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LSS projected basics (#140)
* Move files * Move files * Add galactic to RA/dec * Update sky maps * Add projected fields * Remove old import * Quick update * Add IO * Add imports * Update imports * Add basic file
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Richard Stiskalek
GitHub
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3b46f17ead
commit
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36 changed files with 365 additions and 231 deletions
295
scripts/flow/flow_validation.py
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295
scripts/flow/flow_validation.py
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# Copyright (C) 2024 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Script to run the PV validation model on various catalogues and simulations.
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The script is not MPI parallelised, instead it is best run on a GPU.
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"""
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from argparse import ArgumentParser, ArgumentTypeError
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def none_or_int(value):
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if value.lower() == "none":
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return None
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try:
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return int(value)
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except ValueError:
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raise ArgumentTypeError(f"Invalid value: {value}. Must be an integer or 'none'.") # noqa
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def parse_args():
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parser = ArgumentParser()
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parser.add_argument("--simname", type=str, required=True,
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help="Simulation name.")
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parser.add_argument("--catalogue", type=str, required=True,
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help="PV catalogues.")
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parser.add_argument("--ksmooth", type=int, default=1,
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help="Smoothing index.")
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parser.add_argument("--ksim", type=none_or_int, default=None,
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help="IC iteration number. If 'None', all IC realizations are used.") # noqa
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parser.add_argument("--ndevice", type=int, default=1,
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help="Number of devices to request.")
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parser.add_argument("--device", type=str, default="cpu",
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help="Device to use.")
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args = parser.parse_args()
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# Convert the catalogue to a list of catalogues
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args.catalogue = args.catalogue.split(",")
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return args
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ARGS = parse_args()
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# This must be done before we import JAX etc.
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from numpyro import set_host_device_count, set_platform # noqa
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set_platform(ARGS.device) # noqa
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set_host_device_count(ARGS.ndevice) # noqa
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import sys # noqa
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from os.path import join # noqa
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import csiborgtools # noqa
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import jax # noqa
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from h5py import File # noqa
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from numpyro.infer import MCMC, NUTS, init_to_median # noqa
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def print_variables(names, variables):
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for name, variable in zip(names, variables):
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print(f"{name:<20} {variable}", flush=True)
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print(flush=True)
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def get_models(get_model_kwargs, verbose=True):
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"""Load the data and create the NumPyro models."""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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folder = "/mnt/extraspace/rstiskalek/catalogs/"
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nsims = paths.get_ics(ARGS.simname)
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if ARGS.ksim is None:
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nsim_iterator = [i for i in range(len(nsims))]
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else:
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nsim_iterator = [ARGS.ksim]
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nsims = [nsims[ARGS.ksim]]
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if verbose:
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print(f"{'Simulation:':<20} {ARGS.simname}")
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print(f"{'Catalogue:':<20} {ARGS.catalogue}")
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print(f"{'Num. realisations:':<20} {len(nsims)}")
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print(flush=True)
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# Get models
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models = [None] * len(ARGS.catalogue)
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for i, cat in enumerate(ARGS.catalogue):
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if cat == "A2":
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fpath = join(folder, "A2.h5")
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elif cat in ["LOSS", "Foundation", "Pantheon+", "SFI_gals",
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"2MTF", "SFI_groups", "SFI_gals_masked",
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"Pantheon+_groups", "Pantheon+_groups_zSN",
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"Pantheon+_zSN"]:
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fpath = join(folder, "PV_compilation.hdf5")
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else:
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raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.")
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loader = csiborgtools.flow.DataLoader(ARGS.simname, nsim_iterator,
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cat, fpath, paths,
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ksmooth=ARGS.ksmooth)
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models[i] = csiborgtools.flow.get_model(loader, **get_model_kwargs)
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print(f"\n{'Num. radial steps':<20} {len(loader.rdist)}\n", flush=True)
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return models
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def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, epoch_num):
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"""Compute evidence using the `harmonic` package."""
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data, names = csiborgtools.dict_samples_to_array(samples)
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data = data.reshape(nchains_harmonic, -1, len(names))
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log_posterior = log_posterior.reshape(nchains_harmonic, -1)
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return csiborgtools.harmonic_evidence(
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data, log_posterior, return_flow_samples=False, epochs_num=epoch_num)
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def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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calculate_evidence, nchains_harmonic, epoch_num, kwargs_print):
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"""Run the NumPyro model and save output to a file."""
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try:
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ndata = sum(model.ndata for model in model_kwargs["models"])
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except AttributeError as e:
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raise AttributeError("The models must have an attribute `ndata` "
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"indicating the number of data points.") from e
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nuts_kernel = NUTS(model, init_strategy=init_to_median(num_samples=1000))
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mcmc = MCMC(nuts_kernel, num_warmup=nburn, num_samples=nsteps)
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rng_key = jax.random.PRNGKey(42)
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mcmc.run(rng_key, extra_fields=("potential_energy",), **model_kwargs)
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samples = mcmc.get_samples()
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log_posterior = -mcmc.get_extra_fields()["potential_energy"]
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log_likelihood = samples.pop("ll_values")
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if log_likelihood is None:
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raise ValueError("The samples must contain the log likelihood values under the key `ll_values`.") # noqa
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BIC, AIC = csiborgtools.BIC_AIC(samples, log_likelihood, ndata)
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print(f"{'BIC':<20} {BIC}")
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print(f"{'AIC':<20} {AIC}")
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mcmc.print_summary()
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if calculate_evidence:
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print("Calculating the evidence using `harmonic`.", flush=True)
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neg_ln_evidence, neg_ln_evidence_err = get_harmonic_evidence(
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samples, log_posterior, nchains_harmonic, epoch_num)
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print(f"{'-ln(Z)':<20} {neg_ln_evidence}")
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print(f"{'-ln(Z) error':<20} {neg_ln_evidence_err}")
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else:
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neg_ln_evidence = jax.numpy.nan
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neg_ln_evidence_err = (jax.numpy.nan, jax.numpy.nan)
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fname = f"samples_{ARGS.simname}_{'+'.join(ARGS.catalogue)}_ksmooth{ARGS.ksmooth}.hdf5" # noqa
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if ARGS.ksim is not None:
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fname = fname.replace(".hdf5", f"_nsim{ARGS.ksim}.hdf5")
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if sample_beta:
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fname = fname.replace(".hdf5", "_sample_beta.hdf5")
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fname = join(out_folder, fname)
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print(f"Saving results to `{fname}`.")
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with File(fname, "w") as f:
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# Write samples
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grp = f.create_group("samples")
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for key, value in samples.items():
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grp.create_dataset(key, data=value)
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# Write log likelihood and posterior
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f.create_dataset("log_likelihood", data=log_likelihood)
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f.create_dataset("log_posterior", data=log_posterior)
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# Write goodness of fit
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grp = f.create_group("gof")
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grp.create_dataset("BIC", data=BIC)
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grp.create_dataset("AIC", data=AIC)
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grp.create_dataset("neg_lnZ", data=neg_ln_evidence)
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grp.create_dataset("neg_lnZ_err", data=neg_ln_evidence_err)
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fname_summary = fname.replace(".hdf5", ".txt")
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print(f"Saving summary to `{fname_summary}`.")
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with open(fname_summary, 'w') as f:
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original_stdout = sys.stdout
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sys.stdout = f
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print("User parameters:")
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for kwargs in kwargs_print:
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print_variables(kwargs.keys(), kwargs.values())
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print("HMC summary:")
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print(f"{'BIC':<20} {BIC}")
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print(f"{'AIC':<20} {AIC}")
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print(f"{'-ln(Z)':<20} {neg_ln_evidence}")
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print(f"{'-ln(Z) error':<20} {neg_ln_evidence_err}")
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mcmc.print_summary(exclude_deterministic=False)
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sys.stdout = original_stdout
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###############################################################################
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# Command line interface #
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###############################################################################
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def get_distmod_hyperparams(catalogue):
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alpha_min = -1.0
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alpha_max = 3.0
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sample_alpha = True
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if catalogue in ["LOSS", "Foundation", "Pantheon+", "Pantheon+_groups", "Pantheon+_zSN"]: # noqa
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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"mag_cal_mean": -18.25, "mag_cal_std": 2.0,
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"alpha_cal_mean": 0.148, "alpha_cal_std": 1.0,
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"beta_cal_mean": 3.112, "beta_cal_std": 2.0,
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"alpha_min": alpha_min, "alpha_max": alpha_max,
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"sample_alpha": sample_alpha
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}
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elif catalogue in ["SFI_gals", "2MTF"]:
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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"a_mean": -21., "a_std": 5.0,
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"b_mean": -5.95, "b_std": 3.0,
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"alpha_min": alpha_min, "alpha_max": alpha_max,
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"sample_alpha": sample_alpha
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}
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else:
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raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.")
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if __name__ == "__main__":
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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out_folder = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity" # noqa
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print(f"{'Num. devices:':<20} {jax.device_count()}")
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print(f"{'Devices:':<20} {jax.devices()}")
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###########################################################################
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# Fixed user parameters #
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###########################################################################
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nsteps = 5000
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nburn = 1500
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zcmb_max = 0.05
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calculate_evidence = False
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nchains_harmonic = 10
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num_epochs = 30
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if nsteps % nchains_harmonic != 0:
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raise ValueError(
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"The number of steps must be divisible by the number of chains.")
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main_params = {"nsteps": nsteps, "nburn": nburn, "zcmb_max": zcmb_max,
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"calculate_evidence": calculate_evidence,
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"nchains_harmonic": nchains_harmonic,
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"num_epochs": num_epochs}
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print_variables(main_params.keys(), main_params.values())
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calibration_hyperparams = {"Vext_min": -1000, "Vext_max": 1000,
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"Vmono_min": -1000, "Vmono_max": 1000,
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"beta_min": -1.0, "beta_max": 3.0,
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"sigma_v_min": 1.0, "sigma_v_max": 750.,
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"sample_Vmono": False,
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"sample_beta": True,
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"sample_sigma_v_ext": False,
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}
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print_variables(
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calibration_hyperparams.keys(), calibration_hyperparams.values())
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distmod_hyperparams_per_catalogue = []
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for cat in ARGS.catalogue:
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x = get_distmod_hyperparams(cat)
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print(f"\n{cat} hyperparameters:")
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print_variables(x.keys(), x.values())
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distmod_hyperparams_per_catalogue.append(x)
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kwargs_print = (main_params, calibration_hyperparams,
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*distmod_hyperparams_per_catalogue)
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###########################################################################
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get_model_kwargs = {"zcmb_max": zcmb_max}
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models = get_models(get_model_kwargs, )
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model_kwargs = {
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"models": models,
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"field_calibration_hyperparams": calibration_hyperparams,
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"distmod_hyperparams_per_model": distmod_hyperparams_per_catalogue,
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}
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model = csiborgtools.flow.PV_validation_model
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run_model(model, nsteps, nburn, model_kwargs, out_folder,
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calibration_hyperparams["sample_beta"], calculate_evidence,
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nchains_harmonic, num_epochs, kwargs_print)
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