{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "383306d9", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from iminuit import Minuit" ] }, { "cell_type": "code", "execution_count": 2, "id": "9d130e75", "metadata": {}, "outputs": [], "source": [ "# Read the arrays that were prepared separately\n", "# b_pdf = the default background hist pdf \n", "# b1_pdf = 1st alternative to hist b_pdf\n", "# b2_pdf = 2nd alternative to hist b_pdf\n", "# s_pdf = the hist pdf for signal\n", "# d = the hist for the data\n", "# binCen = the center of the hist bins\n", "# binEdges = the edges of the bins\n", "# The arrays were saved with this command:\n", "# np.savez(\"histForMinuitFit.npz\", b_pdf, b1_pdf, b2_pdf, \n", "# s_pdf, d, binCen, binEdges,\n", "# b_pdf=b_pdf, b1_pdf=b1_pdf, b2_pdf=b2_pdf, \n", "# s_pdf=s_pdf, d=d,\n", "# binCen=binCen, binEdges=binEdges)\n", "npzfile = np.load(\"histForMinuitFit.npz\")\n", "b_pdf = npzfile['b_pdf']\n", "b1_pdf = npzfile['b1_pdf']\n", "b2_pdf = npzfile['b2_pdf']\n", "s_pdf = npzfile['s_pdf']\n", "d = npzfile['d']\n", "binCen = npzfile['binCen']\n", "binEdges = npzfile['binEdges']" ] }, { "cell_type": "code", "execution_count": 3, "id": "cec725f5", "metadata": {}, "outputs": [], "source": [ "# All pdfs should already be normalized to 1.\n", "# But just in case, normalize them again\n", "b_pdf = b_pdf / b_pdf.sum()\n", "b1_pdf = b1_pdf / b1_pdf.sum()\n", "b2_pdf = b2_pdf / b2_pdf.sum()\n", "s_pdf = s_pdf / s_pdf.sum()" ] }, { "cell_type": "code", "execution_count": 4, "id": "95624b29", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a1 = plt.subplot(111)\n", "a1.hist(binCen,binEdges,weights=b_pdf, \n", " histtype='step', log=True, color='blue', label=\"b_pdf\")\n", "a1.hist(binCen,binEdges,weights=b1_pdf, \n", " histtype='step', linestyle='dashed', log=True, color='red', label=\"b1_pdf\")\n", "a1.hist(binCen,binEdges,weights=b2_pdf, \n", " histtype='step', linestyle='dashed', log=True, color='green', label=\"b2_pdf\")\n", "a1.set_xlim(binEdges[0], binEdges[-1])\n", "a1.set_title(\"Background PDF\")\n", "a1.set_xlabel(\"Boosted Decision Tree Score\")\n", "a1.set_ylabel(\"Fraction of events\")\n", "a1.legend()\n", "a1.grid()" ] }, { "cell_type": "code", "execution_count": 5, "id": "8eed1db4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a2 = plt.subplot(111)\n", "a2.hist(binCen,binEdges,weights=s_pdf, histtype='step', log=True, color='red')\n", "a2.set_xlim(binEdges[0], binEdges[-1])\n", "a2.set_title(\"Signal PDF\")\n", "a2.set_xlabel(\"Boosted Decision Tree Score\")\n", "a2.set_ylabel(\"Fraction of events\")\n", "a2.grid()" ] }, { "cell_type": "code", "execution_count": 6, "id": "1b788231", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a4 = plt.subplot(111)\n", "# c4, b4, _ = a4.hist(binCen, binEdges, weights=d, log=True, color='black', histtype='step')\n", "a4.errorbar(binCen, d, yerr=np.sqrt(d), linestyle=\"none\", color='black',\n", " marker='o')\n", "a4.set_yscale('log')\n", "a4.set_xlim(binEdges[0], binEdges[-1])\n", "a4.set_xlabel(\"Boosted Decision Tree Score\")\n", "a4.set_ylabel(\"Number of events\")\n", "a4.set_title(\"(Pseudo) Data\")\n", "a4.grid()" ] }, { "cell_type": "code", "execution_count": 7, "id": "bbdf9176", "metadata": {}, "outputs": [], "source": [ "# Define a class to hold the data, the pdfs, and the NLL function\n", "# Gets the \"morphed\" BG pdf using the method of Section 4 in\n", "# https://indico.cern.ch/event/107747/contributions/32677/attachments/24368/35057/conway.pdf\n", "# Let f be the nuisance parameter associated with morphing\n", "# For abs(f)<1 we have quadratic approximation\n", "# b = 0.5*f*(f-1)*bg1 - (f-1)*(f+1)*bg + 0.5*f(f+1)*bg2\n", "# For abs(f)>1 we linearly extrapolate\n", "# Note:\n", "# db/df = (f-0.5)*bg1 - 2*f*bg + (f+0.5)*bg2\n", "# --> db/df at f=-1 : -1.5*bg1 + 2*bg - 0.5*bg2\n", "# --> fb/df at f=1 : 0.5*bg1 - 2*bg + 1.5*bg2\n", "class myFit:\n", " def __init__(self, data, sig_pdf, bg_pdf, bg1_pdf, bg2_pdf):\n", " self.data = data\n", " self.sig_pdf = sig_pdf\n", " self.bg_pdf = bg_pdf\n", " self.bg1_pdf = bg1_pdf\n", " self.bg2_pdf = bg2_pdf\n", "\n", " def getMorphedPdf(self, f):\n", " if abs(f) <= 1.:\n", " morph = 0.5*f*(f-1)*self.bg1_pdf - (f-1)*(f+1)*self.bg_pdf + 0.5*f*(f+1)*self.bg2_pdf\n", " elif f>1:\n", " slope = 0.5*self.bg1_pdf - 2*self.bg_pdf + 1.5*self.bg2_pdf\n", " morph = self.bg2_pdf + (f-1)*slope\n", " else:\n", " slope = -1.5*self.bg1_pdf + 2*self.bg_pdf - 0.5*self.bg2_pdf\n", " morph = self.bg1_pdf + (f+1)*slope\n", " return morph/morph.sum()\n", " \n", " def NLL(self, p):\n", " S = p[0]\n", " B = p[1]\n", " alpha = p[2]\n", " new_b_pdf = self.getMorphedPdf(alpha)\n", " temp = self.data * np.log(S*s_pdf + B*new_b_pdf)\n", " return S + B - temp.sum() + alpha*alpha/2." ] }, { "cell_type": "code", "execution_count": 8, "id": "23e3fb8a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = -2516 Nfcn = 72
EDM = 1.22e-06 (Goal: 0.0001)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 S 20 12
1 B 705 29
2 alpha 0.4 0.8
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S B alpha
S 140 -120 (-0.352) -4.72 (-0.529)
B -120 (-0.352) 825 4.71 (0.218)
alpha -4.72 (-0.529) 4.71 (0.218) 0.568
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = -2516 │ Nfcn = 72 │\n", "│ EDM = 1.22e-06 (Goal: 0.0001) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ S │ 20 │ 12 │ │ │ │ │ │\n", "│ 1 │ B │ 705 │ 29 │ │ │ │ │ │\n", "│ 2 │ alpha │ 0.4 │ 0.8 │ │ │ │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────┐\n", "│ │ S B alpha │\n", "├───────┼───────────────────┤\n", "│ S │ 140 -120 -4.72 │\n", "│ B │ -120 825 4.71 │\n", "│ alpha │ -4.72 4.71 0.568 │\n", "└───────┴───────────────────┘" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Instantiate the class\n", "thisFit = myFit(d, s_pdf, b_pdf, b1_pdf, b2_pdf)\n", "\n", "# Initialize the parameters (order is S,B,alpha)\n", "pinit = np.array([10, 500, 0])\n", "m = Minuit(thisFit.NLL, pinit, name=(\"S\", \"B\", \"alpha\"))\n", "m.errordef = Minuit.LIKELIHOOD\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 9, "id": "fcc6b118", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = -2516 Nfcn = 209
EDM = 1.22e-06 (Goal: 0.0001)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 S 20 12 -11 13
1 B 705 29 -28 29
2 alpha 0.4 0.8 -0.8 0.7
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S B alpha
Error -11 13 -28 29 -0.8 0.7
Valid True True True True True True
At Limit False False False False False False
Max FCN False False False False False False
New Min False False False False False False
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S B alpha
S 140 -120 (-0.352) -4.72 (-0.529)
B -120 (-0.352) 825 4.71 (0.218)
alpha -4.72 (-0.529) 4.71 (0.218) 0.568
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = -2516 │ Nfcn = 209 │\n", "│ EDM = 1.22e-06 (Goal: 0.0001) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ S │ 20 │ 12 │ -11 │ 13 │ │ │ │\n", "│ 1 │ B │ 705 │ 29 │ -28 │ 29 │ │ │ │\n", "│ 2 │ alpha │ 0.4 │ 0.8 │ -0.8 │ 0.7 │ │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n", "│ │ S │ B │ alpha │\n", "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n", "│ Error │ -11 │ 13 │ -28 │ 29 │ -0.8 │ 0.7 │\n", "│ Valid │ True │ True │ True │ True │ True │ True │\n", "│ At Limit │ False │ False │ False │ False │ False │ False │\n", "│ Max FCN │ False │ False │ False │ False │ False │ False │\n", "│ New Min │ False │ False │ False │ False │ False │ False │\n", "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n", "┌───────┬───────────────────┐\n", "│ │ S B alpha │\n", "├───────┼───────────────────┤\n", "│ S │ 140 -120 -4.72 │\n", "│ B │ -120 825 4.71 │\n", "│ alpha │ -4.72 4.71 0.568 │\n", "└───────┴───────────────────┘" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.minos()" ] }, { "cell_type": "code", "execution_count": 10, "id": "475ae160", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Profile log likelihood (crappy plot)\n", "S_scan, loglik = m.draw_mnprofile(\"S\",size=100, bound=[0,50], subtract_min=True)" ] }, { "cell_type": "code", "execution_count": 11, "id": "fe0ed282", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Redraw it prettier\n", "ax = plt.subplot(111)\n", "ax.plot(S_scan,loglik)\n", "ax.set_xlim(S_scan[0], S_scan[-1])\n", "ax.set_ylim(bottom=0)\n", "ax.grid()\n", "ax.plot([0,0],[0,ax.get_ylim()[1]], color='black',linestyle='dotted')\n", "ax.plot([S_scan[0], S_scan[-1]], [0.5, 0.5], color='red', linestyle='dashed')\n", "ax.plot([S_scan[0], S_scan[-1]], [2.0, 2.0], color='red', linestyle='dashed')\n", "ax.set_xlabel(\"Signal\", size=15)\n", "_ = ax.set_ylabel(\"$-\\Delta$log-lik\", size=15)" ] }, { "cell_type": "code", "execution_count": 12, "id": "671e9f99", "metadata": {}, "outputs": [], "source": [ "# get fit parameters\n", "fittedB = m.values['B']\n", "fittedS = m.values['S']\n", "fitteda = m.values['alpha']\n", "if fitteda>0:\n", " bf_pdf = b_pdf + fitteda*(b1_pdf-b_pdf)\n", "else:\n", " bf_pdf = b_pdf - fitteda*(b2_pdf-b_pdf)\n", "bf_pdf = bf_pdf / bf_pdf.sum()\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "d32c8530", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Then plot stacked histograms of S and B\n", "# lists with the data, colors, and labels of the two hist \n", "blah = [binCen, binCen]\n", "colors = ['blue', 'red']\n", "names = ['Background', 'Signal']\n", "w2 = [fittedB*bf_pdf, fittedS*s_pdf]\n", "\n", "ax42 = plt.subplot(111)\n", "ax42.hist(blah, binEdges, histtype='stepfilled', log=True,\n", " color=colors, stacked='True', label=names, weights=w2, alpha=0.4)\n", "ax42.errorbar(binCen, d, yerr=np.sqrt(d), linestyle='none', marker='o', \n", " color='black', markersize=4)\n", "ax42.set_ylim(0.1)\n", "ax42.set_xlim(binEdges[0], binEdges[-1])\n", "ax42.legend()\n", "_ = ax42.set_ylabel(\"Number of events\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9d0daa88", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 5 }