diff --git a/orbitize/results.py b/orbitize/results.py index fea92e13..7a3aa7fa 100644 --- a/orbitize/results.py +++ b/orbitize/results.py @@ -58,18 +58,24 @@ def __init__( self.param_idx = self.system.param_idx self.standard_param_idx = self.system.basis.standard_basis_idx - def add_samples(self, orbital_params, lnlikes, curr_pos=None): + def add_samples(self, orbital_params, lnlikes, curr_pos=None): """ - Add accepted orbits, their likelihoods, and the orbitize version number + Add accepted orbits, their likelihoods, and the orbitize version number to the results Args: - orbital_params (np.array): add sets of orbital params (could be multiple) + orbital_params (np.array): add sets of orbital params (could be multiple) to results lnlike (np.array): add corresponding lnlike values to results - curr_pos (np.array of float): for MCMC only. A multi-D array of the + curr_pos (np.array of float): for MCMC only. A multi-D array of the current walker positions + .. Note:: ``post``/``lnlike`` are backed by an internal buffer that's + over-allocated and grown by doubling, so repeated calls (e.g. from + ``periodic_save_freq`` during MCMC) append in amortized O(1) time + per row instead of reallocating and copying the full accumulated + array on every call. + Written: Henry Ngo, 2018 API Update: Sarah Blunt, 2021 diff --git a/orbitize/sampler.py b/orbitize/sampler.py index 888f66ab..8c1ab214 100644 --- a/orbitize/sampler.py +++ b/orbitize/sampler.py @@ -6,7 +6,6 @@ import astropy.units as u import astropy.constants as consts import dynesty -import emcee import matplotlib.pyplot as plt import numpy as np import ptemcee @@ -663,9 +662,8 @@ def run_sampler( class MCMC(Sampler): """ - MCMC sampler. Supports either parallel tempering or just regular MCMC. Parallel tempering will be run if ``num_temps`` > 1 - Parallel-Tempered MCMC Sampler uses ptemcee, a fork of the emcee Affine-infariant sampler - Affine-Invariant Ensemble MCMC Sampler uses emcee. + MCMC sampler. Supports either parallel tempering or just regular MCMC. Parallel tempering will + be run if ``num_temps`` > 1 using ptemcee, a fork of the emcee Affine-infariant sampler. .. Warning:: may not work well for multi-modal distributions @@ -719,12 +717,6 @@ def __init__( version_number=orbitize.__version__, ) - if self.num_temps > 1: - self.use_pt = True - else: - self.use_pt = False - self.num_temps = 1 - # get priors from the system class. need to remove and record fixed priors self.priors = [] self.fixed_params = [] @@ -757,15 +749,10 @@ def __init__( init_positions.append(random_init) # save this as the current position for the walkers - if self.use_pt: - # make this an numpy array, but combine the parameters into a shape of (ntemps, nwalkers, nparams) - # we currently have a list of [ntemps, nwalkers] with nparam arrays. We need to make nparams the third dimension - self.curr_pos = np.dstack(init_positions) - else: - # make this an numpy array, but combine the parameters into a shape of (nwalkers, nparams) - # we currently have a list of arrays where each entry is num_walkers prior draws for each parameter - # We need to make nparams the second dimension, so we have to transpose the stacked array - self.curr_pos = np.stack(init_positions).T + # make this an numpy array, but combine the parameters into a shape of (ntemps, nwalkers, nparams) + # we currently have a list of [ntemps, nwalkers] with nparam arrays. We need to make nparams the third dimension + self.curr_pos = np.dstack(init_positions) + else: # restart from previous walker positions self.results.load_results(prev_result_filename, append=True) @@ -773,9 +760,7 @@ def __init__( prev_pos = self.results.curr_pos # check previous positions has the correct dimensions as we need given how this sampler was created. - expected_shape = (self.num_walkers, len(self.priors)) - if self.use_pt: - expected_shape = (self.num_temps,) + expected_shape + expected_shape = (self.num_temps,) + (self.num_walkers, len(self.priors)) if prev_pos.shape != expected_shape: raise ValueError( "Unable to restart chain. Saved walker positions has shape {0}, while current sampler needs {1}".format( @@ -849,13 +834,17 @@ def _logl(self, params, include_logp=False): return super(MCMC, self)._logl(full_params) + logp - def _update_chains_from_sampler(self, sampler, num_steps=None): + def _update_chains_from_sampler(self, sampler, num_steps=None, start_step=0): """ Updates self.post, self.chain, and self.lnlike from the MCMC sampler Args: - sampler (emcee.EnsembleSampler or ptemcee.Sampler): sampler object. + sampler (ptemcee.Sampler): sampler object. num_steps (int): if not None, only stores the first num_steps number of steps + start_step (int): if not 0, only (re)computes steps from this index + onwards, so self.post/self.lnlikes end up covering just the + ``[start_step, num_steps)`` chunk of the chain instead of the + full history. Used during periodic saving. """ if num_steps is None: # use all the steps, grab total number of steps from dimension of chains @@ -864,22 +853,13 @@ def _update_chains_from_sampler(self, sampler, num_steps=None): self.chain = sampler.chain num_params = self.chain.shape[-1] - if self.use_pt: - # chain is shape: Ntemp x Nwalkers x Nsteps x Nparams - self.post = sampler.chain[0, :, :num_steps].reshape( - -1, num_params - ) # the reshaping flattens the chain - # should also be picking out the lowest temperature logps - self.lnlikes = sampler.loglikelihood[0, :, :num_steps].flatten() - self.lnlikes_alltemps = sampler.loglikelihood[:, :, :num_steps] - else: - # chain is shape: Nwalkers x Nsteps x Nparams - self.post = sampler.chain[:, :num_steps].reshape(-1, num_params) - self.lnlikes = sampler.lnprobability[:, :num_steps].flatten() - - # convert posterior probability (returned by sampler objects) to likelihood (required by orbitize.results.Results) - for i, orb in enumerate(self.post): - self.lnlikes[i] -= orbitize.priors.all_lnpriors(orb, self.priors) + # chain has shape Ntemp x Nwalkers x Nsteps x Nparams + self.post = sampler.chain[0, :, start_step:num_steps].reshape( + -1, num_params + ) # the reshaping flattens the chain + # pick out the lowest temperature loglikelihoods + self.lnlikes = sampler.loglikelihood[0, :, start_step:num_steps].flatten() + self.lnlikes_alltemps = sampler.loglikelihood[:, :, start_step:num_steps] # include fixed parameters in posterior self.post = self._fill_in_fixed_params(self.post) @@ -891,62 +871,35 @@ def validate_xyz_positions(self): positions by new randomly generated positions until all are valid. """ if self.system.fitting_basis == "XYZ": - if self.use_pt: - all_valid = False - while not all_valid: - total_invalids = 0 - for temp in range(self.num_temps): - to_stand = self.system.basis.to_standard_basis( - self.curr_pos[temp, :, :].T.copy() - ).T - - # Get invalids by checking ecc values for each companion - indices = [ - ((i * 6) + 1) - for i in range(self.system.num_secondary_bodies) - ] - invalids = np.where( - (to_stand[:, indices] < 0.0) | (to_stand[:, indices] >= 1.0) - )[0] - - # Redraw samples for the invalid ones - if len(invalids) > 0: - newpos = [] - for prior in self.priors: - randompos = prior.draw_samples(len(invalids)) - newpos.append(randompos) - self.curr_pos[temp, invalids, :] = np.stack(newpos).T - total_invalids += len(invalids) - if total_invalids == 0: - all_valid = True - print("All walker positions validated.") - else: - all_valid = False - while not all_valid: - total_invalids = 0 + all_valid = False + while not all_valid: + total_invalids = 0 + for temp in range(self.num_temps): to_stand = self.system.basis.to_standard_basis( - self.curr_pos[:, :].T.copy() + self.curr_pos[temp, :, :].T.copy() ).T # Get invalids by checking ecc values for each companion indices = [ - ((i * 6) + 1) for i in range(self.system.num_secondary_bodies) + ((i * 6) + 1) + for i in range(self.system.num_secondary_bodies) ] invalids = np.where( (to_stand[:, indices] < 0.0) | (to_stand[:, indices] >= 1.0) )[0] - # Redraw saples for the invalid ones + # Redraw samples for the invalid ones if len(invalids) > 0: newpos = [] for prior in self.priors: randompos = prior.draw_samples(len(invalids)) newpos.append(randompos) - self.curr_pos[invalids, :] = np.stack(newpos).T + self.curr_pos[temp, invalids, :] = np.stack(newpos).T total_invalids += len(invalids) - if total_invalids == 0: - all_valid = True - print("All walker positions validated.") + if total_invalids == 0: + all_valid = True + print("All walker positions validated.") + def run_sampler( self, @@ -979,7 +932,7 @@ def run_sampler( every nth step while running, where n is value passed into this variable. Returns: - ``emcee.sampler`` object: the sampler used to run the MCMC + ``ptemcee.sampler`` object: the sampler used to run the MCMC """ if periodic_save_freq is not None and output_filename is None: @@ -989,40 +942,34 @@ def run_sampler( if periodic_save_freq is not None and not isinstance(periodic_save_freq, int): raise TypeError("periodic_save_freq must be an integer") + if output_filename is not None: + if output_filename.endswith('.hdf5'): + output_filename = output_filename.split('.hdf5')[0] + nsteps = int(np.ceil(total_orbits / self.num_walkers)) if nsteps <= 0: raise ValueError("Total_orbits must be greater than num_walkers.") with mp.Pool(processes=self.num_threads) as pool: - if self.use_pt: - sampler = ptemcee.Sampler( - self.num_walkers, - self.num_params, - self._logl, - orbitize.priors.all_lnpriors, - ntemps=self.num_temps, - logpargs=[ - self.priors, - ], - pool=pool - ) - else: - sampler = emcee.EnsembleSampler( - self.num_walkers, - self.num_params, - self._logl, - pool=pool, - kwargs={"include_logp": True}, - ) + sampler = ptemcee.Sampler( + self.num_walkers, + self.num_params, + self._logl, + orbitize.priors.all_lnpriors, + ntemps=self.num_temps, + logpargs=[ + self.priors, + ], + pool=pool + ) + print("Starting Burn in") for i, state in enumerate( sampler.sample(self.curr_pos, iterations=burn_steps, thin=thin) ): - if self.use_pt: - self.curr_pos = state[0] - else: - self.curr_pos = state.coords + self.curr_pos = state[0] + if (i + 1) % 5 == 0: print( @@ -1036,7 +983,7 @@ def run_sampler( if periodic_save_freq is not None: if (i + 1) % periodic_save_freq == 0: # we've completed i+1 steps self.results.curr_pos = self.curr_pos - self.results.save_results(output_filename) + self.results.save_results(f'{output_filename}_{i+1}burnsteps.hdf5') sampler.reset() print("") @@ -1046,76 +993,52 @@ def run_sampler( for i, state in enumerate( sampler.sample(self.curr_pos, iterations=nsteps, thin=thin) ): - if self.use_pt: - self.curr_pos = state[0] - else: - self.curr_pos = state.coords + self.curr_pos = state[0] # print progress statement if (i + 1) % 5 == 0: print(str(i + 1) + "/" + str(nsteps) + " steps completed", end="\r") if periodic_save_freq is not None: + save_start_time = time.time() if (i + 1) % periodic_save_freq == 0: # we've completed i+1 steps - self._update_chains_from_sampler(sampler, num_steps=i + 1) - - # figure out what is the new chunk of the chain and corresponding lnlikes that have been computed before last save - # grab the current posterior and lnlikes and reshape them to have the Nwalkers x Nsteps dimension again - post_shape = self.post.shape - curr_chain_shape = ( - self.num_walkers, - post_shape[0] // self.num_walkers, - post_shape[-1], + + # only (re)compute the chunk of the chain since the last + # save, not the full history + self._update_chains_from_sampler( + sampler, num_steps=i + 1, start_step=saved_upto ) - curr_chain = self.post.reshape(curr_chain_shape) - curr_lnlike_chain = self.lnlikes.reshape(curr_chain_shape[:2]) - # use the reshaped arrays and find the new steps we computed - curr_chunk = curr_chain[:, saved_upto : i + 1] - curr_chunk = curr_chunk.reshape( - -1, curr_chunk.shape[-1] - ) # flatten nwalkers x nsteps dim - curr_lnlike_chunk = curr_lnlike_chain[ - :, saved_upto : i + 1 - ].flatten() # add this current chunk to the results object (which already has all the previous chunks saved) self.results.add_samples( - curr_chunk, curr_lnlike_chunk, curr_pos=self.curr_pos + self.post, self.lnlikes, curr_pos=self.curr_pos ) - self.results.save_results(output_filename) + self.results.save_results(f'{output_filename}_{i+1}steps.hdf5') saved_upto = i + 1 + print(f'saving took {time.time()-save_start_time} s') print("") - self._update_chains_from_sampler(sampler) if periodic_save_freq is None: - # need to save everything + # nothing has been processed/saved yet; need to do the whole chain + self._update_chains_from_sampler(sampler) self.results.add_samples( self.post, self.lnlikes, curr_pos=self.curr_pos ) elif saved_upto < nsteps: - # just need to save the last few - # same code as above except we just need to grab the last few - post_shape = self.post.shape - curr_chain_shape = ( - self.num_walkers, - post_shape[0] // self.num_walkers, - post_shape[-1], - ) - curr_chain = self.post.reshape(curr_chain_shape) - curr_lnlike_chain = self.lnlikes.reshape(curr_chain_shape[:2]) - curr_chunk = curr_chain[:, saved_upto:] - curr_chunk = curr_chunk.reshape( - -1, curr_chunk.shape[-1] - ) # flatten nwalkers x nsteps dim - curr_lnlike_chunk = curr_lnlike_chain[:, saved_upto:].flatten() - + # just need to process and save the leftover chunk since the last save + self._update_chains_from_sampler(sampler, start_step=saved_upto) self.results.add_samples( - curr_chunk, curr_lnlike_chunk, curr_pos=self.curr_pos + self.post, self.lnlikes, curr_pos=self.curr_pos ) + else: + # everything has already been saved; still refresh self.chain, + # self.post, and self.lnlikes to reflect the full run in case + # the caller inspects them after run_sampler() returns + self._update_chains_from_sampler(sampler) if output_filename is not None: - self.results.save_results(output_filename) + self.results.save_results(f'{output_filename}.hdf5') print("Run complete") diff --git a/pyproject.toml b/pyproject.toml index 1e7ec9b6..33dfa15c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ dependencies = [ "numpy", "astropy>=4", "scipy", - "emcee>=3", "ptemcee_for_orbitize", "matplotlib", "corner", diff --git a/requirements.txt b/requirements.txt index 2c215d7e..ac9da352 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,6 @@ astropy>=4 numpy scipy -emcee>=3 ptemcee_for_orbitize matplotlib corner diff --git a/tests/test_mcmc.py b/tests/test_mcmc.py index 329434c2..0fda6327 100644 --- a/tests/test_mcmc.py +++ b/tests/test_mcmc.py @@ -19,13 +19,11 @@ 'sma1': 0, 'ecc1':1, 'inc1':2, 'aop1':3, 'pan1':4, 'tau1':5, 'plx':6, 'mtot':7 } -def do_mcmc_runs(num_temps=0, num_threads=1, make_corner_plot=False): +def do_mcmc_runs(num_temps=1, num_threads=1, make_corner_plot=False): """ Tests the MCMC sampler by making sure it runs Args: num_temps (int): Number of temperatures to use - Uses Parallel Tempering MCMC (ptemcee) if > 1, - otherwises, uses Affine-Invariant Ensemble Sampler (emcee) num_threads (int): number of threads to run make_corner_plot (bool): if True, make a corner plot of the results """ @@ -106,8 +104,6 @@ def do_examine_chop_chains(num_temps=0, num_threads=1): Tests the MCMC sampler's examine_chains and chop_chains methods Args: num_temps: Number of temperatures to use - Uses Parallel Tempering MCMC (ptemcee) if > 1, - otherwises, uses Affine-Invariant Ensemble Sampler (emcee) num_threads: number of threads to run """ @@ -181,7 +177,7 @@ def test_mcmc_param_idx(): # construct Driver with fixed mass and plx n_walkers = 100 myDriver = Driver(input_file, 'MCMC', 1, 1, 0.01, - mcmc_kwargs={'num_temps': 0, 'num_threads': 1, + mcmc_kwargs={'num_temps': 1, 'num_threads': 1, 'num_walkers': n_walkers} ) @@ -190,7 +186,7 @@ def test_mcmc_param_idx(): # construct Driver with no fixed params myDriver = Driver(input_file, 'MCMC', 1, 1, 0.01, mass_err=0.1, plx_err=0.2, - mcmc_kwargs={'num_temps': 0, 'num_threads': 1, + mcmc_kwargs={'num_temps': 1, 'num_threads': 1, 'num_walkers': n_walkers} ) @@ -201,13 +197,13 @@ def test_mcmc_runs(): do_mcmc_runs(num_temps=2, num_threads=1, make_corner_plot=True) do_mcmc_runs(num_temps=2, num_threads=4) # Ensemble MCMC tests - do_mcmc_runs(num_temps=0, num_threads=1) - do_mcmc_runs(num_temps=0, num_threads=8) + do_mcmc_runs(num_temps=1, num_threads=1) + do_mcmc_runs(num_temps=1, num_threads=8) def test_chop_chains(): # Test examine/chop chains do_examine_chop_chains(num_temps=5) # PT - do_examine_chop_chains(num_temps=0) # Ensemble + do_examine_chop_chains(num_temps=1) # Ensemble if __name__ == "__main__": diff --git a/tests/test_multiplanet.py b/tests/test_multiplanet.py index 3315b564..93f37991 100644 --- a/tests/test_multiplanet.py +++ b/tests/test_multiplanet.py @@ -226,16 +226,16 @@ def test_fit_selfconsist(): # start walkers near the true location for the orbital parameters np.random.seed(123) # planet b - samp.curr_pos[:, 0] = np.random.normal(b_params[0], 0.01, n_walkers) # sma - samp.curr_pos[:, 1] = np.random.normal(b_params[-1], 0.01, n_walkers) # tau + samp.curr_pos[0, :, 0] = np.random.normal(b_params[0], 0.01, n_walkers) # sma + samp.curr_pos[0, :, 1] = np.random.normal(b_params[-1], 0.01, n_walkers) # tau # planet c - samp.curr_pos[:, 2] = np.random.normal(c_params[0], 0.01, n_walkers) # sma - samp.curr_pos[:, 3] = np.random.normal(c_params[-1], 0.01, n_walkers) # tau + samp.curr_pos[0, :, 2] = np.random.normal(c_params[0], 0.01, n_walkers) # sma + samp.curr_pos[0, :, 3] = np.random.normal(c_params[-1], 0.01, n_walkers) # tau # we will make a fairly broad mass starting position - samp.curr_pos[:, 4] = np.random.uniform(mass_b * 0.25, mass_b * 4, n_walkers) - samp.curr_pos[:, 5] = np.random.uniform(mass_c * 0.25, mass_c * 4, n_walkers) - samp.curr_pos[0, 4] = mass_b - samp.curr_pos[0, 5] = mass_c + samp.curr_pos[0, :, 4] = np.random.uniform(mass_b * 0.25, mass_b * 4, n_walkers) + samp.curr_pos[0, :, 5] = np.random.uniform(mass_c * 0.25, mass_c * 4, n_walkers) + samp.curr_pos[0, 0, 4] = mass_b + samp.curr_pos[0, 0, 5] = mass_c samp.run_sampler(n_walkers * 50, burn_steps=50) @@ -259,5 +259,5 @@ def test_fit_selfconsist(): if __name__ == "__main__": - # test_compute_model() + test_compute_model() test_fit_selfconsist() diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..7ffea88e --- /dev/null +++ b/uv.lock @@ -0,0 +1,947 @@ +version = 1 +revision = 3 +requires-python = ">=3.14" +resolution-markers = [ + "sys_platform == 'win32'", + "sys_platform == 'emscripten'", + "sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] +name = "astropy" +version = 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