# Copyright (C) 2024 Richard Stiskalek # This program is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 3 of the License, or (at your # option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. """ Script to run the PV validation model on various catalogues and simulations. The script is not MPI parallelised, instead it is best run on a GPU. """ from argparse import ArgumentParser, ArgumentTypeError def none_or_int(value): if value.lower() == "none": return None try: return int(value) except ValueError: raise ArgumentTypeError(f"Invalid value: {value}. Must be an integer or 'none'.") # noqa def parse_args(): parser = ArgumentParser() parser.add_argument("--simname", type=str, required=True, help="Simulation name.") parser.add_argument("--catalogue", type=str, required=True, help="PV catalogue.") parser.add_argument("--ksmooth", type=int, default=1, help="Smoothing index.") parser.add_argument("--ksim", type=none_or_int, default=None, help="IC iteration number. If 'None', all IC realizations are used.") # noqa parser.add_argument("--ndevice", type=int, default=1, help="Number of devices to request.") parser.add_argument("--device", type=str, default="cpu", help="Device to use.") return parser.parse_args() ARGS = parse_args() # This must be done before we import JAX etc. from numpyro import set_host_device_count, set_platform # noqa set_platform(ARGS.device) # noqa set_host_device_count(ARGS.ndevice) # noqa import sys # noqa from os.path import join # noqa import csiborgtools # noqa import jax # noqa from h5py import File # noqa from mpi4py import MPI # noqa from numpyro.infer import MCMC, NUTS, Predictive, init_to_median # noqa def print_variables(names, variables): for name, variable in zip(names, variables): print(f"{name:<20} {variable}", flush=True) print(flush=True) def get_model(paths, get_model_kwargs, verbose=True): """Load the data and create the NumPyro model.""" paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) folder = "/mnt/extraspace/rstiskalek/catalogs/" nsims = paths.get_ics(ARGS.simname) if ARGS.ksim is None: nsim_iterator = [i for i in range(len(nsims))] else: nsim_iterator = [ARGS.ksim] nsims = [nsims[ARGS.ksim]] if verbose: print(f"{'Simulation:':<20} {ARGS.simname}") print(f"{'Catalogue:':<20} {ARGS.catalogue}") print(f"{'Num. realisations:':<20} {len(nsims)}") print(flush=True) if ARGS.catalogue == "A2": fpath = join(folder, "A2.h5") elif ARGS.catalogue in ["LOSS", "Foundation", "Pantheon+", "SFI_gals", "2MTF", "SFI_groups", "SFI_gals_masked", "Pantheon+_groups", "Pantheon+_groups_zSN", "Pantheon+_zSN"]: fpath = join(folder, "PV_compilation.hdf5") else: raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.") loader = csiborgtools.flow.DataLoader(ARGS.simname, nsim_iterator, ARGS.catalogue, fpath, paths, ksmooth=ARGS.ksmooth) return csiborgtools.flow.get_model(loader, **get_model_kwargs) def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, epoch_num): """Compute evidence using the `harmonic` package.""" data, names = csiborgtools.dict_samples_to_array(samples) data = data.reshape(nchains_harmonic, -1, len(names)) log_posterior = log_posterior.reshape(10, -1) return csiborgtools.harmonic_evidence( data, log_posterior, return_flow_samples=False, epochs_num=epoch_num) def get_simulation_weights(samples, model, model_kwargs): """Get the weights per posterior samples for each simulation.""" predictive = Predictive(model, samples) ll_all = predictive( jax.random.PRNGKey(1), store_ll_all=True, **model_kwargs)["ll_all"] # Multiply the likelihood of galaxies ll_per_simulation = jax.numpy.sum(ll_all, axis=-1) # Normalization by summing the likelihood over simulations norm = jax.scipy.special.logsumexp(ll_per_simulation, axis=-1) return ll_per_simulation - norm[:, None] def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta, calculate_evidence, nchains_harmonic, epoch_num, kwargs_print): """Run the NumPyro model and save output to a file.""" try: ndata = model.ndata except AttributeError as e: raise AttributeError("The model must have an attribute `ndata` " "indicating the number of data points.") from e nuts_kernel = NUTS(model, init_strategy=init_to_median(num_samples=1000)) mcmc = MCMC(nuts_kernel, num_warmup=nburn, num_samples=nsteps) rng_key = jax.random.PRNGKey(42) mcmc.run(rng_key, extra_fields=("potential_energy",), **model_kwargs) samples = mcmc.get_samples() simulation_weights = get_simulation_weights(samples, model, model_kwargs) log_posterior = -mcmc.get_extra_fields()["potential_energy"] log_likelihood = samples.pop("ll_values") if log_likelihood is None: raise ValueError("The samples must contain the log likelihood values under the key `ll_values`.") # noqa BIC, AIC = csiborgtools.BIC_AIC(samples, log_likelihood, ndata) print(f"{'BIC':<20} {BIC}") print(f"{'AIC':<20} {AIC}") mcmc.print_summary() if calculate_evidence: print("Calculating the evidence using `harmonic`.", flush=True) neg_ln_evidence, neg_ln_evidence_err = get_harmonic_evidence( samples, log_posterior, nchains_harmonic, epoch_num) print(f"{'-ln(Z)':<20} {neg_ln_evidence}") print(f"{'-ln(Z) error':<20} {neg_ln_evidence_err}") else: neg_ln_evidence = jax.numpy.nan neg_ln_evidence_err = (jax.numpy.nan, jax.numpy.nan) fname = f"samples_{ARGS.simname}_{ARGS.catalogue}_ksmooth{ARGS.ksmooth}.hdf5" # noqa if ARGS.ksim is not None: fname = fname.replace(".hdf5", f"_nsim{ARGS.ksim}.hdf5") if sample_beta: fname = fname.replace(".hdf5", "_sample_beta.hdf5") fname = join(out_folder, fname) print(f"Saving results to `{fname}`.") with File(fname, "w") as f: # Write samples grp = f.create_group("samples") for key, value in samples.items(): grp.create_dataset(key, data=value) # Write log likelihood and posterior f.create_dataset("log_likelihood", data=log_likelihood) f.create_dataset("log_posterior", data=log_posterior) f.create_dataset("simulation_weights", data=simulation_weights) # Write goodness of fit grp = f.create_group("gof") grp.create_dataset("BIC", data=BIC) grp.create_dataset("AIC", data=AIC) grp.create_dataset("neg_lnZ", data=neg_ln_evidence) grp.create_dataset("neg_lnZ_err", data=neg_ln_evidence_err) fname_summary = fname.replace(".hdf5", ".txt") print(f"Saving summary to `{fname_summary}`.") with open(fname_summary, 'w') as f: original_stdout = sys.stdout sys.stdout = f print("User parameters:") for kwargs in kwargs_print: print_variables(kwargs.keys(), kwargs.values()) print("HMC summary:") print(f"{'BIC':<20} {BIC}") print(f"{'AIC':<20} {AIC}") print(f"{'-ln(Z)':<20} {neg_ln_evidence}") print(f"{'-ln(Z) error':<20} {neg_ln_evidence_err}") mcmc.print_summary(exclude_deterministic=False) sys.stdout = original_stdout ############################################################################### # Command line interface # ############################################################################### if __name__ == "__main__": paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) out_folder = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity" # noqa print(f"{'Num. devices:':<20} {jax.device_count()}") print(f"{'Devices:':<20} {jax.devices()}") ########################################################################### # Fixed user parameters # ########################################################################### nsteps = 5000 nburn = 1000 zcmb_max = 0.06 sample_alpha = True sample_beta = True calculate_evidence = False nchains_harmonic = 10 num_epochs = 30 if nsteps % nchains_harmonic != 0: raise ValueError("The number of steps must be divisible by the number of chains.") # noqa main_params = {"nsteps": nsteps, "nburn": nburn, "zcmb_max": zcmb_max, "sample_alpha": sample_alpha, "sample_beta": sample_beta, "calculate_evidence": calculate_evidence, "nchains_harmonic": nchains_harmonic, "num_epochs": num_epochs} print_variables(main_params.keys(), main_params.values()) calibration_hyperparams = {"Vext_std": 250, "alpha_mean": 1.0, "alpha_std": 0.5, "beta_mean": 1.0, "beta_std": 0.5, "sigma_v_mean": 200., "sigma_v_std": 100., "sample_alpha": sample_alpha, "sample_beta": sample_beta, } print_variables( calibration_hyperparams.keys(), calibration_hyperparams.values()) if ARGS.catalogue in ["LOSS", "Foundation", "Pantheon+", "Pantheon+_groups", "Pantheon+_zSN"]: # noqa distmod_hyperparams = {"e_mu_mean": 0.1, "e_mu_std": 0.05, "mag_cal_mean": -18.25, "mag_cal_std": 0.5, "alpha_cal_mean": 0.148, "alpha_cal_std": 0.05, "beta_cal_mean": 3.112, "beta_cal_std": 1.0, } elif ARGS.catalogue in ["SFI_gals", "2MTF"]: distmod_hyperparams = {"e_mu_mean": 0.3, "e_mu_std": 0.15, "a_mean": -21., "a_std": 0.5, "b_mean": -5.95, "b_std": 0.25, } else: raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.") print_variables( distmod_hyperparams.keys(), distmod_hyperparams.values()) kwargs_print = (main_params, calibration_hyperparams, distmod_hyperparams) ########################################################################### model_kwargs = {"calibration_hyperparams": calibration_hyperparams, "distmod_hyperparams": distmod_hyperparams} get_model_kwargs = {"zcmb_max": zcmb_max} model = get_model(paths, get_model_kwargs, ) run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta, calculate_evidence, nchains_harmonic, num_epochs, kwargs_print)