220.0 -234.0  1730.0  13589.0  \n",
      "60   986.0   209.0  228.0 -211.0 -234.0  1897.0  13590.0  \n",
      "61    86.0  -555.0  224.0 -212.0 -232.0  1890.0  13591.0  \n",
      "62  -264.0 -1232.0  222.0 -212.0 -233.0  1885.0  13592.0  \n",
      "63 -1133.0 -1497.0  226.0 -197.0 -223.0  1856.0  13593.0  \n",
      "64  -819.0 -1079.0  225.0 -193.0 -219.0  1833.0  13594.0  \n",
      "65  -791.0  -545.0  227.0 -187.0 -208.0  1789.0  13595.0  \n",
      "66 -1323.0  -649.0  221.0 -185.0 -200.0  1746.0  13596.0  \n",
      "67 -1423.0  -355.0  217.0 -186.0 -193.0  1752.0  13597.0  \n",
      "68 -1146.0    58.0  215.0 -191.0 -185.0  1766.0  13598.0  \n",
      "69   740.0    88.0  213.0 -195.0 -184.0  1799.0  13599.0  \n",
      "70   339.0   131.0  211.0 -200.0 -179.0  1780.0  13600.0  \n",
      "71  -267.0   371.0  207.0 -197.0 -176.0  1759.0  13601.0  \n",
      "72   786.0   277.0  206.0 -200.0 -176.0  1743.0  13602.0  \n",
      "73   326.0    26.0  208.0 -200.0 -174.0  1732.0  13603.0  \n",
      "74  -105.0  -857.0  204.0 -190.0 -173.0  1735.0  13604.0  \n",
      "75  -390.0 -1991.0  207.0 -178.0 -171.0  1824.0  13605.0  \n",
      "76  -899.0 -2674.0  204.0 -170.0 -165.0  1788.0  13606.0  \n",
      "77  -559.0 -2939.0  209.0 -154.0 -167.0  1737.0  13607.0  \n",
      "78   166.0 -2975.0  213.0 -141.0 -166.0  1637.0  13608.0  \n",
      "79   520.0 -2165.0  217.0 -133.0 -171.0  1547.0  13609.0  \n",
      "80   376.0 -1654.0  221.0 -126.0 -171.0  1443.0  13610.0  \n",
      "81   110.0 -1270.0  222.0 -122.0 -174.0  1199.0  13611.0  \n",
      "82   298.0  -664.0  224.0 -123.0 -173.0   862.0  13612.0  \n",
      "83  -275.0   740.0  226.0 -125.0 -178.0   400.0  13613.0  \n",
      "84    99.0  2207.0  222.0 -138.0 -178.0   127.0  13614.0  \n",
      "85   327.0  2197.0  217.0 -148.0 -180.0    15.0  13615.0  \n",
      "86  -874.0   988.0  213.0 -153.0 -181.0    14.0  13616.0  \n",
      "87 -1223.0   445.0  202.0 -157.0 -181.0    17.0  13617.0  \n",
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    {
     "data": {
      "text/plain": [
       "count    13102.000000\n",
       "mean        61.169058\n",
       "std         30.698514\n",
       "min         10.000000\n",
       "50%         57.000000\n",
       "95%        102.000000\n",
       "96%        107.000000\n",
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       "98%        127.000000\n",
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       "max       1522.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print (XX)\n",
    "X_len = np.asarray(list(map(len, XX)))\n",
    "l = []\n",
    "sq_xlen = pd.Series(X_len)\n",
    "ptiles = [x*0.01 for x in range(100)]\n",
    "for i in ptiles:\n",
    "    l.append(sq_xlen.quantile(i))\n",
    "    print(l)\n",
    "plt.plot(l, ptiles)\n",
    "sq_xlen.describe(percentiles=[x*0.01 for x in range(95,100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a8beb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data):\n",
    "    fig, axs = plt.subplots(4, 3, figsize=(3*3, 3*4))\n",
    "    t = data['Millis']\n",
    "    axs[0][0].plot(t, data['Acc1 X'])\n",
    "    axs[0][1].plot(t, data['Acc1 Y'])\n",
    "    axs[0][2].plot(t, data['Acc1 Z'])\n",
    "    axs[1][0].plot(t, data['Acc2 X'])\n",
    "    axs[1][1].plot(t, data['Acc2 Y'])\n",
    "    axs[1][2].plot(t, data['Acc2 Z'])\n",
    "    axs[2][0].plot(t, data['Gyro X'])\n",
    "    axs[2][1].plot(t, data['Gyro Y'])\n",
    "    axs[2][2].plot(t, data['Gyro Z'])\n",
    "    axs[3][0].plot(t, data['Mag X'])\n",
    "    axs[3][1].plot(t, data['Mag Y'])\n",
    "    axs[3][2].plot(t, data['Mag Z'])\n",
    "\n",
    "    for a in axs:\n",
    "        for b in a:\n",
    "            b.plot(t, data['Force'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0dcd7db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12974,), (72, 15))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_p = 0.99\n",
    "threshold = int(sq_xlen.quantile(threshold_p))\n",
    "len_mask = np.where(X_len <= threshold)\n",
    "\n",
    "X_filter = XX[len_mask]\n",
    "y_filter = y[len_mask]\n",
    "\n",
    "X_filter.shape, X_filter[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a0e96ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "a = [x.drop(labels='Millis', axis=1) for x in X_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2d8e64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_filter = pad_sequences(X_filter, dtype=float, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77c502cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data):\n",
    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
    "    axs[0][0].plot(data[0])\n",
    "    axs[0][1].plot(data[1])\n",
    "    axs[0][2].plot(data[2])\n",
    "    axs[1][0].plot(data[3])\n",
    "    axs[1][1].plot(data[4])\n",
    "    axs[1][2].plot(data[5])\n",
    "    axs[2][0].plot(data[6])\n",
    "    axs[2][1].plot(data[7])\n",
    "    axs[2][2].plot(data[8])\n",
    "    axs[3][0].plot(data[9])\n",
    "    axs[3][1].plot(data[10])\n",
    "    axs[3][2].plot(data[11])\n",
    "    axs[4][0].plot(data[12])\n",
    "    axs[4][1].plot(data[13])\n",
    "\n",
    "#     for a in axs:\n",
    "#         for b in a:\n",
    "#             b.plot(t, data['Force'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7235d56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
    "import tensorflow as tf\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "\n",
    "yt_filter = lb.fit_transform(y_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc983f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10379, 156, 15)\n",
      "(2595, 156, 15)\n",
      "(10379, 26)\n",
      "(2595, 26)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_filter, yt_filter, test_size=0.2, random_state=177013)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "plot_data(X_filter[0].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59d6ebca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 2340)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 2340)              9360      \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1560)              3651960   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 750)               1170750   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 300)               225300    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 156)               46956     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 26)                4082      \n",
      "=================================================================\n",
      "Total params: 5,108,408\n",
      "Trainable params: 5,103,728\n",
      "Non-trainable params: 4,680\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Flatten(input_shape=X_filter[0].shape))\n",
    "\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(1560, activation='relu'))\n",
    "\n",
    "model.add(Dense(750, activation='relu'))\n",
    "\n",
    "model.add(Dense(300, activation='relu'))\n",
    "\n",
    "model.add(Dense(156, activation='relu'))\n",
    "\n",
    "model.add(Dense(26, activation='softmax'))\n",
    "\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "    loss=\"categorical_crossentropy\", \n",
    "    metrics=[\"acc\"],\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4032731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "163/163 [==============================] - 2s 4ms/step - loss: 2.8054 - acc: 0.1758\n",
      "Epoch 2/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 1.6546 - acc: 0.4853\n",
      "Epoch 3/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 1.2309 - acc: 0.6210\n",
      "Epoch 4/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.9498 - acc: 0.6970\n",
      "Epoch 5/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.7573 - acc: 0.7576\n",
      "Epoch 6/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.6517 - acc: 0.7947\n",
      "Epoch 7/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.5346 - acc: 0.8296\n",
      "Epoch 8/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.4961 - acc: 0.8433\n",
      "Epoch 9/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.4598 - acc: 0.8598\n",
      "Epoch 10/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.4350 - acc: 0.8617\n",
      "Epoch 11/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.3451 - acc: 0.8938\n",
      "Epoch 12/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.2625 - acc: 0.9171\n",
      "Epoch 13/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.3213 - acc: 0.9061\n",
      "Epoch 14/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.2438 - acc: 0.9214\n",
      "Epoch 15/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.2206 - acc: 0.9301\n",
      "Epoch 16/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.2177 - acc: 0.9317\n",
      "Epoch 17/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.2512 - acc: 0.9239\n",
      "Epoch 18/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.1521 - acc: 0.9502\n",
      "Epoch 19/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.1574 - acc: 0.9545\n",
      "Epoch 20/20\n",
      "163/163 [==============================] - 1s 3ms/step - loss: 0.1940 - acc: 0.9412\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fa1a80ab070>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, \n",
    "          epochs=20,\n",
    "          batch_size=64,\n",
    "          shuffle=True,\n",
    "          verbose=1\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e20cb3cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate on test data\n",
      "82/82 [==============================] - 0s 2ms/step - loss: 1.4639 - acc: 0.7356\n",
      "test loss, test acc: [1.4639196395874023, 0.7356454730033875]\n",
      "Generate predictions for 3 samples\n",
      "predictions shape: (3, 26)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array(['N', 'U', 'I'], dtype='<U1'), array(['N', 'U', 'I'], dtype='<U1'))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on the test data using `evaluate`\n",
    "print(\"Evaluate on test data\")\n",
    "results = model.evaluate(X_test, y_test, batch_size=32)\n",
    "print(\"test loss, test acc:\", results)\n",
    "\n",
    "# Generate predictions (probabilities -- the output of the last layer)\n",
    "# on new data using `predict`\n",
    "print(\"Generate predictions for 3 samples\")\n",
    "predictions = model.predict(X_test[:3])\n",
    "print(\"predictions shape:\", predictions.shape)\n",
    "\n",
    "lb.inverse_transform(y_test[:3]), lb.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f88c9c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "exit()"
   ]
  }
 ],
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