def save_sample(self, fname, name):
sample = np.atleast_2d(np.loadtxt(fname))
if not sample.size:
return None
collection = SampleCollection(self.model, self.output, name=str(name))
for row in sample:
collection.add(
row[2:2 + self.n_sampled],
derived=row[2 + self.n_sampled:2 + self.n_sampled + self.n_derived],
weight=row[0],
logpriors=row[-(self.n_priors + self.n_likes):-self.n_likes],
loglikes=row[-self.n_likes:])
# make sure that the points are written
collection.out_update()
return collection
class MCMC(CovmatSampler):
r"""
Adaptive, speed-hierarchy-aware MCMC sampler (adapted from CosmoMC)
\cite{Lewis:2002ah,Lewis:2013hha}.
"""
_at_resume_prefer_new = CovmatSampler._at_resume_prefer_new + [
"burn_in", "callback_function", "callback_every", "max_tries",
"output_every", "learn_every", "learn_proposal_Rminus1_max",
"learn_proposal_Rminus1_max_early", "learn_proposal_Rminus1_min",
"max_samples", "Rminus1_stop", "Rminus1_cl_stop", "Rminus1_cl_level",
"covmat", "covmat_params"
]
_at_resume_prefer_old = CovmatSampler._at_resume_prefer_new + [
"proposal_scale", "blocking"
]
file_base_name = 'mcmc'
# instance variables from yaml
burn_in: NumberWithUnits
learn_every: NumberWithUnits
output_every: NumberWithUnits
callback_every: NumberWithUnits
max_tries: NumberWithUnits
max_samples: int
drag: bool
callback_function: Optional[Callable]
blocking: Optional[Sequence]
proposal_scale: float
learn_proposal: bool
learn_proposal_Rminus1_max: float
learn_proposal_Rminus1_max_early: float
Rminus1_cl_level: float
Rminus1_stop: float
Rminus1_cl_stop: float
Rminus1_single_split: int
learn_proposal_Rminus1_min: float
measure_speeds: bool
oversample_thin: int
oversample_power: float
def set_instance_defaults(self):
super().set_instance_defaults()
# checkpoint variables
self.converged = False
self.mpi_size = None
self.Rminus1_last = np.inf
def initialize(self):
"""Initializes the sampler:
creates the proposal distribution and draws the initial sample."""
if not self.model.prior.d():
raise LoggedError(self.log,
"No parameters being varied for sampler")
self.log.debug("Initializing")
# MARKED FOR DEPRECATION IN v3.0
if getattr(self, "oversample", None) is not None:
raise LoggedError(
self.log, "`oversample` has been deprecated. "
"Oversampling is now requested by setting "
"`oversample_power` > 0.")
# END OF DEPRECATION BLOCK
# MARKED FOR DEPRECATION IN v3.0
if getattr(self, "check_every", None) is not None:
raise LoggedError(
self.log, "`check_every` has been deprecated. "
"Please use `learn_every` instead.")
# END OF DEPRECATION BLOCK
if self.callback_every is None:
self.callback_every = self.learn_every
self._quants_d_units = []
for q in ["max_tries", "learn_every", "callback_every", "burn_in"]:
number = NumberWithUnits(getattr(self, q), "d", dtype=int)
self._quants_d_units.append(number)
setattr(self, q, number)
self.output_every = NumberWithUnits(self.output_every, "s", dtype=int)
if is_main_process():
if self.output.is_resuming() and (max(self.mpi_size or 0, 1) !=
mpi.size()):
raise LoggedError(
self.log,
"Cannot resume a run with a different number of chains: "
"was %d and now is %d.", max(self.mpi_size or 0, 1),
mpi.size())
sync_processes()
# One collection per MPI process: `name` is the MPI rank + 1
name = str(1 + mpi.rank())
self.collection = SampleCollection(self.model,
self.output,
name=name,
resuming=self.output.is_resuming())
self.current_point = OneSamplePoint(self.model)
# Use standard MH steps by default
self.get_new_sample = self.get_new_sample_metropolis
# Prepare callback function
if self.callback_function:
self.callback_function_callable = (get_external_function(
self.callback_function))
# Useful for getting last points added inside callback function
self.last_point_callback = 0
self.i_learn = 1
# Monitoring/restore progress
if is_main_process():
cols = [
"N", "timestamp", "acceptance_rate", "Rminus1", "Rminus1_cl"
]
self.progress = DataFrame(columns=cols)
if self.output and not self.output.is_resuming():
header_fmt = {
"N": 6 * " " + "N",
"timestamp": 17 * " " + "timestamp"
}
with open(self.progress_filename(), "w",
encoding="utf-8") as progress_file:
progress_file.write("# " + " ".join([
header_fmt.get(col, ((7 + 8) - len(col)) * " " + col)
for col in self.progress.columns
]) + "\n")
# Get first point, to be discarded -- not possible to determine its weight
# Still, we need to compute derived parameters, since, as the proposal "blocked",
# we may be saving the initial state of some block.
# NB: if resuming but nothing was written (burn-in not finished): re-start
if self.output.is_resuming() and len(self.collection):
last = len(self.collection) - 1
initial_point = (self.collection[
self.collection.sampled_params].iloc[last]).to_numpy(
dtype=np.float64, copy=True)
results = LogPosterior(
logpost=-self.collection[OutPar.minuslogpost].iloc[last],
logpriors=-(self.collection[
self.collection.minuslogprior_names].iloc[last].to_numpy(
dtype=np.float64, copy=True)),
loglikes=-0.5 *
(self.collection[self.collection.chi2_names].iloc[last].
to_numpy(dtype=np.float64, copy=True)),
derived=(self.collection[
self.collection.derived_params].iloc[last].to_numpy(
dtype=np.float64, copy=True)))
else:
# NB: max_tries adjusted to dim instead of #cycles (blocking not computed yet)
self.max_tries.set_scale(self.model.prior.d())
self.log.info(
"Getting initial point... (this may take a few seconds)")
initial_point, results = \
self.model.get_valid_point(max_tries=self.max_tries.value,
random_state=self._rng)
# If resuming but no existing chain, assume failed run and ignore blocking
# if speeds measurement requested
if self.output.is_resuming() and not len(self.collection) \
and self.measure_speeds:
self.blocking = None
if self.measure_speeds and self.blocking:
self.mpi_warning(
"Parameter blocking manually fixed: speeds will not be measured."
)
elif self.measure_speeds:
n = None if self.measure_speeds is True else int(
self.measure_speeds)
self.model.measure_and_set_speeds(n=n,
discard=0,
random_state=self._rng)
self.set_proposer_blocking()
self.set_proposer_initial_covmat(load=True)
self.current_point.add(initial_point, results)
self.log.info("Initial point: %s", self.current_point)
# Max #(learn+convergence checks) to wait,
# in case one process dies/hangs without raising error
self.been_waiting = 0
self.max_waiting = max(50, self.max_tries.unit_value)
# Burning-in countdown -- the +1 accounts for the initial point (always accepted)
self.burn_in_left = self.burn_in.value * self.current_point.output_thin + 1
self._msg_ready = ("Ready to check convergence" +
(" and learn a new proposal covmat"
if self.learn_proposal else ""))
# Initial dummy checkpoint
# (needed when 1st "learn point" not reached in prev. run)
self.write_checkpoint()
@property
def i_last_slow_block(self):
if self.drag:
return next(i for i, o in enumerate(self.oversampling_factors)
if o != 1) - 1
self.log.warning(
"`i_last_slow_block` is only well defined when dragging.")
return 0
@property
def slow_blocks(self):
return self.blocks[:1 + self.i_last_slow_block]
@property
def slow_params(self):
return list(chain(*self.slow_blocks))
@property
def n_slow(self):
return len(self.slow_params)
@property
def fast_blocks(self):
return self.blocks[self.i_last_slow_block + 1:]
@property
def fast_params(self):
return list(chain(*self.fast_blocks))
@property
def n_fast(self):
return len(self.fast_params)
def get_acceptance_rate(self, first=0, last=None):
return (((last or self.n()) - (first or 0)) /
self.collection[OutPar.weight][first:last].sum())
def set_proposer_blocking(self):
if self.blocking:
# Includes the case in which we are resuming
self.blocks, self.oversampling_factors = \
self.model.check_blocking(self.blocking)
else:
self.blocks, self.oversampling_factors = \
self.model.get_param_blocking_for_sampler(
oversample_power=self.oversample_power, split_fast_slow=self.drag)
# Turn off dragging if one block, or if speed differences < 2x, or no differences
if self.drag:
if len(self.blocks) == 1:
self.drag = False
self.log.warning(
"Dragging disabled: not possible if there is only one block."
)
if max(self.oversampling_factors) / min(
self.oversampling_factors) < 2:
self.drag = False
self.log.warning("Dragging disabled: speed ratios < 2.")
if self.drag:
# The definition of oversample_power=1 as spending the same amount of time in
# the slow and fast block would suggest a 1/2 factor here, but this additional
# factor of 2 w.r.t. oversampling should produce an equivalent exploration
# efficiency.
self.drag_interp_steps = int(
np.round(
self.oversampling_factors[self.i_last_slow_block + 1] *
self.n_fast / self.n_slow))
if self.drag_interp_steps < 2:
self.drag = False
self.log.warning(
"Dragging disabled: "
"speed ratio and fast-to-slow ratio not large enough.")
# Define proposer and other blocking-related quantities
if self.drag:
# MARKED FOR DEPRECATION IN v3.0
if getattr(self, "drag_limits", None) is not None:
raise LoggedError(
self.log, "`drag_limits` has been deprecated. "
"Use 'oversample_power' to control the amount"
" of dragging steps.")
# END OF DEPRECATION BLOCK
self.get_new_sample = self.get_new_sample_dragging
self.mpi_info("Dragging with number of interpolating steps:")
max_width = len(str(self.drag_interp_steps))
self.mpi_info("* %" + "%d" % max_width + "d : %r", 1,
self.slow_blocks)
self.mpi_info("* %" + "%d" % max_width + "d : %r",
self.drag_interp_steps, self.fast_blocks)
elif np.any(np.array(self.oversampling_factors) > 1):
self.mpi_info("Oversampling with factors:")
max_width = len(str(max(self.oversampling_factors)))
for f, b in zip(self.oversampling_factors, self.blocks):
self.mpi_info("* %" + "%d" % max_width + "d : %r", f, b)
if self.oversample_thin:
self.current_point.output_thin = int(
np.round(
sum(
len(b) * o for b, o in zip(
self.blocks, self.oversampling_factors)) /
self.model.prior.d()))
# Save blocking in updated info, in case we want to resume
self._updated_info["blocking"] = list(
zip(self.oversampling_factors, self.blocks))
sampled_params_list = list(
self.model.parameterization.sampled_params())
blocks_indices = [[sampled_params_list.index(p) for p in b]
for b in self.blocks]
self.proposer = BlockedProposer(
blocks_indices,
self._rng,
oversampling_factors=self.oversampling_factors,
i_last_slow_block=(self.i_last_slow_block if self.drag else None),
proposal_scale=self.proposal_scale)
# Cycle length, taking into account oversampling/dragging
if self.drag:
self.cycle_length = self.n_slow
else:
self.cycle_length = sum(
len(b) * o
for b, o in zip(blocks_indices, self.oversampling_factors))
self.log.debug("Cycle length in steps: %r", self.cycle_length)
for number in self._quants_d_units:
number.set_scale(self.cycle_length //
self.current_point.output_thin)
def set_proposer_initial_covmat(self, load=False):
if load:
# Build the initial covariance matrix of the proposal, or load from checkpoint
self._initial_covmat, where_nan = self._load_covmat(
prefer_load_old=self.output.is_resuming())
if np.any(where_nan) and self.learn_proposal:
# We want to start learning the covmat earlier.
self.mpi_info(
"Covariance matrix " +
("not present" if np.all(where_nan) else "not complete") +
". We will start learning the covariance of the proposal "
"earlier: R-1 = %g (would be %g if all params loaded).",
self.learn_proposal_Rminus1_max_early,
self.learn_proposal_Rminus1_max)
self.learn_proposal_Rminus1_max = self.learn_proposal_Rminus1_max_early
self.log.debug(
"Sampling with covmat:\n%s",
DataFrame(self._initial_covmat,
columns=self.model.parameterization.sampled_params(),
index=self.model.parameterization.sampled_params()).
to_string(line_width=line_width))
self.proposer.set_covariance(self._initial_covmat)
def _get_last_nondragging_block(self, blocks, speeds):
# blocks and speeds are already sorted
log_differences = np.zeros(len(blocks) - 1)
for i in range(len(blocks) - 1):
log_differences[i] = (np.log(np.min(speeds[:i + 1])) -
np.log(np.min(speeds[i + 1:])))
i_max = np.argmin(log_differences)
return i_max
def run(self):
"""
Runs the sampler.
"""
self.mpi_info("Sampling!" + (
" (NB: no accepted step will be saved until %d burn-in samples " %
self.burn_in.value +
"have been obtained)" if self.burn_in.value else ""))
self.n_steps_raw = 0
last_output: float = 0
last_n = self.n()
state_check_every = 1
with mpi.ProcessState(self) as state:
while last_n < self.max_samples and not self.converged:
self.get_new_sample()
self.n_steps_raw += 1
if self.output_every.unit:
# if output_every in sec, print some info
# and dump at fixed time intervals
now = datetime.datetime.now()
now_sec = now.timestamp()
if now_sec >= last_output + self.output_every.value:
self.do_output(now)
last_output = now_sec
state.check_error()
if self.current_point.weight == 1:
# have added new point
# Callback function
n = self.n()
if n != last_n:
# and actually added
last_n = n
if (self.callback_function
and not (max(n, 1) % self.callback_every.value)
and self.current_point.weight == 1):
self.callback_function_callable(self)
self.last_point_callback = len(self.collection)
if more_than_one_process():
# Checking convergence and (optionally) learning
# the covmat of the proposal
if self.check_ready() and state.set(
mpi.State.READY):
self.log.info(self._msg_ready +
" (waiting for the rest...)")
if state.all_ready():
self.mpi_info("All chains are r%s",
self._msg_ready[1:])
self.check_convergence_and_learn_proposal()
self.i_learn += 1
else:
if self.check_ready():
self.log.debug(self._msg_ready)
self.check_convergence_and_learn_proposal()
self.i_learn += 1
elif self.current_point.weight % state_check_every == 0:
state.check_error()
# more frequent checks near beginning
state_check_every = min(10, state_check_every + 1)
if last_n == self.max_samples:
self.log.info(
"Reached maximum number of accepted steps allowed (%s). "
"Stopping.", self.max_samples)
# Write the last batch of samples ( < output_every (not in sec))
self.collection.out_update()
ns = mpi.gather(self.n())
self.mpi_info("Sampling complete after %d accepted steps.", sum(ns))
def n(self, burn_in=False):
"""
Returns the total number of accepted steps taken, including or not burn-in steps
depending on the value of the `burn_in` keyword.
"""
return len(self.collection) + (
0 if not burn_in else self.burn_in.value -
self.burn_in_left // self.current_point.output_thin + 1)
def get_new_sample_metropolis(self):
"""
Draws a new trial point from the proposal pdf and checks whether it is accepted:
if it is accepted, it saves the old one into the collection and sets the new one
as the current state; if it is rejected increases the weight of the current state
by 1.
Returns:
``True`` for an accepted step, ``False`` for a rejected one.
"""
trial = self.current_point.values.copy()
self.proposer.get_proposal(trial)
trial_results = self.model.logposterior(trial)
accept = self.metropolis_accept(trial_results.logpost,
self.current_point.logpost)
self.process_accept_or_reject(accept, trial, trial_results)
return accept
def get_new_sample_dragging(self):
"""
Draws a new trial point in the slow subspace, and gets the corresponding trial
in the fast subspace by "dragging" the fast parameters.
Finally, checks the acceptance of the total step using the "dragging" pdf:
if it is accepted, it saves the old one into the collection and sets the new one
as the current state; if it is rejected increases the weight of the current state
by 1.
Returns:
``True`` for an accepted step, ``False`` for a rejected one.
"""
# Prepare starting and ending points *in the SLOW subspace*
# "start_" and "end_" mean here the extremes in the SLOW subspace
current_start_point = self.current_point.values
current_start_logpost = self.current_point.logpost
current_end_point = current_start_point.copy()
self.proposer.get_proposal_slow(current_end_point)
self.log.debug("Proposed slow end-point: %r", current_end_point)
# Save derived parameters of delta_slow jump, in case I reject all the dragging
# steps but accept the move in the slow direction only
current_end = self.model.logposterior(current_end_point)
if current_end.logpost == -np.inf:
self.current_point.weight += 1
return False
# accumulators for the "dragging" probabilities to be metropolis-tested
# at the end of the interpolation
start_drag_logpost_acc = current_start_logpost
end_drag_logpost_acc = current_end.logpost
# don't compute derived during drag, unless must be computed anyway
derived = self.model.requires_derived
# alloc mem
delta_fast = np.empty(len(current_start_point))
# start dragging
for i_step in range(1, 1 + self.drag_interp_steps):
self.log.debug("Dragging step: %d", i_step)
# take a step in the fast direction in both slow extremes
delta_fast[:] = 0.
self.proposer.get_proposal_fast(delta_fast)
self.log.debug("Proposed fast step delta: %r", delta_fast)
proposal_start_point = current_start_point + delta_fast
# get the new extremes for the interpolated probability
# (reject if any of them = -inf; avoid evaluating both if just one fails)
# Force the computation of the (slow blocks) derived params at the starting
# point, but discard them, since they contain the starting point's fast ones,
# not used later -- save the end point's ones.
proposal_start_logpost = self.model.logposterior(
proposal_start_point,
return_derived=bool(derived),
_no_check=True).logpost
if proposal_start_logpost != -np.inf:
proposal_end_point = current_end_point + delta_fast
proposal_end = self.model.logposterior(
proposal_end_point,
return_derived=bool(derived),
_no_check=True)
if proposal_end.logpost != -np.inf:
# create the interpolated probability and do a Metropolis test
frac = i_step / (1 + self.drag_interp_steps)
proposal_interp_logpost = (
(1 - frac) * proposal_start_logpost +
frac * proposal_end.logpost)
current_interp_logpost = (
(1 - frac) * current_start_logpost +
frac * current_end.logpost)
accept_drag = self.metropolis_accept(
proposal_interp_logpost, current_interp_logpost)
if accept_drag:
# If the dragging step was accepted, do the drag
current_start_point = proposal_start_point
current_start_logpost = proposal_start_logpost
current_end_point = proposal_end_point
current_end = proposal_end
else:
accept_drag = False
else:
accept_drag = False
self.log.debug("Dragging step: %s",
("accepted" if accept_drag else "rejected"))
# In any case, update the dragging probability for the final metropolis test
start_drag_logpost_acc += current_start_logpost
end_drag_logpost_acc += current_end.logpost
# Test for the TOTAL step
n_average = 1 + self.drag_interp_steps
accept = self.metropolis_accept(end_drag_logpost_acc / n_average,
start_drag_logpost_acc / n_average)
if accept and not derived:
# recompute with derived parameters (slow parameter ones should be cached)
current_end = self.model.logposterior(current_end_point)
self.process_accept_or_reject(accept, current_end_point, current_end)
self.log.debug("TOTAL step: %s",
("accepted" if accept else "rejected"))
return accept
def metropolis_accept(self, logp_trial, logp_current):
"""
Symmetric-proposal Metropolis-Hastings test.
Returns:
``True`` or ``False``.
"""
if logp_trial == -np.inf:
return False
elif logp_trial > logp_current:
return True
else:
return self._rng.standard_exponential() > (logp_current -
logp_trial)
def process_accept_or_reject(self, accept_state, trial, trial_results):
"""Processes the acceptance/rejection of the new point."""
if accept_state:
# add the old point to the collection (if not burning or initial point)
if self.burn_in_left <= 0:
if self.current_point.add_to_collection(self.collection):
self.log.debug("New sample, #%d: \n %s", self.n(),
self.current_point)
# Update chain files, if output_every *not* in sec
if not self.output_every.unit:
if self.n() % self.output_every.value == 0:
self.collection.out_update()
else:
self.burn_in_left -= 1
self.log.debug("Burn-in sample:\n %s", self.current_point)
if self.burn_in_left == 0 and self.burn_in:
self.log.info(
"Finished burn-in phase: discarded %d accepted steps.",
self.burn_in.value)
# set the new point as the current one, with weight one
self.current_point.add(trial, trial_results)
else: # not accepted
self.current_point.weight += 1
# Failure criterion: chain stuck! (but be more permissive during burn_in)
max_tries_now = self.max_tries.value * (
1 + (10 - 1) * np.sign(self.burn_in_left))
if self.current_point.weight > max_tries_now:
self.collection.out_update()
raise LoggedError(
self.log,
"The chain has been stuck for %d attempts, stopping sampling. "
"Make sure the reference point is semsible and initial covmat."
"For parameters not included in an initial covmat, the 'proposal' "
"width set for each parameter should be of order of the expected "
"conditional posterior width, which may be much smaller than the "
"marginalized posterior width - choose a smaller "
"rather than larger value if in doubt. You can also decrease the "
"'proposal_scale' option for mcmc, though small values will sample "
"less efficiently once things converge.\n"
"Alternatively (though not advisable) make 'max_tries: np.inf' "
"(or 'max_tries: .inf' in yaml).\n"
"Current point: %s", max_tries_now, self.current_point)
# Functions to check convergence and learn the covariance of the proposal distribution
def check_ready(self):
"""
Checks if the chain(s) is(/are) ready to check convergence and, if requested,
learn a new covariance matrix for the proposal distribution.
"""
n = len(self.collection)
# If *just* (weight==1) got ready to check+learn
if not (n % self.learn_every.value) and n > 0:
self.log.info("Learn + convergence test @ %d samples accepted.", n)
self.model.dump_timing()
if more_than_one_process():
self.been_waiting += 1
if self.been_waiting > self.max_waiting:
raise LoggedError(
self.log,
"Waiting for too long for all chains to be ready. "
"Maybe one of them is stuck or died unexpectedly?")
return True
return False
# noinspection PyUnboundLocalVariable
@np.errstate(all="ignore")
def check_convergence_and_learn_proposal(self):
"""
Checks the convergence of the sampling process, and, if requested,
learns a new covariance matrix for the proposal distribution from the covariance
of the last samples.
"""
# Compute Rminus1 of means
self.been_waiting = 0
if more_than_one_process():
# Compute and gather means and covs
use_first = int(self.n() / 2)
mean = self.collection.mean(first=use_first)
cov = self.collection.cov(first=use_first)
acceptance_rate = self.get_acceptance_rate(use_first)
Ns, means, covs, acceptance_rates = mpi.array_gather(
[self.n(), mean, cov, acceptance_rate])
else:
# Compute and gather means, covs and CL intervals of last m-1 chain fractions
m = 1 + self.Rminus1_single_split
cut = int(len(self.collection) / m)
try:
acceptance_rate = self.get_acceptance_rate(cut)
Ns = np.ones(m - 1) * cut
means = np.array([
self.collection.mean(first=i * cut, last=(i + 1) * cut - 1)
for i in range(1, m)
])
covs = np.array([
self.collection.cov(first=i * cut, last=(i + 1) * cut - 1)
for i in range(1, m)
])
except always_stop_exceptions:
raise
except Exception:
self.log.info(
"Not enough points in chain to check convergence. "
"Waiting for next checkpoint.")
return
acceptance_rates = None
if is_main_process():
self.progress.at[self.i_learn, "N"] = sum(Ns)
self.progress.at[self.i_learn, "timestamp"] = \
datetime.datetime.now().isoformat()
acceptance_rate = (np.average(acceptance_rates, weights=Ns)
if acceptance_rates is not None else
acceptance_rate)
self.log.info(
" - Acceptance rate: %.3f" +
(" = avg(%r)" % list(acceptance_rates)
if acceptance_rates is not None else ""), acceptance_rate)
self.progress.at[self.i_learn, "acceptance_rate"] = acceptance_rate
# "Within" or "W" term -- our "units" for assessing convergence
# and our prospective new covariance matrix
mean_of_covs = np.average(covs, weights=Ns, axis=0)
# "Between" or "B" term
# We don't weight with the number of samples in the chains here:
# shorter chains will likely be outliers, and we want to notice them
cov_of_means = np.atleast_2d(np.cov(means.T)) # , fweights=Ns)
# For numerical stability, we turn mean_of_covs into correlation matrix:
# rho = (diag(Sigma))^(-1/2) * Sigma * (diag(Sigma))^(-1/2)
# and apply the same transformation to the mean of covs (same eigenvals!)
d = np.sqrt(np.diag(cov_of_means))
corr_of_means = (cov_of_means / d).T / d
norm_mean_of_covs = (mean_of_covs / d).T / d
success_means = False
converged_means = False
# Cholesky of (normalized) mean of covs and eigvals of Linv*cov_of_means*L
try:
L = np.linalg.cholesky(norm_mean_of_covs)
except np.linalg.LinAlgError:
self.log.warning(
"Negative covariance eigenvectors. "
"This may mean that the covariance of the samples does not "
"contain enough information at this point. "
"Skipping learning a new covmat for now.")
else:
Linv = np.linalg.inv(L)
try:
eigvals = np.linalg.eigvalsh(
Linv.dot(corr_of_means).dot(Linv.T))
success_means = True
except np.linalg.LinAlgError:
self.log.warning("Could not compute eigenvalues. "
"Skipping learning a new covmat for now.")
else:
Rminus1 = max(np.abs(eigvals))
self.progress.at[self.i_learn, "Rminus1"] = Rminus1
# For real square matrices, a possible def of the cond number is:
condition_number = Rminus1 / min(np.abs(eigvals))
self.log.debug(" - Condition number = %g",
condition_number)
self.log.debug(" - Eigenvalues = %r", eigvals)
self.log.info(
" - Convergence of means: R-1 = %f after %d accepted steps"
% (Rminus1, sum(Ns)) +
(" = sum(%r)" %
list(Ns) if more_than_one_process() else ""))
# Have we converged in means?
# (criterion must be fulfilled twice in a row)
converged_means = max(
Rminus1, self.Rminus1_last) < self.Rminus1_stop
else:
mean_of_covs = None
success_means = None
converged_means = False
Rminus1 = None
success_means, converged_means = mpi.share(
(success_means, converged_means))
# Check the convergence of the bounds of the confidence intervals
# Same as R-1, but with the rms deviation from the mean bound
# in units of the mean standard deviation of the chains
if converged_means:
if more_than_one_process():
mcsamples = self.collection.sampled_to_getdist_mcsamples(
first=use_first)
try:
bound = np.array([[
mcsamples.confidence(i,
limfrac=self.Rminus1_cl_level /
2.,
upper=which)
for i in range(self.model.prior.d())
] for which in [False, True]]).T
success_bounds = True
except:
bound = None
success_bounds = False
bounds = np.array(mpi.gather(bound))
else:
try:
mcsamples_list = [
self.collection.sampled_to_getdist_mcsamples(
first=i * cut, last=(i + 1) * cut - 1)
for i in range(1, m)
]
except always_stop_exceptions:
raise
except:
self.log.info(
"Not enough points in chain to check c.l. convergence. "
"Waiting for next checkpoint.")
return
try:
bounds = [
np.array([[
mcs.confidence(i,
limfrac=self.Rminus1_cl_level / 2.,
upper=which)
for i in range(self.model.prior.d())
] for which in [False, True]]).T
for mcs in mcsamples_list
]
success_bounds = True
except:
bounds = None
success_bounds = False
if is_main_process():
if success_bounds:
Rminus1_cl = (np.std(bounds, axis=0).T /
np.sqrt(np.diag(mean_of_covs)))
self.log.debug(" - normalized std's of bounds = %r",
Rminus1_cl)
Rminus1_cl = np.max(Rminus1_cl)
self.progress.at[self.i_learn, "Rminus1_cl"] = Rminus1_cl
self.log.info(
" - Convergence of bounds: R-1 = %f after %d " %
(Rminus1_cl,
(sum(Ns) if more_than_one_process() else self.n())) +
"accepted steps" +
(" = sum(%r)" %
list(Ns) if more_than_one_process() else ""))
if Rminus1_cl < self.Rminus1_cl_stop:
self.converged = True
self.log.info("The run has converged!")
self._Ns = Ns
else:
self.log.info(
"Computation of the bounds was not possible. "
"Waiting until the next converge check.")
# Broadcast and save the convergence status and the last R-1 of means
if success_means:
self.Rminus1_last, self.converged = mpi.share((
Rminus1, self.converged) if is_main_process() else None)
# Do we want to learn a better proposal pdf?
if self.learn_proposal and not self.converged:
good_Rminus1 = (self.learn_proposal_Rminus1_max >
self.Rminus1_last >
self.learn_proposal_Rminus1_min)
if not good_Rminus1:
self.mpi_info(
"Convergence less than requested for updates: "
"waiting until the next convergence check.")
return
mean_of_covs = mpi.share(mean_of_covs)
try:
self.proposer.set_covariance(mean_of_covs)
self.mpi_info(
" - Updated covariance matrix of proposal pdf.")
self.mpi_debug("%r", mean_of_covs)
except:
self.mpi_debug(
"Updating covariance matrix failed unexpectedly. "
"waiting until next covmat learning attempt.")
# Save checkpoint info
self.write_checkpoint()
def do_output(self, date_time):
self.collection.out_update()
msg = "Progress @ %s : " % date_time.strftime("%Y-%m-%d %H:%M:%S")
msg += "%d steps taken" % self.n_steps_raw
if self.burn_in_left and self.burn_in: # NB: burn_in_left = 1 even if no burn_in
msg += " -- still burning in, %d accepted steps left." % self.burn_in_left
else:
msg += ", and %d accepted." % self.n()
self.log.info(msg)
def write_checkpoint(self):
if is_main_process() and self.output:
checkpoint_filename = self.checkpoint_filename()
self.dump_covmat(self.proposer.get_covariance())
checkpoint_info = {
"sampler": {
self.get_name():
dict([
("converged", self.converged),
("Rminus1_last", self.Rminus1_last),
(
"burn_in",
(
self.burn_in.
value # initial: repeat burn-in if not finished
if not self.n() and self.burn_in_left else 0)
), # to avoid overweighting last point of prev. run
("mpi_size", get_mpi_size())
])
}
}
yaml_dump_file(checkpoint_filename,
checkpoint_info,
error_if_exists=False)
if not self.progress.empty:
with open(self.progress_filename(), "a",
encoding="utf-8") as progress_file:
fmts = {"N": lambda x: "{:9d}".format(x)}
# TODO: next one is ignored when added to the dict
# "acceptance_rate": lambda x: "{:15.8g}".format(x)}
progress_file.write(
self.progress.tail(1).to_string(
header=False, index=False, formatters=fmts) + "\n")
self.log.debug(
"Dumped checkpoint and progress info, and current covmat.")
def converge_info_changed(self, old_info, new_info):
converge_params = [
'Rminus1_stop', "Rminus1_cl_stop", "Rminus1_cl_level",
"max_samples"
]
return any(old_info.get(p) != new_info.get(p) for p in converge_params)
# Finally: returning the computed products ###########################################
def products(self):
"""
Auxiliary function to define what should be returned in a scripted call.
Returns:
The sample ``SampleCollection`` containing the accepted steps.
"""
products = {"sample": self.collection}
if is_main_process():
products["progress"] = self.progress
return products
# Class methods
@classmethod
def output_files_regexps(cls, output, info=None, minimal=False):
regexps = [output.collection_regexp(name=None)]
if minimal:
return [(r, None) for r in regexps]
regexps += [
re.compile(output.prefix_regexp_str + re.escape(ext.lstrip(".")) +
"$") for ext in
[Extension.checkpoint, Extension.progress, Extension.covmat]
]
return [(r, None) for r in regexps]
@classmethod
def get_version(cls):
return get_version()
@classmethod
def _get_desc(cls, info=None):
drag_string = r" using the fast-dragging procedure described in \cite{Neal:2005}"
if info is None:
# Unknown case (no info passed)
string = " [(if drag: True)%s]" % drag_string
else:
string = drag_string if info.get(
"drag",
cls.get_defaults()["drag"]) else ""
return (
"Adaptive, speed-hierarchy-aware MCMC sampler (adapted from CosmoMC) "
r"\cite{Lewis:2002ah,Lewis:2013hha}" + string + ".")
class Evaluate(Sampler):
file_base_name = 'evaluate'
override: Mapping[str, float]
N: int
def initialize(self):
"""
Creates a 1-point collection to store the point
at which the posterior is evaluated.
"""
try:
self.N = int(self.N)
except ValueError:
raise LoggedError(
self.log,
"Could not convert the number of samples to an integer: %r", self.N)
self.one_point = SampleCollection(self.model, self.output, name="1")
self.log.info("Initialized!")
def run(self):
"""
First gets a reference point. If a single reference point is not given,
the point is sampled from the reference pdf. If that one is not defined either,
the point is sampled from the prior.
Then it evaluates the prior and likelihood(s) and stores them in the one-member
sample collection.
"""
for i in range(self.N):
if self.N > 1:
self.log.info("Evaluating sample #%d ------------------------------",
i + 1)
self.log.info("Looking for a reference point with non-zero prior.")
reference_values = self.model.prior.reference(random_state=self._rng)
reference_point = dict(
zip(self.model.parameterization.sampled_params(), reference_values))
for p, v in (self.override or {}).items():
if p not in reference_point:
raise LoggedError(
self.log, "Parameter '%s' used in override not known. "
"Known parameters names are %r.",
p, self.model.parameterization.sampled_params())
reference_point[p] = v
self.log.info("Reference point:\n " + "\n ".join(
["%s = %g" % pv for pv in reference_point.items()]))
self.log.info("Evaluating prior and likelihoods...")
self.logposterior = self.model.logposterior(reference_point)
self.one_point.add(
list(reference_point.values()), derived=self.logposterior.derived,
logpost=self.logposterior.logpost, logpriors=self.logposterior.logpriors,
loglikes=self.logposterior.loglikes)
self.log.info("log-posterior = %g", self.logposterior.logpost)
self.log.info("log-prior = %g", self.logposterior.logprior)
for j, name in enumerate(self.model.prior):
self.log.info(
" logprior_" + name + " = %g", self.logposterior.logpriors[j])
if self.logposterior.logprior > -np.inf:
self.log.info("log-likelihood = %g", self.logposterior.loglike)
for j, name in enumerate(self.model.likelihood):
self.log.info(
" chi2_" + name + " = %g", (-2 * self.logposterior.loglikes[j]))
self.log.info("Derived params:")
for name, value in zip(self.model.parameterization.derived_params(),
self.logposterior.derived):
self.log.info(" " + name + " = %g", value)
else:
self.log.info("Likelihoods and derived parameters not computed, "
"since the prior is null.")
# Write the output: the point and its prior, posterior and likelihood.
self.one_point.out_update()
def products(self):
"""
Auxiliary function to define what should be returned in a scripted call.
Returns:
The sample ``SampleCollection`` containing the
sequentially discarded live points.
"""
return {"sample": self.one_point}