{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates how to draw receiver operating characteristic curves (ROC) directly from a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from freeforestml import Variable, Process, Cut, roc\n",
    "from freeforestml import toydata, example_style\n",
    "example_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = toydata.get()\n",
    "df['noise'] = df.fpid + 10 * (df.random - 0.5)**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_bkg = Process(r\"Background\", range=(0, 0))\n",
    "p_sig = Process(r\"Signal\", range=(1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")\n",
    "roc(df.compute(), p_sig, p_bkg, v_higgs_m, axes=ax, steps=400)\n",
    "roc(df.compute(), p_sig, p_bkg, Variable(\"Noise ID\", \"noise\"), axes=ax, enlarge=1.5)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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