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# -*- coding: utf-8 -*- |
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# vim:fileencoding=utf-8 |
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# |
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# Copyright (c) 2017-2018 Stefan Bender |
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# |
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# This module is part of sciapy. |
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# sciapy is free software: you can redistribute it or modify |
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# it under the terms of the GNU General Public License as published |
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# by the Free Software Foundation, version 2. |
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# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html. |
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"""SCIAMACHY data regression MCMC sampler |
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Markov Chain Monte Carlo (MCMC) routines to sample |
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the posterior probability of SCIAMACHY data regression fits. |
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Uses the :class:`emcee.EnsembleSampler` [#]_ do do the real work. |
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.. [#] https://emcee.readthedocs.io |
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""" |
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import logging |
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from multiprocessing import Pool |
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import numpy as np |
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from scipy.optimize._differentialevolution import DifferentialEvolutionSolver |
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from scipy.special import logsumexp |
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import celerite |
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import george |
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import emcee |
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try: |
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from tqdm import tqdm |
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have_tqdm = True |
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except ImportError: |
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have_tqdm = False |
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__all__ = ["mcmc_sample_model"] |
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def _lpost(p, model, y=None, beta=1.): |
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model.set_parameter_vector(p) |
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lprior = model.log_prior() |
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if not np.isfinite(lprior): |
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return (-np.inf, np.nan) |
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log_likelihood = model.log_likelihood(y, quiet=True) |
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return (beta * (log_likelihood + lprior), [log_likelihood]) |
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View Code Duplication |
def _sample_mcmc(sampler, nsamples, p0, rst0, |
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show_progress, progress_mod, debug=False): |
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smpl = sampler.sample(p0, rstate0=rst0, iterations=nsamples) |
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if have_tqdm: |
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smpl = tqdm(smpl, total=nsamples, disable=None) |
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for i, result in enumerate(smpl): |
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if show_progress and (i + 1) % progress_mod == 0: |
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if not have_tqdm and not debug: |
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logging.info("%5.1f%%", 100 * (float(i + 1) / nsamples)) |
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if debug: |
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_pos, _logp, _, _ = result |
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logging.debug("%5.1f%% lnpmax: %s, p(lnpmax): %s", |
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100 * (float(i + 1) / nsamples), |
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np.max(_logp), _pos[np.argmax(_logp)]) |
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return result |
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View Code Duplication |
def mcmc_sample_model(model, y, beta=1., |
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nwalkers=100, nburnin=200, nprod=800, |
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nthreads=1, optimized=False, |
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bounds=None, |
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return_logpost=False, |
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show_progress=False, progress_mod=10): |
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"""Markov Chain Monte Carlo sampling interface |
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MCMC sampling interface to sample posterior probabilities using the |
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:class:`emcee.EnsembleSampler` [#]_. |
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.. [#] https://emcee.readthedocs.io |
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Arguments |
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--------- |
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model : celerite.GP, george.GP, or sciapy.regress.RegressionModel instance |
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The model to draw posterior samples from. It should provide either |
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`log_likelihood()` and `log_prior()` functions or be directly callable |
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via `__call__()`. |
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y : (N,) array_like |
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The data to condition the probabilities on. |
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beta : float, optional |
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Tempering factor for the probability, default: 1. |
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nwalkers : int, optional |
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The number of MCMC walkers (default: 100). If this number is smaller |
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than 4 times the number of parameters, it is multiplied by the number |
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of parameters. Otherwise it specifies the number of parameters directly. |
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nburnin : int, optional |
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The number of burn-in samples to draw, default: 200. |
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nprod : int, optional |
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The number of production samples to draw, default: 800. |
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nthreads : int, optional |
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The number of threads to use with a `multiprocessing.Pool`, |
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used as `pool` for `emcee.EnsembleSampler`. Default: 1. |
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optimized : bool, optional |
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Indicate whether the actual (starting) position was determined with an |
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optimization algorithm beforehand. If `False` (the default), a |
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pre-burn-in run optimizes the starting position. Sampling continues |
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from there with the normal burn-in and production runs. |
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In that case, latin hypercube sampling is used to distribute the walker |
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starting positions equally in parameter space. |
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bounds : iterable, optional |
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The parameter bounds as a list of (min, max) entries. |
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Default: None |
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return_logpost : bool, optional |
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Indicate whether or not to return the sampled log probabilities as well. |
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Default: False |
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show_progress : bool, optional |
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Print the percentage of samples every `progress_mod` samples. |
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Default: False |
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progress_mod : int, optional |
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Interval in samples to print the percentage of samples. |
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Default: 10 |
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Returns |
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------- |
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samples or (samples, logpost) : array_like or tuple |
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(nwalkers * nprod, ndim) array of the sampled parameters from the |
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production run if return_logpost is `False`. |
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A tuple of an (nwalkers * nprod, ndim) array (the same as above) |
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and an (nwalkers,) array with the second entry containing the |
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log posterior probabilities if return_logpost is `True`. |
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""" |
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v = model.get_parameter_vector() |
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ndim = len(v) |
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if nwalkers < 4 * ndim: |
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nwalkers *= ndim |
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logging.info("MCMC parameters: %s walkers, %s burn-in samples, " |
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"%s production samples using %s threads.", |
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nwalkers, nburnin, nprod, nthreads) |
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if isinstance(model, celerite.GP) or isinstance(model, george.GP): |
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mod_func = _lpost |
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mod_args = (model, y, beta) |
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else: |
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mod_func = model |
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mod_args = (beta,) |
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# Initialize the walkers. |
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if not optimized: |
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# scipy.optimize's DifferentialEvolutionSolver uses |
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# latin hypercube sampling as starting positions. |
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# We just use their initialization to avoid duplicating code. |
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if bounds is None: |
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bounds = model.get_parameter_bounds() |
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de_solver = DifferentialEvolutionSolver(_lpost, |
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bounds=bounds, |
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popsize=nwalkers // ndim) |
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# The initial population should reflect latin hypercube sampling |
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p0 = de_solver.population |
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else: |
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p0 = np.array([v + 1e-2 * np.random.randn(ndim) for _ in range(nwalkers)]) |
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# set up the sampling pool |
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pool = Pool(processes=nthreads) |
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sampler = emcee.EnsembleSampler(nwalkers, ndim, mod_func, args=mod_args, |
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pool=pool) |
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rst0 = np.random.get_state() |
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if not optimized: |
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logging.info("Running MCMC fit (%s samples)", nburnin) |
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p0, lnp0, rst0, _ = _sample_mcmc(sampler, nburnin, p0, rst0, |
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show_progress, progress_mod, debug=True) |
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logging.info("MCMC fit finished.") |
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p = p0[np.argmax(lnp0)] |
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logging.info("Fit max logpost: %s, params: %s, exp(params): %s", |
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np.max(lnp0), p, np.exp(p)) |
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model.set_parameter_vector(p) |
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logging.debug("params: %s", model.get_parameter_dict()) |
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logging.debug("log_likelihood: %s", model.log_likelihood(y)) |
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p0 = [p + 1e-4 * np.random.randn(ndim) for _ in range(nwalkers)] |
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sampler.reset() |
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logging.info("Running burn-in (%s samples)", nburnin) |
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p0, lnp0, rst0, _ = _sample_mcmc(sampler, nburnin, p0, rst0, |
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show_progress, progress_mod) |
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logging.info("Burn-in finished.") |
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p = p0[np.argmax(lnp0)] |
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logging.info("burn-in max logpost: %s, params: %s, exp(params): %s", |
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np.max(lnp0), p, np.exp(p)) |
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model.set_parameter_vector(p) |
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logging.debug("params: %s", model.get_parameter_dict()) |
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logging.debug("log_likelihood: %s", model.log_likelihood(y)) |
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sampler.reset() |
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logging.info("Running production chain (%s samples)", nprod) |
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_sample_mcmc(sampler, nprod, p0, rst0, show_progress, progress_mod) |
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logging.info("Production run finished.") |
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samples = sampler.flatchain |
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lnp = sampler.flatlnprobability |
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# first column in the blobs are the log likelihoods |
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lnlh = np.array(sampler.blobs)[..., 0].ravel().astype(float) |
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post_expect_loglh = np.nanmean(np.array(lnlh)) |
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logging.info("total samples: %s", samples.shape) |
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samplmean = np.mean(samples, axis=0) |
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logging.info("mean: %s, exp(mean): %s, sqrt(exp(mean)): %s", |
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samplmean, np.exp(samplmean), np.sqrt(np.exp(samplmean))) |
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samplmedian = np.median(samples, axis=0) |
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logging.info("median: %s, exp(median): %s, sqrt(exp(median)): %s", |
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samplmedian, np.exp(samplmedian), np.sqrt(np.exp(samplmedian))) |
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logging.info("max logpost: %s, params: %s, exp(params): %s", |
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np.max(lnp), samples[np.argmax(lnp)], |
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np.exp(samples[np.argmax(lnp)])) |
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logging.info("AIC: %s", 2 * ndim - 2 * np.max(lnp)) |
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logging.info("BIC: %s", np.log(len(y)) * ndim - 2 * np.max(lnp)) |
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logging.info("poor man's evidence 1 sum: %s, mean: %s", |
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np.sum(np.exp(lnp)), np.mean(np.exp(lnp))) |
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logging.info("poor man's evidence 2 max: %s, std: %s", |
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np.max(np.exp(lnp)), np.std(np.exp(lnp))) |
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logging.info("poor man's evidence 3: %s", |
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np.max(np.exp(lnp)) / np.std(np.exp(lnp))) |
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logging.info("poor man's evidence 4 sum: %s", |
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logsumexp(lnp, b=1. / lnp.shape[0], axis=0)) |
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# mode |
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model.set_parameter_vector(samples[np.argmax(lnp)]) |
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log_lh = model.log_likelihood(y) |
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# Use the likelihood instead of the posterior |
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# https://doi.org/10.3847/1538-3881/aa9332 |
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logging.info("BIC lh: %s", np.log(len(y)) * ndim - 2 * log_lh) |
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# DIC |
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sample_deviance = -2 * np.max(lnp) |
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deviance_at_sample = -2 * (model.log_prior() + log_lh) |
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pd = sample_deviance - deviance_at_sample |
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dic = 2 * sample_deviance - deviance_at_sample |
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logging.info("max logpost log_lh: %s, AIC: %s, DIC: %s, pd: %s", |
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model.log_likelihood(y), 2 * ndim - 2 * log_lh, dic, pd) |
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# mean |
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model.set_parameter_vector(samplmean) |
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log_lh = model.log_likelihood(y) |
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log_lh_mean = log_lh |
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# DIC |
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sample_deviance = -2 * np.nanmean(lnp) |
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deviance_at_sample = -2 * (model.log_prior() + log_lh) |
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pd = sample_deviance - deviance_at_sample |
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dic = 2 * sample_deviance - deviance_at_sample |
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logging.info("mean log_lh: %s, AIC: %s, DIC: %s, pd: %s", |
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model.log_likelihood(y), 2 * ndim - 2 * log_lh, dic, pd) |
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# median |
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model.set_parameter_vector(samplmedian) |
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log_lh = model.log_likelihood(y) |
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# DIC |
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sample_deviance = -2 * np.nanmedian(lnp) |
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deviance_at_sample = -2 * (model.log_prior() + log_lh) |
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dic = 2 * sample_deviance - deviance_at_sample |
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pd = sample_deviance - deviance_at_sample |
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logging.info("median log_lh: %s, AIC: %s, DIC: %s, pd: %s", |
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model.log_likelihood(y), 2 * ndim - 2 * log_lh, dic, pd) |
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# (4)--(6) in Ando2011 doi:10.1080/01966324.2011.10737798 |
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pd_ando = 2 * (log_lh_mean - post_expect_loglh) |
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ic5 = - 2 * post_expect_loglh + 2 * pd_ando |
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ic6 = - 2 * post_expect_loglh + 2 * ndim |
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logging.info("Ando2011: pd: %s, IC(5): %s, IC(6): %s", |
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pd_ando, ic5, ic6) |
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if return_logpost: |
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return samples, lnp |
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return samples |
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st-bender /
sciapy
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