{
"cells": [
{
"cell_type": "markdown",
"id": "a9799f10",
"metadata": {},
"source": [
"# rf702_efficiencyfit_2D\n",
"Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function\n",
"to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function\n",
"of x should be described by eff(x)\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Clemens Lange, Wouter Verkerke (C++ version) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:17 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f3fa6d24",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:23.354916Z",
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"iopub.status.idle": "2024-03-19T19:17:26.239478Z",
"shell.execute_reply": "2024-03-19T19:17:26.232269Z"
}
},
"outputs": [],
"source": [
"import ROOT\n",
"\n",
"\n",
"flat = False"
]
},
{
"cell_type": "markdown",
"id": "48de9edf",
"metadata": {},
"source": [
"Construct efficiency function e(x,y)\n",
"-----------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "ac5a9481",
"metadata": {},
"source": [
"Declare variables x,mean, with associated name, title, value and allowed\n",
"range"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9e2ded39",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:26.251972Z",
"iopub.status.busy": "2024-03-19T19:17:26.251311Z",
"iopub.status.idle": "2024-03-19T19:17:26.762332Z",
"shell.execute_reply": "2024-03-19T19:17:26.758262Z"
}
},
"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)\n",
"y = ROOT.RooRealVar(\"y\", \"y\", -10, 10)"
]
},
{
"cell_type": "markdown",
"id": "3289bf33",
"metadata": {},
"source": [
"Efficiency function eff(x;a,b)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "41cc6e0a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:26.773019Z",
"iopub.status.busy": "2024-03-19T19:17:26.772597Z",
"iopub.status.idle": "2024-03-19T19:17:27.175208Z",
"shell.execute_reply": "2024-03-19T19:17:27.172687Z"
}
},
"outputs": [],
"source": [
"ax = ROOT.RooRealVar(\"ax\", \"ay\", 0.6, 0, 1)\n",
"bx = ROOT.RooRealVar(\"bx\", \"by\", 5)\n",
"cx = ROOT.RooRealVar(\"cx\", \"cy\", -1, -10, 10)\n",
"\n",
"ay = ROOT.RooRealVar(\"ay\", \"ay\", 0.2, 0, 1)\n",
"by = ROOT.RooRealVar(\"by\", \"by\", 5)\n",
"cy = ROOT.RooRealVar(\"cy\", \"cy\", -1, -10, 10)\n",
"\n",
"effFunc = ROOT.RooFormulaVar(\n",
" \"effFunc\", \"((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))\", [ax, bx, cx, x, ay, by, cy, y]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ab94b2ad",
"metadata": {},
"source": [
"Acceptance state cut (1 or 0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b3a78a5e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:27.180908Z",
"iopub.status.busy": "2024-03-19T19:17:27.180509Z",
"iopub.status.idle": "2024-03-19T19:17:27.522052Z",
"shell.execute_reply": "2024-03-19T19:17:27.520337Z"
}
},
"outputs": [],
"source": [
"cut = ROOT.RooCategory(\"cut\", \"cutr\", {\"accept\": 1, \"reject\": 0})"
]
},
{
"cell_type": "markdown",
"id": "73f691be",
"metadata": {},
"source": [
"Construct conditional efficiency pdf E(cut|x,y)\n",
"---------------------------------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "a56b7be0",
"metadata": {},
"source": [
"Construct efficiency pdf eff(cut|x)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2e122c4b",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:27.530097Z",
"iopub.status.busy": "2024-03-19T19:17:27.529722Z",
"iopub.status.idle": "2024-03-19T19:17:27.729770Z",
"shell.execute_reply": "2024-03-19T19:17:27.727830Z"
}
},
"outputs": [],
"source": [
"effPdf = ROOT.RooEfficiency(\"effPdf\", \"effPdf\", effFunc, cut, \"accept\")"
]
},
{
"cell_type": "markdown",
"id": "e2ea543c",
"metadata": {},
"source": [
"Generate data(x,y,cut) from a toy model\n",
"-------------------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "da84ac33",
"metadata": {},
"source": [
"Construct global shape pdf shape(x) and product model(x,cut) = eff(cut|x)*shape(x)\n",
"(These are _only_ needed to generate some toy MC here to be used later)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9b24d9c0",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:27.797416Z",
"iopub.status.busy": "2024-03-19T19:17:27.796894Z",
"iopub.status.idle": "2024-03-19T19:17:28.146774Z",
"shell.execute_reply": "2024-03-19T19:17:28.142842Z"
}
},
"outputs": [],
"source": [
"shapePdfX = ROOT.RooPolynomial(\"shapePdfX\", \"shapePdfX\", x, [0 if flat else -0.095])\n",
"shapePdfY = ROOT.RooPolynomial(\"shapePdfY\", \"shapePdfY\", y, [0 if flat else +0.095])\n",
"shapePdf = ROOT.RooProdPdf(\"shapePdf\", \"shapePdf\", [shapePdfX, shapePdfY])\n",
"model = ROOT.RooProdPdf(\"model\", \"model\", {shapePdf}, Conditional=({effPdf}, {cut}))"
]
},
{
"cell_type": "markdown",
"id": "82f5eb42",
"metadata": {},
"source": [
"Generate some toy data from model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "415981ab",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:28.169528Z",
"iopub.status.busy": "2024-03-19T19:17:28.168685Z",
"iopub.status.idle": "2024-03-19T19:17:28.611539Z",
"shell.execute_reply": "2024-03-19T19:17:28.606855Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]) WARNING: performing accept/reject sampling on a p.d.f in 2 dimensions without prior knowledge on maximum value of p.d.f. Determining maximum value by taking 200000 trial samples. If p.d.f contains sharp peaks smaller than average distance between trial sampling points these may be missed and p.d.f. may be sampled incorrectly.\n",
"[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]): WARNING: 200000 trial samples requested by p.d.f for 2-dimensional accept/reject sampling, this may take some time\n"
]
}
],
"source": [
"data = model.generate({x, y, cut}, 10000)"
]
},
{
"cell_type": "markdown",
"id": "540cbe8f",
"metadata": {},
"source": [
"Fit conditional efficiency pdf to data\n",
"--------------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "cdcb7da4",
"metadata": {},
"source": [
"Fit conditional efficiency pdf to data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "706413b9",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:28.616351Z",
"iopub.status.busy": "2024-03-19T19:17:28.616017Z",
"iopub.status.idle": "2024-03-19T19:17:29.338137Z",
"shell.execute_reply": "2024-03-19T19:17:29.336985Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(effPdf_over_effPdf_Int[cut]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_effPdf_over_effPdf_Int[cut]_modelData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization\n"
]
}
],
"source": [
"effPdf.fitTo(data, ConditionalObservables={x, y}, PrintLevel=-1)"
]
},
{
"cell_type": "markdown",
"id": "41d0bf2a",
"metadata": {},
"source": [
"Plot fitted, data efficiency\n",
"--------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "1cef5879",
"metadata": {},
"source": [
"Make 2D histograms of all data, data and efficiency function"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "90a18d65",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:29.343731Z",
"iopub.status.busy": "2024-03-19T19:17:29.343413Z",
"iopub.status.idle": "2024-03-19T19:17:29.707454Z",
"shell.execute_reply": "2024-03-19T19:17:29.706232Z"
}
},
"outputs": [],
"source": [
"hh_data_all = data.createHistogram(\"hh_data_all\", x, Binning=8, YVar=dict(var=y, Binning=8))\n",
"hh_data_sel = data.createHistogram(\"hh_data_sel\", x, Binning=8, YVar=dict(var=y, Binning=8), Cut=\"cut==cut::accept\")\n",
"hh_eff = effFunc.createHistogram(\"hh_eff\", x, Binning=50, YVar=dict(var=y, Binning=50))"
]
},
{
"cell_type": "markdown",
"id": "3614695b",
"metadata": {},
"source": [
"Some adjustsment for good visualization"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d014e1d5",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:29.713544Z",
"iopub.status.busy": "2024-03-19T19:17:29.713153Z",
"iopub.status.idle": "2024-03-19T19:17:29.887549Z",
"shell.execute_reply": "2024-03-19T19:17:29.886283Z"
}
},
"outputs": [],
"source": [
"hh_data_all.SetMinimum(0)\n",
"hh_data_sel.SetMinimum(0)\n",
"hh_eff.SetMinimum(0)\n",
"hh_eff.SetLineColor(ROOT.kBlue)"
]
},
{
"cell_type": "markdown",
"id": "5613a4bf",
"metadata": {},
"source": [
"Draw all frames on a canvas"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a7a9dd8f",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:29.905159Z",
"iopub.status.busy": "2024-03-19T19:17:29.904525Z",
"iopub.status.idle": "2024-03-19T19:17:30.558435Z",
"shell.execute_reply": "2024-03-19T19:17:30.553233Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rf702_efficiency_2D.png has been created\n"
]
}
],
"source": [
"ca = ROOT.TCanvas(\"rf702_efficiency_2D\", \"rf702_efficiency_2D\", 1200, 400)\n",
"ca.Divide(3)\n",
"ca.cd(1)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"hh_data_all.GetZaxis().SetTitleOffset(1.8)\n",
"hh_data_all.Draw(\"lego\")\n",
"ca.cd(2)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"hh_data_sel.GetZaxis().SetTitleOffset(1.8)\n",
"hh_data_sel.Draw(\"lego\")\n",
"ca.cd(3)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"hh_eff.GetZaxis().SetTitleOffset(1.8)\n",
"hh_eff.Draw(\"surf\")\n",
"\n",
"ca.SaveAs(\"rf702_efficiency_2D.png\")"
]
},
{
"cell_type": "markdown",
"id": "cd6e34ae",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ebf40584",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:30.580402Z",
"iopub.status.busy": "2024-03-19T19:17:30.579977Z",
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"shell.execute_reply": "2024-03-19T19:17:30.922141Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}