Evaluator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb44c8fc-7fd5-40b1-a047-16c566f3c4e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-01 19:31:27.889277: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Necessary modules loaded.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import scipy.sparse as sparse #for numpy.array - pd.dataframe column conversion\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys, math, json\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "print('Necessary modules loaded.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7b5e873-4d94-461d-aae8-25cbf604ed51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folders ready.\n"
     ]
    }
   ],
   "source": [
    "os.makedirs('plots/performance', exist_ok=True)\n",
    "os.makedirs('plots/kinematics', exist_ok=True)\n",
    "os.makedirs('plots/input_variables', exist_ok=True)\n",
    "os.makedirs('plots/per_sample_score', exist_ok=True)\n",
    "os.makedirs('plots/efficiency', exist_ok=True)\n",
    "print('Folders ready.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ebfd08-d6a8-45e8-aabb-51e9e03a7013",
   "metadata": {},
   "source": [
    "## Preparing the dataframe\n",
    "### Reading the input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2dc78858-b311-42e9-a400-62ce4e6c88a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files read.\n"
     ]
    }
   ],
   "source": [
    "###########################################\n",
    "#Set global parameters here:\n",
    "model_to_use = 'var11_endcap'\n",
    "region_name  = 'barrel region'\n",
    "figtype      = region_name.split(' ')[0]\n",
    "debug = False\n",
    "###########################################\n",
    "\n",
    "def csv_to_df(samplename_, nrows_):\n",
    "    df = pd.read_csv(f'FlattenedCSVFiles/df_{samplename_}.csv', nrows=nrows_)\n",
    "    df['sample'] = samplename_\n",
    "    df = df[df['phoPt']>10] #Global pT cut\n",
    "    return df\n",
    "\n",
    "df_gjet = pd.concat([\n",
    "    csv_to_df('GJet_PT-10to40', 150000),\n",
    "    csv_to_df('GJet_PT-40', 150000)\n",
    "])\n",
    "df_gjet = df_gjet.reset_index(drop=True)\n",
    "\n",
    "df_qcd = pd.concat([\n",
    "    csv_to_df('QCD_PT-80to120', 50000),\n",
    "    csv_to_df('QCD_PT-120to170', 50000),\n",
    "    csv_to_df('QCD_PT-170to300', 50000),\n",
    "    csv_to_df('QCD_PT-300toInf', 50000)\n",
    "])\n",
    "df_qcd = df_qcd.reset_index(drop=True)\n",
    "\n",
    "df_taugun = pd.concat([\n",
    "    csv_to_df('TauGun_E-10to100', 50000),\n",
    "    csv_to_df('TauGun_E-100to3000', 50000)\n",
    "])\n",
    "df_taugun = df_taugun.reset_index(drop=True)\n",
    "\n",
    "print('Files read.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ddabb5-3fc6-4c42-9864-ccb8762fdf7c",
   "metadata": {},
   "source": [
    "### Defining signal and background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3210d8f3-3bca-4e5f-aca0-288a7d6c54dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal and background defined.\n"
     ]
    }
   ],
   "source": [
    "def filter_signal(df):\n",
    "    # Note: When a candidate matches with a gen-particle, it's genpt is NOT -999\n",
    "    signal_flag = (df['isPhotonMatching']==1) & (df['isPromptFinalState'] == 1)\n",
    "    filtered_df = df[signal_flag]\n",
    "    return filtered_df\n",
    "\n",
    "def filter_background(df):\n",
    "    background_flag1 = (df['isPhotonMatching']==0) # In case of fakes\n",
    "    background_flag2 = (df['isPhotonMatching']==1) & (df['isPromptFinalState'] == 0) # Matches with gen, but non-prompt\n",
    "    filtered_df = df[background_flag1 | background_flag2]\n",
    "    return filtered_df\n",
    "    \n",
    "df_sig = filter_signal(df_gjet)\n",
    "df_sig['truth'] = 1 #Target is what the NN tries to predict\n",
    "df_sig = df_sig.reset_index(drop=True)\n",
    "\n",
    "df_bkg = pd.concat([filter_background(df_qcd), filter_background(df_taugun)])\n",
    "df_bkg['truth'] = 0\n",
    "df_bkg = df_bkg.reset_index(drop=True)\n",
    "\n",
    "data_all = pd.concat([df_sig, df_bkg])\n",
    "data_all = data_all[data_all['phoPt']>10] #Offline Pt cut\n",
    "data_all = data_all.sample(frac=1).reset_index(drop=True) #Randomizing the rows\n",
    "\n",
    "print('Signal and background defined.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b85493d0-4809-432b-b761-443ce4e9355d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['phoE', 'phoEt', 'phoPt', 'phoEta', 'phoPhi', 'phoHoverE',\n",
       "       'phoTrkSumPtHollow', 'phoEcalRecHit', 'phoSigmaIEtaIEta3x3',\n",
       "       'phoSigmaIEtaIPhiFull5x5', 'phoSigmaIEtaIEtaFull5x5',\n",
       "       'phoEcalPFClusterIso', 'phoHcalPFClusterIso', 'phoHasPixelSeed',\n",
       "       'phoR93x3', 'phoHcalTower', 'phoHoverECone', 'phoHoverEValid',\n",
       "       'phoR9Full5x5', 'phoSigmaIPhiIPhiFull5x5', 'phoTrkSumPtSolid',\n",
       "       'isPromptFinalState', 'isDirectHardProcessTauDecayProductFinalState',\n",
       "       'isDirectHadronDecayProduct', 'isTauDecayProduct',\n",
       "       'isPromptTauDecayProduct', 'isDirectTauDecayProduct',\n",
       "       'isDirectPromptTauDecayProduct',\n",
       "       'isDirectPromptTauDecayProductFinalState', 'isHardProcess',\n",
       "       'fromHardProcessFinalState', 'fromHardProcessDecayed',\n",
       "       'phoMatchingGenPt', 'isPionMother', 'isPFPhoton', 'isPhotonMatching',\n",
       "       'sample', 'truth'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4245c5b1-0eca-4c56-a987-823e916aea09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phoPt</th>\n",
       "      <th>phoEta</th>\n",
       "      <th>phoPhi</th>\n",
       "      <th>isPhotonMatching</th>\n",
       "      <th>isPromptFinalState</th>\n",
       "      <th>isPFPhoton</th>\n",
       "      <th>sample</th>\n",
       "      <th>truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>371.838837</td>\n",
       "      <td>-1.479682</td>\n",
       "      <td>-1.387012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-300toInf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.048531</td>\n",
       "      <td>-0.941561</td>\n",
       "      <td>2.282521</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GJet_PT-40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.437684</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>2.650225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.172512</td>\n",
       "      <td>-1.433932</td>\n",
       "      <td>-2.322273</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.627117</td>\n",
       "      <td>0.578476</td>\n",
       "      <td>0.386640</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446362</th>\n",
       "      <td>38.330750</td>\n",
       "      <td>2.001185</td>\n",
       "      <td>-1.340963</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446363</th>\n",
       "      <td>34.884296</td>\n",
       "      <td>-0.777811</td>\n",
       "      <td>-2.109243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>GJet_PT-10to40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446364</th>\n",
       "      <td>45.891792</td>\n",
       "      <td>0.441985</td>\n",
       "      <td>1.248863</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446365</th>\n",
       "      <td>12.367259</td>\n",
       "      <td>0.271302</td>\n",
       "      <td>-1.079471</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446366</th>\n",
       "      <td>26.876453</td>\n",
       "      <td>1.143012</td>\n",
       "      <td>1.603428</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GJet_PT-10to40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446367 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             phoPt    phoEta    phoPhi  isPhotonMatching  isPromptFinalState  \\\n",
       "0       371.838837 -1.479682 -1.387012                 1                   0   \n",
       "1        39.048531 -0.941561  2.282521                 1                   1   \n",
       "2        12.437684  0.029402  2.650225                 1                   0   \n",
       "3        14.172512 -1.433932 -2.322273                 1                   0   \n",
       "4        51.627117  0.578476  0.386640                 1                   0   \n",
       "...            ...       ...       ...               ...                 ...   \n",
       "446362   38.330750  2.001185 -1.340963                 1                   0   \n",
       "446363   34.884296 -0.777811 -2.109243                 1                   1   \n",
       "446364   45.891792  0.441985  1.248863                 1                   0   \n",
       "446365   12.367259  0.271302 -1.079471                 0                -999   \n",
       "446366   26.876453  1.143012  1.603428                 1                   1   \n",
       "\n",
       "        isPFPhoton            sample  truth  \n",
       "0                0   QCD_PT-300toInf      0  \n",
       "1                1        GJet_PT-40      1  \n",
       "2                0  TauGun_E-10to100      0  \n",
       "3                0  TauGun_E-10to100      0  \n",
       "4                0    QCD_PT-80to120      0  \n",
       "...            ...               ...    ...  \n",
       "446362           0    QCD_PT-80to120      0  \n",
       "446363           0    GJet_PT-10to40      1  \n",
       "446364           0  TauGun_E-10to100      0  \n",
       "446365           0    QCD_PT-80to120      0  \n",
       "446366           1    GJet_PT-10to40      1  \n",
       "\n",
       "[446367 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all[['phoPt','phoEta','phoPhi','isPhotonMatching','isPromptFinalState','isPFPhoton','sample','truth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f29d326-4eb1-4357-9528-c5343f0b39a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics of all the candidates:\n",
      "nPhoton gen-matching = 338459\n",
      "nPhoton gen-matching and prompt (sig) = 154175 (45.6 % of all matching candidates)\n",
      "nPhoton gen-matching and non-prompt (bkg) = 184284 (54.4 % of all matching candidates)\n",
      "nPhoton not gen-matching (bkg) = 107908\n",
      "------------------------------------------------------------\n",
      "Total signal     = 154175 (all prompt, gen-matching)\n",
      "Total background = 292192 (36.9 % fake + 63.1 % non-prompt)\n",
      "------------------------------------------------------------\n",
      "Signal and background from each sample:\n",
      "Sample                    Signal     Background (non-prompt, fake)\n",
      "QCD_PT-300toInf           0          46865 (74.6 % + 25.4 %)\n",
      "GJet_PT-40                79384      0\n",
      "TauGun_E-10to100          0          50000 (58.0 % + 42.0 %)\n",
      "QCD_PT-80to120            0          48878 (60.2 % + 39.8 %)\n",
      "QCD_PT-170to300           0          47950 (69.9 % + 30.1 %)\n",
      "TauGun_E-100to3000        0          50000 (50.6 % + 49.4 %)\n",
      "QCD_PT-120to170           0          48499 (66.1 % + 33.9 %)\n",
      "GJet_PT-10to40            74791      0\n"
     ]
    }
   ],
   "source": [
    "print('Statistics of all the candidates:')\n",
    "nGenMatching = len(data_all.query(\"(isPhotonMatching==1)\"))\n",
    "nNotMatching = len(data_all.query(\"(isPhotonMatching==0)\"))\n",
    "nMatchingPrompt = len(data_all.query(\"(isPhotonMatching==1) & (isPromptFinalState) == 1\"))\n",
    "nMatchingNonPrompt = len(data_all.query(\"(isPhotonMatching==1) & (isPromptFinalState) == 0\"))\n",
    "nSig = len(data_all.query(\"(truth==1)\"))\n",
    "nBkg = len(data_all.query(\"(truth==0)\"))\n",
    "\n",
    "print(f'nPhoton gen-matching = {nGenMatching}')\n",
    "print(f'nPhoton gen-matching and prompt (sig) = {nMatchingPrompt} ({nMatchingPrompt*100/nGenMatching:.1f} % of all matching candidates)')\n",
    "print(f'nPhoton gen-matching and non-prompt (bkg) = {nMatchingNonPrompt} ({nMatchingNonPrompt*100/nGenMatching:.1f} % of all matching candidates)')\n",
    "print(f'nPhoton not gen-matching (bkg) = {nNotMatching}')\n",
    "print('-'*60)\n",
    "print(f'Total signal     = {nSig} (all prompt, gen-matching)')\n",
    "print(f'Total background = {nBkg} ({nNotMatching*100/nBkg:.1f} % fake + {nMatchingNonPrompt*100/nBkg:.1f} % non-prompt)')\n",
    "print('-'*60)\n",
    "\n",
    "unique_samples = data_all['sample'].unique()\n",
    "\n",
    "print('Signal and background from each sample:')\n",
    "print('Sample'.ljust(25),'Signal'.ljust(10),'Background (non-prompt, fake)')\n",
    "for s in unique_samples:\n",
    "    nsig = len(data_all.query(f'(sample==\"{s}\") & (truth == 1)'))\n",
    "    nbkg = len(data_all.query(f'(sample == \"{s}\") & (truth == 0)'))\n",
    "    genmatch = len(data_all.query(f'(sample == \"{s}\") & (truth == 0) & (isPhotonMatching==1)'))\n",
    "    fake = len(data_all.query(f'(sample == \"{s}\") & (truth == 0) & (isPhotonMatching==0)'))\n",
    "    if nbkg != 0:\n",
    "        genfrac = genmatch*100/nbkg\n",
    "        fakefrac = fake*100/nbkg\n",
    "    sigstat = str(nsig)\n",
    "    bkgstat = str(0)\n",
    "    if nbkg!=0 : bkgstat = f'{nbkg}'.ljust(6) +f'({genfrac:.1f} % + {fakefrac:.1f} %)'\n",
    "    print(s.ljust(25), sigstat.ljust(10), bkgstat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b466d425-4663-4af9-9a49-bc5e351a2574",
   "metadata": {},
   "source": [
    "### Filtering against barrel, endcap, or forward region\n",
    "- For barrel, `abs(eta) < 1.442`\n",
    "- For endcap, `abs(eta) > 1.566`\n",
    "- For foward region, `abs(eta) > 2.5`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c04252e5-f7bb-4c75-b4bc-2fff291269dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eta-cut applied between 0 - 1.442\n",
      "Dataframe is ready for evaluation.\n",
      "----------------------------------------\n",
      "Statistics of the cadidates chosen for evaluation:\n",
      "nSig = 86992 (all GJet)\n",
      "nBkg = 172256 (61.5% QCD + 38.5% TauGun)\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "eta_range=None\n",
    "if 'barrel' in region_name:\n",
    "    data = data_all[abs(data_all['phoEta']) < 1.442 ]\n",
    "    eta_range = '[abs(eta)<1.442]'\n",
    "    print('Eta-cut applied between 0 - 1.442')\n",
    "elif 'endcap' in region_name:\n",
    "    data = data_all[(abs(data_all['phoEta']) > 1.556) & (abs(data_all['phoEta']) < 2.4)]\n",
    "    eta_range = '[1.556<abs(eta)<2.5]'\n",
    "    print('Eta-cut applied between 1.556 - 2.5')\n",
    "elif 'forward' in region_name:\n",
    "    data = data_all[(abs(data_all['phoEta']) > 2.5) & (abs(data_all['phoEta']) < 3.0)]\n",
    "    eta_range = '[2.5<abs(eta)<3.0]'\n",
    "    print('Eta-cut applied between 2.5 - 3.0')\n",
    "\n",
    "data = data.reset_index(drop=True)\n",
    "print('Dataframe is ready for evaluation.')\n",
    "\n",
    "nsig = len(data.query('(truth==1)'))\n",
    "nbkg = len(data.query('(truth==0)'))\n",
    "nqcd = len(data.query('sample.str.contains(\"QCD\")', engine='python'))\n",
    "ntau = len(data.query('sample.str.contains(\"Tau\")', engine='python'))\n",
    "frac_qcd = 0\n",
    "frac_tau = 0\n",
    "if nbkg !=0 :\n",
    "    frac_qcd = (nqcd/nbkg)*100\n",
    "    frac_tau = (ntau/nbkg)*100\n",
    "\n",
    "print('-'*40)\n",
    "print('Statistics of the cadidates chosen for evaluation:')\n",
    "print(f'nSig = {nsig} (all GJet)')\n",
    "print(f'nBkg = {nbkg} ({frac_qcd:.1f}% QCD + {frac_tau:.1f}% TauGun)')\n",
    "print('-'*40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4576a1-5294-4ece-addd-accc62b88f0c",
   "metadata": {},
   "source": [
    "### Kinematics of the candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a798163b-62e0-4e97-950c-ed57fcd886ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_kinematics(df):\n",
    "    kinematic_var = [\n",
    "        (\"phoPt\", 0, 200),\n",
    "        (\"phoEta\", -4, 4),\n",
    "        (\"phoPhi\", -4, 4),\n",
    "        (\"phoE\", 0, 500),\n",
    "    ]\n",
    "    ncol = 4\n",
    "    nrow = (len(kinematic_var) + ncol - 1) // ncol\n",
    "    fig, axs = plt.subplots(nrow, ncol, figsize=(15, nrow * 3.5))\n",
    "    axs = axs.flatten()\n",
    "    \n",
    "    for i, (var, xmin, xmax) in enumerate(kinematic_var):\n",
    "        ax = axs[i]\n",
    "        sig_data = df[df['truth']==1][var].dropna()\n",
    "        bkg_data = df[df['truth']==0][var].dropna()\n",
    "        nbins = 50\n",
    "        mybins = np.linspace(xmin, xmax, nbins)\n",
    "    \n",
    "        #Decorations:\n",
    "        sig_label = f'sig ({len(sig_data)})'\n",
    "        bkg_label = f'bkg ({len(bkg_data)})'\n",
    "        sig_color = 'xkcd:green'\n",
    "        bkg_color = 'xkcd:blue'\n",
    "        common_hist_props = {\n",
    "        'histtype': 'step',\n",
    "        'density': True,\n",
    "        'linewidth': 2,\n",
    "        }\n",
    "        ax.hist(sig_data, bins=mybins, label=sig_label, color=sig_color, **common_hist_props)\n",
    "        ax.hist(bkg_data, bins=mybins, label=bkg_label, color=bkg_color, **common_hist_props)\n",
    "        ax.set_xlim(xmin, xmax)\n",
    "        ax.set_title(f'{var}')\n",
    "        ax.legend(loc='best')\n",
    "\n",
    "    # Remove any unused subplots\n",
    "    for i in range(len(kinematic_var), len(axs)): fig.delaxes(axs[i])\n",
    "    fig.suptitle(f'Kinematics in the {region_name} {eta_range}', fontsize=16)\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.97])\n",
    "    plt.subplots_adjust(hspace=0.25) #do this after tight layout   \n",
    "    if not debug: plt.savefig(f'plots/kinematics/kinematics_{figtype}.png', dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "103e5837-7ddd-4037-a463-fd2b705653dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kinematics(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f01308-96eb-4680-8d2e-f11c8bc4a981",
   "metadata": {},
   "source": [
    "## Loading the model and the training variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcc39475-9aba-4680-8892-3aada5c04a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model: var11_endcap ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-01 19:31:36.969848: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is loaded.\n",
      "\n",
      "0. Updated the the min-max values for phoHoverE\n",
      "1. Updated the the min-max values for phoTrkSumPtHollow\n",
      "2. Updated the the min-max values for phoEcalRecHit\n",
      "3. Updated the the min-max values for phoSigmaIEtaIEta3x3\n",
      "4. Updated the the min-max values for phoSigmaIEtaIEtaFull5x5\n",
      "5. Updated the the min-max values for phoSigmaIEtaIPhiFull5x5\n",
      "6. Updated the the min-max values for phoEcalPFClusterIso\n",
      "7. Updated the the min-max values for phoHcalPFClusterIso\n",
      "8. Updated the the min-max values for phoHasPixelSeed\n",
      "9. Updated the the min-max values for phoR9Full5x5\n",
      "10. Updated the the min-max values for phoHcalTower\n",
      "\n",
      "The following normalizations will be used in the min-max scaler.\n",
      "Variable                      min               max\n",
      "phoHoverE                     0.0               0.14999437\n",
      "phoTrkSumPtHollow             0.0               111922.79\n",
      "phoEcalRecHit                 0.0               1078.8676\n",
      "phoSigmaIEtaIEta3x3           0.0               0.074436404\n",
      "phoSigmaIEtaIEtaFull5x5       0.0               0.07791951\n",
      "phoSigmaIEtaIPhiFull5x5       -0.0023394742     0.0021037813\n",
      "phoEcalPFClusterIso           0.0               1080.1742\n",
      "phoHcalPFClusterIso           0.0               697.84314\n",
      "phoHasPixelSeed               0.0               1.0\n",
      "phoR9Full5x5                  0.18182698        16.86208\n",
      "phoHcalTower                  0.06542291        461.85028\n"
     ]
    }
   ],
   "source": [
    "#varname, min, max, xmin, xmax\n",
    "# The last two are for plotting\n",
    "train_var = [\n",
    "    (\"phoHoverE\",               5, 6, 0, 0.2),\n",
    "    (\"phoTrkSumPtHollow\",       5, 6, 0, 60),\n",
    "    (\"phoEcalRecHit\",           5, 6, 0, 60),\n",
    "    (\"phoSigmaIEtaIEta3x3\",     5, 6, -0.01, 0.05),\n",
    "    (\"phoSigmaIEtaIEtaFull5x5\", 5, 6, -0.01, 0.05),\n",
    "    (\"phoSigmaIEtaIPhiFull5x5\", 5, 6, -0.0006, 0.0006), #for barrel\n",
    "    (\"phoEcalPFClusterIso\",     5, 6, 0, 60),\n",
    "    (\"phoHcalPFClusterIso\",     5, 6, 0, 60),\n",
    "    (\"phoHasPixelSeed\",         5, 6, 0, 5),\n",
    "    (\"phoR9Full5x5\",            5, 6, 0, 5),\n",
    "    (\"phoHcalTower\",            5, 6, 0, 60),\n",
    "]\n",
    "\n",
    "#Intializing with max and min values 5,6 in order to find debugs.\n",
    "\n",
    "jsonfile = 'model_info.json'\n",
    "with open(jsonfile,'r') as infile: modeldata = json.load(infile)\n",
    "\n",
    "def load_model(modelname):\n",
    "    for model, norm in modeldata.items():\n",
    "        #print(model, modelname)\n",
    "        if model == modelname:\n",
    "            print(f'Loading model: {modelname} ...')\n",
    "            mymodel = tf.keras.models.load_model(f'TrainedModels/{modelname}/{modelname}.h5')\n",
    "            mymodel.load_weights(f'TrainedModels/{modelname}/{modelname}.h5')\n",
    "            print('The model is loaded.\\n')\n",
    "\n",
    "            #Updating the min-max scaling numbers:\n",
    "            for i, (var_code, _, _, xmin, xmax) in enumerate(train_var):\n",
    "                for var_json, val in norm.items():\n",
    "                    if var_code == var_json:\n",
    "                        train_var[i] = (var_code, val['min'], val['max'], xmin, xmax)\n",
    "                        print(f'{i}. Updated the the min-max values for {var_code}')\n",
    "            \n",
    "            return mymodel\n",
    "    \n",
    "model = load_model(model_to_use)\n",
    "print('\\nThe following normalizations will be used in the min-max scaler.')\n",
    "print('Variable'.ljust(29), 'min'.ljust(17), 'max')\n",
    "for var, min_val, max_val, xmin, xmax in train_var:\n",
    "    print(f'{var.ljust(30)}{str(min_val).ljust(18)}{str(max_val)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669bcf54-0652-4893-9c51-fbc42be5245d",
   "metadata": {},
   "source": [
    "### Looking at the input variables shapes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4c7381d-f7f4-4717-85e3-026033625f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1050 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 4\n",
    "n_rows = (len(train_var) + n_cols - 1) // n_cols\n",
    "\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(15, n_rows * 3.5))\n",
    "axs = axs.flatten()\n",
    "\n",
    "for i, (col, _, _, xmin, xmax) in enumerate(train_var):\n",
    "    ax = axs[i]\n",
    "    \n",
    "    #Automatically adjusting bins and x-range:\n",
    "    sig_data = data[data['truth']==1][col].dropna()\n",
    "    bkg_data = data[data['truth']==0][col].dropna()\n",
    "    #min_val = min(sig_data.min(), bkg_data.min())\n",
    "    #max_val = max(sig_data.max(), bkg_data.max())\n",
    "    #nbins = np.histogram_bin_edges(np.concatenate((sig_data, bkg_data)), bins='auto').size - 1\n",
    "    nbins = 50\n",
    "    mybins = np.linspace(xmin, xmax, nbins)\n",
    "    \n",
    "    #Decorations:\n",
    "    sig_label = f'sig ({len(sig_data)})'\n",
    "    bkg_label = f'bkg ({len(bkg_data)})'\n",
    "    sig_color = 'xkcd:green'\n",
    "    bkg_color = 'xkcd:blue'\n",
    "    common_hist_props = {\n",
    "    'histtype': 'step',\n",
    "    'density': True,\n",
    "    'linewidth': 2,\n",
    "    }\n",
    "    \n",
    "    #Exceptions:\n",
    "    if col == 'phoHasPixelSeed' : mybins = np.arange(xmin, xmax + 1, 1)\n",
    "\n",
    "    ax.hist(sig_data, bins=mybins, label=sig_label, color=sig_color, **common_hist_props)\n",
    "    ax.hist(bkg_data, bins=mybins, label=bkg_label, color=bkg_color, **common_hist_props)\n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_title(f'{col}')\n",
    "    ax.legend(loc='best')\n",
    "\n",
    "# Remove any unused subplots\n",
    "for i in range(len(train_var), len(axs)):\n",
    "    fig.delaxes(axs[i])\n",
    "    \n",
    "fig.suptitle(f'Input variables (normalized) in the {region_name} {eta_range}', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.97])\n",
    "plt.subplots_adjust(hspace=0.25) #do this after tight layout\n",
    "\n",
    "if not debug: plt.savefig(f'plots/input_variables/input_variables_{figtype}.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bead6d89-abbd-47c4-a6a0-c16b88aed934",
   "metadata": {},
   "source": [
    "### Preparing normalized X (input parameters) and truth (target) arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5da59adf-03e5-4a42-9f01-58dbc41847e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input variables are fed into the neural-network in the following order.\n",
      "0 phoHoverE\n",
      "1 phoTrkSumPtHollow\n",
      "2 phoEcalRecHit\n",
      "3 phoSigmaIEtaIEta3x3\n",
      "4 phoSigmaIEtaIEtaFull5x5\n",
      "5 phoSigmaIEtaIPhiFull5x5\n",
      "6 phoEcalPFClusterIso\n",
      "7 phoHcalPFClusterIso\n",
      "8 phoHasPixelSeed\n",
      "9 phoR9Full5x5\n",
      "10 phoHcalTower\n",
      "Shapes of the input parameters X and y :\n",
      "(259248, 11) (259248, 1)\n"
     ]
    }
   ],
   "source": [
    "train_cols = [entry[0] for entry in train_var]\n",
    "print('The input variables are fed into the neural-network in the following order.')\n",
    "for i, var in enumerate(train_cols) : print(i, var)\n",
    "\n",
    "X, y = data[train_cols].values, data[['truth']].values\n",
    "\n",
    "# Extract min and max values into NumPy arrays\n",
    "min_values = np.array([entry[1] for entry in train_var])\n",
    "max_values = np.array([entry[2] for entry in train_var])\n",
    "\n",
    "def min_max_scaler(X, min_values, max_values):\n",
    "    MaxMinusMin = max_values - min_values\n",
    "    normedX = 2 * ((X - min_values) / MaxMinusMin) - 1.0\n",
    "    return normedX\n",
    "\n",
    "X = min_max_scaler(X, min_values, max_values)\n",
    "\n",
    "print('Shapes of the input parameters X and y :')\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12206560-d5d5-484d-928f-6838db8cab7d",
   "metadata": {},
   "source": [
    "### Evaulating the model and looking at the cut-based ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe8e719f-38ef-4eb3-a6f5-784832911934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8102/8102 [==============================] - 30s 4ms/step\n",
      "The model is evulated, and TPR, FNR are calculated.\n",
      "The dataframe is updated with the NNscore.\n"
     ]
    }
   ],
   "source": [
    "def get_score_and_roc(model, X, y):\n",
    "    #Predict using the existing model:\n",
    "    y_truth = y\n",
    "    y_predicted = model.predict(X)\n",
    "    \n",
    "    fpr, tpr, wps = roc_curve(y_truth, y_predicted) \n",
    "    auc_score = auc(tpr,1-fpr)    \n",
    "    tpr=tpr*100      #True positve rate = How many signal passes the threshold - Signal Efficiency\n",
    "    fnr=(1-fpr)*100  #False negative rate = How many background fails the threshold - Background Rejection\n",
    "    \n",
    "    return y_predicted, fnr, tpr, wps, auc_score\n",
    "\n",
    "nnscore, fnr, tpr, wps, auc_score = get_score_and_roc(model, X, y)\n",
    "print('The model is evulated, and TPR, FNR are calculated.')\n",
    "\n",
    "data[\"NNScore\"] = nnscore\n",
    "print('The dataframe is updated with the NNscore.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51d530db-30bf-45ab-8eb5-da63d4ce86c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phoPt</th>\n",
       "      <th>phoEta</th>\n",
       "      <th>phoPhi</th>\n",
       "      <th>phoMatchingGenPt</th>\n",
       "      <th>isPromptFinalState</th>\n",
       "      <th>isPFPhoton</th>\n",
       "      <th>sample</th>\n",
       "      <th>truth</th>\n",
       "      <th>NNScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.048531</td>\n",
       "      <td>-0.941561</td>\n",
       "      <td>2.282521</td>\n",
       "      <td>39.090454</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GJet_PT-40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.261713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.437684</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>2.650225</td>\n",
       "      <td>11.055320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.194729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.172512</td>\n",
       "      <td>-1.433932</td>\n",
       "      <td>-2.322273</td>\n",
       "      <td>14.011247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.477523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.627117</td>\n",
       "      <td>0.578476</td>\n",
       "      <td>0.386640</td>\n",
       "      <td>40.746407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.618759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.342331</td>\n",
       "      <td>-0.320708</td>\n",
       "      <td>-0.461945</td>\n",
       "      <td>68.198250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GJet_PT-40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259243</th>\n",
       "      <td>27.641838</td>\n",
       "      <td>0.623153</td>\n",
       "      <td>-0.421087</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.385198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259244</th>\n",
       "      <td>34.884296</td>\n",
       "      <td>-0.777811</td>\n",
       "      <td>-2.109243</td>\n",
       "      <td>36.139481</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>GJet_PT-10to40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.531776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259245</th>\n",
       "      <td>45.891792</td>\n",
       "      <td>0.441985</td>\n",
       "      <td>1.248863</td>\n",
       "      <td>37.036655</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TauGun_E-10to100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.337454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259246</th>\n",
       "      <td>12.367259</td>\n",
       "      <td>0.271302</td>\n",
       "      <td>-1.079471</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>QCD_PT-80to120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.588750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259247</th>\n",
       "      <td>26.876453</td>\n",
       "      <td>1.143012</td>\n",
       "      <td>1.603428</td>\n",
       "      <td>0.349689</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GJet_PT-10to40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.139569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259248 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            phoPt    phoEta    phoPhi  phoMatchingGenPt  isPromptFinalState  \\\n",
       "0       39.048531 -0.941561  2.282521         39.090454                   1   \n",
       "1       12.437684  0.029402  2.650225         11.055320                   0   \n",
       "2       14.172512 -1.433932 -2.322273         14.011247                   0   \n",
       "3       51.627117  0.578476  0.386640         40.746407                   0   \n",
       "4       68.342331 -0.320708 -0.461945         68.198250                   1   \n",
       "...           ...       ...       ...               ...                 ...   \n",
       "259243  27.641838  0.623153 -0.421087       -999.000000                -999   \n",
       "259244  34.884296 -0.777811 -2.109243         36.139481                   1   \n",
       "259245  45.891792  0.441985  1.248863         37.036655                   0   \n",
       "259246  12.367259  0.271302 -1.079471       -999.000000                -999   \n",
       "259247  26.876453  1.143012  1.603428          0.349689                   1   \n",
       "\n",
       "        isPFPhoton            sample  truth   NNScore  \n",
       "0                1        GJet_PT-40      1  0.261713  \n",
       "1                0  TauGun_E-10to100      0  0.194729  \n",
       "2                0  TauGun_E-10to100      0  0.477523  \n",
       "3                0    QCD_PT-80to120      0  0.618759  \n",
       "4                1        GJet_PT-40      1  0.692724  \n",
       "...            ...               ...    ...       ...  \n",
       "259243           0    QCD_PT-80to120      0  0.385198  \n",
       "259244           0    GJet_PT-10to40      1  0.531776  \n",
       "259245           0  TauGun_E-10to100      0  0.337454  \n",
       "259246           0    QCD_PT-80to120      0  0.588750  \n",
       "259247           1    GJet_PT-10to40      1  0.139569  \n",
       "\n",
       "[259248 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['phoPt','phoEta','phoPhi','phoMatchingGenPt','isPromptFinalState','isPFPhoton','sample','truth','NNScore']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df1c725-90e3-4df3-87e1-1acf56bdc93b",
   "metadata": {},
   "source": [
    "### Defining working points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fe43c8d3-4cbd-453d-bcab-b3f609d1b594",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information on the cut-based ID :\n",
      "Signal efficiency and background rejection of the cut-based ID : 82.25 % and 81.08%\n"
     ]
    }
   ],
   "source": [
    "# The cut-based PF-ID :\n",
    "print('Information on the cut-based ID :')\n",
    "PF_bkgpass =len(data.query('(isPFPhoton == 1) & (truth == 0)'))\n",
    "PF_bkgfail =len(data.query('(isPFPhoton == 0) & (truth == 0)'))\n",
    "PF_sigpass =len(data.query('(isPFPhoton == 1) & (truth == 1)'))\n",
    "PF_sigfail =len(data.query('(isPFPhoton == 0) & (truth == 1)'))\n",
    "PF_bkgrej  =( PF_bkgfail/(PF_bkgpass+PF_bkgfail) )*100\n",
    "PF_sigeff  =( PF_sigpass/(PF_sigpass+PF_sigfail) )*100\n",
    "print(f'Signal efficiency and background rejection of the cut-based ID : {PF_sigeff:.2f} % and {PF_bkgrej:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "497d30b6-dee0-4c44-82ce-ebdd8427fd71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal efficiency and background rejection of the working point: 81.91 % and 85.65%\n"
     ]
    }
   ],
   "source": [
    "#EGamma working point\n",
    "if 'barrel' in model_to_use: example_wp = 0.22\n",
    "elif 'endcap' in model_to_use: example_wp = 0.35\n",
    "\n",
    "nn_bkgpass =len(data.query(f'(NNScore > {example_wp}) & (truth == 0)'))\n",
    "nn_bkgfail =len(data.query(f'(NNScore < {example_wp}) & (truth == 0)'))\n",
    "nn_sigpass =len(data.query(f'(NNScore > {example_wp}) & (truth == 1)'))\n",
    "nn_sigfail =len(data.query(f'(NNScore < {example_wp}) & (truth == 1)'))\n",
    "nn_bkgrej  =( nn_bkgfail/(nn_bkgpass+nn_bkgfail) )*100\n",
    "nn_sigeff  =( nn_sigpass/(nn_sigpass+nn_sigfail) )*100\n",
    "print(f'Signal efficiency and background rejection of the working point: {nn_sigeff:.2f} % and {nn_bkgrej:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0c0b616-d480-4c00-930d-7e91fb30e42d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Define custom binning for the NNScore\n",
    "mybins = np.arange(0, 1.02, 0.02)\n",
    "\n",
    "# Create the figure and subplots\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "#Common ax decorations:\n",
    "for ax in axs:\n",
    "    ax.spines['top'].set_linewidth(1.2)    # Top boundary\n",
    "    ax.spines['right'].set_linewidth(1.2)  # Right boundary\n",
    "    ax.spines['bottom'].set_linewidth(1.2) # Bottom boundary\n",
    "    ax.spines['left'].set_linewidth(1.2)   # Left boundary\n",
    "    ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "    ax.grid(True, alpha=0.5, linestyle='--')\n",
    "\n",
    "# Define common histogram properties\n",
    "hist_props = {\n",
    "    'histtype': 'step',\n",
    "    'density': True,\n",
    "    'linewidth': 3,\n",
    "}\n",
    "nsig = len(data[data['truth']==1])\n",
    "nbkg = len(data[data['truth']==0])\n",
    "# Left subplot: NNScore distribution\n",
    "axs[0].hist(data[data['truth']==1]['NNScore'], bins=mybins, color='xkcd:green', label=f'Signal ({nsig})', **hist_props)\n",
    "axs[0].hist(data[data['truth']==0]['NNScore'], bins=mybins, color='xkcd:blue', label=f'Background ({nbkg})', **hist_props)\n",
    "axs[0].axvline(x=example_wp, color='xkcd:black', linestyle='--', linewidth=2, alpha=0.5)\n",
    "axs[0].set_xlabel('NNScore', fontsize=14)\n",
    "axs[0].set_ylabel('Frequency', fontsize=14)\n",
    "axs[0].legend(title=f'model: {model_to_use} {eta_range}', loc='upper center', fontsize=12, title_fontsize=12)\n",
    "#axs[0].set_title('Signal and background separation', fontsize=18)\n",
    "\n",
    "'''\n",
    "axs[1].plot(wps, tpr, label='Signal efficiency',    marker='o', markersize=1.5, color='xkcd:green')\n",
    "axs[1].plot(wps, fnr, label='Background rejection', marker='o', markersize=1.5, color='xkcd:blue')    \n",
    "axs[1].axvline(x=example_wp, color='xkcd:black', linestyle='--', linewidth=2, alpha=0.5)\n",
    "axs[1].set_xlabel('Working Points (wp)', fontsize=14)\n",
    "axs[1].set_ylabel('Efficiency (%)', fontsize=14)\n",
    "axs[1].set_xlim([0, 1])\n",
    "axs[1].set_ylim([0, 100])\n",
    "axs[1].legend(title=f'model: {model_to_use} {eta_range}', loc='best', fontsize=12, title_fontsize=12)\n",
    "#axs[1].set_title('Scanning thorugh working points', fontsize=18)\n",
    "'''\n",
    "\n",
    "# ROC\n",
    "axs[1].plot(tpr, fnr, color='darkorange', lw=3, label=f'AUC = {auc_score:.2f}')\n",
    "#axs[1].plot([0, 100], [100, 0], color='navy', lw=2, linestyle='--', alpha=0.2)\n",
    "axs[1].plot(PF_sigeff, PF_bkgrej, marker='X', color=\"red\",  markersize=8, label=f'cut-based ID ({PF_sigeff:.0f}%, {PF_bkgrej:.0f}%)')\n",
    "axs[1].plot(nn_sigeff, nn_bkgrej, marker='X', color=\"xkcd:black\", markersize=8, label=f'Example wp @{example_wp} ({nn_sigeff:.0f}%, {nn_bkgrej:.0f}%)')\n",
    "axs[1].set_xlim([0, 100])\n",
    "axs[1].set_ylim([0, 100])\n",
    "axs[1].set_xlabel('Signal Efficiency (%)', fontsize=14)\n",
    "axs[1].set_ylabel('Background Rejection (%)', fontsize=14)\n",
    "axs[1].legend(title=f'model: {model_to_use} {eta_range}', loc='center left', fontsize=12, title_fontsize=12, bbox_to_anchor=(0.0, 0.20))\n",
    "#axs[1].set_title('ROC curve', fontsize=18)\n",
    "\n",
    "# Adjust layout\n",
    "fig.suptitle(f'Performance of the model: {model_to_use} in the {region_name} {eta_range}', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.30) #do this after tight layout\n",
    "\n",
    "# Display the plot after saving.\n",
    "if not debug: plt.savefig(f'plots/performance/performance_{model_to_use}_in_{figtype}.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c68f62-99d7-4d00-8bf1-a010a47c8133",
   "metadata": {},
   "source": [
    "### NN-score across different samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cc9e1078-69a3-425a-921d-985b0a8a70a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define common histogram properties\n",
    "hist_props = {\n",
    "    'histtype': 'step',\n",
    "    'density': True,\n",
    "    'linewidth': 2,\n",
    "    'alpha': 0.7\n",
    "}\n",
    "\n",
    "'''\n",
    "samples = {\n",
    "    'GJet_PT-10to40': 'xkcd:light olive green',\n",
    "    'GJet_PT-40': 'xkcd:green',\n",
    "    'QCD_PT-80to120': 'xkcd:sky blue',\n",
    "    'QCD_PT-120to170': 'xkcd:azure',\n",
    "    'QCD_PT-170to300': 'xkcd:royal blue',\n",
    "    'QCD_PT-300toInf': 'xkcd:navy',\n",
    "    'TauGun_E-10to100': 'xkcd:salmon',\n",
    "    'TauGun_E-100to3000': 'xkcd:crimson'\n",
    "}\n",
    "'''\n",
    "samples = {\n",
    "    'GJet': 'xkcd:green',\n",
    "    'QCD': 'xkcd:blue',\n",
    "    'TauGun': 'xkcd:red'\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "mybins = np.arange(0, 1.02, 0.02)\n",
    "\n",
    "for sample_, color_ in samples.items() :\n",
    "    count = len(data[data['sample'].str.contains(sample_)])\n",
    "    ax.hist(data[data['sample'].str.contains(sample_)]['NNScore'], bins=mybins, color=color_, label=sample_.ljust(2)+f' [{count}]', **hist_props)\n",
    "\n",
    "ax.axvline(x=example_wp, color='xkcd:black', linestyle='--', linewidth=2, alpha=0.5)\n",
    "ax.set_xlabel('NNScore', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)\n",
    "ax.set_yscale('log')\n",
    "ax.spines['top'].set_linewidth(1.2)    # Top boundary\n",
    "ax.spines['right'].set_linewidth(1.2)  # Right boundary\n",
    "ax.spines['bottom'].set_linewidth(1.2) # Bottom boundary\n",
    "ax.spines['left'].set_linewidth(1.2)   # Left boundary\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "ax.grid(True, alpha=0.5, linestyle='--')\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "\n",
    "ax.set_title(f'model: {model_to_use}, region: {figtype} {eta_range}', fontsize=12, pad=15)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.0) #do this after tight layout\n",
    "\n",
    "# Display the plot after saving.\n",
    "if not debug: plt.savefig(f'plots/per_sample_score/per_sample_score_{model_to_use}_in_{figtype}.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b443bb07-456d-49c8-b1d4-8d91b11ae510",
   "metadata": {},
   "source": [
    "### Efficiency in pT bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2dc5002-b3e5-42a7-8c1b-61cb105a6544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptbins=[10, 15, 20, 25, 30, 40, 60, 80, 100, 150, 200]\n",
    "ptbinslist=list(range(len(ptbins)-1))\n",
    "#print(f'pT bin names = {ptbinslist}')\n",
    "data['pt_bin'] =  pd.cut(data['phoPt'], bins=ptbins, labels=ptbinslist)\n",
    "\n",
    "pt_xerr = np.diff(ptbins)\n",
    "pt_xval = []\n",
    "i = 0\n",
    "while i<len(pt_xerr):\n",
    "    x_val = ptbins[i] + (pt_xerr[i]/2)\n",
    "    pt_xval.append(x_val)\n",
    "    i = i+1\n",
    "\n",
    "#print(f'x vals = {pt_xval} (npoints = {len(pt_xval)})')\n",
    "#print(f'x err  = {pt_xerr} (npoints = {len(pt_xerr)})')\n",
    "\n",
    "def efficiency_in_bin(num, den):\n",
    "    return num / den if den != 0 else 0\n",
    "\n",
    "sample_groups = {\n",
    "    'GJet': data[data['sample'].str.contains('GJet')],\n",
    "    'QCD': data[data['sample'].str.contains('QCD')],\n",
    "    'TauGun': data[data['sample'].str.contains('TauGun')],\n",
    "}\n",
    "\n",
    "# Group by 'sample' and calculate efficiency in each pt bin\n",
    "efficiencies = {}\n",
    "for group_name, group_data in sample_groups.items():\n",
    "    eff_per_bin = []\n",
    "    for pt_bin in ptbinslist:\n",
    "        bin_data = group_data[group_data['pt_bin'] == pt_bin]\n",
    "        num = bin_data[bin_data['NNScore'] > example_wp].shape[0]\n",
    "        den = bin_data.shape[0]\n",
    "        eff = efficiency_in_bin(num, den)\n",
    "        eff_per_bin.append(eff)\n",
    "    efficiencies[group_name] = eff_per_bin\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "colors = {\n",
    "    'GJet':   'xkcd:green',\n",
    "    'QCD':    'xkcd:blue',\n",
    "    'TauGun': 'xkcd:red'\n",
    "}\n",
    "for group_name, eff in efficiencies.items():\n",
    "    label_ = f'{group_name} [{len(data[data[\"sample\"].str.contains(group_name)])}]'\n",
    "    ax.errorbar(pt_xval, eff, xerr=pt_xerr / 2, label=label_, fmt='o', color=colors[group_name])\n",
    "ax.spines['top'].set_linewidth(1.2)    # Top boundary\n",
    "ax.spines['right'].set_linewidth(1.2)  # Right boundary\n",
    "ax.spines['bottom'].set_linewidth(1.2) # Bottom boundary\n",
    "ax.spines['left'].set_linewidth(1.2)   # Left boundary\n",
    "ax.set_xlabel('pT bins', fontsize=12)\n",
    "ax.set_ylabel('Efficiency', fontsize=12)\n",
    "ax.set_xlim(0, 200)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "ax.grid(True, alpha=0.5, linestyle='--')\n",
    "\n",
    "legendheader = f'wp = {example_wp}'\n",
    "ax.legend(title=legendheader, loc='upper left', bbox_to_anchor=(1, 1), fontsize=12, title_fontsize=12)\n",
    "ax.set_title(f'model: {model_to_use}, region: {figtype} {eta_range}', fontsize=12, pad=15)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.0)\n",
    "if not debug: plt.savefig(f'plots/efficiency/efficiency_{model_to_use}_in_{figtype}.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34972c0-01a8-4995-afa5-77f594e00cc0",
   "metadata": {},
   "source": [
    "## Finding potential variables to improve the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d371ab5-83fd-498b-8511-8143113b8432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "potential_var = [\n",
    "    (\"phoHoverE\",               0, 0.2),\n",
    "    (\"phoTrkSumPtHollow\",       0, 50),\n",
    "    (\"phoEcalRecHit\",           0, 50),\n",
    "    (\"phoSigmaIEtaIEta3x3\",     0, 0.03),\n",
    "    (\"phoSigmaIEtaIPhiFull5x5\", 0, 0.03),\n",
    "    (\"phoSigmaIEtaIEtaFull5x5\", 0, 0.03),\n",
    "    (\"phoEcalPFClusterIso\",     0, 50),\n",
    "    (\"phoHcalPFClusterIso\",     0, 50),\n",
    "    (\"phoHasPixelSeed\",         0, 5),\n",
    "    (\"phoR93x3\",                0, 2),\n",
    "    (\"phoHcalTower\",            0, 50),\n",
    "    (\"phoHoverECone\",           0, 0.5),\n",
    "    (\"phoHoverEValid\",          0, 5),\n",
    "    (\"phoR9Full5x5\",            0, 2),\n",
    "    (\"phoSigmaIPhiIPhiFull5x5\", 0, 0.1),\n",
    "    (\"phoTrkSumPtSolid\",        0, 1),\n",
    "    (\"phoMatchingGenPt\",        0, 200),\n",
    "    #(\"sample\",                  0, 1),\n",
    "    #(\"truth\",                   0, 1),\n",
    "    (\"isPromptFinalState\",      0, 5),\n",
    "    (\"isDirectHardProcessTauDecayProductFinalState\", 0, 5),\n",
    "    (\"isDirectHadronDecayProduct\", 0, 5),\n",
    "    (\"isTauDecayProduct\",       0, 5),\n",
    "    (\"isPromptTauDecayProduct\", 0, 5),\n",
    "    (\"isDirectTauDecayProduct\", 0, 5),\n",
    "    (\"isDirectPromptTauDecayProduct\", 0, 5),\n",
    "    (\"isDirectPromptTauDecayProductFinalState\", 0, 5),\n",
    "    (\"isHardProcess\",           0, 5),\n",
    "    (\"fromHardProcessFinalState\", 0, 5),\n",
    "    (\"fromHardProcessDecayed\",  0, 5),\n",
    "    (\"isPionMother\",            0, 5),\n",
    "    (\"isPFPhoton\",              0, 5),\n",
    "    (\"isPhotonMatching\",        0, 5),\n",
    "]\n",
    "\n",
    "booleans = [\n",
    "    \"phoHoverEValid\",\n",
    "    \"isPromptFinalState\",\n",
    "    \"isDirectHardProcessTauDecayProductFinalState\",\n",
    "    \"isDirectHadronDecayProduct\",\n",
    "    \"isTauDecayProduct\",\n",
    "    \"isPromptTauDecayProduct\",\n",
    "    \"isDirectTauDecayProduct\",\n",
    "    \"isDirectPromptTauDecayProduct\",\n",
    "    \"isDirectPromptTauDecayProductFinalState\",\n",
    "    \"isHardProcess\",\n",
    "    \"fromHardProcessFinalState\",\n",
    "    \"fromHardProcessDecayed\",\n",
    "    \"isPionMother\",\n",
    "    \"isPFPhoton\",\n",
    "    \"isPhotonMatching\"\n",
    "]\n",
    "\n",
    "for i, (var, xmin, xmax) in enumerate(potential_var):\n",
    "    \n",
    "    #Berak the loop for already existing training variables:\n",
    "    if any(var == item[0] for item in train_var): continue\n",
    "    if var in booleans: continue\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n",
    "    #ax = ax.flatten()\n",
    "    \n",
    "    sig_data = data[data['truth']==1][var].dropna()\n",
    "    bkg_data = data[data['truth']==0][var].dropna()\n",
    "\n",
    "    nbins = 50\n",
    "    mybins = np.linspace(xmin, xmax, nbins)\n",
    "    \n",
    "    #Decorations:\n",
    "    sig_label = f'sig ({len(sig_data)})'\n",
    "    bkg_label = f'bkg ({len(bkg_data)})'\n",
    "    sig_color = 'xkcd:green'\n",
    "    bkg_color = 'xkcd:blue'\n",
    "    common_hist_props = {\n",
    "    'histtype': 'step',\n",
    "    'density': True,\n",
    "    'linewidth': 2,\n",
    "    }\n",
    "    \n",
    "    #Custom binning for booleans:\n",
    "    if var in booleans: mybins = np.arange(xmin, xmax + 1, 1)\n",
    "\n",
    "    ax.hist(sig_data, bins=mybins, label=sig_label, color=sig_color, **common_hist_props)\n",
    "    ax.hist(bkg_data, bins=mybins, label=bkg_label, color=bkg_color, **common_hist_props)\n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_title(f'{var} {eta_range}')\n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "    #fig.suptitle(f'Input variables (normalized) in the {plotinfo}', fontsize=16)\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.97])\n",
    "    plt.subplots_adjust(hspace=0.25) #do this after tight layout\n",
    "\n",
    "    if not debug:\n",
    "        os.makedirs(f'plots/potential_variables/{figtype}', exist_ok=True)\n",
    "        plt.savefig(f'plots/potential_variables/{figtype}/{var}.png', dpi=300)\n",
    "    plt.show()\n",
    "    #break\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a83c3c3-a427-4314-bb67-6d86b047a15b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}