Batch_Generator_Task-04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fae018f",
   "metadata": {},
   "source": [
    "### Import the necessary modules\n",
    "\n",
    "We start with importing the necessary modules required for the tutorial. Here we imported ROOT and TMVA(Toolkit for Multivariate Data Analysis). If you want to know more about TMVA, you can refer the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "160f77fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT\n",
    "from ROOT import TMVA \n",
    "import os\n",
    "from array import array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e861e83c",
   "metadata": {},
   "source": [
    "### Setting up TMVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d80f1d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT.TMVA.Tools.Instance()\n",
    "TMVA.PyMethodBase.PyInitialize()\n",
    "\n",
    "## For PYMVA methods\n",
    "TMVA.PyMethodBase.PyInitialize();\n",
    "\n",
    "\n",
    "outputFile = ROOT.TFile.Open(\"BatchdataOutput.root\", \"RECREATE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c230a2",
   "metadata": {},
   "source": [
    "### Define the input datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "66104597",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputFileName = \"images_data.root\"\n",
    "inputFile = ROOT.TFile.Open(inputFileName)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f407c8",
   "metadata": {},
   "source": [
    "### Setting up the Signal and background Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e606645d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************************************************************\n",
      "*Tree    :sig_tree  : signal_tree                                            *\n",
      "*Entries :    10000 : Total =         2601382 bytes  File  Size =    2572423 *\n",
      "*        :          : Tree compression factor =   1.00                       *\n",
      "******************************************************************************\n",
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      "*Br    2 :var2      : var2/F                                                 *\n",
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    }
   ],
   "source": [
    "signalTree     = inputFile.Get(\"sig_tree\")\n",
    "backgroundTree = inputFile.Get(\"bkg_tree\")\n",
    "\n",
    "signalTree.Print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d152f9b5",
   "metadata": {},
   "source": [
    "### Extracting the data into a RDataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91c7bb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ROOT.RDataFrame(\"sig_tree\", \"images_data.root\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c40787",
   "metadata": {},
   "source": [
    "### Use of RDataFrame.Range to select a chunk (batch of data)\n",
    "Here we are considering a batch of 100 for each branch of signal Tree (Total there are 10000 entries in each branch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "96ee9319",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.Range(0,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d8d82f",
   "metadata": {},
   "source": [
    "### Use of AsNumpy to extract the data from the dataframe into Numpy dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9b2a239d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'var0': ndarray([-1.33602500e+00, -4.09288311e+00,  1.96556091e+00,\n",
      "          3.89667678e+00,  5.52924681e+00,  1.16362429e+00,\n",
      "          2.64346790e+00,  2.47144938e+00, -4.85097289e-01,\n",
      "          2.89741921e+00,  4.73280478e+00,  3.01877904e+00,\n",
      "          3.12367535e+00, -4.48586035e+00,  3.51635313e+00,\n",
      "          5.71682882e+00,  4.31257057e+00,  1.01603842e+01,\n",
      "          2.73509383e+00,  6.47822976e-01,  2.66566372e+00,\n",
      "          1.39952393e+01,  3.16755414e+00,  1.00602722e+01,\n",
      "          4.35392761e+00,  9.60262775e+00,  5.18892527e-01,\n",
      "          1.23200884e+01,  5.39695919e-02,  1.45863571e+01,\n",
      "          6.90780067e+00,  1.40009069e+00, -2.25628781e+00,\n",
      "          2.57242751e+00,  3.16384578e+00,  2.44943380e+00,\n",
      "         -1.92530617e-01,  6.89263868e+00,  3.01004720e+00,\n",
      "         -2.65800071e+00,  5.18959045e+00,  3.72071290e+00,\n",
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      "          0.5386625 ,  8.937819  ,  5.2293773 ,  3.6668882 , -0.34742337,\n",
      "          4.007183  ,  2.4195082 ,  6.5238614 , -2.0297444 , 10.797482  ,\n",
      "          4.433654  ,  5.1760597 , -1.0870094 ,  5.443906  , -1.9362038 ,\n",
      "          6.89844   ,  0.98148245,  1.6902308 , -1.5609577 ,  7.8460584 ,\n",
      "         -0.0320105 ,  2.3186631 ,  2.7212493 ,  4.8411407 ,  3.3749027 ,\n",
      "          1.8639872 ,  5.328671  ,  8.065269  ,  9.062336  , -1.8882672 ,\n",
      "          9.954922  ,  0.20660207, 12.664468  ,  4.2156305 , -0.69962496,\n",
      "         12.322704  ,  8.444481  ,  6.6476407 ,  0.6614095 , 10.148309  ,\n",
      "          9.935019  , -1.8965873 ,  6.7750897 ,  7.185781  ,  2.165942  ,\n",
      "         -2.3466928 ,  3.1386673 ,  0.9539083 ,  9.443414  ,  5.336501  ,\n",
      "          6.418779  ,  1.6574222 , -1.5947658 ,  1.9445434 , 10.749699  ,\n",
      "          1.9888995 ,  5.3236346 ,  7.3754983 ,  4.5600443 ,  3.5752614 ,\n",
      "          5.287681  ,  8.865847  , -1.0691007 ,  8.022894  , -0.5037178 ,\n",
      "          5.7777615 , -3.787546  , -6.918577  , 11.655182  ,  4.929537  ,\n",
      "          8.694362  ,  5.475015  ,  4.219879  ,  5.888823  ,  7.014098  ,\n",
      "         -0.35337   ,  7.7107773 , -1.3497161 , -2.8597112 ,  0.33545634,\n",
      "          5.4363184 , -0.43299812,  0.3935349 ,  4.4715953 ,  1.4462457 ],\n",
      "        dtype=float32), 'var60': ndarray([15.436429  ,  9.268894  ,  2.3146772 ,  1.1114541 , 11.39684   ,\n",
      "          6.519977  ,  7.223328  ,  0.34981328,  7.2884035 ,  2.4883926 ,\n",
      "          8.041922  , 10.394989  , -0.1802347 , 13.2519655 ,  3.189981  ,\n",
      "          4.896209  ,  6.9198422 , -1.3426815 ,  3.4052105 ,  3.6151702 ,\n",
      "          2.0254982 ,  7.019965  ,  7.9437532 ,  5.6853433 , 12.365249  ,\n",
      "          2.371799  , 17.744196  , -0.93830305, 13.642285  ,  4.80065   ,\n",
      "         -2.8931699 ,  7.936455  ,  7.5865293 ,  4.0848136 ,  6.436248  ,\n",
      "         12.01267   , 15.689127  ,  8.9161    , 12.5543165 , 12.860755  ,\n",
      "          9.752504  , 13.394672  ,  1.3266524 ,  9.621723  , 10.834963  ,\n",
      "          5.0053163 ,  9.720461  ,  6.8629346 ,  3.9816625 , 12.080554  ,\n",
      "          5.9457927 , 14.773674  ,  9.689384  , 10.074113  ,  6.7664137 ,\n",
      "          5.7508736 , 17.908285  ,  3.1320553 ,  7.9812937 ,  7.322514  ,\n",
      "          5.336592  , 11.409703  ,  4.8168526 ,  9.037175  , 17.019894  ,\n",
      "          9.970308  ,  3.28731   , 13.070816  , 10.417643  ,  8.374656  ,\n",
      "         13.090572  , 10.313902  ,  9.500452  ,  2.9745777 ,  0.20474899,\n",
      "          2.6784477 ,  4.948782  , 14.995966  ,  4.481029  ,  6.173233  ,\n",
      "          2.888967  , -5.77984   ,  8.745662  ,  6.8882747 ,  8.883075  ,\n",
      "          1.3519217 ,  2.125023  ,  9.205697  ,  7.812046  ,  3.5470324 ,\n",
      "         -0.7986566 , 12.775872  , 10.51967   ,  5.2552814 , 11.545071  ,\n",
      "          2.6502461 ,  8.5608635 ,  8.061635  , 13.821352  ,  8.379011  ],\n",
      "        dtype=float32), 'var61': ndarray([14.009829  ,  7.8593807 ,  1.5066624 ,  5.4400363 ,  2.1784582 ,\n",
      "         11.037003  ,  3.8179739 ,  0.7611648 ,  5.434688  ,  2.5502462 ,\n",
      "          1.1875238 , 10.778647  ,  1.8485832 ,  9.261728  ,  4.986491  ,\n",
      "          6.4663405 ,  5.0957866 ,  4.5074134 ,  8.571215  ,  7.564327  ,\n",
      "          1.0297843 , -3.1485555 , 11.89893   , 13.661438  ,  9.80046   ,\n",
      "          4.307857  ,  5.263019  , -3.8519263 , 13.827135  ,  4.3899865 ,\n",
      "         -0.08003512,  6.2231345 , 14.461805  ,  5.9116697 ,  5.453703  ,\n",
      "          7.4002137 , 13.29208   ,  0.7391841 ,  9.706541  , 11.285454  ,\n",
      "          2.100898  ,  6.9208107 , 10.833453  ,  9.434606  , -2.085561  ,\n",
      "          5.158596  , 12.132984  , -0.05072197, -1.6392926 ,  0.04892471,\n",
      "          6.8555207 ,  1.5547806 ,  5.526166  ,  5.536096  , -2.799482  ,\n",
      "          4.8852153 , 11.7061    ,  3.1210234 ,  1.9417411 ,  4.479533  ,\n",
      "          5.421451  ,  4.0027575 ,  4.5539794 , 12.248272  , 10.691067  ,\n",
      "         14.687545  ,  7.8985786 ,  2.9510448 ,  6.5275955 ,  2.646234  ,\n",
      "         -0.77141637, 19.966969  ,  4.622608  ,  1.7053051 , -4.2019124 ,\n",
      "         12.973988  ,  8.111324  , 12.90243   ,  5.4076447 , 12.286547  ,\n",
      "          8.079803  ,  1.8468567 , 11.252567  ,  7.1256156 , 12.678133  ,\n",
      "          5.46361   ,  5.2576175 ,  7.4399743 ,  0.92756104,  1.4687203 ,\n",
      "         -1.6767122 ,  2.6676252 ,  9.803901  ,  2.4968505 , 13.161783  ,\n",
      "          8.402061  ,  4.641445  ,  7.293891  ,  7.797103  ,  4.0031276 ],\n",
      "        dtype=float32), 'var62': ndarray([ 7.3402386e+00, -2.3586526e+00,  2.8096406e+00,  4.1903882e+00,\n",
      "          1.2631989e+00, -1.4915495e+00,  2.0450842e+00,  8.9384995e+00,\n",
      "         -9.6608275e-01,  2.7410855e+00, -2.2481248e+00,  8.9756737e+00,\n",
      "          1.3543673e+00,  8.6977444e+00,  5.9360213e+00, -6.2731080e+00,\n",
      "          4.8074937e+00,  2.0230107e+00,  8.8565245e+00, -6.4379120e-01,\n",
      "          3.6768949e+00,  1.1831640e+00,  7.5492659e+00,  1.4465284e+00,\n",
      "         -3.6237711e-01,  3.2561731e+00,  1.0438352e+01,  3.0170643e+00,\n",
      "          4.9030800e+00,  4.6576285e+00, -5.8351141e-01,  2.6768143e+00,\n",
      "          4.2460990e+00,  4.6731973e+00,  2.4970169e+00,  1.3072253e+01,\n",
      "          8.4573689e+00, -2.6869202e+00,  7.1266136e+00,  1.0630117e+01,\n",
      "          3.0621627e-01,  8.8691378e+00, -1.1586267e+00,  5.0154099e+00,\n",
      "          3.5414793e+00,  1.2676976e+00,  5.5514989e+00,  2.7794201e+00,\n",
      "          9.5403500e+00,  5.3561516e+00,  3.5860219e+00,  2.7286265e+00,\n",
      "         -1.5235987e+00, -2.5602245e+00, -5.2270323e-01,  1.1587778e+01,\n",
      "          4.6043506e+00,  2.1365061e+00,  4.8221321e+00,  1.8070974e+00,\n",
      "          2.7254183e+00,  5.8296700e+00,  8.4157305e+00,  6.2974038e+00,\n",
      "          3.2128439e+00,  1.3071736e+01,  1.0184519e+01,  3.3857448e+00,\n",
      "          1.2548492e+00,  4.7627015e+00, -1.6382858e+00,  8.3040237e+00,\n",
      "          1.7714348e+00,  4.0148559e+00,  4.0161438e+00,  4.2049208e+00,\n",
      "         -6.5161473e-01,  3.1501956e+00,  6.6012254e+00,  3.4477429e+00,\n",
      "          4.0986618e-01,  2.9471786e+00,  4.9320226e+00,  6.6518745e+00,\n",
      "         -5.8319122e-03, -1.2940590e+00,  3.1662641e+00,  5.9913540e+00,\n",
      "         -3.7942069e+00,  5.6925030e+00, -2.1407931e+00, -2.6702037e-01,\n",
      "          7.0829554e+00,  2.8568425e+00,  1.7788148e+00,  4.8951259e+00,\n",
      "         -1.8390276e+00,  4.9365168e+00,  6.2834868e+00,  5.9976602e+00],\n",
      "        dtype=float32), 'var63': ndarray([11.776831  ,  4.457062  ,  7.105893  ,  3.3314452 , -2.9659853 ,\n",
      "         -1.9542532 ,  5.974733  ,  3.448907  ,  0.97731894, -5.515773  ,\n",
      "          2.6329374 ,  5.532877  ,  1.032427  ,  3.298695  ,  4.5495753 ,\n",
      "         -5.417611  , -1.2853824 ,  2.7953777 ,  2.6578586 , -2.8124764 ,\n",
      "         -2.299124  , -0.8558312 ,  3.7590444 ,  2.743489  , -1.4003446 ,\n",
      "          0.09159054, -0.80668473,  0.17634447,  1.1486367 ,  1.2403947 ,\n",
      "         -1.5217235 , -0.23019189,  5.708383  ,  3.2790637 ,  3.3973005 ,\n",
      "         13.4294815 ,  2.8884127 , -1.0367528 ,  2.1160748 ,  4.737776  ,\n",
      "         -0.66434807, 10.3560505 ,  1.8475975 , -0.7932218 , -0.37389383,\n",
      "          0.1313663 ,  5.919147  ,  4.6777897 ,  0.01715809,  4.894583  ,\n",
      "          0.82200295, -2.2361166 ,  5.934931  ,  5.4719443 ,  4.124365  ,\n",
      "          2.2379599 , 10.869823  , -4.221881  ,  0.6524362 ,  0.83598775,\n",
      "          5.7135873 ,  6.7941246 ,  5.5018706 , 10.287429  ,  5.347937  ,\n",
      "          1.1871407 ,  7.592323  , -7.8341455 , -1.6891419 , -1.8830174 ,\n",
      "         -0.652319  ,  7.557936  ,  3.0974646 ,  5.9296913 , 13.15926   ,\n",
      "          6.5460806 ,  1.9338148 ,  1.8803229 , -2.764281  ,  9.716088  ,\n",
      "          1.3826873 ,  0.25743178,  2.5692406 ,  2.1687515 ,  2.0216584 ,\n",
      "         -0.7652899 , -1.32543   ,  1.8097073 ,  5.4105387 ,  8.412412  ,\n",
      "          2.1443725 ,  2.3501594 ,  9.200739  , -0.9630503 ,  4.1275535 ,\n",
      "          2.9220917 ,  0.5553367 , -1.3468401 ,  3.5990021 ,  5.1870656 ],\n",
      "        dtype=float32), 'var7': ndarray([-3.8706129 ,  1.7222944 , 11.104798  ,  8.115085  , -3.678604  ,\n",
      "         -2.7019355 , -1.0410215 ,  3.216709  ,  4.4652605 , -0.895703  ,\n",
      "          3.0602262 ,  5.3117704 ,  2.8941827 ,  3.1147153 , 11.018576  ,\n",
      "          3.0916312 ,  2.5746949 ,  1.02875   ,  4.2495008 , -4.4233227 ,\n",
      "         -1.4591769 , -2.2054613 , -0.965844  ,  5.5204506 ,  0.7422531 ,\n",
      "          5.4654136 ,  3.6337166 , -4.0532947 ,  0.12862986,  1.6018144 ,\n",
      "         -3.3703377 ,  4.8883696 ,  7.3663554 , -1.1922121 ,  1.9770063 ,\n",
      "         10.763402  ,  2.433932  ,  5.7484374 ,  1.0264055 , 12.454345  ,\n",
      "          2.0044308 , -2.021657  ,  0.48334822,  4.0337667 ,  6.382395  ,\n",
      "          4.651493  , -2.5640583 , -2.8908994 ,  1.2195828 ,  2.1503956 ,\n",
      "          3.4336505 ,  8.010502  ,  2.1601696 ,  1.5743265 , 12.630704  ,\n",
      "          3.4959235 , -3.9170332 ,  1.4714364 ,  8.137321  ,  3.2443914 ,\n",
      "          1.0146037 ,  1.8207242 ,  2.0556822 ,  0.8626197 , -1.7522597 ,\n",
      "         -2.0441563 ,  8.921143  , -0.29195404,  8.312192  , 11.273935  ,\n",
      "          2.0416908 ,  3.5036013 ,  4.311276  , -1.0765638 ,  8.117626  ,\n",
      "          3.7125082 , 10.741019  ,  3.853403  ,  7.8241925 ,  2.8213737 ,\n",
      "         -2.098398  ,  5.1615825 , -2.8831542 ,  3.088076  , -1.134341  ,\n",
      "          5.8232    , -0.8272207 , -3.1250277 ,  8.738219  ,  1.3042839 ,\n",
      "         -0.5401917 , -0.93947315,  4.7066636 ,  7.384037  , -1.4465046 ,\n",
      "          0.4051541 , -0.9977582 , -2.9287522 ,  0.8441311 ,  0.94262445],\n",
      "        dtype=float32), 'var8': ndarray([ 3.5277545 ,  5.1762023 ,  6.3105745 , -2.1527226 ,  6.3409805 ,\n",
      "          7.4722776 ,  2.7854292 ,  2.356114  ,  2.5652447 , -0.98682   ,\n",
      "          5.565075  ,  2.9794705 ,  5.3649573 , -0.23960173,  2.3705237 ,\n",
      "         12.231049  ,  9.374529  ,  7.308846  ,  7.987199  ,  8.471669  ,\n",
      "         12.620919  ,  0.9395782 , -0.11713606,  9.119242  ,  9.61665   ,\n",
      "         14.453317  ,  2.1190395 ,  7.669835  ,  1.4022301 ,  6.539013  ,\n",
      "          8.96227   , -3.1711667 , -1.5182697 , 10.4376545 , -0.46228653,\n",
      "         -0.59757835,  1.7518762 ,  5.321137  ,  4.856264  ,  3.4153345 ,\n",
      "         -0.762214  ,  0.32598385,  5.8384852 ,  0.95796347,  5.0500183 ,\n",
      "         10.164502  ,  6.175001  ,  4.5609736 ,  5.4498124 ,  3.385318  ,\n",
      "          7.774784  ,  0.0352103 , 12.633614  ,  3.6238377 ,  6.6707253 ,\n",
      "          0.0598757 ,  1.4208997 , -3.9107988 ,  8.3117075 ,  2.870352  ,\n",
      "          9.299902  ,  1.2172247 ,  2.4966354 , -4.7530007 ,  1.5899401 ,\n",
      "          0.48879272,  4.160146  ,  9.692989  ,  2.6680825 ,  2.7132227 ,\n",
      "          1.5447147 ,  4.056847  ,  3.2255685 ,  4.226393  ,  0.25698048,\n",
      "         -0.56106895,  1.011786  ,  6.401852  ,  2.337875  ,  1.7232426 ,\n",
      "         -4.935454  ,  4.404043  , -1.1084725 ,  5.763113  , 10.03216   ,\n",
      "          1.763566  , 10.500269  , -0.6689196 ,  4.584959  ,  3.9896464 ,\n",
      "          5.4912906 ,  8.473221  , -0.78870344,  7.770946  , -1.9524641 ,\n",
      "          2.244284  , 14.1062355 ,  2.496738  , -4.3461485 ,  1.6584157 ],\n",
      "        dtype=float32), 'var9': ndarray([ 1.0955719 ,  6.0525527 ,  6.063516  ,  2.577957  , 18.353664  ,\n",
      "          5.3339257 ,  5.3836017 ,  3.312161  ,  3.013343  , 10.258756  ,\n",
      "          9.211502  ,  4.175771  , -0.8854961 , -5.144957  ,  3.7229621 ,\n",
      "         13.778134  , 14.607194  , 14.781036  ,  4.9331284 , 10.995706  ,\n",
      "         15.234086  ,  6.834692  ,  5.9980288 , 14.690919  ,  8.570616  ,\n",
      "         16.870743  ,  3.8948975 , 19.348242  ,  7.962297  , 13.36349   ,\n",
      "         16.582493  ,  4.865073  ,  5.641529  , 15.5664015 ,  5.747851  ,\n",
      "          3.9235988 ,  0.44625744,  8.923414  ,  2.7274773 , -1.5424113 ,\n",
      "          7.5507603 , -1.0606674 ,  8.295086  ,  2.9873915 ,  6.8703437 ,\n",
      "          7.40263   , 11.447325  ,  9.772659  ,  3.46665   , 11.229876  ,\n",
      "         10.679466  ,  4.2913246 , 14.666542  ,  4.272879  ,  3.4846275 ,\n",
      "          0.72545105,  1.3013228 ,  0.9322011 , 10.518837  ,  8.280435  ,\n",
      "         10.140101  ,  1.8827375 ,  8.185017  ,  0.805833  ,  0.2785677 ,\n",
      "          3.3408215 , -0.1735302 ,  5.164084  ,  7.0672154 ,  2.7378612 ,\n",
      "         10.881442  ,  5.7798033 ,  4.52181   ,  4.574581  ,  1.4770907 ,\n",
      "          6.0671706 , 13.961192  ,  3.968545  ,  3.3128345 , -0.45598257,\n",
      "          4.5879164 , 11.913872  ,  5.3307014 ,  0.7300546 ,  4.761807  ,\n",
      "         10.074269  , 23.491642  , 10.809     ,  7.9728494 , -2.1066437 ,\n",
      "         10.323015  ,  3.6886134 ,  8.726615  , 13.829346  , -0.5517488 ,\n",
      "         13.964094  , 13.478389  , -0.19227293,  1.9838908 ,  1.111334  ],\n",
      "        dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "# Take all columns\n",
    "npy1 = df1.AsNumpy()\n",
    "print(npy1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11974785",
   "metadata": {},
   "source": [
    "Extracting the name of each branch in an array which are the keys of Dictionary npy1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "df9c5fd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "['var0', 'var1', 'var10', 'var11', 'var12', 'var13', 'var14', 'var15', 'var16', 'var17', 'var18', 'var19', 'var2', 'var20', 'var21', 'var22', 'var23', 'var24', 'var25', 'var26', 'var27', 'var28', 'var29', 'var3', 'var30', 'var31', 'var32', 'var33', 'var34', 'var35', 'var36', 'var37', 'var38', 'var39', 'var4', 'var40', 'var41', 'var42', 'var43', 'var44', 'var45', 'var46', 'var47', 'var48', 'var49', 'var5', 'var50', 'var51', 'var52', 'var53', 'var54', 'var55', 'var56', 'var57', 'var58', 'var59', 'var6', 'var60', 'var61', 'var62', 'var63', 'var7', 'var8', 'var9']\n"
     ]
    }
   ],
   "source": [
    "print(type(npy1))\n",
    "rows=list(npy1.keys())\n",
    "print(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8738ea",
   "metadata": {},
   "source": [
    "### Create a numpy array extracting all the data from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "60576c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "vec = df1.AsNumpy(columns=[\"var0\"])\n",
    "res1_array=np.array(list(vec.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d9655bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.336025   -4.092883    1.9655609  ... -2.5696828   1.357026\n",
      "   6.254604  ]\n",
      " [ 0.7632829   5.66362     1.4061441  ...  5.484785    0.95200175\n",
      "   3.5430255 ]\n",
      " [ 7.2729025   5.52698    10.708342   ...  4.2346277   5.5558853\n",
      "  12.274164  ]\n",
      " ...\n",
      " [19.657343    3.6865606  15.7146845  ... 13.756122   18.308765\n",
      "  11.770398  ]\n",
      " [14.433575   18.267435    9.263986   ... 13.805253   17.320747\n",
      "  20.793114  ]\n",
      " [10.888389   19.185621   10.288151   ...  4.6916385  22.37562\n",
      "  17.388264  ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "for i in rows[1:32]:\n",
    "    vec = df1.AsNumpy(columns=[i])\n",
    "    res1_array = np.r_[res1_array,list(vec.values())]\n",
    "print(res1_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4c400b4d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>92</th>\n",
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       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
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       "      <th>0</th>\n",
       "      <td>-1.336025</td>\n",
       "      <td>-4.092883</td>\n",
       "      <td>1.965561</td>\n",
       "      <td>3.896677</td>\n",
       "      <td>5.529247</td>\n",
       "      <td>1.163624</td>\n",
       "      <td>2.643468</td>\n",
       "      <td>2.471449</td>\n",
       "      <td>-0.485097</td>\n",
       "      <td>2.897419</td>\n",
       "      <td>...</td>\n",
       "      <td>6.278726</td>\n",
       "      <td>3.972255</td>\n",
       "      <td>-1.039392</td>\n",
       "      <td>4.305660</td>\n",
       "      <td>5.746236</td>\n",
       "      <td>2.795549</td>\n",
       "      <td>1.561632</td>\n",
       "      <td>-2.569683</td>\n",
       "      <td>1.357026</td>\n",
       "      <td>6.254604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.763283</td>\n",
       "      <td>5.663620</td>\n",
       "      <td>1.406144</td>\n",
       "      <td>-0.971143</td>\n",
       "      <td>7.293005</td>\n",
       "      <td>7.006859</td>\n",
       "      <td>2.172393</td>\n",
       "      <td>6.329552</td>\n",
       "      <td>12.701866</td>\n",
       "      <td>-2.058933</td>\n",
       "      <td>...</td>\n",
       "      <td>19.785761</td>\n",
       "      <td>4.204327</td>\n",
       "      <td>1.677374</td>\n",
       "      <td>6.501729</td>\n",
       "      <td>2.189398</td>\n",
       "      <td>6.799557</td>\n",
       "      <td>8.629866</td>\n",
       "      <td>5.484785</td>\n",
       "      <td>0.952002</td>\n",
       "      <td>3.543025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.272902</td>\n",
       "      <td>5.526980</td>\n",
       "      <td>10.708342</td>\n",
       "      <td>3.709020</td>\n",
       "      <td>13.599938</td>\n",
       "      <td>8.297036</td>\n",
       "      <td>2.951663</td>\n",
       "      <td>1.785292</td>\n",
       "      <td>22.514666</td>\n",
       "      <td>9.435592</td>\n",
       "      <td>...</td>\n",
       "      <td>12.652964</td>\n",
       "      <td>14.756213</td>\n",
       "      <td>7.102365</td>\n",
       "      <td>14.396164</td>\n",
       "      <td>3.771356</td>\n",
       "      <td>5.983877</td>\n",
       "      <td>9.122152</td>\n",
       "      <td>4.234628</td>\n",
       "      <td>5.555885</td>\n",
       "      <td>12.274164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.218680</td>\n",
       "      <td>11.854890</td>\n",
       "      <td>9.511338</td>\n",
       "      <td>7.482359</td>\n",
       "      <td>9.167512</td>\n",
       "      <td>7.954143</td>\n",
       "      <td>14.677694</td>\n",
       "      <td>4.751048</td>\n",
       "      <td>14.485039</td>\n",
       "      <td>13.261776</td>\n",
       "      <td>...</td>\n",
       "      <td>14.795762</td>\n",
       "      <td>12.287502</td>\n",
       "      <td>2.439374</td>\n",
       "      <td>14.008286</td>\n",
       "      <td>4.328670</td>\n",
       "      <td>9.699433</td>\n",
       "      <td>11.308884</td>\n",
       "      <td>10.453323</td>\n",
       "      <td>-1.160988</td>\n",
       "      <td>13.371491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.300408</td>\n",
       "      <td>3.502065</td>\n",
       "      <td>18.303375</td>\n",
       "      <td>10.689672</td>\n",
       "      <td>12.923079</td>\n",
       "      <td>8.774014</td>\n",
       "      <td>11.061160</td>\n",
       "      <td>9.289441</td>\n",
       "      <td>4.465584</td>\n",
       "      <td>16.648767</td>\n",
       "      <td>...</td>\n",
       "      <td>8.612619</td>\n",
       "      <td>10.756506</td>\n",
       "      <td>10.025352</td>\n",
       "      <td>7.474237</td>\n",
       "      <td>3.651299</td>\n",
       "      <td>10.048705</td>\n",
       "      <td>7.685595</td>\n",
       "      <td>6.862747</td>\n",
       "      <td>7.853603</td>\n",
       "      <td>7.977712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0          1          2          3          4         5          6   \\\n",
       "0 -1.336025  -4.092883   1.965561   3.896677   5.529247  1.163624   2.643468   \n",
       "1  0.763283   5.663620   1.406144  -0.971143   7.293005  7.006859   2.172393   \n",
       "2  7.272902   5.526980  10.708342   3.709020  13.599938  8.297036   2.951663   \n",
       "3  2.218680  11.854890   9.511338   7.482359   9.167512  7.954143  14.677694   \n",
       "4  4.300408   3.502065  18.303375  10.689672  12.923079  8.774014  11.061160   \n",
       "\n",
       "         7          8          9   ...         90         91         92  \\\n",
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       "2  1.785292  22.514666   9.435592  ...  12.652964  14.756213   7.102365   \n",
       "3  4.751048  14.485039  13.261776  ...  14.795762  12.287502   2.439374   \n",
       "4  9.289441   4.465584  16.648767  ...   8.612619  10.756506  10.025352   \n",
       "\n",
       "          93        94         95         96         97        98         99  \n",
       "0   4.305660  5.746236   2.795549   1.561632  -2.569683  1.357026   6.254604  \n",
       "1   6.501729  2.189398   6.799557   8.629866   5.484785  0.952002   3.543025  \n",
       "2  14.396164  3.771356   5.983877   9.122152   4.234628  5.555885  12.274164  \n",
       "3  14.008286  4.328670   9.699433  11.308884  10.453323 -1.160988  13.371491  \n",
       "4   7.474237  3.651299  10.048705   7.685595   6.862747  7.853603   7.977712  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# convert array into dataframe\n",
    "DF = pd.DataFrame(res1_array[:])\n",
    "\n",
    "# save the dataframe as a csv file\n",
    "DF.to_csv(\"data1.csv\")\n",
    "\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "250f2ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = df1.AsNumpy(columns=[rows[32]])\n",
    "res2_array=np.array(list(vec.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3cb4c254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17.552488   14.8517     20.258608   ...  7.862945   19.474665\n",
      "  13.752408  ]\n",
      " [ 2.4140606   7.3459044   9.887703   ... -4.709083    5.254788\n",
      "  -0.80547893]\n",
      " [ 3.816624    4.2820153  16.960608   ...  8.691112    6.98026\n",
      "  10.415255  ]\n",
      " ...\n",
      " [-3.8706129   1.7222944  11.104798   ... -2.9287522   0.8441311\n",
      "   0.94262445]\n",
      " [ 3.5277545   5.1762023   6.3105745  ...  2.496738   -4.3461485\n",
      "   1.6584157 ]\n",
      " [ 1.0955719   6.0525527   6.063516   ... -0.19227293  1.9838908\n",
      "   1.111334  ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "for i in rows[33:64]:\n",
    "    vec = df1.AsNumpy(columns=[i])\n",
    "    res2_array = np.r_[res2_array,list(vec.values())]\n",
    "print(res2_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c37a86a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.552488</td>\n",
       "      <td>14.851700</td>\n",
       "      <td>20.258608</td>\n",
       "      <td>16.023281</td>\n",
       "      <td>5.414101</td>\n",
       "      <td>1.079743</td>\n",
       "      <td>10.099811</td>\n",
       "      <td>10.457681</td>\n",
       "      <td>2.390532</td>\n",
       "      <td>8.605271</td>\n",
       "      <td>...</td>\n",
       "      <td>4.244608</td>\n",
       "      <td>13.882496</td>\n",
       "      <td>7.606391</td>\n",
       "      <td>1.598511</td>\n",
       "      <td>11.439191</td>\n",
       "      <td>8.202053</td>\n",
       "      <td>4.529440</td>\n",
       "      <td>7.862945</td>\n",
       "      <td>19.474665</td>\n",
       "      <td>13.752408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.414061</td>\n",
       "      <td>7.345904</td>\n",
       "      <td>9.887703</td>\n",
       "      <td>21.604706</td>\n",
       "      <td>3.834147</td>\n",
       "      <td>4.448325</td>\n",
       "      <td>4.780985</td>\n",
       "      <td>9.219211</td>\n",
       "      <td>4.723229</td>\n",
       "      <td>-1.488317</td>\n",
       "      <td>...</td>\n",
       "      <td>5.234775</td>\n",
       "      <td>4.028111</td>\n",
       "      <td>4.222233</td>\n",
       "      <td>5.426785</td>\n",
       "      <td>4.505137</td>\n",
       "      <td>5.415120</td>\n",
       "      <td>1.866752</td>\n",
       "      <td>-4.709083</td>\n",
       "      <td>5.254788</td>\n",
       "      <td>-0.805479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.816624</td>\n",
       "      <td>4.282015</td>\n",
       "      <td>16.960608</td>\n",
       "      <td>5.388222</td>\n",
       "      <td>8.992385</td>\n",
       "      <td>0.138740</td>\n",
       "      <td>11.342526</td>\n",
       "      <td>1.958259</td>\n",
       "      <td>5.763241</td>\n",
       "      <td>4.522415</td>\n",
       "      <td>...</td>\n",
       "      <td>8.702728</td>\n",
       "      <td>9.011817</td>\n",
       "      <td>-1.770969</td>\n",
       "      <td>6.026810</td>\n",
       "      <td>8.011896</td>\n",
       "      <td>10.832191</td>\n",
       "      <td>-0.312128</td>\n",
       "      <td>8.691112</td>\n",
       "      <td>6.980260</td>\n",
       "      <td>10.415255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.254239</td>\n",
       "      <td>-5.478763</td>\n",
       "      <td>-0.673511</td>\n",
       "      <td>2.807751</td>\n",
       "      <td>8.727755</td>\n",
       "      <td>9.313919</td>\n",
       "      <td>8.015204</td>\n",
       "      <td>-2.202890</td>\n",
       "      <td>3.171855</td>\n",
       "      <td>3.808939</td>\n",
       "      <td>...</td>\n",
       "      <td>6.344444</td>\n",
       "      <td>6.643681</td>\n",
       "      <td>3.915831</td>\n",
       "      <td>8.439391</td>\n",
       "      <td>7.805997</td>\n",
       "      <td>3.536092</td>\n",
       "      <td>15.459486</td>\n",
       "      <td>6.146331</td>\n",
       "      <td>3.789025</td>\n",
       "      <td>3.055388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.036800</td>\n",
       "      <td>2.747641</td>\n",
       "      <td>1.845184</td>\n",
       "      <td>8.925235</td>\n",
       "      <td>8.612143</td>\n",
       "      <td>12.617948</td>\n",
       "      <td>9.465577</td>\n",
       "      <td>8.436515</td>\n",
       "      <td>6.059497</td>\n",
       "      <td>4.187626</td>\n",
       "      <td>...</td>\n",
       "      <td>10.573262</td>\n",
       "      <td>7.483535</td>\n",
       "      <td>14.596214</td>\n",
       "      <td>8.319124</td>\n",
       "      <td>7.631743</td>\n",
       "      <td>9.465684</td>\n",
       "      <td>18.026104</td>\n",
       "      <td>12.423092</td>\n",
       "      <td>1.754870</td>\n",
       "      <td>7.623420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0          1          2          3         4          5          6   \\\n",
       "0  17.552488  14.851700  20.258608  16.023281  5.414101   1.079743  10.099811   \n",
       "1   2.414061   7.345904   9.887703  21.604706  3.834147   4.448325   4.780985   \n",
       "2   3.816624   4.282015  16.960608   5.388222  8.992385   0.138740  11.342526   \n",
       "3  -2.254239  -5.478763  -0.673511   2.807751  8.727755   9.313919   8.015204   \n",
       "4  17.036800   2.747641   1.845184   8.925235  8.612143  12.617948   9.465577   \n",
       "\n",
       "          7         8         9   ...         90         91         92  \\\n",
       "0  10.457681  2.390532  8.605271  ...   4.244608  13.882496   7.606391   \n",
       "1   9.219211  4.723229 -1.488317  ...   5.234775   4.028111   4.222233   \n",
       "2   1.958259  5.763241  4.522415  ...   8.702728   9.011817  -1.770969   \n",
       "3  -2.202890  3.171855  3.808939  ...   6.344444   6.643681   3.915831   \n",
       "4   8.436515  6.059497  4.187626  ...  10.573262   7.483535  14.596214   \n",
       "\n",
       "         93         94         95         96         97         98         99  \n",
       "0  1.598511  11.439191   8.202053   4.529440   7.862945  19.474665  13.752408  \n",
       "1  5.426785   4.505137   5.415120   1.866752  -4.709083   5.254788  -0.805479  \n",
       "2  6.026810   8.011896  10.832191  -0.312128   8.691112   6.980260  10.415255  \n",
       "3  8.439391   7.805997   3.536092  15.459486   6.146331   3.789025   3.055388  \n",
       "4  8.319124   7.631743   9.465684  18.026104  12.423092   1.754870   7.623420  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# convert array into dataframe\n",
    "DF = pd.DataFrame(res2_array[:])\n",
    "\n",
    "# save the dataframe as a csv file\n",
    "DF.to_csv(\"data1.csv\")\n",
    "\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af685e1a",
   "metadata": {},
   "source": [
    "### Create a Keras Model for Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3026e527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 12)                1212      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 12)                156       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 13        \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,381\n",
      "Trainable params: 1,381\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Flatten\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=100, activation=\"relu\"))\n",
    "model.add(Dense(12, activation=\"relu\"))\n",
    "model.add(Dense(1, activation=\"sigmoid\"))\n",
    "model.add(Flatten())\n",
    "model.summary()\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2523e40a",
   "metadata": {},
   "source": [
    "### Training of Model without using a Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dbb77abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 [==============================] - 0s 466ms/step - loss: -9.0071 - accuracy: 0.0000e+00\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 11ms/step - loss: -13.8341 - accuracy: 0.0000e+00\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 6ms/step - loss: -18.5350 - accuracy: 0.0000e+00\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 9ms/step - loss: -23.1150 - accuracy: 0.0000e+00\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 15ms/step - loss: -27.5665 - accuracy: 0.0000e+00\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 7ms/step - loss: -32.0266 - accuracy: 0.0000e+00\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 7ms/step - loss: -36.2284 - accuracy: 0.0000e+00\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 7ms/step - loss: -40.2420 - accuracy: 0.0000e+00\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 9ms/step - loss: -44.0684 - accuracy: 0.0000e+00\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 9ms/step - loss: -47.7520 - accuracy: 0.0000e+00\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 8ms/step - loss: -51.2669 - accuracy: 0.0000e+00\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 6ms/step - loss: -54.6838 - accuracy: 0.0000e+00\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 6ms/step - loss: -57.5916 - accuracy: 0.0000e+00\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 8ms/step - loss: -60.0882 - accuracy: 0.0000e+00\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 7ms/step - loss: -62.3071 - accuracy: 0.0000e+00\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 9ms/step - loss: -64.3650 - accuracy: 0.0000e+00\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 8ms/step - loss: -66.1853 - accuracy: 0.0000e+00\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 9ms/step - loss: -67.8441 - accuracy: 0.0000e+00\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 6ms/step - loss: -69.5130 - accuracy: 0.0000e+00\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 6ms/step - loss: -71.0639 - accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9c577b8b20>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tf_ds = pd.read_csv(\"data1.csv\")\n",
    "# tf_ds2=pd.read_csv(\"data.csv\")\n",
    "\n",
    "model.fit(res1_array,res2_array,batch_size=32, epochs=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111f20b4",
   "metadata": {},
   "source": [
    "### Declaring a Generator for Training the Model in Batches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2d3bff",
   "metadata": {},
   "source": [
    "Here we consider the batch size as number of rows present in the numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "81101d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dc5b7b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(array1,array2, batch_size):\n",
    "  inputs = []\n",
    "  targets = []\n",
    "  batchcount = 0\n",
    "  while True:\n",
    "       for x in range(batch_size):\n",
    "         inputs.append(array1[x])\n",
    "         targets.append(array2[x])\n",
    "         batchcount += 1\n",
    "         if batchcount > batch_size:\n",
    "                  X = np.array(inputs, dtype='float32')\n",
    "                  y = np.array(targets, dtype='float32')\n",
    "                  yield (X,y)\n",
    "                  inputs = []\n",
    "                  targets = []\n",
    "                  batchcount = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cdaa8d6",
   "metadata": {},
   "source": [
    "### Training a Model using a generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e241af5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8513/2353086351.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  model.fit_generator(generator(res1_array,res2_array,batch_size=32),steps_per_epoch=100/batch_size ,epochs=20)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 6ms/step - loss: -73.9768 - accuracy: 0.0000e+00\n",
      "Epoch 2/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -80.4282 - accuracy: 0.0000e+00\n",
      "Epoch 3/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -80.8494 - accuracy: 0.0000e+00\n",
      "Epoch 4/20\n",
      "3/3 [==============================] - 0s 6ms/step - loss: -82.7483 - accuracy: 0.0000e+00\n",
      "Epoch 5/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -83.4099 - accuracy: 0.0000e+00\n",
      "Epoch 6/20\n",
      "3/3 [==============================] - 0s 7ms/step - loss: -82.8071 - accuracy: 0.0000e+00\n",
      "Epoch 7/20\n",
      "3/3 [==============================] - 0s 7ms/step - loss: -83.2369 - accuracy: 0.0000e+00\n",
      "Epoch 8/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -82.6931 - accuracy: 0.0000e+00\n",
      "Epoch 9/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -83.8492 - accuracy: 0.0000e+00\n",
      "Epoch 10/20\n",
      "3/3 [==============================] - 0s 9ms/step - loss: -86.0170 - accuracy: 0.0000e+00\n",
      "Epoch 11/20\n",
      "3/3 [==============================] - 0s 7ms/step - loss: -83.9771 - accuracy: 0.0000e+00\n",
      "Epoch 12/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -84.2595 - accuracy: 0.0000e+00\n",
      "Epoch 13/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -84.0173 - accuracy: 0.0000e+00\n",
      "Epoch 14/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -82.9863 - accuracy: 0.0000e+00\n",
      "Epoch 15/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -83.2885 - accuracy: 0.0000e+00\n",
      "Epoch 16/20\n",
      "3/3 [==============================] - 0s 9ms/step - loss: -82.6931 - accuracy: 0.0000e+00\n",
      "Epoch 17/20\n",
      "3/3 [==============================] - 0s 9ms/step - loss: -83.8492 - accuracy: 0.0000e+00\n",
      "Epoch 18/20\n",
      "3/3 [==============================] - 0s 7ms/step - loss: -86.0170 - accuracy: 0.0000e+00\n",
      "Epoch 19/20\n",
      "3/3 [==============================] - 0s 8ms/step - loss: -83.9771 - accuracy: 0.0000e+00\n",
      "Epoch 20/20\n",
      "3/3 [==============================] - 0s 6ms/step - loss: -84.2595 - accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9cc44ca9a0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.fit_generator(generator(res1_array,res2_array,batch_size=32),steps_per_epoch=100/batch_size ,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db2f47dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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