{
"cells": [
{
"cell_type": "markdown",
"id": "8ff0409b",
"metadata": {},
"source": [
"# rf707_kernelestimation\n",
"Special pdf's: using non-parametric (multi-dimensional) kernel estimation pdfs\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Wouter Verkerke \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": "ebe78786",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:40.093397Z",
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"shell.execute_reply": "2024-03-19T19:17:40.132422Z"
}
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"outputs": [],
"source": [
"%%cpp -d\n",
"#include \"RooRealVar.h\"\n",
"#include \"RooDataSet.h\"\n",
"#include \"RooGaussian.h\"\n",
"#include \"RooPolynomial.h\"\n",
"#include \"RooKeysPdf.h\"\n",
"#include \"RooNDKeysPdf.h\"\n",
"#include \"RooProdPdf.h\"\n",
"#include \"TCanvas.h\"\n",
"#include \"TAxis.h\"\n",
"#include \"TH1.h\"\n",
"#include \"RooPlot.h\"\n",
"using namespace RooFit;"
]
},
{
"cell_type": "markdown",
"id": "16da3287",
"metadata": {},
"source": [
"Create low stats 1-D dataset\n",
"-------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "ee7687a0",
"metadata": {},
"source": [
"Create a toy pdf for sampling"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d1ca07cd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:40.141449Z",
"iopub.status.busy": "2024-03-19T19:17:40.141062Z",
"iopub.status.idle": "2024-03-19T19:17:41.026020Z",
"shell.execute_reply": "2024-03-19T19:17:41.007255Z"
}
},
"outputs": [],
"source": [
"RooRealVar x(\"x\", \"x\", 0, 20);\n",
"RooPolynomial p(\"p\", \"p\", x, RooArgList(0.01, -0.01, 0.0004));"
]
},
{
"cell_type": "markdown",
"id": "3c1cb1c5",
"metadata": {},
"source": [
"Sample 500 events from p"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2277ea93",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.045922Z",
"iopub.status.busy": "2024-03-19T19:17:41.045527Z",
"iopub.status.idle": "2024-03-19T19:17:41.261589Z",
"shell.execute_reply": "2024-03-19T19:17:41.260483Z"
}
},
"outputs": [],
"source": [
"std::unique_ptr data1{p.generate(x, 200)};"
]
},
{
"cell_type": "markdown",
"id": "9e1ae563",
"metadata": {},
"source": [
"Create 1-D kernel estimation pdf\n",
"---------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "39ec314b",
"metadata": {},
"source": [
"Create adaptive kernel estimation pdf. In this configuration the input data\n",
"is mirrored over the boundaries to minimize edge effects in distribution\n",
"that do not fall to zero towards the edges"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f1e92da8",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.268810Z",
"iopub.status.busy": "2024-03-19T19:17:41.268444Z",
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"shell.execute_reply": "2024-03-19T19:17:41.484355Z"
}
},
"outputs": [],
"source": [
"RooKeysPdf kest1(\"kest1\", \"kest1\", x, *data1, RooKeysPdf::MirrorBoth);"
]
},
{
"cell_type": "markdown",
"id": "79075830",
"metadata": {},
"source": [
"An adaptive kernel estimation pdf on the same data without mirroring option\n",
"for comparison"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8371c17c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.490563Z",
"iopub.status.busy": "2024-03-19T19:17:41.490220Z",
"iopub.status.idle": "2024-03-19T19:17:41.737377Z",
"shell.execute_reply": "2024-03-19T19:17:41.736323Z"
}
},
"outputs": [],
"source": [
"RooKeysPdf kest2(\"kest2\", \"kest2\", x, *data1, RooKeysPdf::NoMirror);"
]
},
{
"cell_type": "markdown",
"id": "b515bceb",
"metadata": {},
"source": [
"Adaptive kernel estimation pdf with increased bandwidth scale factor\n",
"(promotes smoothness over detail preservation)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9aa12641",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.741709Z",
"iopub.status.busy": "2024-03-19T19:17:41.741411Z",
"iopub.status.idle": "2024-03-19T19:17:41.962815Z",
"shell.execute_reply": "2024-03-19T19:17:41.960954Z"
}
},
"outputs": [],
"source": [
"RooKeysPdf kest3(\"kest1\", \"kest1\", x, *data1, RooKeysPdf::MirrorBoth, 2);"
]
},
{
"cell_type": "markdown",
"id": "699632c6",
"metadata": {},
"source": [
"Plot kernel estimation pdfs with and without mirroring over data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0bd85814",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.004075Z",
"iopub.status.busy": "2024-03-19T19:17:42.003687Z",
"iopub.status.idle": "2024-03-19T19:17:42.263980Z",
"shell.execute_reply": "2024-03-19T19:17:42.262701Z"
}
},
"outputs": [],
"source": [
"RooPlot *frame = x.frame(Title(\"Adaptive kernel estimation pdf with and w/o mirroring\"), Bins(20));\n",
"data1->plotOn(frame);\n",
"kest1.plotOn(frame);\n",
"kest2.plotOn(frame, LineStyle(kDashed), LineColor(kRed));"
]
},
{
"cell_type": "markdown",
"id": "a6ffa620",
"metadata": {},
"source": [
"Plot kernel estimation pdfs with regular and increased bandwidth"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b11ee51e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.285511Z",
"iopub.status.busy": "2024-03-19T19:17:42.285121Z",
"iopub.status.idle": "2024-03-19T19:17:42.518104Z",
"shell.execute_reply": "2024-03-19T19:17:42.515301Z"
}
},
"outputs": [],
"source": [
"RooPlot *frame2 = x.frame(Title(\"Adaptive kernel estimation pdf with regular, increased bandwidth\"));\n",
"kest1.plotOn(frame2);\n",
"kest3.plotOn(frame2, LineColor(kMagenta));"
]
},
{
"cell_type": "markdown",
"id": "394fa0fc",
"metadata": {},
"source": [
"Create low stats 2-D dataset\n",
"-------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "13280d39",
"metadata": {},
"source": [
"Construct a 2D toy pdf for sampling"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a7bec5e9",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.524732Z",
"iopub.status.busy": "2024-03-19T19:17:42.524144Z",
"iopub.status.idle": "2024-03-19T19:17:42.878335Z",
"shell.execute_reply": "2024-03-19T19:17:42.868217Z"
}
},
"outputs": [],
"source": [
"RooRealVar y(\"y\", \"y\", 0, 20);\n",
"RooPolynomial py(\"py\", \"py\", y, RooArgList(0.01, 0.01, -0.0004));\n",
"RooProdPdf pxy(\"pxy\", \"pxy\", RooArgSet(p, py));\n",
"std::unique_ptr data2{pxy.generate({x, y}, 1000)};"
]
},
{
"cell_type": "markdown",
"id": "81956e83",
"metadata": {},
"source": [
"Create 2-D kernel estimation pdf\n",
"---------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "63fdff0a",
"metadata": {},
"source": [
"Create 2D adaptive kernel estimation pdf with mirroring"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dd150f3a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.918473Z",
"iopub.status.busy": "2024-03-19T19:17:42.918043Z",
"iopub.status.idle": "2024-03-19T19:17:43.461134Z",
"shell.execute_reply": "2024-03-19T19:17:43.459552Z"
}
},
"outputs": [],
"source": [
"RooNDKeysPdf kest4(\"kest4\", \"kest4\", RooArgSet(x, y), *data2, \"am\");"
]
},
{
"cell_type": "markdown",
"id": "0b542bca",
"metadata": {},
"source": [
"Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "16f7e336",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:43.466135Z",
"iopub.status.busy": "2024-03-19T19:17:43.465748Z",
"iopub.status.idle": "2024-03-19T19:17:44.234708Z",
"shell.execute_reply": "2024-03-19T19:17:44.228448Z"
}
},
"outputs": [],
"source": [
"RooNDKeysPdf kest5(\"kest5\", \"kest5\", RooArgSet(x, y), *data2, \"am\", 2);"
]
},
{
"cell_type": "markdown",
"id": "c66f962b",
"metadata": {},
"source": [
"Create a histogram of the data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "61955f3b",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:44.244048Z",
"iopub.status.busy": "2024-03-19T19:17:44.243670Z",
"iopub.status.idle": "2024-03-19T19:17:44.464355Z",
"shell.execute_reply": "2024-03-19T19:17:44.463012Z"
}
},
"outputs": [],
"source": [
"TH1 *hh_data = data2->createHistogram(\"hh_data\", x, Binning(10), YVar(y, Binning(10)));"
]
},
{
"cell_type": "markdown",
"id": "2a4ce016",
"metadata": {},
"source": [
"Create histogram of the 2d kernel estimation pdfs"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "97df01dd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:44.475085Z",
"iopub.status.busy": "2024-03-19T19:17:44.474694Z",
"iopub.status.idle": "2024-03-19T19:17:45.348683Z",
"shell.execute_reply": "2024-03-19T19:17:45.346186Z"
}
},
"outputs": [],
"source": [
"TH1 *hh_pdf = kest4.createHistogram(\"hh_pdf\", x, Binning(25), YVar(y, Binning(25)));\n",
"TH1 *hh_pdf2 = kest5.createHistogram(\"hh_pdf2\", x, Binning(25), YVar(y, Binning(25)));\n",
"hh_pdf->SetLineColor(kBlue);\n",
"hh_pdf2->SetLineColor(kMagenta);\n",
"\n",
"TCanvas *c = new TCanvas(\"rf707_kernelestimation\", \"rf707_kernelestimation\", 800, 800);\n",
"c->Divide(2, 2);\n",
"c->cd(1);\n",
"gPad->SetLeftMargin(0.15);\n",
"frame->GetYaxis()->SetTitleOffset(1.4);\n",
"frame->Draw();\n",
"c->cd(2);\n",
"gPad->SetLeftMargin(0.15);\n",
"frame2->GetYaxis()->SetTitleOffset(1.8);\n",
"frame2->Draw();\n",
"c->cd(3);\n",
"gPad->SetLeftMargin(0.15);\n",
"hh_data->GetZaxis()->SetTitleOffset(1.4);\n",
"hh_data->Draw(\"lego\");\n",
"c->cd(4);\n",
"gPad->SetLeftMargin(0.20);\n",
"hh_pdf->GetZaxis()->SetTitleOffset(2.4);\n",
"hh_pdf->Draw(\"surf\");\n",
"hh_pdf2->Draw(\"surfsame\");"
]
},
{
"cell_type": "markdown",
"id": "ac289d0a",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "08b17f5a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:45.363817Z",
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"shell.execute_reply": "2024-03-19T19:17:45.871253Z"
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"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gROOT->GetListOfCanvases()->Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ROOT C++",
"language": "c++",
"name": "root"
},
"language_info": {
"codemirror_mode": "text/x-c++src",
"file_extension": ".C",
"mimetype": " text/x-c++src",
"name": "c++"
}
},
"nbformat": 4,
"nbformat_minor": 5
}