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[RF] Update SBI tutorials with improved plot styling and labels #16692

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[RF] Update SBI tutorials with improved plot styling and labels #16692

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89 changes: 64 additions & 25 deletions tutorials/roofit/rf615_simulation_based_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@
from sklearn.neural_network import MLPClassifier

# The samples used for training the classifier in this tutorial / rescale for more accuracy
n_samples = 1000
n_samples = 10000

# Kills warning messages
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.WARNING)
Expand Down Expand Up @@ -85,6 +85,15 @@ def morphing(setting):
morph = ROOT.RooWrapperPdf("morph", "morph", morph_func, True)
workspace.Import(morph, Silence=True)

# Uncomment to see input plots for the first dimension (you might need to increase the morphed samples)
# f1 = x_var.frame(Title="linear morphing;x;pdf", Range=(-4, 8))
# for i in range(n_grid):
# workspace[f"histpdf{i}"].plotOn(f1)
# workspace["morph"].plotOn(f1, LineColor="r")
# c0 = ROOT.TCanvas()
# f1.Draw()
# input() # Wait for user input to proceed


# Class used in this case to demonstrate the use of SBI in Root
class SBI:
Expand Down Expand Up @@ -139,15 +148,16 @@ def train_classifier(self):
# Define the "observed" data in a workspace
def build_ws(mu_observed, sigma):
# using a workspace for easier processing inside the class
workspace = ROOT.RooWorkspace()
workspace.factory(f"Gaussian::gauss(x[-5,15], mu[0,4], {sigma})")
workspace.factory("Uniform::uniform(x)")
workspace.Print("v")
obs_data = workspace["gauss"].generate(workspace["x"], n_samples)
ws = ROOT.RooWorkspace()
ws.factory(f"Gaussian::gauss(x[-5,15], mu[0,4], {sigma})")
ws.factory("Uniform::uniform(x)")
ws["mu"].setVal(mu_observed)
ws.Print("v")
obs_data = ws["gauss"].generate(ws["x"], 1000)
obs_data.SetName("obs_data")
workspace.Import(obs_data, Silence=True)
ws.Import(obs_data, Silence=True)

return workspace
return ws


# The "observed" data
Expand Down Expand Up @@ -201,30 +211,59 @@ def learned_likelihood_ratio(x, mu):
ROOT.SetOwnership(nll_morph, True)

# Plot the negative logarithmic summed likelihood
frame1 = mu_var.frame(Title="NLL of SBI vs. Morphing", Range=(1.5, 2.5))
nll_gauss.plotOn(frame1, LineColor="g", ShiftToZero=True, Name="gauss")
nllr_learned.plotOn(frame1, LineColor="r", LineStyle="--", ShiftToZero=True, Name="learned")
frame1 = mu_var.frame(Title="NLL of SBI vs. Morphing;mu;NLL", Range=(2.2, 2.8))
nllr_learned.plotOn(frame1, LineColor="kP6Blue", ShiftToZero=True, Name="learned")
nll_gauss.plotOn(frame1, LineColor="kP6Blue+1", ShiftToZero=True, Name="gauss")
ROOT.RooAbsReal.setEvalErrorLoggingMode("Ignore") # Silence some warnings
nll_morph.plotOn(frame1, LineColor="c", ShiftToZero=True, Name="morphed")
nll_morph.plotOn(frame1, LineColor="kP6Blue+2", ShiftToZero=True, Name="morphed")
ROOT.RooAbsReal.setEvalErrorLoggingMode("PrintErrors")

# Plot the likelihood functions
frame2 = x_var.frame(Title="Learned vs analytical likelihhood function")
llhr_learned.plotOn(frame2, LineColor="r", LineStyle="--", Name="learned_ratio")
llhr_calc.plotOn(frame2, LineColor="g", Name="exact")

# Write the plots into one canvas
c = ROOT.TCanvas("rf615_simulation_based_inference", "rf615_simulation_based_inference", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame2 = x_var.frame(Title="Likelihood ratio r(x|#mu=2.5);x;p_{gauss}/p_{uniform}")
llhr_learned.plotOn(frame2, LineColor="kP6Blue", Name="learned_ratio")
llhr_calc.plotOn(frame2, LineColor="kP6Blue+1", Name="exact")

# Write the plots into one canvas to show, or into separate canvases for saving.
single_canvas = True

c = ROOT.TCanvas("", "", 1200 if single_canvas else 600, 600)
if single_canvas:
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)

legend1 = ROOT.TLegend(0.43, 0.63, 0.8, 0.87)
legend1.SetFillColor(ROOT.kWhite)
legend1.SetLineColor(ROOT.kWhite)
legend1.SetTextSize(0.04)
legend1.AddEntry("learned", "learned (SBI)", "L")
legend1.AddEntry("gauss", "true NLL", "L")
legend1.AddEntry("morphed", "moment morphing", "L")
legend1.Draw()

if single_canvas:
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)
else:
c.SaveAs("rf615_plot_1.png")
c = ROOT.TCanvas("", "", 600, 600)

frame2.Draw()

legend2 = ROOT.TLegend(0.53, 0.73, 0.87, 0.87)
legend2.SetFillColor(ROOT.kWhite)
legend2.SetLineColor(ROOT.kWhite)
legend2.SetTextSize(0.04)
legend2.AddEntry("learned_ratio", "learned (SBI)", "L")
legend2.AddEntry("exact", "true ratio", "L")
legend2.Draw()

if not single_canvas:
c.SaveAs("rf615_plot_2.png")

# Compute the minimum via minuit and display the results
for nll in [nll_gauss, nllr_learned, nll_morph]:
minimizer = ROOT.RooMinimizer(nll)
Expand Down
68 changes: 48 additions & 20 deletions tutorials/roofit/rf617_simulation_based_inference_multidimensional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@
# Kills warning messages
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.WARNING)

n_samples_morph = 1000 # Number of samples for morphing
n_samples_morph = 10000 # Number of samples for morphing
n_bins = 4 # Number of 'sampled' Gaussians
n_samples_train = n_samples_morph * n_bins # To have a fair comparison

Expand Down Expand Up @@ -172,7 +172,7 @@ def train_classifier(self):
# Define the "observed" data in a workspace
def build_ws(mu_observed):
n_vars = len(mu_observed)
x_vars = [ROOT.RooRealVar(f"x{i}", f"x{i}", -2, 6) for i in range(n_vars)]
x_vars = [ROOT.RooRealVar(f"x{i}", f"x{i}", -5, 15) for i in range(n_vars)]
mu_vars = [ROOT.RooRealVar(f"mu{i}", f"mu{i}", mu_observed[i], 0, 4) for i in range(n_vars)]
gaussians = [ROOT.RooGaussian(f"gauss{i}", f"gauss{i}", x_vars[i], mu_vars[i], sigmas[i]) for i in range(n_vars)]
uniforms = [ROOT.RooUniform(f"uniform{i}", f"uniform{i}", x_vars[i]) for i in range(n_vars)]
Expand Down Expand Up @@ -257,14 +257,14 @@ def learned_likelihood_ratio(*args):

# Plot the learned and analytical summed negativelogarithmic likelihood
frame1 = mu_vars[0].frame(
Title="Negative logarithmic Likelihood",
Title="NLL of SBI vs. Morphing;#mu_{1};NLL",
Range=(mu_observed[0] - 1, mu_observed[0] + 1),
)
nll_gauss.plotOn(frame1, ShiftToZero=True, LineColor="g", Name="gauss")
nll_gauss.plotOn(frame1, ShiftToZero=True, LineColor="kP6Blue+1", Name="gauss")
ROOT.RooAbsReal.setEvalErrorLoggingMode("Ignore") # Silence some warnings
nll_morph.plotOn(frame1, ShiftToZero=True, LineColor="c", Name="morph")
nll_morph.plotOn(frame1, ShiftToZero=True, LineColor="kP6Blue+2", Name="morph")
ROOT.RooAbsReal.setEvalErrorLoggingMode("PrintErrors")
nllr_learned.plotOn(frame1, LineColor="r", ShiftToZero=True, LineStyle="--", Name="learned")
nllr_learned.plotOn(frame1, LineColor="kP6Blue", ShiftToZero=True, Name="learned")


# Declare a helper function in ROOT to dereference unique_ptr
Expand All @@ -281,23 +281,51 @@ def learned_likelihood_ratio(*args):
lhr_calc_final.recursiveRedirectServers(norm_set)

# Plot the likelihood ratio functions
frame2 = x_vars[0].frame(Title="Learned vs analytical likelihood function")
lhr_learned.plotOn(frame2, LineColor="r", LineStyle="--", Name="learned")
lhr_calc_final.plotOn(frame2, LineColor="g", Name="analytical")

c = ROOT.TCanvas(
"rf617_simulation_based_inference_multidimensional", "rf617_simulation_based_inference_multidimensional", 800, 400
)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame2 = x_vars[0].frame(Title="Likelihood ratio r(x_{1}|#mu_{1}=2.5);x_{1};p_{gauss}/p_{uniform}")
lhr_learned.plotOn(frame2, LineColor="kP6Blue", Name="learned_ratio")
lhr_calc_final.plotOn(frame2, LineColor="kP6Blue+1", Name="exact")

# Write the plots into one canvas to show, or into separate canvases for saving.
single_canvas = True

c = ROOT.TCanvas("", "", 1200 if single_canvas else 600, 600)
if single_canvas:
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)

legend1 = ROOT.TLegend(0.43, 0.63, 0.8, 0.87)
legend1.SetFillColor(ROOT.kWhite)
legend1.SetLineColor(ROOT.kWhite)
legend1.SetTextSize(0.04)
legend1.AddEntry("learned", "learned (SBI)", "L")
legend1.AddEntry("gauss", "true NLL", "L")
legend1.AddEntry("morphed", "moment morphing", "L")
legend1.Draw()

if single_canvas:
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)
else:
c.SaveAs("rf617_plot_1.png")
c = ROOT.TCanvas("", "", 600, 600)

frame2.Draw()

legend2 = ROOT.TLegend(0.53, 0.73, 0.87, 0.87)
legend2.SetFillColor(ROOT.kWhite)
legend2.SetLineColor(ROOT.kWhite)
legend2.SetTextSize(0.04)
legend2.AddEntry("learned_ratio", "learned (SBI)", "L")
legend2.AddEntry("exact", "true ratio", "L")
legend2.Draw()

if not single_canvas:
c.SaveAs("rf617_plot_2.png")


# Use ROOT's minimizer to compute the minimum and display the results
for nll in [nll_gauss, nllr_learned, nll_morph]:
Expand Down
50 changes: 31 additions & 19 deletions tutorials/roofit/rf618_mixture_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,17 +112,13 @@


# Define functions to compute the learned likelihood.
def calculate_likelihood_xgb(m4l_arr):
def calculate_likelihood_xgb(m4l_arr: np.ndarray) -> np.ndarray:
prob = model_xgb.predict_proba(m4l_arr.T)[:, 0]
return (1 - prob) / prob


llh = ROOT.RooFit.bindFunction(f"llh", calculate_likelihood_xgb, m4l)

# Plot the likelihood
frame1 = m4l.frame(Title=f"Likelihood", Range=(0, 200))
llh.plotOn(frame1, ShiftToZero=False)

# Number of signals and background
n_signal = results["higgs"]["weight"].sum()
n_back = results["zz"]["weight"].sum()
Expand All @@ -138,7 +134,7 @@ def weight_signal(mu):


# Define the likelihood ratio accordingly to mixture models
def likelihood_ratio(llr, mu):
def likelihood_ratio(llr: np.ndarray, mu: np.ndarray) -> np.ndarray:

m = 2

Expand All @@ -159,7 +155,12 @@ def likelihood_ratio(llr, mu):
mu_var = ROOT.RooRealVar("mu", "mu", 0.1, 5)

nll_ratio = ROOT.RooFit.bindFunction(f"nll", likelihood_ratio, llh, mu_var)
pdf_learned = ROOT.RooWrapperPdf("learned_pdf", "learned_pdf", nll_ratio, True)
pdf_learned = ROOT.RooWrapperPdf("learned_pdf", "learned_pdf", nll_ratio, selfNormalized=True)

# Plot the likelihood
frame1 = m4l.frame(Title="Likelihood ratio r(m_{4l}|#mu=1);m_{4l};p(#mu=1)/p(#mu=0)", Range=(80, 170))
# llh.plotOn(frame1, ShiftToZero=False, LineColor="kP6Blue")
nll_ratio.plotOn(frame1, ShiftToZero=False, LineColor="kP6Blue")

n_pred = ROOT.RooFormulaVar("n_pred", f"{n_back} + mu * {n_signal}", [mu_var])
pdf_learned_extended = ROOT.RooExtendPdf("final_pdf", "final_pdf", pdf_learned, n_pred)
Expand All @@ -169,21 +170,32 @@ def likelihood_ratio(llr, mu):
nll = pdf_learned_extended.createNLL(data, Extended=True)

# Plot the nll computet by the mixture model
frame2 = mu_var.frame(Title="NLL")
nll.plotOn(frame2)

# Write the plots into one canvas
c = ROOT.TCanvas("rf618_sbi_higgs", "rf618_sbi_higgs", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame2 = mu_var.frame(Title="NLL sum;#mu (signal strength);#Delta NLL", Range=(0.5, 4))
nll.plotOn(frame2, ShiftToZero=True, LineColor="kP6Blue")

# Write the plots into one canvas to show, or into separate canvases for saving.
single_canvas = True

c = ROOT.TCanvas("", "", 1200 if single_canvas else 600, 600)
if single_canvas:
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.8)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)

if single_canvas:
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)
else:
c.SaveAs("rf618_plot_1.png")
c = ROOT.TCanvas("", "", 600, 600)

frame2.Draw()

if not single_canvas:
c.SaveAs("rf618_plot_2.png")

# Compute the minimum via minuit and display the results
minimizer = ROOT.RooMinimizer(nll)
Expand Down
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