ann_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/02\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import tensorflow\n",
    "\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.backend as k\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model, Sequential\n",
    "from tensorflow.keras.layers import Input, Dense, Activation, Dropout\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import uproot\n",
    "import ROOT\n",
    "from array import array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from numpy.random import seed\n",
    "#import random as python_random\n",
    "#number = 1\n",
    "#seed(number)\n",
    "#python_random.seed(number)\n",
    "#tensorflow.random.set_seed(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#variables_test = ([\"n30\", \"n35\", \"n40\", \"n45\", \"n50\", \"n55\", \"n60\", \"n65\", \"n70\", \"n75\", \"n80\", \"nav\", \"c30\", \n",
    "#\"c35\", \"c40\", \"c45\", \"c50\", \"c55\", \"c60\", \"c65\", \"c70\", \"c75\", \"c80\", ])\n",
    "#branches = [ \"energy\", \"eecal\", \"ehcal\", \"nhits\", \"nhits_ecal\", \"nhits_hcal\", \"time\", \"etime\", \"htime\", \"radius\", \n",
    "#\"eradius\", \"hradius\", \"elayer\", \"hlayer\", \"energy_hit\", \"energy_hit_ecal\", \"energy_hit_hcal\", \n",
    "#\"energy_hit_ecal_max\", \"energy_hit_hcal_max\", \"muon_energy\", \"muon_energy_hit\", \"muon_nhits\", \"muon_time\", \n",
    "#\"muon_layer\" ]\n",
    "\n",
    "#branches = [ \"energy\", \"eecal\", \"ehcal\", \"nhits\", \"nhits_ecal\", \"nhits_hcal\", \"radius\", \"eradius\", \"hradius\", \n",
    "#\"time\", \"etime\", \"htime\", \"elayer\", \"hlayer\", \"energy_hit\", \"energy_hit_ecal\", \"energy_hit_hcal\", \n",
    "#\"energy_hit_ecal_max\", \"energy_hit_hcal_max\", \"muon_energy\", \"muon_energy_hit\", \"muon_nhits\", \"muon_time\", \n",
    "#\"muon_layer\" ]\n",
    "\n",
    "#branches = [\"energy\", \"ehcal\", \"eecal\", \"nhits\", \"nhits_ecal\", \"nhits_hcal\", \"htime\", \"radius\", \"hradius\", \n",
    "#            \"hlayer\", \"c30\", \"energy_hit\", \"energy_hit_hcal\", \"muon_energy\", \"muon_energy_hit\", \"muon_nhits\", \n",
    "#            \"muon_time\", \"muon_layer\"]\n",
    "\n",
    "branches = [\"eecal\", \"ehcal\", \"energy_hit_ecal\", \"energy_hit_hcal\", \"muon_energy\", \"muon_energy_hit\", \n",
    "           \"nhits_ecal\", \"nhits_hcal\", \"etime\", \"htime\", \"eradius\", \"hradius\", \"elayer\", \"hlayer\",\n",
    "           \"muon_nhits\", \"muon_time\", \"muon_layer\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ./gun_k0L_allgev_FTFP_BERT_90000evt_ILD_l5_v02steel.root\n",
    "\n",
    "fTreeName = \"tree\"\n",
    "\n",
    "data = uproot.pandas.iterate(\"./gun_k0L_allgev_FTFP_BERT_260000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "data = pd.concat(list(data))\n",
    "\n",
    "data_mc = uproot.pandas.iterate(\"./gun_k0L_allgev_FTFP_BERT_260000evt_ILD_l5_v02steel.root\", fTreeName, [\"mc_energy\"])\n",
    "data_mc = pd.concat(list(data_mc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Rewrite this within new code!!!!!!!!!!!!!!!!!!!!!\n",
    "### Rewrite this within new code!!!!!!!!!!!!!!!!!!!!!\n",
    "### Rewrite this within new code!!!!!!!!!!!!!!!!!!!!!\n",
    "\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "mc_energy_scaled = min_max_scaler.fit_transform(data_mc[['mc_energy']].values.astype(float))\n",
    "\n",
    "#energy_scaled = min_max_scaler.fit_transform(data[['energy']].values.astype(float))\n",
    "eecal_scaled = min_max_scaler.fit_transform(data[['eecal']].values.astype(float))\n",
    "ehcal_scaled = min_max_scaler.fit_transform(data[['ehcal']].values.astype(float))\n",
    "\n",
    "#nhits_scaled = min_max_scaler.fit_transform(data[['nhits']].values.astype(int))\n",
    "#nhits_ecal_scaled = min_max_scaler.fit_transform(data[['nhits_ecal']].values.astype(int))\n",
    "#nhits_hcal_scaled = min_max_scaler.fit_transform(data[['nhits_hcal']].values.astype(int))\n",
    "\n",
    "#time_scaled = min_max_scaler.fit_transform(data[['time']].values.astype(float))\n",
    "#etime_scaled = min_max_scaler.fit_transform(data[['etime']].values.astype(float))\n",
    "#htime_scaled = min_max_scaler.fit_transform(data[['htime']].values.astype(float))\n",
    "\n",
    "#radius_scaled = min_max_scaler.fit_transform(data[['radius']].values.astype(float))\n",
    "#eradius_scaled = min_max_scaler.fit_transform(data[['eradius']].values.astype(float))\n",
    "#hradius_scaled = min_max_scaler.fit_transform(data[['hradius']].values.astype(float))\n",
    "\n",
    "#elayer_scaled = min_max_scaler.fit_transform(data[['elayer']].values.astype(float))\n",
    "#hlayer_scaled = min_max_scaler.fit_transform(data[['hlayer']].values.astype(float))\n",
    "\n",
    "#energy_hit_scaled = min_max_scaler.fit_transform(data[['energy_hit']].values.astype(float))\n",
    "energy_hit_ecal_scaled = min_max_scaler.fit_transform(data[['energy_hit_ecal']].values.astype(float))\n",
    "energy_hit_hcal_scaled = min_max_scaler.fit_transform(data[['energy_hit_hcal']].values.astype(float))\n",
    "\n",
    "#n30_scaled = min_max_scaler.fit_transform(data[['n30']].values.astype(int))\n",
    "#n35_scaled = min_max_scaler.fit_transform(data[['n35']].values.astype(int))\n",
    "#n40_scaled = min_max_scaler.fit_transform(data[['n40']].values.astype(int))\n",
    "#n45_scaled = min_max_scaler.fit_transform(data[['n45']].values.astype(int))\n",
    "#n50_scaled = min_max_scaler.fit_transform(data[['n50']].values.astype(int))\n",
    "#n55_scaled = min_max_scaler.fit_transform(data[['n55']].values.astype(int))\n",
    "#n60_scaled = min_max_scaler.fit_transform(data[['n60']].values.astype(int))\n",
    "#n65_scaled = min_max_scaler.fit_transform(data[['n65']].values.astype(int))\n",
    "#n70_scaled = min_max_scaler.fit_transform(data[['n70']].values.astype(int))\n",
    "#n75_scaled = min_max_scaler.fit_transform(data[['n75']].values.astype(int))\n",
    "#n80_scaled = min_max_scaler.fit_transform(data[['n80']].values.astype(int))\n",
    "#nav_scaled = min_max_scaler.fit_transform(data[['nav']].values.astype(int))\n",
    "#c30_scaled = min_max_scaler.fit_transform(data[['c30']].values.astype(float))\n",
    "#c35_scaled = min_max_scaler.fit_transform(data[['c35']].values.astype(float))\n",
    "#c40_scaled = min_max_scaler.fit_transform(data[['c40']].values.astype(float))\n",
    "#c45_scaled = min_max_scaler.fit_transform(data[['c45']].values.astype(float))\n",
    "#c50_scaled = min_max_scaler.fit_transform(data[['c50']].values.astype(float))\n",
    "#c55_scaled = min_max_scaler.fit_transform(data[['c55']].values.astype(float))\n",
    "#c60_scaled = min_max_scaler.fit_transform(data[['c60']].values.astype(float))\n",
    "#c65_scaled = min_max_scaler.fit_transform(data[['c65']].values.astype(float))\n",
    "#c70_scaled = min_max_scaler.fit_transform(data[['c70']].values.astype(float))\n",
    "#c75_scaled = min_max_scaler.fit_transform(data[['c75']].values.astype(float))\n",
    "#c80_scaled = min_max_scaler.fit_transform(data[['c80']].values.astype(float))\n",
    "\n",
    "muon_energy_scaled = min_max_scaler.fit_transform(data[['muon_energy']].values.astype(float))\n",
    "muon_energy_hit_scaled = min_max_scaler.fit_transform(data[['muon_energy_hit']].values.astype(float))\n",
    "#muon_nhits_scaled = min_max_scaler.fit_transform(data[['muon_nhits']].values.astype(int))\n",
    "#muon_time_scaled = min_max_scaler.fit_transform(data[['muon_time']].values.astype(float))\n",
    "#muon_layer_scaled = min_max_scaler.fit_transform(data[['muon_layer']].values.astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc_energy_scaled = np.squeeze(mc_energy_scaled)\n",
    "\n",
    "#energy_scaled = np.squeeze(energy_scaled)\n",
    "eecal_scaled = np.squeeze(eecal_scaled)\n",
    "ehcal_scaled = np.squeeze(ehcal_scaled)\n",
    "\n",
    "#nhits_scaled = np.squeeze(nhits_scaled)\n",
    "#nhits_ecal_scaled = np.squeeze(nhits_ecal_scaled)\n",
    "#nhits_hcal_scaled = np.squeeze(nhits_hcal_scaled)\n",
    "\n",
    "#time_scaled = np.squeeze(time_scaled)\n",
    "#etime_scaled = np.squeeze(etime_scaled)\n",
    "#htime_scaled = np.squeeze(htime_scaled)\n",
    "\n",
    "#radius_scaled = np.squeeze(radius_scaled)\n",
    "#eradius_scaled = np.squeeze(eradius_scaled)\n",
    "#hradius_scaled = np.squeeze(hradius_scaled)\n",
    "\n",
    "#elayer_scaled = np.squeeze(elayer_scaled)\n",
    "#hlayer_scaled = np.squeeze(hlayer_scaled)\n",
    "\n",
    "#n30_scaled = np.squeeze(n30_scaled)\n",
    "#n35_scaled = np.squeeze(n35_scaled)\n",
    "#n40_scaled = np.squeeze(n40_scaled)\n",
    "#n45_scaled = np.squeeze(n45_scaled)\n",
    "#n50_scaled = np.squeeze(n50_scaled)\n",
    "#n55_scaled = np.squeeze(n55_scaled)\n",
    "#n60_scaled = np.squeeze(n60_scaled)\n",
    "#n65_scaled = np.squeeze(n65_scaled)\n",
    "#n70_scaled = np.squeeze(n70_scaled)\n",
    "#n75_scaled = np.squeeze(n75_scaled)\n",
    "#n80_scaled = np.squeeze(n80_scaled)\n",
    "#nav_scaled = np.squeeze(nav_scaled)\n",
    "#c30_scaled = np.squeeze(c30_scaled)\n",
    "#c35_scaled = np.squeeze(c35_scaled)\n",
    "#c40_scaled = np.squeeze(c40_scaled)\n",
    "#c45_scaled = np.squeeze(c45_scaled)\n",
    "#c50_scaled = np.squeeze(c50_scaled)\n",
    "#c55_scaled = np.squeeze(c55_scaled)\n",
    "#c60_scaled = np.squeeze(c60_scaled)\n",
    "#c65_scaled = np.squeeze(c65_scaled)\n",
    "#c70_scaled = np.squeeze(c70_scaled)\n",
    "#c75_scaled = np.squeeze(c75_scaled)\n",
    "#c80_scaled = np.squeeze(c80_scaled)\n",
    "\n",
    "#energy_hit_scaled = np.squeeze(energy_hit_scaled)\n",
    "energy_hit_ecal_scaled = np.squeeze(energy_hit_ecal_scaled)\n",
    "energy_hit_hcal_scaled = np.squeeze(energy_hit_hcal_scaled)\n",
    "\n",
    "muon_energy_scaled = np.squeeze(muon_energy_scaled)\n",
    "muon_energy_hit_scaled = np.squeeze(muon_energy_hit_scaled)\n",
    "#muon_nhits_scaled = np.squeeze(muon_nhits_scaled)\n",
    "#muon_time_scaled = np.squeeze(muon_time_scaled)\n",
    "#muon_layer_scaled = np.squeeze(muon_layer_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc_energy_scaled = pd.Series(mc_energy_scaled, name='mc_energy', dtype='float32')\n",
    "\n",
    "#energy_scaled = pd.Series(energy_scaled, name='energy', dtype='float32')\n",
    "eecal_scaled = pd.Series(eecal_scaled, name='eecal', dtype='float32')\n",
    "ehcal_scaled = pd.Series(ehcal_scaled, name='ehcal', dtype='float32')\n",
    "\n",
    "#nhits_scaled = pd.Series(eecal_scaled, name='nhits', dtype='int32')\n",
    "#nhits_ecal_scaled = pd.Series(eecal_scaled, name='nhits_ecal', dtype='int32')\n",
    "#nhits_hcal_scaled = pd.Series(eecal_scaled, name='nhits_hcal', dtype='int32')\n",
    "\n",
    "#time_scaled = pd.Series(time_scaled, name='time', dtype='float32')\n",
    "#etime_scaled = pd.Series(etime_scaled, name='etime', dtype='float32')\n",
    "#htime_scaled = pd.Series(htime_scaled, name='htime', dtype='float32')\n",
    "\n",
    "#radius_scaled = pd.Series(radius_scaled, name='radius', dtype='float32')\n",
    "#eradius_scaled = pd.Series(eradius_scaled, name='eradius', dtype='float32')\n",
    "#hradius_scaled = pd.Series(hradius_scaled, name='hradius', dtype='float32')\n",
    "\n",
    "#elayer_scaled = pd.Series(elayer_scaled, name='elayer', dtype='float32')\n",
    "#hlayer_scaled = pd.Series(hlayer_scaled, name='hlayer', dtype='float32')\n",
    "\n",
    "#n30_scaled = pd.Series(n30_scaled, name='n30', dtype='int32')\n",
    "#n35_scaled = pd.Series(n35_scaled, name='n35', dtype='int32')\n",
    "#n40_scaled = pd.Series(n40_scaled, name='n40', dtype='int32')\n",
    "#n45_scaled = pd.Series(n45_scaled, name='n45', dtype='int32')\n",
    "#n50_scaled = pd.Series(n50_scaled, name='n50', dtype='int32')\n",
    "#n55_scaled = pd.Series(n55_scaled, name='n55', dtype='int32')\n",
    "#n60_scaled = pd.Series(n60_scaled, name='n60', dtype='int32')\n",
    "#n65_scaled = pd.Series(n65_scaled, name='n65', dtype='int32')\n",
    "#n70_scaled = pd.Series(n70_scaled, name='n70', dtype='int32')\n",
    "#n75_scaled = pd.Series(n75_scaled, name='n75', dtype='int32')\n",
    "#n80_scaled = pd.Series(n80_scaled, name='n70', dtype='int32')\n",
    "#nav_scaled = pd.Series(nav_scaled, name='n30', dtype='int32')\n",
    "#c30_scaled = pd.Series(c30_scaled, name='c30', dtype='float32')\n",
    "#c35_scaled = pd.Series(c35_scaled, name='c35', dtype='float32')\n",
    "#c40_scaled = pd.Series(c40_scaled, name='c40', dtype='float32')\n",
    "#c45_scaled = pd.Series(c45_scaled, name='c45', dtype='float32')\n",
    "#c50_scaled = pd.Series(c50_scaled, name='c50', dtype='float32')\n",
    "#c55_scaled = pd.Series(c55_scaled, name='c55', dtype='float32')\n",
    "#c60_scaled = pd.Series(c60_scaled, name='c60', dtype='float32')\n",
    "#c65_scaled = pd.Series(c65_scaled, name='c65', dtype='float32')\n",
    "#c70_scaled = pd.Series(c70_scaled, name='c70', dtype='float32')\n",
    "#c75_scaled = pd.Series(c75_scaled, name='c75', dtype='float32')\n",
    "#c80_scaled = pd.Series(c80_scaled, name='c80', dtype='float32')\n",
    "\n",
    "#energy_hit_scaled = pd.Series(energy_hit_scaled, name='energy_hit', dtype='float32')\n",
    "energy_hit_ecal_scaled = pd.Series(energy_hit_ecal_scaled, name='energy_hit_ecal', dtype='float32')\n",
    "energy_hit_hcal_scaled = pd.Series(energy_hit_hcal_scaled, name='energy_hit_hcal', dtype='float32')\n",
    "\n",
    "muon_energy_scaled = pd.Series(muon_energy_scaled, name='muon_energy', dtype='float32')\n",
    "muon_energy_hit_scaled = pd.Series(muon_energy_hit_scaled, name='muon_energy_hit', dtype='float32')\n",
    "#muon_nhits_scaled = pd.Series(muon_nhits_scaled, name='muon_nhits', dtype='int32')\n",
    "#muon_time_scaled = pd.Series(muon_time_scaled, name='muon_time', dtype='float32')\n",
    "#muon_layer_scaled = pd.Series(muon_layer_scaled, name='muon_layer', dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mc_scaled = pd.DataFrame({'mc_energy' : mc_energy_scaled})\n",
    "\n",
    "data_scaled = pd.DataFrame({\n",
    "#                            'energy' : energy_scaled,\n",
    "                            'eecal' : eecal_scaled,\n",
    "                            'ehcal' : ehcal_scaled,\n",
    "    \n",
    "#                            'nhits' : nhits_scaled, \n",
    "#                            'nhits_ecal' : nhits_ecal_scaled, \n",
    "#                            'nhits_hcal' : nhits_hcal_scaled, \n",
    "    \n",
    "#                            'time' : time_scaled,\n",
    "#                            'etime' : etime_scaled,\n",
    "#                            'htime' : htime_scaled, \n",
    "    \n",
    "#                            'radius' : radius_scaled, \n",
    "#                            'eradius' : eradius_scaled, \n",
    "#                            'hradius' : hradius_scaled, \n",
    "    \n",
    "#                            'elayer' : elayer_scaled, \n",
    "#                            'hlayer' : hlayer_scaled,\n",
    "    \n",
    "#                            'n30' : n30_scaled,\n",
    "#                            'n35' : n35_scaled,\n",
    "#                            'n40' : n40_scaled,\n",
    "#                            'c45' : n45_scaled,\n",
    "#                            'c50' : n50_scaled,\n",
    "#                            'c55' : n55_scaled,\n",
    "#                            'c60' : n60_scaled,\n",
    "#                            'c65' : n65_scaled,\n",
    "#                            'c70' : n70_scaled,\n",
    "#                            'c75' : n75_scaled,    \n",
    "#                            'c80' : n80_scaled,\n",
    "#                            'nav' : nav_scaled,\n",
    "#                            'c30' : c30_scaled,\n",
    "#                            'c35' : c35_scaled,\n",
    "#                            'c40' : c40_scaled,    \n",
    "#                            'c45' : c45_scaled,\n",
    "#                            'c50' : c50_scaled,\n",
    "#                            'c55' : c55_scaled,\n",
    "#                            'c60' : c60_scaled,\n",
    "#                            'c65' : c65_scaled,\n",
    "#                            'c70' : c70_scaled,\n",
    "#                            'c75' : c75_scaled,\n",
    "#                            'c80' : c80_scaled,    \n",
    "    \n",
    "#                            'energy_hit' : energy_hit_scaled, \n",
    "                            'energy_hit_ecal' : energy_hit_ecal_scaled, \n",
    "                            'energy_hit_hcal' : energy_hit_hcal_scaled, \n",
    "    \n",
    "                            'muon_energy' : muon_energy_scaled,\n",
    "                            'muon_energy_hit' : muon_energy_hit_scaled\n",
    "#                            'muon_nhits' : muon_nhits_scaled,\n",
    "#                            'muon_time' : muon_time_scaled, \n",
    "#                            'muon_layer' : muon_layer_scaled\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data, data_mc, train_size = 0.2)\n",
    "#x_train, x_test, y_train, y_test = train_test_split(data_scaled, data_mc_scaled, train_size = 0.9)\n",
    "#x = data[data.columns[:2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>eecal</th>\n",
       "      <th>ehcal</th>\n",
       "      <th>energy_hit_ecal</th>\n",
       "      <th>energy_hit_hcal</th>\n",
       "      <th>muon_energy</th>\n",
       "      <th>muon_energy_hit</th>\n",
       "      <th>nhits_ecal</th>\n",
       "      <th>nhits_hcal</th>\n",
       "      <th>etime</th>\n",
       "      <th>htime</th>\n",
       "      <th>eradius</th>\n",
       "      <th>hradius</th>\n",
       "      <th>elayer</th>\n",
       "      <th>hlayer</th>\n",
       "      <th>muon_nhits</th>\n",
       "      <th>muon_time</th>\n",
       "      <th>muon_layer</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37324</th>\n",
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       "      <th>68400</th>\n",
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       "    <tr>\n",
       "      <th>223003</th>\n",
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       "      <th>65542</th>\n",
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       "      <td>98</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>182434 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eecal      ehcal  energy_hit_ecal  energy_hit_hcal  muon_energy  \\\n",
       "37324    2.914389   0.305532         0.030358         0.033948     0.000000   \n",
       "208019   0.000000  68.893440         0.000000         0.121291     1.732349   \n",
       "38203    1.621294   4.622135         0.030590         0.060028     0.000000   \n",
       "185171  23.421452  40.212723         0.049308         0.077932     0.000000   \n",
       "101988   9.408077   4.870014         0.026880         0.054719     0.000000   \n",
       "...           ...        ...              ...              ...          ...   \n",
       "68400    3.436684   6.982409         0.070136         0.059679     0.000000   \n",
       "223003  66.953644  28.599413         0.049376         0.063554     0.000000   \n",
       "65542    3.458077   4.611125         0.070573         0.047052     0.000000   \n",
       "115624   0.012646  20.409302         0.012646         0.074216     0.000000   \n",
       "53604    0.574047   8.058315         0.047837         0.071949     0.000000   \n",
       "\n",
       "        muon_energy_hit  nhits_ecal  nhits_hcal      etime      htime  \\\n",
       "37324          0.000000          96           9  15.205139  44.782043   \n",
       "208019         0.133258           0         568   0.000000  22.011225   \n",
       "38203          0.000000          53          77   9.281345  25.135580   \n",
       "185171         0.000000         475         516   9.563914  18.914932   \n",
       "101988         0.000000         350          89   8.626285  21.802670   \n",
       "...                 ...         ...         ...        ...        ...   \n",
       "68400          0.000000          49         117  11.043403  19.213751   \n",
       "223003         0.000000        1356         450  11.049956  24.582951   \n",
       "65542          0.000000          49          98  11.189964  22.607147   \n",
       "115624         0.000000           1         275  16.392115  22.096706   \n",
       "53604          0.000000          12         112   7.316762  14.093345   \n",
       "\n",
       "           eradius     hradius     elayer     hlayer  muon_nhits  muon_time  \\\n",
       "37324    88.815994  310.207581  19.828377   7.136472           0   0.000000   \n",
       "208019    0.000000   55.142788   0.000000  26.186007          13  25.917233   \n",
       "38203   256.757141  175.863968  12.963758  22.333775           0   0.000000   \n",
       "185171   50.139786  142.712326  21.546888  10.397236           0   0.000000   \n",
       "101988   78.490585  141.245331  10.557379  16.012953           0   0.000000   \n",
       "...            ...         ...        ...        ...         ...        ...   \n",
       "68400    27.818356  118.388916  27.087358   6.015252           0   0.000000   \n",
       "223003   59.246185  257.289093  15.307486  15.783568           0   0.000000   \n",
       "65542   147.913712  304.664612  27.080057   7.283298           0   0.000000   \n",
       "115624  953.665222  123.521118  28.000000  17.071726           0   0.000000   \n",
       "53604    88.400169   83.402130  26.837088  15.119639           0   0.000000   \n",
       "\n",
       "        muon_layer  \n",
       "37324     0.000000  \n",
       "208019    3.447482  \n",
       "38203     0.000000  \n",
       "185171    0.000000  \n",
       "101988    0.000000  \n",
       "...            ...  \n",
       "68400     0.000000  \n",
       "223003    0.000000  \n",
       "65542     0.000000  \n",
       "115624    0.000000  \n",
       "53604     0.000000  \n",
       "\n",
       "[182434 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>425</td>\n",
       "      <td>13.651529</td>\n",
       "      <td>17.755495</td>\n",
       "      <td>727.467102</td>\n",
       "      <td>75.444916</td>\n",
       "      <td>7.185410</td>\n",
       "      <td>10.982880</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>157005</th>\n",
       "      <td>9.669407</td>\n",
       "      <td>34.932144</td>\n",
       "      <td>0.050361</td>\n",
       "      <td>0.080489</td>\n",
       "      <td>0.177945</td>\n",
       "      <td>0.035589</td>\n",
       "      <td>192</td>\n",
       "      <td>434</td>\n",
       "      <td>11.010762</td>\n",
       "      <td>22.845562</td>\n",
       "      <td>46.143959</td>\n",
       "      <td>158.003296</td>\n",
       "      <td>24.036331</td>\n",
       "      <td>15.406244</td>\n",
       "      <td>5</td>\n",
       "      <td>33.920021</td>\n",
       "      <td>3.852748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59397</th>\n",
       "      <td>5.559443</td>\n",
       "      <td>1.724728</td>\n",
       "      <td>0.025156</td>\n",
       "      <td>0.041065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>221</td>\n",
       "      <td>42</td>\n",
       "      <td>8.777745</td>\n",
       "      <td>20.461739</td>\n",
       "      <td>59.363453</td>\n",
       "      <td>185.823914</td>\n",
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       "    <tr>\n",
       "      <th>151592</th>\n",
       "      <td>8.174483</td>\n",
       "      <td>30.920469</td>\n",
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       "      <td>0.077108</td>\n",
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       "      <td>401</td>\n",
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       "      <td>127.215721</td>\n",
       "      <td>27.295904</td>\n",
       "      <td>4.246043</td>\n",
       "      <td>2</td>\n",
       "      <td>23.559845</td>\n",
       "      <td>2.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>45608 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eecal      ehcal  energy_hit_ecal  energy_hit_hcal  muon_energy  \\\n",
       "123461  12.259786   9.654978         0.076624         0.062695     0.000000   \n",
       "148824   4.047160  30.053387         0.049965         0.082564     0.000000   \n",
       "207268  27.687239  42.209007         0.055485         0.064937     0.045219   \n",
       "99193    2.644205  13.449503         0.047218         0.090265     0.000000   \n",
       "32063    0.000000   7.798792         0.000000         0.082093     0.000000   \n",
       "...           ...        ...              ...              ...          ...   \n",
       "91886    6.499557   5.079955         0.022967         0.062715     0.000000   \n",
       "180165   0.447745  57.314960         0.223873         0.134859     0.000000   \n",
       "157005   9.669407  34.932144         0.050361         0.080489     0.177945   \n",
       "59397    5.559443   1.724728         0.025156         0.041065     0.000000   \n",
       "151592   8.174483  30.920469         0.113534         0.077108     2.075611   \n",
       "\n",
       "        muon_energy_hit  nhits_ecal  nhits_hcal      etime      htime  \\\n",
       "123461         0.000000         160         154   8.622712  20.278191   \n",
       "148824         0.000000          81         364  11.966895  22.502575   \n",
       "207268         0.045219         499         650  11.621511  22.869896   \n",
       "99193          0.000000          56         149  10.622511  15.423512   \n",
       "32063          0.000000           0          95   0.000000  25.424236   \n",
       "...                 ...         ...         ...        ...        ...   \n",
       "91886          0.000000         283          81   8.834362  18.324047   \n",
       "180165         0.000000           2         425  13.651529  17.755495   \n",
       "157005         0.035589         192         434  11.010762  22.845562   \n",
       "59397          0.000000         221          42   8.777745  20.461739   \n",
       "151592         1.037806          72         401  15.470201  21.325752   \n",
       "\n",
       "           eradius     hradius     elayer     hlayer  muon_nhits  muon_time  \\\n",
       "123461   34.656376  184.786499  27.157253   4.484210           0   0.000000   \n",
       "148824   57.356392  104.984100  25.338644   6.731803           0   0.000000   \n",
       "207268   45.360428  136.934753  22.027695  13.927596           1  62.610962   \n",
       "99193    46.187733   87.939583  23.089109   7.743214           0   0.000000   \n",
       "32063     0.000000   83.363396   0.000000  19.874790           0   0.000000   \n",
       "...            ...         ...        ...        ...         ...        ...   \n",
       "91886    83.154213  120.151749   9.001735   5.907115           0   0.000000   \n",
       "180165  727.467102   75.444916   7.185410  10.982880           0   0.000000   \n",
       "157005   46.143959  158.003296  24.036331  15.406244           5  33.920021   \n",
       "59397    59.363453  185.823914  14.497105   6.984890           0   0.000000   \n",
       "151592   49.095310  127.215721  27.295904   4.246043           2  23.559845   \n",
       "\n",
       "        muon_layer  \n",
       "123461    0.000000  \n",
       "148824    0.000000  \n",
       "207268    3.000000  \n",
       "99193     0.000000  \n",
       "32063     0.000000  \n",
       "...            ...  \n",
       "91886     0.000000  \n",
       "180165    0.000000  \n",
       "157005    3.852748  \n",
       "59397     0.000000  \n",
       "151592    2.000000  \n",
       "\n",
       "[45608 rows x 17 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228042"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "182434 + 45608"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eecal</th>\n",
       "      <th>ehcal</th>\n",
       "      <th>energy_hit_ecal</th>\n",
       "      <th>energy_hit_hcal</th>\n",
       "      <th>muon_energy</th>\n",
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       "      <td>31</td>\n",
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       "      <th>228037</th>\n",
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       "      <th>228038</th>\n",
       "      <td>56.527229</td>\n",
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       "      <th>228039</th>\n",
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       "      <td>53</td>\n",
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       "      <th>228040</th>\n",
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       "<p>228042 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eecal      ehcal  energy_hit_ecal  energy_hit_hcal  muon_energy  \\\n",
       "0        0.670394   0.017444         0.024829         0.017444     0.000000   \n",
       "1        0.645810   0.202328         0.071757         0.028904     0.000000   \n",
       "2        0.566482   0.398062         0.024630         0.079612     0.000000   \n",
       "3        0.849447   0.051002         0.027402         0.025501     0.000000   \n",
       "4        0.000000   0.895484         0.000000         0.052676     0.000000   \n",
       "...           ...        ...              ...              ...          ...   \n",
       "228037  19.359770  74.361397         0.031634         0.130688     0.000000   \n",
       "228038  56.527229  32.589684         0.048984         0.059362     0.147955   \n",
       "228039   3.095529  85.028496         0.058406         0.089410     2.128779   \n",
       "228040   0.553285  69.528854         0.024056         0.108979     7.743567   \n",
       "228041  51.303375  39.076424         0.082349         0.059933     0.029082   \n",
       "\n",
       "        muon_energy_hit  nhits_ecal  nhits_hcal      etime      htime  \\\n",
       "0              0.000000          27           1  15.301003  29.174778   \n",
       "1              0.000000           9           7   9.229284  35.510986   \n",
       "2              0.000000          23           5   8.524140  21.881247   \n",
       "3              0.000000          31           2   9.958794  28.391405   \n",
       "4              0.000000           0          17   0.000000  26.000984   \n",
       "...                 ...         ...         ...        ...        ...   \n",
       "228037         0.000000         612         569  11.173767  22.173275   \n",
       "228038         0.036989        1154         549  11.727120  26.792801   \n",
       "228039         0.066524          53         951  18.986591  19.182997   \n",
       "228040         0.106076          23         638  33.004810  18.920288   \n",
       "228041         0.029082         623         652  11.849463  23.059702   \n",
       "\n",
       "           eradius     hradius     elayer     hlayer  muon_nhits  muon_time  \\\n",
       "0        27.506840  132.961609  11.368681   2.000000           0   0.000000   \n",
       "1       132.959625  454.003021   7.469445   8.567707           0   0.000000   \n",
       "2        44.695396   57.010426  19.194912   5.374790           0   0.000000   \n",
       "3        31.147676  304.719849  10.912801   5.256326           0   0.000000   \n",
       "4         0.000000   90.133797   0.000000  22.711790           0   0.000000   \n",
       "...            ...         ...        ...        ...         ...        ...   \n",
       "228037   97.840607  125.466568  13.646028  14.669214           0   0.000000   \n",
       "228038   29.344259  178.373383  13.846959  14.270522           4  42.479671   \n",
       "228039  269.131134  113.978600  26.097837  15.419724          32  26.529984   \n",
       "228040  421.208832  100.217827  23.028711  11.405708          73  28.414501   \n",
       "228041   44.015198  210.070618  22.386030  14.718886           1   0.000000   \n",
       "\n",
       "        muon_layer  \n",
       "0         0.000000  \n",
       "1         0.000000  \n",
       "2         0.000000  \n",
       "3         0.000000  \n",
       "4         0.000000  \n",
       "...            ...  \n",
       "228037    0.000000  \n",
       "228038    3.602151  \n",
       "228039    4.323201  \n",
       "228040    4.981774  \n",
       "228041    2.000000  \n",
       "\n",
       "[228042 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.ehcal.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = [x_train, x_test]\n",
    "#y = [y_train, y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#xr = pd.concat(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yr = pd.concat(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = np.concatenate((x_train, x_test))\n",
    "#y = np.concatenate((y_train, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "full_size = data.shape[0]\n",
    "n = full_size // 5\n",
    "\n",
    "data1 = data.iloc[0:n]\n",
    "data2 = data.iloc[n:(n*2)]\n",
    "data3 = data.iloc[(n*2):(n*3)]\n",
    "data4 = data.iloc[(n*3):(n*4)]\n",
    "data5 = data.iloc[(n*4):(n*5)]\n",
    "\n",
    "data1_mc = data_mc.iloc[0:n]\n",
    "data2_mc = data_mc.iloc[n:(n*2)]\n",
    "data3_mc = data_mc.iloc[(n*2):(n*3)]\n",
    "data4_mc = data_mc.iloc[(n*3):(n*4)]\n",
    "data5_mc = data_mc.iloc[(n*4):(n*5)]\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "x1_train = ((data2.append(data3)).append(data4)).append(data5)\n",
    "y1_train = ((data2_mc.append(data3_mc)).append(data4_mc)).append(data5_mc)\n",
    "x1_test = data1\n",
    "y1_test = data1_mc\n",
    "\n",
    "x2_train = ((data1.append(data3)).append(data4)).append(data5)\n",
    "y2_train = ((data1_mc.append(data3_mc)).append(data4_mc)).append(data5_mc)\n",
    "x2_test = data2\n",
    "y2_test = data2_mc\n",
    "\n",
    "x3_train = ((data1.append(data2)).append(data4)).append(data5)\n",
    "y3_train = ((data1_mc.append(data2_mc)).append(data4_mc)).append(data5_mc)\n",
    "x3_test = data3\n",
    "y3_test = data3_mc\n",
    "\n",
    "x4_train = ((data1.append(data2)).append(data3)).append(data5)\n",
    "y4_train = ((data1_mc.append(data2_mc)).append(data3_mc)).append(data5_mc)\n",
    "x4_test = data4\n",
    "y4_test = data4_mc\n",
    "\n",
    "x5_train = ((data1.append(data2)).append(data3)).append(data4)\n",
    "y5_train = ((data1_mc.append(data2_mc)).append(data3_mc)).append(data4_mc)\n",
    "x5_test = data5\n",
    "y5_test = data5_mc\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#num_folds = 5\n",
    "#kfold = KFold(n_splits=num_folds, shuffle=True)\n",
    "#fold_no = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = 0.25\n",
    "\n",
    "def loss1(y_true, y_pred):\n",
    "    return k.mean(k.square((y_pred - y_true) / (y_true)))\n",
    "\n",
    "def loss2(y_true, y_pred):\n",
    "    return k.mean(k.square((y_pred - y_true)) / (y_true))\n",
    "\n",
    "def loss3(y_true, y_pred):\n",
    "    return k.mean(k.square((y_pred - y_true)) / (a * y_true))\n",
    "\n",
    "def metric1(y_true, y_pred):\n",
    "    return k.mean((y_pred - y_true) / (y_true))\n",
    "\n",
    "def r2(y_true, y_pred):\n",
    "    SS_res =  k.sum(k.square(y_true - y_pred)) \n",
    "    SS_tot = k.sum(k.square(y_true - k.mean(y_true))) \n",
    "    return (1 - SS_res/(SS_tot + k.epsilon()))\n",
    "\n",
    "mse = tensorflow.keras.metrics.MeanSquaredError()\n",
    "rmse = tensorflow.keras.metrics.RootMeanSquaredError()\n",
    "mae = tensorflow.keras.metrics.MeanAbsoluteError()\n",
    "mape = tensorflow.keras.metrics.MeanAbsolutePercentageError()\n",
    "msle = tensorflow.keras.metrics.MeanSquaredLogarithmicError()\n",
    "cs = tensorflow.keras.metrics.CosineSimilarity()\n",
    "lce = tensorflow.keras.metrics.LogCoshError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear = tensorflow.keras.activations.linear\n",
    "\n",
    "leaky_relu = tensorflow.keras.layers.LeakyReLU()\n",
    "\n",
    "model = Sequential()\n",
    "#model.add(Dense(32, activation='tanh', input_dim=data1.shape[1]))\n",
    "model.add(Dense(128, activation=leaky_relu, input_dim=x_train.shape[1]))\n",
    "#model.add(Dense(32, activation='tanh', input_dim=data.shape[1]))\n",
    "model.add(Dense(64, activation=leaky_relu))\n",
    "#model.add(Dropout(0.1))\n",
    "model.add(Dense(32, activation=leaky_relu))\n",
    "#model.add(Dense(16, activation=leaky_relu))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(16, activation=leaky_relu))\n",
    "#model.add(Dropout(0.1))\n",
    "model.add(Dense(4, activation=leaky_relu))\n",
    "model.add(Dense(1, activation=linear))\n",
    "\n",
    "'''\n",
    "a = Input(shape=x_train.shape[1])\n",
    "b = Dense(8)(a)\n",
    "b = Dropout(rate=0.5)(b)\n",
    "b = Activation('tanh')(b)\n",
    "b = Dense(4)(a)\n",
    "b = Dropout(rate=0.5)(b)\n",
    "b = Activation('tanh')(b)\n",
    "c = Dense(1)(b)\n",
    "model = Model(inputs = a, outputs = c)\n",
    "'''\n",
    "\n",
    "#model.compile(loss='mean_squared_error', metrics=[metric1], optimizer='adam')\n",
    "model.compile(loss=loss3, metrics=[metric1, r2, mse, rmse, mae, mape, msle, cs, lce], optimizer='nadam')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#for train, test in kfold.split(x, y):\n",
    "#    history = model.fit(x_train, y_train, epochs = 200, validation_data = (x_test, y_test))\n",
    "#    history = model.fit(x[train], y[train], epochs=50, validation_split=0.6)\n",
    "    \n",
    "#    plt.plot(history.history['loss'])\n",
    "#    plt.plot(history.history['val_loss'])\n",
    "#    plt.title('Model loss')\n",
    "#    plt.ylabel('Loss')\n",
    "#    plt.xlabel('Epoch')\n",
    "#    plt.legend(['loss', 'val_loss', 'r2'], loc='upper right')\n",
    "#    plt.savefig('loss.png', dpi=100, format='png')\n",
    "#    plt.show()\n",
    "    #Image('loss.png')\n",
    "    \n",
    "#    fold_no = fold_no + 1\n",
    "history = model.fit(x_train, y_train, epochs = 100, validation_data = (x_test, y_test))\n",
    "'''\n",
    "n_epochs = 100\n",
    "history1 = model.fit(x1_train, y1_train, epochs = n_epochs, validation_data = (x1_test, y1_test))\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "history2 = model.fit(x2_train, y2_train, epochs = n_epochs, validation_data = (x2_test, y2_test))\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "history3 = model.fit(x3_train, y3_train, epochs = n_epochs, validation_data = (x3_test, y3_test))\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "history4 = model.fit(x4_train, y4_train, epochs = n_epochs, validation_data = (x4_test, y4_test))\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "history5 = model.fit(x5_train, y5_train, epochs = n_epochs, validation_data = (x5_test, y5_test))\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "print(\"------------------------------------------------------------------\")\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import GridSearchCV\n",
    "#from sklearn.metrics import r2_score\n",
    "#from sklearn.metrics import make_scorer\n",
    "#ftwo_scorer = make_scorer(r2_score, beta=2)\n",
    "#type(ftwo_scorer)\n",
    "#param_grid = dict(epochs=[10, 20, 30])\n",
    "#grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=ftwo_scorer, n_jobs=-1, cv=3)\n",
    "#grid_result = grid.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "#from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "#estimator = KerasRegressor(build_fn=model, nb_epoch=100, batch_size=100, verbose=False)\n",
    "# Create a KerasClassifier object\n",
    "#model = KerasRegressor(build_fn = model,\n",
    "#                       verbose = 0)\n",
    "#from sklearn.model_selection import RandomizedSearchCV\n",
    "#from sklearn.metrics import r2_score\n",
    "#from sklearn.metrics import make_scorer\n",
    "#ftwo_scorer = make_scorer(r2_score, beta=2)\n",
    "#type(ftwo_scorer)\n",
    "# Define the parambias_initializereters to try out\n",
    "#params = {'activataion': [\"relu\", \"tanh\"],\n",
    "#          'batch_size': [16, 32, 64], \n",
    "#          'epochs': [50, 100],\n",
    "#          'learning_rate': [0.01, 0.001, 0.0001]}\n",
    "#random_search = RandomizedSearchCV(model,\n",
    "#                                   param_distributions = params,\n",
    "#                                   scoring = ftwo_scorer,\n",
    "#                                   cv = KFold(10))\n",
    "#random_search_results = random_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#error = model.evaluate( x_test, y_test )\n",
    "#print(\"\\n\")\n",
    "#print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#history1.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#history2.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#history3.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#history4.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#history5.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['loss', 'val_loss'], loc='upper right')\n",
    "plt.savefig('./ann_pict/loss.png', dpi=100, format='png')\n",
    "plt.show()\n",
    "#Image('loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['metric1'])\n",
    "plt.plot(history.history['val_metric1'])\n",
    "plt.title('Model metric1')\n",
    "plt.ylabel('Metric1')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['metric1', 'val_metric1'], loc='lower right')\n",
    "plt.savefig('./ann_pict/metric1.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['r2'])\n",
    "plt.plot(history.history['val_r2'])\n",
    "plt.title('Model r2')\n",
    "plt.ylabel('r2')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['r2', 'val_r2'], loc='lower right')\n",
    "plt.savefig('./ann_pict/r2.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['mean_squared_error'])\n",
    "plt.plot(history.history['val_mean_squared_error'])\n",
    "plt.title('Model mean_squared_error')\n",
    "plt.ylabel('mean_squared_error')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['mean_squared_error', 'val_mean_squared_error'], loc='upper right')\n",
    "plt.savefig('./ann_pict/mean_squared_error.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['root_mean_squared_error'])\n",
    "plt.plot(history.history['val_root_mean_squared_error'])\n",
    "plt.title('Model root_mean_squared_error')\n",
    "plt.ylabel('root_mean_squared_error')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['root_mean_squared_error', 'val_root_mean_squared_error'], loc='upper right')\n",
    "plt.savefig('./ann_pict/root_mean_squared_error.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['mean_absolute_error'])\n",
    "plt.plot(history.history['val_mean_absolute_error'])\n",
    "plt.title('Model mean_absolute_error')\n",
    "plt.ylabel('mean_absolute_error')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['mean_absolute_error', 'val_mean_absolute_error'], loc='upper right')\n",
    "plt.savefig('./ann_pict/mean_absolute_error.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['mean_absolute_percentage_error'])\n",
    "plt.plot(history.history['val_mean_absolute_percentage_error'])\n",
    "plt.title('Model mean_absolute_percentage_error')\n",
    "plt.ylabel('mean_absolute_percentage_error')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['mean_absolute_percentage_error', 'val_mean_absolute_percentage_error'], loc='upper right')\n",
    "plt.savefig('./ann_pict/mean_absolute_percentage_error.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['mean_squared_logarithmic_error'])\n",
    "plt.plot(history.history['val_mean_squared_logarithmic_error'])\n",
    "plt.title('Model mean_squared_logarithmic_error')\n",
    "plt.ylabel('mean_squared_logarithmic_error')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['mean_squared_logarithmic_error', 'val_mean_squared_logarithmic_error'], loc='upper right')\n",
    "plt.savefig('./ann_pict/mean_squared_logarithmic_error.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['cosine_similarity'])\n",
    "plt.plot(history.history['val_cosine_similarity'])\n",
    "plt.title('Model cosine_similarity')\n",
    "plt.ylabel('cosine_similarity')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['cosine_similarity', 'val_cosine_similarity'], loc='lower right')\n",
    "plt.savefig('./ann_pict/cosine_similarity.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(history.history['logcosh'])\n",
    "plt.plot(history.history['val_logcosh'])\n",
    "plt.title('Model logcosh')\n",
    "plt.ylabel('logcosh')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['logcosh', 'val_logcosh'], loc='upper right')\n",
    "plt.savefig('./ann_pict/logcosh.png', dpi=100, format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('ANN.h5')\n",
    "#model = load_model('ANN.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_model(model, to_file='./ann_pict/model.png', show_shapes=True)\n",
    "#Image('model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file5 = uproot.open(\"./data/gun_k0L_5gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file10 = uproot.open(\"./data/gun_k0L_10gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file15 = uproot.open(\"./data/gun_k0L_15gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file20 = uproot.open(\"./data/gun_k0L_20gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file25 = uproot.open(\"./data/gun_k0L_25gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file30 = uproot.open(\"./data/gun_k0L_30gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file40 = uproot.open(\"./data/gun_k0L_40gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file50 = uproot.open(\"./data/gun_k0L_50gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file60 = uproot.open(\"./data/gun_k0L_60gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file1 = uproot.open(\"./data/data_other/gun_k0L_1gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file2 = uproot.open(\"./data/data_other/gun_k0L_2gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file3 = uproot.open(\"./data/data_other/gun_k0L_3gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file4 = uproot.open(\"./data/data_other/gun_k0L_4gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file70 = uproot.open(\"./data/data_other/gun_k0L_70gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file80 = uproot.open(\"./data/data_other/gun_k0L_80gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file90 = uproot.open(\"./data/data_other/gun_k0L_90gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "\n",
    "file7 = uproot.open(\"./data/data_other/gun_k0L_7gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file45 = uproot.open(\"./data/data_other/gun_k0L_45gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "\n",
    "file120 = uproot.open(\"./data/data_other/gun_k0L_120gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file6 = uproot.open(\"./data/data_other/gun_k0L_6gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file8 = uproot.open(\"./data/data_other/gun_k0L_8gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file9 = uproot.open(\"./data/data_other/gun_k0L_9gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file12 = uproot.open(\"./data/data_other/gun_k0L_12gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file14 = uproot.open(\"./data/data_other/gun_k0L_14gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file16 = uproot.open(\"./data/data_other/gun_k0L_16gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file18 = uproot.open(\"./data/data_other/gun_k0L_18gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file35 = uproot.open(\"./data/data_other/gun_k0L_35gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file55 = uproot.open(\"./data/data_other/gun_k0L_55gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")\n",
    "file65 = uproot.open(\"./data/data_other/gun_k0L_65gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f5 = uproot.pandas.iterate(\"./data/gun_k0L_5gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file5 = pd.concat(list(f5))\n",
    "\n",
    "f10 = uproot.pandas.iterate(\"./data/gun_k0L_10gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file10 = pd.concat(list(f10))\n",
    "\n",
    "f15 = uproot.pandas.iterate(\"./data/gun_k0L_15gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file15 = pd.concat(list(f15))\n",
    "\n",
    "f20 = uproot.pandas.iterate(\"./data/gun_k0L_20gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file20 = pd.concat(list(f20))\n",
    "\n",
    "f25 = uproot.pandas.iterate(\"./data/gun_k0L_25gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file25 = pd.concat(list(f25))\n",
    "\n",
    "f30 = uproot.pandas.iterate(\"./data/gun_k0L_30gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file30 = pd.concat(list(f30))\n",
    "\n",
    "f40 = uproot.pandas.iterate(\"./data/gun_k0L_40gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file40 = pd.concat(list(f40))\n",
    "\n",
    "f50 = uproot.pandas.iterate(\"./data/gun_k0L_50gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file50 = pd.concat(list(f50))\n",
    "\n",
    "f60 = uproot.pandas.iterate(\"./data/gun_k0L_60gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file60 = pd.concat(list(f60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_1gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file1 = pd.concat(list(f1))\n",
    "\n",
    "f2 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_2gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file2 = pd.concat(list(f2))\n",
    "\n",
    "f3 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_3gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file3 = pd.concat(list(f3))\n",
    "\n",
    "f4 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_4gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file4 = pd.concat(list(f4))\n",
    "\n",
    "f70 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_70gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file70 = pd.concat(list(f70))\n",
    "\n",
    "f80 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_80gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file80 = pd.concat(list(f80))\n",
    "\n",
    "f90 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_90gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file90 = pd.concat(list(f90))\n",
    "\n",
    "\n",
    "f7 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_7gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file7 = pd.concat(list(f7))\n",
    "\n",
    "f45 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_45gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file45 = pd.concat(list(f45))\n",
    "\n",
    "\n",
    "f120 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_120gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file120 = pd.concat(list(f120))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f6 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_6gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file6 = pd.concat(list(f6))\n",
    "\n",
    "f8 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_8gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file8 = pd.concat(list(f8))\n",
    "\n",
    "f9 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_9gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file9 = pd.concat(list(f9))\n",
    "\n",
    "f12 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_12gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file12 = pd.concat(list(f12))\n",
    "\n",
    "f14 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_14gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file14 = pd.concat(list(f14))\n",
    "\n",
    "f16 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_16gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file16 = pd.concat(list(f16))\n",
    "\n",
    "f18 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_18gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file18 = pd.concat(list(f18))\n",
    "\n",
    "f35 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_35gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file35 = pd.concat(list(f35))\n",
    "\n",
    "f55 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_55gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file55 = pd.concat(list(f55))\n",
    "\n",
    "f65 = uproot.pandas.iterate(\"./data/data_other/gun_k0L_65gev_FTFP_BERT_5000evt_ILD_l5_v02steel.root\", fTreeName, branches)\n",
    "file65 = pd.concat(list(f65))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out5 = model.predict( file5 )\n",
    "out10 = model.predict( file10 )\n",
    "out15 = model.predict( file15 )\n",
    "out20 = model.predict( file20 )\n",
    "out25 = model.predict( file25 )\n",
    "out30 = model.predict( file30 )\n",
    "out40 = model.predict( file40 )\n",
    "out50 = model.predict( file50 )\n",
    "out60 = model.predict( file60 )\n",
    "\n",
    "out_train = model.predict( x_train )\n",
    "out_test = model.predict( x_test )\n",
    "#out_test = model.predict( x_test[ branches ].values )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out1 = model.predict( file1 )\n",
    "out2 = model.predict( file2 )\n",
    "out3 = model.predict( file3 )\n",
    "out4 = model.predict( file4 )\n",
    "out70 = model.predict( file70 )\n",
    "out80 = model.predict( file80 )\n",
    "out90 = model.predict( file90 )\n",
    "\n",
    "out7 = model.predict( file7 )\n",
    "out45 = model.predict( file45 )\n",
    "\n",
    "out120 = model.predict( file120 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out6 = model.predict( file6 )\n",
    "out8 = model.predict( file8 )\n",
    "out9 = model.predict( file9 )\n",
    "out12 = model.predict( file12 )\n",
    "out14 = model.predict( file14 )\n",
    "out16 = model.predict( file16 )\n",
    "out18 = model.predict( file18 )\n",
    "out35 = model.predict( file35 )\n",
    "out55 = model.predict( file55 )\n",
    "out65 = model.predict( file65 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = ROOT.TFile('ann.root', 'recreate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree5 = ROOT.TTree( 'tree5', 'tree5' )\n",
    "tree10 = ROOT.TTree( 'tree10', 'tree10' )\n",
    "tree15 = ROOT.TTree( 'tree15', 'tree15' )\n",
    "tree20 = ROOT.TTree( 'tree20', 'tree20' )\n",
    "tree25 = ROOT.TTree( 'tree25', 'tree25' )\n",
    "tree30 = ROOT.TTree( 'tree30', 'tree30' )\n",
    "tree40 = ROOT.TTree( 'tree40', 'tree40' )\n",
    "tree50 = ROOT.TTree( 'tree50', 'tree50' )\n",
    "tree60 = ROOT.TTree( 'tree60', 'tree60' )\n",
    "\n",
    "tree_train = ROOT.TTree( 'tree_train', 'tree_train' )\n",
    "tree_test = ROOT.TTree( 'tree_test', 'tree_test' )\n",
    "\n",
    "before_train = ROOT.TTree( 'before_train', 'before_train' )\n",
    "before_test = ROOT.TTree( 'before_test', 'before_test' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = ROOT.TTree( 'tree1', 'tree1' )\n",
    "tree2 = ROOT.TTree( 'tree2', 'tree2' )\n",
    "tree3 = ROOT.TTree( 'tree3', 'tree3' )\n",
    "tree4 = ROOT.TTree( 'tree4', 'tree4' )\n",
    "tree70 = ROOT.TTree( 'tree70', 'tree70' )\n",
    "tree80 = ROOT.TTree( 'tree80', 'tree80' )\n",
    "tree90 = ROOT.TTree( 'tree90', 'tree90' )\n",
    "\n",
    "tree7 = ROOT.TTree( 'tree7', 'tree7' )\n",
    "tree45 = ROOT.TTree( 'tree45', 'tree45' )\n",
    "\n",
    "tree120 = ROOT.TTree( 'tree120', 'tree120' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree6 = ROOT.TTree( 'tree6', 'tree6' )\n",
    "tree8 = ROOT.TTree( 'tree8', 'tree8' )\n",
    "tree9 = ROOT.TTree( 'tree9', 'tree9' )\n",
    "tree12 = ROOT.TTree( 'tree12', 'tree12' )\n",
    "tree14 = ROOT.TTree( 'tree14', 'tree14' )\n",
    "tree16 = ROOT.TTree( 'tree16', 'tree16' )\n",
    "tree18 = ROOT.TTree( 'tree18', 'tree18' )\n",
    "tree35 = ROOT.TTree( 'tree35', 'tree35' )\n",
    "tree55 = ROOT.TTree( 'tree55', 'tree55' )\n",
    "tree65 = ROOT.TTree( 'tree65', 'tree65' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann5 = array( 'f', [ 0.0 ] )\n",
    "ann10 = array( 'f', [ 0.0 ] )\n",
    "ann15 = array( 'f', [ 0.0 ] )\n",
    "ann20 = array( 'f', [ 0.0 ] )\n",
    "ann25 = array( 'f', [ 0.0 ] )\n",
    "ann30 = array( 'f', [ 0.0 ] )\n",
    "ann40 = array( 'f', [ 0.0 ] )\n",
    "ann50 = array( 'f', [ 0.0 ] )\n",
    "ann60 = array( 'f', [ 0.0 ] )\n",
    "\n",
    "ann_train = array( 'f', [ 0.0 ] )\n",
    "ann_test = array( 'f', [ 0.0 ] )\n",
    "\n",
    "train = array( 'f', [ 0.0 ] )\n",
    "test = array( 'f', [ 0.0 ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann1 = array( 'f', [ 0.0 ] )\n",
    "ann2 = array( 'f', [ 0.0 ] )\n",
    "ann3 = array( 'f', [ 0.0 ] )\n",
    "ann4 = array( 'f', [ 0.0 ] )\n",
    "ann70 = array( 'f', [ 0.0 ] )\n",
    "ann80 = array( 'f', [ 0.0 ] )\n",
    "ann90 = array( 'f', [ 0.0 ] )\n",
    "\n",
    "ann7 = array( 'f', [ 0.0 ] )\n",
    "ann45 = array( 'f', [ 0.0 ] )\n",
    "\n",
    "ann120 = array( 'f', [ 0.0 ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann6 = array( 'f', [ 0.0 ] )\n",
    "ann8 = array( 'f', [ 0.0 ] )\n",
    "ann9 = array( 'f', [ 0.0 ] )\n",
    "ann12 = array( 'f', [ 0.0 ] )\n",
    "ann14 = array( 'f', [ 0.0 ] )\n",
    "ann16 = array( 'f', [ 0.0 ] )\n",
    "ann18 = array( 'f', [ 0.0 ] )\n",
    "ann35 = array( 'f', [ 0.0 ] )\n",
    "ann55 = array( 'f', [ 0.0 ] )\n",
    "ann65 = array( 'f', [ 0.0 ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree5.Branch( 'ann5', ann5, 'ann5/F' )\n",
    "tree10.Branch( 'ann10', ann10, 'ann10/F' )\n",
    "tree15.Branch( 'ann15', ann15, 'ann15/F' )\n",
    "tree20.Branch( 'ann20', ann20, 'ann20/F' )\n",
    "tree25.Branch( 'ann25', ann25, 'ann25/F' )\n",
    "tree30.Branch( 'ann30', ann30, 'ann30/F' )\n",
    "tree40.Branch( 'ann40', ann40, 'ann40/F' )\n",
    "tree50.Branch( 'ann50', ann50, 'ann50/F' )\n",
    "tree60.Branch( 'ann60', ann60, 'ann60/F' )\n",
    "\n",
    "tree_train.Branch( 'ann_train', ann_train, 'ann_train/F' )\n",
    "tree_test.Branch( 'ann_test', ann_test, 'ann_test/F' )\n",
    "\n",
    "before_train.Branch( 'train', train, 'train/F' )\n",
    "before_test.Branch( 'test', test, 'test/F' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1.Branch( 'ann1', ann1, 'ann1/F' )\n",
    "tree2.Branch( 'ann2', ann2, 'ann2/F' )\n",
    "tree3.Branch( 'ann3', ann3, 'ann3/F' )\n",
    "tree4.Branch( 'ann4', ann4, 'ann4/F' )\n",
    "tree70.Branch( 'ann70', ann70, 'ann70/F' )\n",
    "tree80.Branch( 'ann80', ann80, 'ann80/F' )\n",
    "tree90.Branch( 'ann90', ann90, 'ann90/F' )\n",
    "\n",
    "tree7.Branch( 'ann7', ann7, 'ann7/F' )\n",
    "tree45.Branch( 'ann45', ann45, 'ann45/F' )\n",
    "\n",
    "tree120.Branch( 'ann120', ann120, 'ann120/F' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree6.Branch( 'ann6', ann6, 'ann6/F' )\n",
    "tree8.Branch( 'ann8', ann8, 'ann8/F' )\n",
    "tree9.Branch( 'ann9', ann9, 'ann9/F' )\n",
    "tree12.Branch( 'ann12', ann12, 'ann12/F' )\n",
    "tree14.Branch( 'ann14', ann14, 'ann14/F' )\n",
    "tree16.Branch( 'ann16', ann16, 'ann16/F' )\n",
    "tree18.Branch( 'ann18', ann18, 'ann18/F' )\n",
    "tree35.Branch( 'ann35', ann35, 'ann35/F' )\n",
    "tree55.Branch( 'ann55', ann55, 'ann55/F' )\n",
    "tree65.Branch( 'ann65', ann65, 'ann65/F' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train_energy = x_train.energy.to_numpy(x_train.energy)\n",
    "#x_test_energy = x_test.energy.to_numpy(x_test.energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range( 0, len( out5 ) ):\n",
    "    ann5[ 0 ] = out5[ i ]\n",
    "    tree5.Fill()\n",
    "\n",
    "for i in range( 0, len( out10 ) ):\n",
    "    ann10[ 0 ] = out10[ i ]\n",
    "    tree10.Fill()\n",
    "\n",
    "for i in range( 0, len( out15 ) ):\n",
    "    ann15[ 0 ] = out15[ i ]\n",
    "    tree15.Fill()\n",
    "\n",
    "for i in range( 0, len( out20 ) ):\n",
    "    ann20[ 0 ] = out20[ i ]\n",
    "    tree20.Fill()\n",
    "\n",
    "for i in range( 0, len( out25 ) ):\n",
    "    ann25[ 0 ] = out25[ i ]\n",
    "    tree25.Fill()\n",
    "\n",
    "for i in range( 0, len( out30 ) ):\n",
    "    ann30[ 0 ] = out30[ i ]\n",
    "    tree30.Fill()\n",
    "\n",
    "for i in range( 0, len( out40 ) ):\n",
    "    ann40[ 0 ] = out40[ i ]\n",
    "    tree40.Fill()\n",
    "\n",
    "for i in range( 0, len( out50 ) ):\n",
    "    ann50[ 0 ] = out50[ i ]\n",
    "    tree50.Fill()\n",
    "\n",
    "for i in range( 0, len( out60 ) ):\n",
    "    ann60[ 0 ] = out60[ i ]\n",
    "    tree60.Fill()\n",
    "    \n",
    "    \n",
    "for i in range( 0, len( out_train ) ):\n",
    "    ann_train[ 0 ] = out_train[ i ]\n",
    "    tree_train.Fill()\n",
    "    \n",
    "for i in range( 0, len( out_test ) ):\n",
    "    ann_test[ 0 ] = out_test[ i ]\n",
    "    tree_test.Fill()\n",
    "\n",
    "    \n",
    "#for i in range( 0, len( x_train_energy) ):\n",
    "#    train[ 0 ] = x_train_energy[ i ]\n",
    "#    before_train.Fill()\n",
    "    \n",
    "#for i in range( 0, len( x_test_energy ) ):\n",
    "#    test[ 0 ] = x_test_energy[ i ]\n",
    "#    before_test.Fill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range( 0, len( out1 ) ):\n",
    "    ann1[ 0 ] = out1[ i ]\n",
    "    tree1.Fill()\n",
    "\n",
    "for i in range( 0, len( out2 ) ):\n",
    "    ann2[ 0 ] = out2[ i ]\n",
    "    tree2.Fill()\n",
    "\n",
    "for i in range( 0, len( out3 ) ):\n",
    "    ann3[ 0 ] = out3[ i ]\n",
    "    tree3.Fill()\n",
    "\n",
    "for i in range( 0, len( out4 ) ):\n",
    "    ann4[ 0 ] = out4[ i ]\n",
    "    tree4.Fill()\n",
    "\n",
    "for i in range( 0, len( out70 ) ):\n",
    "    ann70[ 0 ] = out70[ i ]\n",
    "    tree70.Fill()\n",
    "\n",
    "for i in range( 0, len( out80 ) ):\n",
    "    ann80[ 0 ] = out80[ i ]\n",
    "    tree80.Fill()\n",
    "\n",
    "for i in range( 0, len( out90 ) ):\n",
    "    ann90[ 0 ] = out90[ i ]\n",
    "    tree90.Fill()\n",
    "\n",
    "    \n",
    "for i in range( 0, len( out7 ) ):\n",
    "    ann7[ 0 ] = out7[ i ]\n",
    "    tree7.Fill()\n",
    "\n",
    "for i in range( 0, len( out45 ) ):\n",
    "    ann45[ 0 ] = out45[ i ]\n",
    "    tree45.Fill()\n",
    "    \n",
    "\n",
    "for i in range( 0, len( out120 ) ):\n",
    "    ann120[ 0 ] = out120[ i ]\n",
    "    tree120.Fill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range( 0, len( out6 ) ):\n",
    "    ann6[ 0 ] = out6[ i ]\n",
    "    tree6.Fill()\n",
    "    \n",
    "for i in range( 0, len( out8 ) ):\n",
    "    ann8[ 0 ] = out8[ i ]\n",
    "    tree8.Fill()\n",
    "    \n",
    "for i in range( 0, len( out9 ) ):\n",
    "    ann9[ 0 ] = out9[ i ]\n",
    "    tree9.Fill()\n",
    "    \n",
    "for i in range( 0, len( out12 ) ):\n",
    "    ann12[ 0 ] = out12[ i ]\n",
    "    tree12.Fill()\n",
    "    \n",
    "for i in range( 0, len( out14 ) ):\n",
    "    ann14[ 0 ] = out14[ i ]\n",
    "    tree14.Fill()\n",
    "    \n",
    "for i in range( 0, len( out16 ) ):\n",
    "    ann16[ 0 ] = out16[ i ]\n",
    "    tree16.Fill()\n",
    "    \n",
    "for i in range( 0, len( out18 ) ):\n",
    "    ann18[ 0 ] = out18[ i ]\n",
    "    tree18.Fill()\n",
    "    \n",
    "for i in range( 0, len( out35 ) ):\n",
    "    ann35[ 0 ] = out35[ i ]\n",
    "    tree35.Fill()\n",
    "    \n",
    "for i in range( 0, len( out55 ) ):\n",
    "    ann55[ 0 ] = out55[ i ]\n",
    "    tree55.Fill()\n",
    "    \n",
    "for i in range( 0, len( out65 ) ):\n",
    "    ann65[ 0 ] = out65[ i ]\n",
    "    tree65.Fill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.Write()\n",
    "ann.Close()"
   ]
  },
  {
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   "outputs": [],
   "source": []
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