rf617_simulation_based_inference_multidimensional.py File Reference

Detailed Description

Use Simulation Based Inference (SBI) in multiple dimensions in RooFit.

This tutorial shows how to use SBI in higher dimension in ROOT. This tutorial transfers the simple concepts of the 1D case introduced in rf615_simulation_based_inference.py onto the higher dimensional case.

Again as reference distribution we choose a simple uniform distribution. The target distribution is chosen to be Gaussian with different mean values. The classifier is trained to discriminate between the reference and target distribution. We see how the neural networks generalize to unknown mean values.

Furthermore, we compare SBI to the approach of moment morphing. In this case, we can conclude, that SBI is way more sample eficcient when it comes to estimating the negative log likelihood ratio.

For an introductory background see rf615_simulation_based_inference.py

 
import ROOT
import numpy as np
from sklearn.neural_network import MLPClassifier
import itertools
 
# Kills warning messages
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.WARNING)
 
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
 
 
# Morphing as baseline
def morphing(setting, n_dimensions):
    # Define binning for morphing
 
    binning = [ROOT.RooBinning(n_bins, 0.0, n_bins - 1.0) for dim in range(n_dimensions)]
    grid = ROOT.RooMomentMorphFuncND.Grid(*binning)
 
    # Set bins for each x variable
    for x_var in x_vars:
        x_var.setBins(50)
 
    # Define mu values as input for morphing for each dimension
    mu_helps = [ROOT.RooRealVar(f"mu{i}", f"mu{i}", 0.0) for i in range(n_dimensions)]
 
    # Create a product of Gaussians for all dimensions
    gaussians = []
    for j in range(n_dimensions):
        gaussian = ROOT.RooGaussian(f"gdim{j}", f"gdim{j}", x_vars[j], mu_helps[j], sigmas[j])
        gaussians.append(gaussian)
 
    # Create a product PDF for the multidimensional Gaussian
    gauss_product = ROOT.RooProdPdf("gauss_product", "gauss_product", ROOT.RooArgList(*gaussians))
 
    templates = dict()
 
    # Iterate through each tuple
    for idx, nd_idx in enumerate(itertools.product(range(n_bins), repeat=n_dimensions)):
        for i_dim in range(n_dimensions):
            mu_helps[i_dim].setVal(nd_idx[i_dim])
 
        # Fill the histograms
        hist = gauss_product.generateBinned(ROOT.RooArgSet(*x_vars), n_samples_morph)
 
        # Ensure that every bin is filled and there are no zero probabilities
        for i_bin in range(hist.numEntries()):
            hist.add(hist.get(i_bin), 1.0)
 
        templates[nd_idx] = ROOT.RooHistPdf(f"histpdf{idx}", f"histpdf{idx}", ROOT.RooArgSet(*x_vars), hist, 1)
 
        # Add the PDF to the grid
        grid.addPdf(templates[nd_idx], *nd_idx)
 
    # Create the morphing function and add it to the ws
    morph_func = ROOT.RooMomentMorphFuncND("morph_func", "morph_func", [*mu_vars], [*x_vars], grid, setting)
    morph_func.setPdfMode(True)
    morph = ROOT.RooWrapperPdf("morph", "morph", morph_func, True)
 
    ws.Import(morph)
 
    # Uncomment to see input plots for the first dimension (you might need to increase the morphed samples)
    # f1 = x_vars[0].frame()
    # for i in range(n_bins):
    #    templates[(i, 0)].plotOn(f1)
    # ws["morph"].plotOn(f1, LineColor="r")
    # c0 = ROOT.TCanvas()
    # f1.Draw()
    # input() # Wait for user input to proceed
 
 
# Define the observed mean values for the Gaussian distributions
mu_observed = [2.5, 2.0]
sigmas = [1.5, 1.5]
 
 
# Class used in this case to demonstrate the use of SBI in Root
class SBI:
    # Initializing the class SBI
    def __init__(self, ws, n_vars):
        # Choose the hyperparameters for training the neural network
        self.classifier = MLPClassifier(hidden_layer_sizes=(20, 20), max_iter=1000, random_state=42)
        self.data_model = None
        self.data_ref = None
        self.X_train = None
        self.y_train = None
        self.ws = ws
        self.n_vars = n_vars
        self._training_mus = None
        self._reference_mu = None
 
    # Defining the target / training data for different values of mean value mu
    def model_data(self, model, x_vars, mu_vars, n_samples):
        ws = self.ws
        samples_gaussian = (
            ws[model].generate([ws[x] for x in x_vars] + [ws[mu] for mu in mu_vars], n_samples).to_numpy()
        )
 
        self._training_mus = np.array([samples_gaussian[mu] for mu in mu_vars]).T
        data_test_model = np.array([samples_gaussian[x] for x in x_vars]).T
 
        self.data_model = data_test_model.reshape(-1, self.n_vars)
 
    # Generating samples for the reference distribution
    def reference_data(self, model, x_vars, mu_vars, n_samples, help_model):
        ws = self.ws
        # Ensuring the normalization with generating as many reference data as target data
        samples_uniform = ws[model].generate([ws[x] for x in x_vars], n_samples)
        data_reference_model = np.array(
            [samples_uniform.get(i).getRealValue(x) for x in x_vars for i in range(samples_uniform.numEntries())]
        )
 
        self.data_ref = data_reference_model.reshape(-1, self.n_vars)
 
        samples_mu = ws[help_model].generate([ws[mu] for mu in mu_vars], n_samples)
        mu_data = np.array(
            [samples_mu.get(i).getRealValue(mu) for mu in mu_vars for i in range(samples_mu.numEntries())]
        )
 
        self._reference_mu = mu_data.reshape(-1, self.n_vars)
 
    # Bringing the data in the right format for training
    def preprocessing(self):
        thetas = np.concatenate((self._training_mus, self._reference_mu))
        X = np.concatenate([self.data_model, self.data_ref])
 
        self.y_train = np.concatenate([np.ones(len(self.data_model)), np.zeros(len(self.data_ref))])
        self.X_train = np.concatenate([X, thetas], axis=1)
 
    # Train the classifier
    def train_classifier(self):
        self.classifier.fit(self.X_train, self.y_train)
 
 
# 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}", -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)]
    uniforms_help = [ROOT.RooUniform(f"uniformh{i}", f"uniformh{i}", mu_vars[i]) for i in range(n_vars)]
    # Create multi-dimensional PDFs
    gauss = ROOT.RooProdPdf("gauss", "gauss", ROOT.RooArgList(*gaussians))
    uniform = ROOT.RooProdPdf("uniform", "uniform", ROOT.RooArgList(*uniforms))
    uniform_help = ROOT.RooProdPdf("uniform_help", "uniform_help", ROOT.RooArgList(*uniforms_help))
    obs_data = gauss.generate(ROOT.RooArgSet(*x_vars), n_samples_morph)
    obs_data.SetName("obs_data")
 
    # Create and return the workspace
    ws = ROOT.RooWorkspace()
    ws.Import(x_vars)
    ws.Import(mu_vars)
    ws.Import(gauss)
    ws.Import(uniform)
    ws.Import(uniform_help)
    ws.Import(obs_data)
 
    return ws
 
 
# Build the workspace and extract variables
ws = build_ws(mu_observed)
 
 
# Export the varibles from ws
x_vars = [ws[f"x{i}"] for i in range(len(mu_observed))]
mu_vars = [ws[f"mu{i}"] for i in range(len(mu_observed))]
 
# Do the morphing
morphing(ROOT.RooMomentMorphFuncND.Linear, len(mu_observed))
 
# Calculate the nll for the moprhed distribution
# TODO: Fix RooAddPdf::fixCoefNormalization(nset) warnings with new CPU backend
nll_morph = ws["morph"].createNLL(ws["obs_data"], EvalBackend="legacy")
 
# Initialize the SBI model
model = SBI(ws, len(mu_observed))
 
# Generate and preprocess training data
model.model_data("gauss", [x.GetName() for x in x_vars], [mu.GetName() for mu in mu_vars], n_samples_train)
model.reference_data(
    "uniform", [x.GetName() for x in x_vars], [mu.GetName() for mu in mu_vars], n_samples_train, "uniform_help"
)
model.preprocessing()
 
# Train the neural network classifier
model.train_classifier()
sbi_model = model
 
 
# Function to compute the likelihood ratio using the trained classifier
def learned_likelihood_ratio(*args):
    n = max(*(len(a) for a in args))
    X = np.zeros((n, len(args)))
    for i in range(len(args)):
        X[:, i] = args[i]
    prob = sbi_model.classifier.predict_proba(X)[:, 1]
    return prob / (1.0 - prob)
 
 
# Create combined variable list for ROOT
combined_vars = ROOT.RooArgList()
for var in x_vars + mu_vars:
    combined_vars.add(var)
 
# Create a custom likelihood ratio function using the trained classifier
lhr_learned = ROOT.RooFit.bindFunction("MyBinFunc", learned_likelihood_ratio, combined_vars)
 
# Calculate the 'analytical' likelihood ratio
lhr_calc = ROOT.RooFormulaVar("lhr_calc", "x[0] / x[1]", [ws["gauss"], ws["uniform"]])
 
# Define the 'analytical' negative logarithmic likelihood ratio
nll_gauss = ws["gauss"].createNLL(ws["obs_data"])
 
# Create the learned pdf and NLL sum based on the learned likelihood ratio
pdf_learned = ROOT.RooWrapperPdf("learned_pdf", "learned_pdf", lhr_learned, True)
 
nllr_learned = pdf_learned.createNLL(ws["obs_data"])
 
# Plot the learned and analytical summed negativelogarithmic likelihood
frame1 = mu_vars[0].frame(
    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="kP6Blue+1", Name="gauss")
ROOT.RooAbsReal.setEvalErrorLoggingMode("Ignore")  # Silence some warnings
nll_morph.plotOn(frame1, ShiftToZero=True, LineColor="kP6Blue+2", Name="morph")
ROOT.RooAbsReal.setEvalErrorLoggingMode("PrintErrors")
nllr_learned.plotOn(frame1, LineColor="kP6Blue", ShiftToZero=True, Name="learned")
 
 
# Declare a helper function in ROOT to dereference unique_ptr
ROOT.gInterpreter.Declare(
    """
RooAbsArg &my_deref(std::unique_ptr<RooAbsArg> const& ptr) { return *ptr; }
"""
)
 
# Choose normalization set for lhr_calc to plot over
norm_set = ROOT.RooArgSet(x_vars)
lhr_calc_final_ptr = ROOT.RooFit.Detail.compileForNormSet(lhr_calc, norm_set)
lhr_calc_final = ROOT.my_deref(lhr_calc_final_ptr)
lhr_calc_final.recursiveRedirectServers(norm_set)
 
# Plot the likelihood ratio functions
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()
 
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]:
    minimizer = ROOT.RooMinimizer(nll)
    minimizer.setErrorLevel(0.5)
    minimizer.setPrintLevel(-1)
    minimizer.minimize("Minuit2")
    result = minimizer.save()
    result.Print()
 
import sys
 
# Hack to bypass ClearProxiedObjects()
del sys.modules["libROOTPythonizations"]

 
  RooFitResult: minimized FCN value: 36418.9, estimated distance to minimum: 1.43276e-06
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                   mu0    2.5116e+00 +/-  1.50e-02
                   mu1    2.0000e+00 +/-  1.50e-02
 
 
  RooFitResult: minimized FCN value: -23474.8, estimated distance to minimum: 1.23305e-05
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                   mu0    2.3674e+00 +/-  1.43e-02
                   mu1    2.0127e+00 +/-  1.41e-02
 
 
  RooFitResult: minimized FCN value: 38093, estimated distance to minimum: 0.000220953
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                   mu0    2.3835e+00 +/-  3.71e-03
                   mu1    1.8343e+00 +/-  1.78e-02
 

Date

July 2024

Author

Robin Syring

Definition in file rf617_simulation_based_inference_multidimensional.py.