synthetic_samples_summary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2354087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import torch\n",
    "import os\n",
    "from numba import cuda \n",
    "plt.style.use(\"science.mplstyle\")\n",
    "\n",
    "from helpers.synthesis_plots import *\n",
    "\n",
    "\n",
    "dpi = 800\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c256b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "results_dir = \"/global/ml4hep/spss/rrmastandrea/NF_results_FETA/\"\n",
    "\n",
    "n_sig_inj = [0, 300, 500, 750, 1000, 1200, 1500, 2000, 2500, 3000]#, 3500, 4000, 4500, 5000]# 10000]\n",
    "seeds = [x for x in range(1)]\n",
    "\n",
    "\n",
    "num_bkg_events = 20000\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b931d94d",
   "metadata": {},
   "source": [
    "# Load in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "676f030d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#x_axis = np.logspace(-8, 0, 400)\n",
    "x_axis = np.linspace(1e-5, 1, 100)\n",
    "\n",
    "\n",
    "keys = [\"feta\",\"cathode\", \"curtains\", \"salad\"]\n",
    "\n",
    "\n",
    "rejections_dict = {k:{n:[] for n in n_sig_inj} for k in keys}                \n",
    "sics_dict = {k:{n:[] for n in n_sig_inj} for k in keys}\n",
    "max_sics_dict = {k:{n:[] for n in n_sig_inj} for k in keys}\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d81e8f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for n in n_sig_inj:\n",
    "    \n",
    "    for seed in seeds:\n",
    "        \n",
    "        for k in keys:\n",
    "            \n",
    "            if k == \"full_sup\":\n",
    "                tpr, sic, rejection, maxsic = get_sic_rejection(k, seed, 0, results_dir, num_bkg_events = num_bkg_events)\n",
    "            else: \n",
    "                tpr, sic, rejection, maxsic = get_sic_rejection(k, seed, n, results_dir, num_bkg_events = num_bkg_events)\n",
    "                \n",
    "            \n",
    "            interp_sic = np.interp(x_axis, tpr, sic)\n",
    "            interp_rej = np.interp(x_axis, tpr, rejection)\n",
    "            rejections_dict[k][n].append(interp_rej)\n",
    "            sics_dict[k][n].append(interp_sic)\n",
    "            max_sics_dict[k][n].append(maxsic)\n",
    "            \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f229e9d",
   "metadata": {},
   "source": [
    "# Take the avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00d60124",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "med_rejections_dict = {k:{n:[] for n in n_sig_inj} for k in keys}\n",
    "med_sics_dict = {k:{n:[] for n in n_sig_inj} for k in keys}\n",
    "med_max_sics_dict = {k:[[],[],[]] for k in keys} # mean, lower_p, upper_p\n",
    "    \n",
    "   \n",
    "\n",
    "for n in n_sig_inj:\n",
    "    \n",
    "    for k in keys:\n",
    "        \n",
    "        med_rejections_dict[k][n] = get_med_percentile(rejections_dict[k][n])\n",
    "        med_sics_dict[k][n] = get_med_percentile(sics_dict[k][n])\n",
    "        \n",
    "                 \n",
    "        med_max_sics_dict[k][0].append(np.median(max_sics_dict[k][n]))\n",
    "        med_max_sics_dict[k][1].append(np.percentile(max_sics_dict[k][n], 16))\n",
    "        med_max_sics_dict[k][2].append(np.percentile(max_sics_dict[k][n], 84))\n",
    "                        \n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "for k in keys:\n",
    "    med_max_sics_dict[k][0] = np.array(med_max_sics_dict[k][0])\n",
    "    med_max_sics_dict[k][1] = np.array(med_max_sics_dict[k][1])\n",
    "    med_max_sics_dict[k][2] = np.array(med_max_sics_dict[k][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb6f9dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "colors_dict = {\"full_sup\":\"green\",\n",
    "               \"salad\": \"blue\",\n",
    "               \"feta\": \"purple\",\n",
    "               \"cathode\":\"deeppink\",\n",
    "               \"curtains\":\"orange\",\n",
    "                \"combined\": \"red\"}\n",
    "\n",
    "labels_dict = {\"full_sup\":\"Full. Sup. \",\n",
    "               \"salad\": \"SALAD\",\n",
    "               \"feta\": \"FETA\",\n",
    "               \"cathode\":\"CATHODE\",\n",
    "               \"curtains\":\"CURTAINs\",\n",
    "                \"combined\": \"Combined\"}\n",
    "\n",
    "\n",
    "l = 3\n",
    "f_1 = 20\n",
    "f_2 = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69a32dad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/home/users/rrmastandrea/computingML2/lib64/python3.6/site-packages/ipykernel_launcher.py:24: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bkg = 101339\n",
    "\n",
    "x_vals = [n / num_bkg for n in n_sig_inj]\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (15, 10)) \n",
    "for k in keys:\n",
    "    \n",
    "    if k == \"feta\": alpha = 0.5\n",
    "    else: alpha = 0.3\n",
    "\n",
    "    plt.plot(x_vals, med_max_sics_dict[k][0], label = labels_dict[k], color = colors_dict[k], linewidth = l, alpha = alpha + 0.4)\n",
    "\n",
    "        \n",
    "    plt.fill_between(x_vals, med_max_sics_dict[k][1],\n",
    "                        med_max_sics_dict[k][2],\n",
    "                         alpha = alpha, color = colors_dict[k], linewidth = 0)\n",
    "   \n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twiny()\n",
    "ax2.set_xticks(n_sig_inj)\n",
    "ax2.set_xticklabels(n_sig_inj, size=f_1, rotation = 45)\n",
    "ax2.set_xlabel(r\"$n_\\mathrm{sig}$\", fontsize = f_2, labelpad = 10)\n",
    "\n",
    "\n",
    "ax1.set_xticks(x_vals)\n",
    "ax1.set_xticklabels([f\"{np.round(100*x, 2)}\\%\" for x in x_vals], size=f_1, rotation = 45)\n",
    "ax1.set_xlabel(\"S/B\", fontsize = f_2)\n",
    "\n",
    "\"\"\"\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "order = [0, 4, 1, 2, 3]\n",
    "ax1.legend([handles[idx] for idx in order],[labels[idx] for idx in order],fontsize = f_1, loc = \"lower right\")\n",
    "\"\"\"\n",
    "ax1.legend(fontsize = f_1, loc = \"lower right\")\n",
    "\n",
    "ax1.set_ylabel(\"max(SIC)\", fontsize = f_2)\n",
    "ax1.tick_params(axis='both', which='major', labelsize=f_1)\n",
    "fig.savefig(f\"paper_plots/maxsic.pdf\", dpi = dpi)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "397928c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0\n",
      "300 0.9424046454402709\n",
      "500 1.6036448811696862\n",
      "750 3.4964699003465074\n",
      "1000 23.177176828381345\n",
      "1200 24.033555869763426\n",
      "1500 51.514927196316094\n",
      "2000 85.55029727746901\n",
      "2500 94.286064052438\n",
      "3000 137.37231332567524\n"
     ]
    }
   ],
   "source": [
    "for i, n in enumerate(n_sig_inj):\n",
    "    \n",
    "    significance = n / np.sqrt(num_bkg)\n",
    "    \n",
    "    print(n, med_max_sics_dict[\"feta\"][0][i]*significance)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb795eaf",
   "metadata": {},
   "source": [
    "# SIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffcdda23",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_to_plot = [2500]\n",
    "\n",
    "for n in n_to_plot:\n",
    "\n",
    "    fig = plt.figure(figsize = (15, 10))\n",
    "\n",
    "    for k in keys:\n",
    "        \n",
    "        if k == \"feta\": alpha = 0.5\n",
    "        else: alpha = 0.3\n",
    "        \n",
    "        plt.plot(x_axis, med_sics_dict[k][n][0], label = labels_dict[k], color = colors_dict[k], linewidth = l, alpha = alpha + 0.4)\n",
    "\n",
    "        \n",
    "        plt.fill_between(x_axis, med_sics_dict[k][n][1],\n",
    "                         med_sics_dict[k][n][2],\n",
    "                         alpha = alpha, color = colors_dict[k], linewidth = 0)\n",
    "  \n",
    "\n",
    "    plt.plot(x_axis, x_axis/np.sqrt(x_axis), label = \"Random\", color = \"black\", linewidth = l+1)\n",
    "    \n",
    "    plt.text(0.5, 17, f\"$n_S = {n}$\\n$S/B = {np.round(100*n/num_bkg, 2)}\\%$\", fontsize = f_2)\n",
    "\n",
    "    \"\"\"\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    order = [0, 4, 1, 2, 3]\n",
    "    plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],fontsize = f_1)\n",
    "    \"\"\"\n",
    "    \n",
    "    plt.legend(fontsize = f_1)\n",
    "    \n",
    "    plt.xlabel(\"Signal Efficiency (True Positive Rate)\", fontsize = f_2)    \n",
    "    plt.ylabel(\"Significance Improvement Characteristic\", fontsize = f_2)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=f_1)\n",
    "    #plt.title(f\"Num injected events: {n}\")\n",
    "    \n",
    "    fig.savefig(f\"paper_plots/sic_{n}.pdf\", dpi = dpi)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587d733e",
   "metadata": {},
   "source": [
    "# Rejection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64240d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in n_to_plot:\n",
    "\n",
    "    fig = plt.figure(figsize = (15, 10)) \n",
    "    for k in keys:\n",
    "        \n",
    "        if k == \"feta\": alpha = 0.5\n",
    "        else: alpha = 0.3\n",
    "            \n",
    "            \n",
    "        plt.plot(x_axis, med_rejections_dict[k][n][0], label = labels_dict[k], color = colors_dict[k], linewidth = l, alpha = alpha + 0.4)\n",
    "\n",
    "        plt.fill_between(x_axis, med_rejections_dict[k][n][1] ,\n",
    "                         med_rejections_dict[k][n][2] ,\n",
    "                         alpha = alpha, color = colors_dict[k], linewidth = 0)\n",
    "\n",
    "    plt.plot(x_axis, 1.0/x_axis, label = \"Random\", color = \"black\", linewidth = l+1)\n",
    "    \n",
    "    plt.text(0.5, 4000, f\"$n_S = {n}$\\n$S/B = {np.round(100*n/num_bkg, 2)}\\%$\", fontsize = f_2)\n",
    "\n",
    "    \"\"\"\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    order = [0, 4, 1, 2, 3]\n",
    "    plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],fontsize = f_1)  \n",
    "    \"\"\"   \n",
    "    plt.legend(fontsize = f_1)  \n",
    "\n",
    "    \n",
    "    plt.ylim(5e-1, 5e4)\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlabel(\"Signal Efficiency (True Positive Rate)\", fontsize = f_2)  \n",
    "    plt.ylabel(\"Rejection (1 / False Positive Rate)\", fontsize = f_2)  \n",
    "    #plt.title(f\"Num injected events: {n}\")\n",
    "    plt.tick_params(axis='both', which='major', labelsize=f_1)\n",
    "    \n",
    "    fig.savefig(f\"paper_plots/rej_{n}.pdf\", dpi = dpi)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421ab531",
   "metadata": {},
   "source": [
    "## SIC vs rej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7e71876",
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in n_to_plot:\n",
    "\n",
    "    fig = plt.figure(figsize = (15, 10)) \n",
    "    for k in keys:\n",
    "        \n",
    "        if k == \"feta\": alpha = 0.5\n",
    "        else: alpha = 0.3\n",
    "            \n",
    "            \n",
    "        plt.plot(med_rejections_dict[k][n][0], med_sics_dict[k][n][0], label = labels_dict[k], color = colors_dict[k], linewidth = l, alpha = alpha + 0.4)\n",
    "\n",
    "        plt.fill_between(med_rejections_dict[k][n][0], med_sics_dict[k][n][1],\n",
    "                         med_sics_dict[k][n][2] ,\n",
    "                         alpha = alpha, color = colors_dict[k], linewidth = 0)\n",
    "\n",
    "    plt.plot(1.0/x_axis[184:], x_axis[184:]/np.sqrt(x_axis[184:]), label = \"Random\", color = \"black\", linewidth = l+1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    plt.text(40, 15, f\"$n_S = {n}$\\n$S/B = {np.round(100*n/num_bkg, 2)}\\%$\", fontsize = f_2)\n",
    "    \n",
    "    \"\"\"\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    order = [0, 4, 1, 2, 3]\n",
    "    plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],fontsize = f_1)\n",
    "    \"\"\"\n",
    "\n",
    "    plt.legend(fontsize = f_1)\n",
    "    plt.xscale(\"log\")\n",
    "    plt.xlabel(\"Rejection (1 / False Positive Rate)\", fontsize = f_2)  \n",
    "    plt.ylabel(\"Significance Improvement Characteristic\", fontsize = f_2)\n",
    "    #plt.title(f\"Num injected events: {n}\")\n",
    "    plt.tick_params(axis='both', which='major', labelsize=f_1)\n",
    "    fig.savefig(f\"paper_plots/sic_vs_rej_{n}.pdf\", dpi = dpi)\n",
    "    \n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b69bf7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09df7f21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2cc7c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3beba185",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64c45ffa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2023336b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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