def completed_bn090217206_bayesian_analysis_multicomp( fitted_joint_likelihood_bn090217206_nai_multicomp, ): jl, _, _ = fitted_joint_likelihood_bn090217206_nai_multicomp # This is necessary because other tests/functions might have messed up with the # model stored within jl.restore_best_fit() model = jl.likelihood_model data_list = jl.data_list spectrum = jl.likelihood_model.bn090217206.spectrum.main.shape spectrum.index_1.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0) spectrum.K_1.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10) spectrum.K_2.prior = Log_uniform_prior(lower_bound=1e-20, upper_bound=10) spectrum.kT_2.prior = Log_uniform_prior(lower_bound=1e0, upper_bound=1e3) bayes = BayesianAnalysis(model, data_list) bayes.set_sampler("emcee") bayes.sampler.setup(n_walkers=50, n_burn_in=50, n_iterations=100, seed=1234) samples = bayes.sample() return bayes, bayes.samples
def xy_completed_bayesian_analysis(xy_fitted_joint_likelihood): jl, _, _ = xy_fitted_joint_likelihood jl.restore_best_fit() model = jl.likelihood_model data = jl.data_list model.fake.spectrum.main.composite.a_1.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.b_1.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.F_2.set_uninformative_prior( Log_uniform_prior) model.fake.spectrum.main.composite.mu_2.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.sigma_2.set_uninformative_prior( Log_uniform_prior) bs = BayesianAnalysis(model, data) bs.set_sampler("emcee") bs.sampler.setup(n_burn_in=100, n_iterations=100, n_walkers=20) samples = bs.sample() return bs, samples
def xy_completed_bayesian_analysis(xy_fitted_joint_likelihood): jl, _, _ = xy_fitted_joint_likelihood jl.restore_best_fit() model = jl.likelihood_model data = jl.data_list model.fake.spectrum.main.composite.a_1.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.b_1.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.F_2.set_uninformative_prior( Log_uniform_prior) model.fake.spectrum.main.composite.mu_2.set_uninformative_prior( Uniform_prior) model.fake.spectrum.main.composite.sigma_2.set_uninformative_prior( Log_uniform_prior) bs = BayesianAnalysis(model, data) samples = bs.sample(20, 100, 1000) return bs, samples
def completed_bn090217206_bayesian_analysis(fitted_joint_likelihood_bn090217206_nai): jl, _, _ = fitted_joint_likelihood_bn090217206_nai jl.restore_best_fit() model = jl.likelihood_model data_list = jl.data_list powerlaw = jl.likelihood_model.bn090217206.spectrum.main.Powerlaw powerlaw.index.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0) powerlaw.K.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10) bayes = BayesianAnalysis(model, data_list) samples = bayes.sample(n_walkers=50, burn_in=50, n_samples=100, seed=1234) return bayes, samples
def completed_bn090217206_bayesian_analysis( fitted_joint_likelihood_bn090217206_nai): jl, _, _ = fitted_joint_likelihood_bn090217206_nai jl.restore_best_fit() model = jl.likelihood_model data_list = jl.data_list powerlaw = jl.likelihood_model.bn090217206.spectrum.main.Powerlaw powerlaw.index.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0) powerlaw.K.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10) bayes = BayesianAnalysis(model, data_list) samples = bayes.sample(n_walkers=50, burn_in=50, n_samples=100, seed=1234) return bayes, samples
def xy_completed_bayesian_analysis(xy_fitted_joint_likelihood): jl, _, _ = xy_fitted_joint_likelihood jl.restore_best_fit() model = jl.likelihood_model data = jl.data_list model.fake.spectrum.main.composite.a_1.set_uninformative_prior(Uniform_prior) model.fake.spectrum.main.composite.b_1.set_uninformative_prior(Uniform_prior) model.fake.spectrum.main.composite.F_2.set_uninformative_prior(Log_uniform_prior) model.fake.spectrum.main.composite.mu_2.set_uninformative_prior(Uniform_prior) model.fake.spectrum.main.composite.sigma_2.set_uninformative_prior(Log_uniform_prior) bs = BayesianAnalysis(model, data) samples = bs.sample(20, 100, 1000) return bs, samples
def bayes_fitter(fitted_joint_likelihood_bn090217206_nai): jl, fit_results, like_frame = fitted_joint_likelihood_bn090217206_nai datalist = jl.data_list model = jl.likelihood_model jl.restore_best_fit() set_priors(model) bayes = BayesianAnalysis(model, datalist) return bayes
def completed_bn090217206_bayesian_analysis_multicomp(fitted_joint_likelihood_bn090217206_nai_multicomp): jl, _, _ = fitted_joint_likelihood_bn090217206_nai_multicomp # This is necessary because other tests/functions might have messed up with the # model stored within jl.restore_best_fit() model = jl.likelihood_model data_list = jl.data_list spectrum = jl.likelihood_model.bn090217206.spectrum.main.shape spectrum.index_1.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0) spectrum.K_1.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10) spectrum.K_2.prior = Log_uniform_prior(lower_bound=1E-20, upper_bound=10) spectrum.kT_2.prior = Log_uniform_prior(lower_bound=1E0, upper_bound=1E3) bayes = BayesianAnalysis(model, data_list) samples = bayes.sample(n_walkers=50, burn_in=50, n_samples=100, seed=1234) return bayes, samples
def unbinned_polyfit(events: Iterable[float], grade: int, t_start: float, t_stop: float, exposure: float, bayes: bool) -> Tuple[Polynomial, float]: """ function to fit a polynomial to unbinned event data. not a member to allow parallel computation :param events: the events to fit :param grade: the polynomical order or grade :param t_start: the start time to fit over :param t_stop: the end time to fit over :param expousure: the exposure of the interval :param bayes: to do a bayesian fit or not """ log.debug(f"starting unbinned_polyfit with grade {grade}") log.debug(f"have {len(events)} events with {exposure} exposure") # create 3ML plugins and fit them with 3ML! # should eventuallly allow better config # select the model based on the grade if threeML_config.time_series.default_fit_method is not None: bayes = threeML_config.time_series.default_fit_method log.debug("using a default poly fit method") if len(events) == 0: log.debug("no events! returning zero") return Polynomial([0] * (grade + 1)), 0 shape = _grade_model_lookup[grade]() with silence_console_log(): ps = PointSource("dummy", 0, 0, spectral_shape=shape) model = Model(ps) observation = EventObservation(events, exposure, t_start, t_stop) xy = UnbinnedPoissonLike("series", observation=observation) if not bayes: # make sure the model is positive for i, (k, v) in enumerate(model.free_parameters.items()): if i == 0: v.bounds = (0, None) v.value = 10 else: v.value = 0.0 # we actually use a line here # because a constant is returns a # single number if grade == 0: shape.b = 0 shape.b.fix = True jl: JointLikelihood = JointLikelihood(model, DataList(xy)) grid_minimizer = GlobalMinimization("grid") local_minimizer = LocalMinimization("minuit") my_grid = { model.dummy.spectrum.main.shape.a: np.logspace(0, 3, 10)} grid_minimizer.setup( second_minimization=local_minimizer, grid=my_grid) jl.set_minimizer(grid_minimizer) # if the fit falis, retry and then just accept try: jl.fit(quiet=True) except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance): try: jl.fit(quiet=True) except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance): log.debug("all MLE fits failed, returning zero") return Polynomial([0]*(grade + 1)), 0 coeff = [v.value for _, v in model.free_parameters.items()] log.debug(f"got coeff: {coeff}") final_polynomial = Polynomial(coeff) final_polynomial.set_covariace_matrix(jl.results.covariance_matrix) min_log_likelihood = xy.get_log_like() else: # set smart priors for i, (k, v) in enumerate(model.free_parameters.items()): if i == 0: v.bounds = (0, None) v.prior = Log_normal(mu=np.log(5), sigma=np.log(5)) v.value = 1 else: v.prior = Gaussian(mu=0, sigma=.5) v.value = 0.1 # we actually use a line here # because a constant is returns a # single number if grade == 0: shape.b = 0 shape.b.fix = True ba: BayesianAnalysis = BayesianAnalysis(model, DataList(xy)) ba.set_sampler("emcee") ba.sampler.setup(n_iterations=500, n_burn_in=200, n_walkers=20) ba.sample(quiet=True) ba.restore_median_fit() coeff = [v.value for _, v in model.free_parameters.items()] log.debug(f"got coeff: {coeff}") final_polynomial = Polynomial(coeff) final_polynomial.set_covariace_matrix( ba.results.estimate_covariance_matrix()) min_log_likelihood = xy.get_log_like() log.debug(f"-min loglike: {-min_log_likelihood}") return final_polynomial, -min_log_likelihood
def polyfit(x: Iterable[float], y: Iterable[float], grade: int, exposure: Iterable[float], bayes: bool = False) -> Tuple[Polynomial, float]: """ function to fit a polynomial to data. not a member to allow parallel computation :param x: the x coord of the data :param y: teh y coord of the data :param grade: the polynomical order or grade :param expousure: the exposure of the interval :param bayes: to do a bayesian fit or not """ # Check that we have enough counts to perform the fit, otherwise # return a "zero polynomial" log.debug(f"starting polyfit with grade {grade} ") if threeML_config.time_series.default_fit_method is not None: bayes = threeML_config.time_series.default_fit_method log.debug("using a default poly fit method") nan_mask = np.isnan(y) y = y[~nan_mask] x = x[~nan_mask] exposure = exposure[~nan_mask] non_zero_mask = y > 0 n_non_zero = non_zero_mask.sum() if n_non_zero == 0: log.debug("no counts, return 0") # No data, nothing to do! return Polynomial([0.0]*(grade+1)), 0.0 # create 3ML plugins and fit them with 3ML! # should eventuallly allow better config # seelct the model based on the grade shape = _grade_model_lookup[grade]() ps = PointSource("_dummy", 0, 0, spectral_shape=shape) model = Model(ps) avg = np.mean(y/exposure) log.debug(f"starting polyfit with avg norm {avg}") with silence_console_log(): xy = XYLike("series", x=x, y=y, exposure=exposure, poisson_data=True, quiet=True) if not bayes: # make sure the model is positive for i, (k, v) in enumerate(model.free_parameters.items()): if i == 0: v.bounds = (0, None) v.value = avg else: v.value = 0.0 # we actually use a line here # because a constant is returns a # single number if grade == 0: shape.b = 0 shape.b.fix = True jl: JointLikelihood = JointLikelihood(model, DataList(xy)) jl.set_minimizer("minuit") # if the fit falis, retry and then just accept try: jl.fit(quiet=True) except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance): log.debug("1st fit failed") try: jl.fit(quiet=True) except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance): log.debug("all MLE fits failed") pass coeff = [v.value for _, v in model.free_parameters.items()] log.debug(f"got coeff: {coeff}") final_polynomial = Polynomial(coeff) try: final_polynomial.set_covariace_matrix( jl.results.covariance_matrix) except: log.exception(f"Fit failed in channel") raise FitFailed() min_log_likelihood = xy.get_log_like() else: # set smart priors for i, (k, v) in enumerate(model.free_parameters.items()): if i == 0: v.bounds = (0, None) v.prior = Log_normal( mu=np.log(avg), sigma=np.max([np.log(avg/2), 1])) v.value = 1 else: v.prior = Gaussian(mu=0, sigma=2) v.value = 1e-2 # we actually use a line here # because a constant is returns a # single number if grade == 0: shape.b = 0 shape.b.fix = True ba: BayesianAnalysis = BayesianAnalysis(model, DataList(xy)) ba.set_sampler("emcee") ba.sampler.setup(n_iterations=500, n_burn_in=200, n_walkers=20) ba.sample(quiet=True) ba.restore_median_fit() coeff = [v.value for _, v in model.free_parameters.items()] log.debug(f"got coeff: {coeff}") final_polynomial = Polynomial(coeff) final_polynomial.set_covariace_matrix( ba.results.estimate_covariance_matrix()) min_log_likelihood = xy.get_log_like() log.debug(f"-min loglike: {-min_log_likelihood}") return final_polynomial, -min_log_likelihood
def test_ubinned_poisson_full(event_observation_contiguous, event_observation_split): s = Line() ps = PointSource("s", 0, 0, spectral_shape=s) s.a.bounds = (0, None) s.a.value = .1 s.b.value = .1 s.a.prior = Log_normal(mu=np.log(10), sigma=1) s.b.prior = Gaussian(mu=0, sigma=1) m = Model(ps) ###### ###### ###### ub1 = UnbinnedPoissonLike("test", observation=event_observation_contiguous) jl = JointLikelihood(m, DataList(ub1)) jl.fit(quiet=True) np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5) ba = BayesianAnalysis(m, DataList(ub1)) ba.set_sampler("emcee") ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500) ba.sample(quiet=True) ba.restore_median_fit() np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5) ###### ###### ###### ub2 = UnbinnedPoissonLike("test", observation=event_observation_split) jl = JointLikelihood(m, DataList(ub2)) jl.fit(quiet=True) np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5) ba = BayesianAnalysis(m, DataList(ub2)) ba.set_sampler("emcee") ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500) ba.sample(quiet=True) ba.restore_median_fit() np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)