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Add more about evidence and selection to flow (#142)
* Add Laplace evidence * Numerically stable laplace evidence * Minor edits to Laplace * Remove rmax * Rm old things * Rm comments * Add script * Add super toy selection * Add super toy selection * Update script
This commit is contained in:
Richard Stiskalek
GitHub
parent
d13246a394
commit
3d1e1c0ae3
8 changed files with 243 additions and 57 deletions
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@ -72,7 +72,7 @@ def print_variables(names, variables):
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print(flush=True)
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def get_models(get_model_kwargs, verbose=True):
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def get_models(get_model_kwargs, toy_selection, 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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@ -110,7 +110,8 @@ def get_models(get_model_kwargs, verbose=True):
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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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models[i] = csiborgtools.flow.get_model(
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loader, toy_selection=toy_selection[i], **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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@ -127,7 +128,7 @@ def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, 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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calculate_harmonic, 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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@ -148,12 +149,12 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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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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if calculate_harmonic:
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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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print(f"{'-ln(Z_h)':<20} {neg_ln_evidence}")
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print(f"{'-ln(Z_h) 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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@ -180,8 +181,8 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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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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grp.create_dataset("neg_lnZ_harmonic", data=neg_ln_evidence)
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grp.create_dataset("neg_lnZ_harmonic_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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@ -206,7 +207,7 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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# Command line interface #
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###############################################################################
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def get_distmod_hyperparams(catalogue, sample_alpha):
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def get_distmod_hyperparams(catalogue, sample_alpha, sample_mag_dipole):
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alpha_min = -1.0
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alpha_max = 3.0
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@ -225,7 +226,7 @@ def get_distmod_hyperparams(catalogue, sample_alpha):
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"c_mean": 0., "c_std": 20.0,
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"sample_curvature": False,
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"a_dipole_mean": 0., "a_dipole_std": 1.0,
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"sample_a_dipole": True,
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"sample_a_dipole": sample_mag_dipole,
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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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@ -233,7 +234,7 @@ def get_distmod_hyperparams(catalogue, sample_alpha):
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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"dmu_min": -3.0, "dmu_max": 3.0,
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"dmu_dipole_mean": 0., "dmu_dipole_std": 1.0,
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"sample_dmu_dipole": True,
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"sample_dmu_dipole": sample_mag_dipole,
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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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@ -241,6 +242,16 @@ def get_distmod_hyperparams(catalogue, sample_alpha):
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raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.")
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def get_toy_selection(toy_selection, catalogue):
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if not toy_selection:
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return None
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if catalogue == "SFI_gals":
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return [1.221e+01, 1.297e+01, -2.708e-01]
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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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@ -251,18 +262,23 @@ if __name__ == "__main__":
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# Fixed user parameters #
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###########################################################################
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nsteps = 1500
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nburn = 1000
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nsteps = 1000
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nburn = 500
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zcmb_min = 0
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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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inference_method = "mike"
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num_epochs = 50
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inference_method = "bayes"
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calculate_harmonic = True if inference_method == "mike" else False
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maxmag_selection = None
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sample_alpha = True
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sample_alpha = False
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sample_beta = True
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sample_Vmono = False
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sample_mag_dipole = False
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toy_selection = True
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if toy_selection and inference_method == "mike":
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raise ValueError("Toy selection is not supported with `mike` inference.") # noqa
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if nsteps % nchains_harmonic != 0:
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raise ValueError(
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@ -272,10 +288,12 @@ if __name__ == "__main__":
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"zcmb_min": zcmb_min,
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"zcmb_max": zcmb_max,
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"maxmag_selection": maxmag_selection,
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"calculate_evidence": calculate_evidence,
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"calculate_harmonic": calculate_harmonic,
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"nchains_harmonic": nchains_harmonic,
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"num_epochs": num_epochs,
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"inference_method": inference_method}
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"inference_method": inference_method,
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"sample_mag_dipole": sample_mag_dipole,
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"toy_selection": toy_selection}
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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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@ -290,7 +308,7 @@ if __name__ == "__main__":
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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, sample_alpha)
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x = get_distmod_hyperparams(cat, sample_alpha, sample_mag_dipole)
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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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@ -301,7 +319,11 @@ if __name__ == "__main__":
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get_model_kwargs = {"zcmb_min": zcmb_min, "zcmb_max": zcmb_max,
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"maxmag_selection": maxmag_selection}
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models = get_models(get_model_kwargs, )
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toy_selection = [get_toy_selection(toy_selection, cat)
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for cat in ARGS.catalogue]
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models = get_models(get_model_kwargs, toy_selection)
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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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@ -312,5 +334,5 @@ if __name__ == "__main__":
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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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calibration_hyperparams["sample_beta"], calculate_harmonic,
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nchains_harmonic, num_epochs, kwargs_print)
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