rf618_mixture_models.py File Reference

Detailed Description

Use of mixture models in RooFit.

This tutorial shows, how to use mixture models for Likelihood Calculation in ROOT. Instead of directly calculating the likelihood we use simulation based inference (SBI) as shown in tutorial 'rf615_simulation_based_inference.py'. We train the classifier to discriminate between samples from an background hypothesis here the zz samples and a target hypothesis, here the higgs samples. The data preparation is based on the tutorial 'df106_HiggsToFourLeptons.py'.

An introduction to mixture models can be found here https://arxiv.org/pdf/1506.02169.

A short summary: We assume the whole probability distribution can be written as a mixture of several components, i.e. $$p(x|\theta)= \sum_{c}w_{c}(\theta)p_{c}(x|\theta)$$ We can write the likelihood ratio in terms of pairwise classification problems \begin{align*} \frac{p(x|\mu)}{p(x|0)}&= \frac{\sum_{c}w_{c}(\mu)p_{c}(x|\mu)}{\sum_{c'}w_{c'}(0)p_{c'}(x|0)}\ &=\sum_{c}\Bigg[\sum_{c'}\frac{w_{c'}(0)}{w_{c}(\mu)}\frac{p_{c'}(x|0)}{p_{c}(x|\mu)}\Bigg]^{-1}, \end{align*} where mu is the signal strength, and a value of 0 corresponds to the background hypothesis. Using this decomposition, one is able to use the pairwise likelihood ratios.

Since the only free parameter in our case is mu, the distributions are independent of this parameter and the dependence on the signal strength can be encoded into the weights. Thus, the subratios simplify dramatically since they are independent of theta and these ratios can be pre-computed and the classifier does not need to be parametrized.

If you wish to see an analysis done with template histograms see 'hf001_example.py'.

 
import ROOT
import os
import numpy as np
import xgboost as xgb
 
# Get Dataframe from tutorial df106_HiggsToFourLeptons.py
# Adjust the path if running locally
df = ROOT.RDataFrame("tree", ROOT.gROOT.GetTutorialDir().Data() + "/analysis/dataframe/df106_HiggsToFourLeptons.root")
 
# Initialize a dictionary to store counts and weight sums for each category
results = {}
 
 
# Extract the relevant columns once and avoid repeated calls
data_dict = df.AsNumpy(columns=["m4l", "sample_category", "weight"])
 
 
weights_dict = {
    name: data_dict["weight"][data_dict["sample_category"] == [name]].sum() for name in ("data", "zz", "other", "higgs")
}
 
# Loop over each sample category
for sample_category in ["data", "higgs", "zz", "other"]:
 
    weight_sum = weights_dict[sample_category]
 
    mask = data_dict["sample_category"] == sample_category
    # Normalize each weight
    weights = data_dict["weight"][mask]
    # Extract the weight_modified
    weight_modified = weights / weight_sum
 
    count = np.sum(mask)
 
    # Store the count and weight sum in the dictionary
    results[sample_category] = {
        "weight_sum": weight_sum,
        "weight_modified": weight_modified,
        "count": count,
        "weight": weights,
    }
 
 
# Extract the mass for higgs and zz
higgs_data = data_dict["m4l"][data_dict["sample_category"] == ["higgs"]]
zz_data = data_dict["m4l"][data_dict["sample_category"] == ["zz"]]
 
 
# Prepare sample weights
sample_weight_higgs = np.array([results["higgs"]["weight_modified"]]).flatten()
sample_weight_zz = np.array([results["zz"]["weight_modified"]]).flatten()
 
# Putting sample weights together in the same manner as the training data
sample_weight = np.concatenate([sample_weight_higgs, sample_weight_zz])
 
# For Training purposes we have to get rid of the negative weights, since xgb can't handle them
sample_weight[sample_weight < 0] = 1e-6
 
# Prepare the features and labels
X = np.concatenate((higgs_data, zz_data), axis=0).reshape(-1, 1)
y = np.concatenate([np.ones(len(higgs_data)), np.zeros(len(zz_data))])
 
# Train the Classifier to discriminate between higgs and zz
model_xgb = xgb.XGBClassifier(n_estimators=1000, max_depth=5, eta=0.2, min_child_weight=1e-6, nthread=1)
model_xgb.fit(X, y, sample_weight=sample_weight)
 
 
# Building a RooRealVar based on the observed data
m4l = ROOT.RooRealVar("m4l", "Four Lepton Invariant Mass", 0.0)
 
 
# Define functions to compute the learned likelihood.
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)
 
# Number of signals and background
n_signal = results["higgs"]["weight"].sum()
n_back = results["zz"]["weight"].sum()
 
 
# Define weight functions
def weight_back(mu):
    return n_back / (n_back + mu * n_signal)
 
 
def weight_signal(mu):
    return 1 - weight_back(mu)
 
 
# Define the likelihood ratio accordingly to mixture models
def likelihood_ratio(llr: np.ndarray, mu: np.ndarray) -> np.ndarray:
 
    m = 2
 
    w_0 = np.array([weight_back(0), weight_signal(0)])
    w_1 = np.array([weight_back(mu[0]), weight_signal(mu[0])])
 
    w = np.outer(w_1, 1.0 / w_0)
 
    p = np.ones((m, m, len(llr)))
    p[1, 0] = llr
    for i in range(m):
        for j in range(i):
            p[j, i] = 1.0 / p[i, j]
 
    return 1.0 / np.sum(1.0 / np.sum(np.expand_dims(w, axis=2) * p, axis=0), axis=0)
 
 
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, 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)
 
# Prepare the observed data set and NLL
data = ROOT.RooDataSet.from_numpy({"m4l": data_dict["m4l"][data_dict["sample_category"] == ["data"]]}, [m4l])
nll = pdf_learned_extended.createNLL(data, Extended=True)
 
# Plot the nll computet by the mixture model
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()
 
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)
minimizer.setErrorLevel(0.5)  # Adjust the error level in the minimization to work with likelihoods
minimizer.setPrintLevel(-1)
minimizer.minimize("Minuit2")
result = minimizer.save()
ROOT.SetOwnership(result, True)
result.Print()
 
del minimizer
del nll
del pdf_learned_extended
del n_pred
del llh
del nll_ratio

[#1] INFO:Fitting -- RooAbsPdf::fitTo(final_pdf) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx512
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_final_pdf_) Summation contains a RooNLLVar, using its error level
 
  RooFitResult: minimized FCN value: -1538.41, estimated distance to minimum: 1.10146e-09
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                    mu    1.7130e+00 +/-  5.93e-01
 

Date

September 2024

Author

Robin Syring

Definition in file rf618_mixture_models.py.