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| Original file line number | Diff line number | Diff line change |
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| import numpy as np | ||
| import pytest | ||
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| def test_loss_registry(): | ||
| _ = pytest.importorskip("ROOT") | ||
| # Copyright (c) 2024 zfit | ||
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| import zfit | ||
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| import zfit_physics.roofit as zroofit | ||
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| # create space | ||
| obs = zfit.Space("x", -2, 3) | ||
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| # parameters | ||
| mu = zfit.Parameter("mu", 1.2, -4, 6) | ||
| sigma = zfit.Parameter("sigma", 1.3, 0.5, 10) | ||
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| # model building, pdf creation | ||
| gauss = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs) | ||
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| # data | ||
| ndraw = 10_000 | ||
| data = np.random.normal(loc=2.0, scale=3.0, size=ndraw) | ||
| data = obs.filter(data) # works also for pandas DataFrame | ||
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| from ROOT import RooArgSet, RooDataSet, RooFit, RooGaussian, RooRealVar | ||
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| mur = RooRealVar("mu", "mu", 1.2, -4, 6) | ||
| sigmar = RooRealVar("sigma", "sigma", 1.3, 0.5, 10) | ||
| obsr = RooRealVar("x", "x", -2, 3) | ||
| gaussr = RooGaussian("gauss", "gauss", obsr, mur, sigmar) | ||
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| datar = RooDataSet("data", "data", {obsr}) | ||
| for d in data: | ||
| obsr.setVal(d) | ||
| datar.add(RooArgSet(obsr)) | ||
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| # create a loss function | ||
| nll = gaussr.createNLL(datar) | ||
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| nllz = zfit.loss.UnbinnedNLL(model=gauss, data=data) | ||
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| # create a minimizer | ||
| tol = 1e-3 | ||
| verbosity = 0 | ||
| minimizer = zfit.minimize.Minuit(gradient=True, verbosity=verbosity, tol=tol, mode=1) | ||
| minimizerzgrad = zfit.minimize.Minuit(gradient=False, verbosity=verbosity, tol=tol, mode=1) | ||
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| params = nllz.get_params() | ||
| initvals = np.array(params) | ||
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| with zfit.param.set_values(params, initvals): | ||
| result = minimizer.minimize(nllz) | ||
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| with zfit.param.set_values(params, initvals): | ||
| result2 = minimizer.minimize(nll) | ||
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| assert result.params['mu']['value'] == pytest.approx(result2.params['mu']['value'], rel=1e-3) | ||
| assert result.params['sigma']['value'] == pytest.approx(result2.params['sigma']['value'], rel=1e-3) | ||
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| with zfit.param.set_values(params, params): | ||
| result4 = minimizerzgrad.minimize(nll) | ||
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| assert result.params['mu']['value'] == pytest.approx(result4.params['mu']['value'], rel=1e-3) | ||
| assert result.params['sigma']['value'] == pytest.approx(result4.params['sigma']['value'], rel=1e-3) |
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| from . import loss, variables |
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| # Copyright (c) 2024 zfit | ||
| from __future__ import annotations | ||
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| from contextlib import suppress | ||
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| import zfit | ||
| from zfit.util.container import convert_to_container | ||
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| from .variables import roo2z_param | ||
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| def nll_from_roofit(nll, params=None): | ||
| """ | ||
| Converts a RooFit NLL (negative log-likelihood) to a Zfit loss object. | ||
| Args: | ||
| nll: The RooFit NLL object to be converted. | ||
| Returns: | ||
| zfit.loss.SimpleLoss: The converted Zfit loss object. | ||
| Raises: | ||
| TypeError: If the provided RooFit loss does not have an error level. | ||
| """ | ||
| params = {} if params is None else {p.name: p for p in convert_to_container(params)} | ||
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| ROOT = None | ||
| if "cppyy.gbl.RooAbsReal" in str(type(nll)): | ||
| with suppress(ImportError): | ||
| import ROOT | ||
| if ROOT is None or not isinstance(nll, ROOT.RooAbsReal): | ||
| return False # not a RooFit loss | ||
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| import zfit | ||
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| def roofit_eval(x): | ||
| for par, arg in zip(nll.getVariables(), x): | ||
| par.setVal(arg) | ||
| # following RooMinimizerFcn.cxx | ||
| nll.setHideOffset(False) | ||
| r = nll.getVal() | ||
| nll.setHideOffset(True) | ||
| return r | ||
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| paramsall = [] | ||
| for v in nll.getVariables(): | ||
| param = params[name] if (name := v.GetName()) in params else roo2z_param(v) | ||
| paramsall.append(param) | ||
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| if (errordef := getattr(nll, "defaultErrorLevel", lambda: None)()) is None and ( | ||
| errordef := getattr(nll, "errordef", lambda: None)() | ||
| ) is None: | ||
| msg = ( | ||
| "Provided loss is RooFit loss but has not error level. " | ||
| "Either set it or create an attribute on the fly (like `nllroofit.errordef = 0.5` " | ||
| ) | ||
| raise TypeError(msg) | ||
| return zfit.loss.SimpleLoss(roofit_eval, paramsall, errordef=errordef, jit=False, gradient="num", hessian="num") | ||
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| zfit.loss.SimpleLoss.register_convertable_loss(nll_from_roofit, priority=50) |
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| Original file line number | Diff line number | Diff line change |
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| from __future__ import annotations | ||
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| def roo2z_param(v): | ||
| """ | ||
| Converts a RooFit RooRealVar to a zfit parameter. | ||
| Args: | ||
| v: RooFit RooRealVar to convert. | ||
| Returns: | ||
| A zfit.Parameter object with properties copied from the RooFit variable. | ||
| """ | ||
| import zfit | ||
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| name = v.GetName() | ||
| value = v.getVal() | ||
| label = v.GetTitle() | ||
| lower = v.getMin() | ||
| upper = v.getMax() | ||
| floating = not v.isConstant() | ||
| stepsize = None | ||
| if v.hasError(): | ||
| stepsize = v.getError() | ||
| elif v.hasAsymError(): # just take average | ||
| stepsize = (v.getErrorHi() - v.getErrorLo()) / 2 | ||
| return zfit.Parameter(name, value, lower=lower, upper=upper, floating=floating, step_size=stepsize, label=label) |