tracking-parametrisation-tuner/thesis/TMVA_stuff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mplhep\n",
    "import awkward as ak\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_bestprecuts_NozMagCut/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TrainTree\"\n",
    "})\n",
    "test_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_bestprecuts_NozMagCut/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TestTree\"\n",
    "})\n",
    "train_array = train_tree.arrays()\n",
    "test_array = test_tree.arrays()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_bkg = train_array[train_array.classID == 1]\n",
    "train_sig = train_array[train_array.classID == 0]\n",
    "test_bkg = test_array[test_array.classID == 1]\n",
    "test_sig = test_array[test_array.classID == 0]\n",
    "plt.hist(\n",
    "    train_sig.matching_mlp,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "plt.hist(\n",
    "    train_bkg.matching_mlp,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "mplhep.histplot(\n",
    "    np.histogram(np.array(test_sig.matching_mlp), 50),\n",
    "    histtype=\"errorbar\",\n",
    "    density=True,\n",
    "    yerr=True,\n",
    "    color=\"#107E7D\",\n",
    "    marker=\"^\",\n",
    "    markersize=7,\n",
    "    label=\"test sample, true pairs\",\n",
    ")\n",
    "mplhep.histplot(\n",
    "    np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
    "    histtype=\"errorbar\",\n",
    "    density=True,\n",
    "    yerr=True,\n",
    "    color=\"#F05342\",\n",
    "    label=\"test sample, wrong pairs\",\n",
    ")\n",
    "plt.xlabel(\"neural network response\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.legend(loc=\"upper center\")\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/thesis/new_electron_NN_response.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cut is set to 0.5, thereby rejecting 0.901 of fake tracks, while keeping 0.935 of the true matches.\n"
     ]
    }
   ],
   "source": [
    "response = 0.5\n",
    "\n",
    "n_sig_keep = ak.num(train_sig[train_sig.matching_mlp >= response], axis=0)\n",
    "n_bkg_rej = ak.num(train_bkg[train_bkg.matching_mlp < response], axis=0)\n",
    "nevents = 115e3\n",
    "\n",
    "print(\n",
    "    f\"The cut is set to {response}, thereby rejecting {np.round(n_bkg_rej/nevents,3)} of fake tracks, while keeping {np.round(n_sig_keep/nevents,3)} of the true matches.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1400x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
    "# 0,0\n",
    "axes[0, 0].hist(\n",
    "    train_sig.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 0].hist(\n",
    "    train_bkg.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
    "axes[0, 0].legend(prop={\"size\": 20})\n",
    "# 1,0\n",
    "axes[1, 0].hist(\n",
    "    train_sig.distX,\n",
    "    bins=50,\n",
    "    range=(0, 150),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 0].hist(\n",
    "    train_bkg.distX,\n",
    "    bins=50,\n",
    "    range=(0, 150),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
    "axes[1, 0].set_ylabel(\"Number of Tracks (normalised)\",\n",
    "                      va=\"bottom\",\n",
    "                      ha=\"center\")\n",
    "# 0,1\n",
    "axes[0, 1].hist(\n",
    "    train_sig.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 1].hist(\n",
    "    train_bkg.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
    "# 1,1\n",
    "axes[1, 1].hist(\n",
    "    train_sig.distY,\n",
    "    bins=50,\n",
    "    range=(0, 150),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 1].hist(\n",
    "    train_bkg.distY,\n",
    "    bins=50,\n",
    "    range=(0, 150),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
    "# 2,0\n",
    "axes[2, 0].hist(\n",
    "    train_sig.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[2, 0].hist(\n",
    "    train_bkg.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
    "# 2,1\n",
    "axes[2, 1].hist(\n",
    "    train_sig.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.03),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[2, 1].hist(\n",
    "    train_bkg.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.03),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
    "plt.savefig(\n",
    "    \"/work/cetin/LHCb/reco_tuner/thesis/new_electron_NN_variables.pdf\",\n",
    "    format=\"PDF\",\n",
    ")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 3, figsize=(25, 13), sharey=False)\n",
    "# 0,0\n",
    "axes[0, 0].hist(\n",
    "    train_sig.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 0].hist(\n",
    "    train_bkg.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
    "axes[0, 0].legend(prop={\"size\": 20})\n",
    "\n",
    "# 0,1\n",
    "axes[0, 1].hist(\n",
    "    train_sig.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 1].hist(\n",
    "    train_bkg.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
    "# 0,2\n",
    "axes[0, 2].hist(\n",
    "    train_sig.distX,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 2].hist(\n",
    "    train_bkg.distX,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 2].set_xlabel(r\"$D_{x}$ [mm]\")\n",
    "axes[0, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n",
    "# 1,0\n",
    "axes[1, 0].hist(\n",
    "    train_sig.distY,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 0].hist(\n",
    "    train_bkg.distY,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 0].set_xlabel(r\"$D_{y}$ [mm]\")\n",
    "# 2,0\n",
    "axes[1, 1].hist(\n",
    "    train_sig.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 1].hist(\n",
    "    train_bkg.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 1].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
    "# 2,1\n",
    "axes[1, 2].hist(\n",
    "    train_sig.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 2].hist(\n",
    "    train_bkg.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 2].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
    "plt.savefig(\n",
    "    \"/work/cetin/LHCb/reco_tuner/thesis/newparams_filtered_NN_elec_variables_landscape.pdf\",\n",
    "    format=\"PDF\",\n",
    ")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}