from __future__ import print_function
import math
import os.path as op
import os
import logging
import time
import numpy as num
from collections import OrderedDict
from pyrocko.guts import StringChoice, Int, Float, Object, List
from pyrocko.guts_array import Array
from grond.meta import GrondError, Forbidden, has_get_plot_classes
from grond.problems.base import ModelHistory
from grond.optimisers.base import Optimiser, OptimiserConfig, BadProblem, \
OptimiserStatus
guts_prefix = 'grond'
logger = logging.getLogger('grond.optimisers.highscore.optimiser')
def nextpow2(i):
return 2**int(math.ceil(math.log(i)/math.log(2.)))
def excentricity_compensated_probabilities(xs, sbx, factor):
inonflat = num.where(sbx != 0.0)[0]
scale = num.zeros_like(sbx)
scale[inonflat] = 1.0 / (sbx[inonflat] * (factor if factor != 0. else 1.0))
distances_sqr_all = num.sum(
((xs[num.newaxis, :, :] - xs[:, num.newaxis, :]) *
scale[num.newaxis, num.newaxis, :])**2, axis=2)
probabilities = 1.0 / num.sum(distances_sqr_all < 1.0, axis=1)
# print(num.sort(num.sum(distances_sqr_all < 1.0, axis=1)))
probabilities /= num.sum(probabilities)
return probabilities
def excentricity_compensated_choice(xs, sbx, factor, rstate):
probabilities = excentricity_compensated_probabilities(
xs, sbx, factor)
r = rstate.random_sample()
ichoice = num.searchsorted(num.cumsum(probabilities), r)
ichoice = min(ichoice, xs.shape[0]-1)
return ichoice
def local_std(xs):
ssbx = num.sort(xs, axis=0)
dssbx = num.diff(ssbx, axis=0)
mdssbx = num.median(dssbx, axis=0)
return mdssbx * dssbx.shape[0] / 2.6
[docs]class SamplerDistributionChoice(StringChoice):
choices = ['multivariate_normal', 'normal']
[docs]class StandardDeviationEstimatorChoice(StringChoice):
choices = [
'median_density_single_chain',
'standard_deviation_all_chains',
'standard_deviation_single_chain']
class SamplerStartingPointChoice(StringChoice):
choices = ['excentricity_compensated', 'random', 'mean']
class BootstrapTypeChoice(StringChoice):
choices = ['bayesian', 'classic']
def fnone(i):
return i if i is not None else -1
class Sample(Object):
'''Sample model with context about how it was generated.'''
model = Array.T(shape=(None,), dtype=num.float, serialize_as='list')
iphase = Int.T(optional=True)
ichain_base = Int.T(optional=True)
ilink_base = Int.T(optional=True)
imodel_base = Int.T(optional=True)
def preconstrain(self, problem):
self.model = problem.preconstrain(self.model)
def pack_context(self):
i = num.zeros(4, dtype=num.int)
i[:] = (
fnone(self.iphase),
fnone(self.ichain_base),
fnone(self.ilink_base),
fnone(self.imodel_base))
return i
[docs]class SamplerPhase(Object):
niterations = Int.T(
help='Number of iteration for this phase.')
ntries_preconstrain_limit = Int.T(
default=1000,
help='Tries to find a valid preconstrained sample.')
seed = Int.T(
optional=True,
help='Random state seed.')
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
self._rstate = None
def get_rstate(self):
if self._rstate is None:
self._rstate = num.random.RandomState(self.seed)
return self._rstate
def get_raw_sample(self, problem, iiter, chains):
raise NotImplementedError
def get_sample(self, problem, iiter, chains):
assert 0 <= iiter < self.niterations
ntries_preconstrain = 0
for ntries_preconstrain in range(self.ntries_preconstrain_limit):
try:
sample = self.get_raw_sample(problem, iiter, chains)
sample.preconstrain(problem)
return sample
except Forbidden:
pass
raise GrondError(
'could not find any suitable candidate sample within %i tries' % (
self.ntries_preconstrain_limit))
[docs]class InjectionSamplerPhase(SamplerPhase):
xs_inject = Array.T(
dtype=num.float, shape=(None, None),
help='Array with the reference model.')
def get_raw_sample(self, problem, iiter, chains):
return Sample(model=self.xs_inject[iiter, :])
[docs]class DirectedSamplerPhase(SamplerPhase):
scatter_scale = Float.T(
optional=True,
help='Scales search radius around the current `highscore` models')
scatter_scale_begin = Float.T(
optional=True,
help='Scaling factor at beginning of the phase.')
scatter_scale_end = Float.T(
optional=True,
help='Scaling factor at the end of the directed phase.')
starting_point = SamplerStartingPointChoice.T(
default='excentricity_compensated',
help='Tunes to the center value of the sampler distribution.'
'May increase the likelihood to draw a highscore member model'
' off-center to the mean value')
sampler_distribution = SamplerDistributionChoice.T(
default='normal',
help='Distribution new models are drawn from.')
standard_deviation_estimator = StandardDeviationEstimatorChoice.T(
default='median_density_single_chain')
ntries_sample_limit = Int.T(default=1000)
def get_scatter_scale_factor(self, iiter):
s = self.scatter_scale
sa = self.scatter_scale_begin
sb = self.scatter_scale_end
assert s is None or (sa is None and sb is None)
if sa != sb:
tb = float(self.niterations-1)
tau = tb/(math.log(sa) - math.log(sb))
t0 = math.log(sa) * tau
t = float(iiter)
return num.exp(-(t-t0) / tau)
else:
return s or 1.0
def get_raw_sample(self, problem, iiter, chains):
rstate = self.get_rstate()
factor = self.get_scatter_scale_factor(iiter)
npar = problem.nparameters
pnames = problem.parameter_names
xbounds = problem.get_parameter_bounds()
ilink_choice = None
ichain_choice = num.argmin(chains.accept_sum)
if self.starting_point == 'excentricity_compensated':
models = chains.models(ichain_choice)
ilink_choice = excentricity_compensated_choice(
models,
chains.standard_deviation_models(
ichain_choice, self.standard_deviation_estimator),
2., rstate)
xchoice = chains.model(ichain_choice, ilink_choice)
elif self.starting_point == 'random':
ilink_choice = rstate.randint(0, chains.nlinks)
xchoice = chains.model(ichain_choice, ilink_choice)
elif self.starting_point == 'mean':
xchoice = chains.mean_model(ichain_choice)
else:
assert False, 'invalid starting_point choice: %s' % (
self.starting_point)
ntries_sample = 0
if self.sampler_distribution == 'normal':
x = num.zeros(npar, dtype=num.float)
sx = chains.standard_deviation_models(
ichain_choice, self.standard_deviation_estimator)
for ipar in range(npar):
ntries = 0
while True:
if sx[ipar] > 0.:
v = rstate.normal(
xchoice[ipar],
factor*sx[ipar])
else:
v = xchoice[ipar]
if xbounds[ipar, 0] <= v and \
v <= xbounds[ipar, 1]:
break
if ntries > self.ntries_sample_limit:
logger.warning(
'failed to produce a suitable '
'candidate sample from normal '
'distribution for parameter \'%s\''
'- drawing from uniform instead.' %
pnames[ipar])
v = rstate.uniform(xbounds[ipar, 0],
xbounds[ipar, 1])
break
ntries += 1
x[ipar] = v
elif self.sampler_distribution == 'multivariate_normal':
ok_mask_sum = num.zeros(npar, dtype=num.int)
while True:
ntries_sample += 1
xcandi = rstate.multivariate_normal(
xchoice, factor**2 * chains.cov(ichain_choice))
ok_mask = num.logical_and(
xbounds[:, 0] <= xcandi, xcandi <= xbounds[:, 1])
if num.all(ok_mask):
break
ok_mask_sum += ok_mask
if ntries_sample > self.ntries_sample_limit:
logger.warning(
'failed to produce a suitable candidate '
'sample from multivariate normal '
'distribution, (%s) - drawing from uniform instead' %
', '.join('%s:%i' % xx for xx in
zip(pnames, ok_mask_sum)))
xbounds = problem.get_parameter_bounds()
xcandi = problem.random_uniform(xbounds, rstate)
break
x = xcandi
imodel_base = None
if ilink_choice is not None:
imodel_base = chains.imodel(ichain_choice, ilink_choice)
return Sample(
model=x,
ichain_base=ichain_choice,
ilink_base=ilink_choice,
imodel_base=imodel_base)
def make_bayesian_weights(nbootstrap, nmisfits,
type='bayesian', rstate=None):
ws = num.zeros((nbootstrap, nmisfits))
if rstate is None:
rstate = num.random.RandomState()
for ibootstrap in range(nbootstrap):
if type == 'classic':
ii = rstate.randint(0, nmisfits, size=nmisfits)
ws[ibootstrap, :] = num.histogram(
ii, nmisfits, (-0.5, nmisfits - 0.5))[0]
elif type == 'bayesian':
f = rstate.uniform(0., 1., size=nmisfits+1)
f[0] = 0.
f[-1] = 1.
f = num.sort(f)
g = f[1:] - f[:-1]
ws[ibootstrap, :] = g * nmisfits
else:
assert False
return ws
class Chains(object):
def __init__(
self, problem, history, nchains, nlinks_cap):
self.problem = problem
self.history = history
self.nchains = nchains
self.nlinks_cap = nlinks_cap
self.chains_m = num.zeros(
(self.nchains, nlinks_cap), dtype=num.float)
self.chains_i = num.zeros(
(self.nchains, nlinks_cap), dtype=num.int)
self.nlinks = 0
self.nread = 0
self.accept_sum = num.zeros(self.nchains, dtype=num.int)
self._acceptance_history = num.zeros(
(self.nchains, 1024), dtype=num.bool)
history.add_listener(self)
def goto(self, n=None):
if n is None:
n = self.history.nmodels
n = min(self.history.nmodels, n)
assert self.nread <= n
while self.nread < n:
nread = self.nread
gbms = self.history.bootstrap_misfits[nread, :]
self.chains_m[:, self.nlinks] = gbms
self.chains_i[:, self.nlinks] = nread
nbootstrap = self.chains_m.shape[0]
self.nlinks += 1
chains_m = self.chains_m
chains_i = self.chains_i
for ichain in range(nbootstrap):
isort = num.argsort(chains_m[ichain, :self.nlinks])
chains_m[ichain, :self.nlinks] = chains_m[ichain, isort]
chains_i[ichain, :self.nlinks] = chains_i[ichain, isort]
if self.nlinks == self.nlinks_cap:
accept = (chains_i[:, self.nlinks_cap-1] != nread) \
.astype(num.bool)
self.nlinks -= 1
else:
accept = num.ones(self.nchains, dtype=num.bool)
self._append_acceptance(accept)
self.accept_sum += accept
self.nread += 1
def load(self):
return self.goto()
def extend(self, ioffset, n, models, misfits, sampler_contexts):
self.goto(ioffset + n)
def indices(self, ichain):
if ichain is not None:
return self.chains_i[ichain, :self.nlinks]
else:
return self.chains_i[:, :self.nlinks].ravel()
def models(self, ichain=None):
return self.history.models[self.indices(ichain), :]
def model(self, ichain, ilink):
return self.history.models[self.chains_i[ichain, ilink], :]
def imodel(self, ichain, ilink):
return self.chains_i[ichain, ilink]
def misfits(self, ichain=0):
return self.chains_m[ichain, :self.nlinks]
def misfit(self, ichain, ilink):
assert ilink < self.nlinks
return self.chains_m[ichain, ilink]
def mean_model(self, ichain=None):
xs = self.models(ichain)
return num.mean(xs, axis=0)
def best_model(self, ichain=0):
xs = self.models(ichain)
return xs[0]
def best_model_misfit(self, ichain=0):
return self.chains_m[ichain, 0]
def standard_deviation_models(self, ichain, estimator):
if estimator == 'median_density_single_chain':
xs = self.models(ichain)
return local_std(xs)
elif estimator == 'standard_deviation_all_chains':
bxs = self.models()
return num.std(bxs, axis=0)
elif estimator == 'standard_deviation_single_chain':
xs = self.models(ichain)
return num.std(xs, axis=0)
else:
assert False, 'invalid standard_deviation_estimator choice'
def covariance_models(self, ichain):
xs = self.models(ichain)
return num.cov(xs.T)
@property
def acceptance_history(self):
return self._acceptance_history[:, :self.nread]
def _append_acceptance(self, acceptance):
if self.nread >= self._acceptance_history.shape[1]:
new_buf = num.zeros(
(self.nchains, nextpow2(self.nread+1)), dtype=num.bool)
new_buf[:, :self._acceptance_history.shape[1]] = \
self._acceptance_history
self._acceptance_history = new_buf
self._acceptance_history[:, self.nread] = acceptance
[docs]@has_get_plot_classes
class HighScoreOptimiser(Optimiser):
'''Monte-Carlo-based directed search optimisation with bootstrap.'''
sampler_phases = List.T(SamplerPhase.T())
chain_length_factor = Float.T(default=8.)
nbootstrap = Int.T(default=100)
bootstrap_type = BootstrapTypeChoice.T(default='bayesian')
bootstrap_seed = Int.T(default=23)
SPARKS = u'\u2581\u2582\u2583\u2584\u2585\u2586\u2587\u2588'
ACCEPTANCE_AVG_LEN = 100
def __init__(self, **kwargs):
Optimiser.__init__(self, **kwargs)
self._bootstrap_weights = None
self._bootstrap_residuals = None
self._correlated_weights = None
self._status_chains = None
self._rstate_bootstrap = None
def get_rstate_bootstrap(self):
if self._rstate_bootstrap is None:
self._rstate_bootstrap = num.random.RandomState(
self.bootstrap_seed)
return self._rstate_bootstrap
def init_bootstraps(self, problem):
self.init_bootstrap_weights(problem)
self.init_bootstrap_residuals(problem)
def init_bootstrap_weights(self, problem):
logger.info('Initializing Bayesian bootstrap weights.')
nmisfits_w = sum(
t.nmisfits for t in problem.targets if t.can_bootstrap_weights)
ws = make_bayesian_weights(
self.nbootstrap,
nmisfits=nmisfits_w,
rstate=self.get_rstate_bootstrap())
imf = 0
for t in problem.targets:
if t.can_bootstrap_weights:
t.set_bootstrap_weights(ws[:, imf:imf+t.nmisfits])
imf += t.nmisfits
else:
t.set_bootstrap_weights(
num.ones((self.nbootstrap, t.nmisfits)))
def init_bootstrap_residuals(self, problem):
logger.info('Initializing Bayesian bootstrap residuals.')
for t in problem.targets:
if t.can_bootstrap_residuals:
t.init_bootstrap_residuals(
self.nbootstrap, rstate=self.get_rstate_bootstrap(),
nthreads=self._nthreads)
else:
t.set_bootstrap_residuals(
num.zeros((self.nbootstrap, t.nmisfits)))
def get_bootstrap_weights(self, problem):
if self._bootstrap_weights is None:
try:
problem.targets[0].get_bootstrap_weights()
except Exception:
self.init_bootstraps(problem)
bootstrap_weights = num.hstack(
[t.get_bootstrap_weights()
for t in problem.targets])
self._bootstrap_weights = num.vstack((
num.ones((1, problem.nmisfits)),
bootstrap_weights))
return self._bootstrap_weights
def get_bootstrap_residuals(self, problem):
if self._bootstrap_residuals is None:
try:
problem.targets[0].get_bootstrap_residuals()
except Exception:
self.init_bootstraps(problem)
bootstrap_residuals = num.hstack(
[t.get_bootstrap_residuals()
for t in problem.targets])
self._bootstrap_residuals = num.vstack((
num.zeros((1, problem.nmisfits)),
bootstrap_residuals))
return self._bootstrap_residuals
def get_correlated_weights(self, problem):
if self._correlated_weights is None:
corr = dict()
misfit_idx = num.cumsum(
[0.] + [t.nmisfits for t in problem.targets], dtype=num.int)
for it, target in enumerate(problem.targets):
weights = target.get_correlated_weights(
nthreads=self._nthreads)
if weights is None:
continue
corr[misfit_idx[it]] = weights
self._correlated_weights = corr
return self._correlated_weights
@property
def nchains(self):
return self.nbootstrap + 1
def chains(self, problem, history):
nlinks_cap = int(round(
self.chain_length_factor * problem.nparameters + 1))
return Chains(
problem, history,
nchains=self.nchains, nlinks_cap=nlinks_cap)
def get_sampler_phase(self, iiter):
niter = 0
for iphase, phase in enumerate(self.sampler_phases):
if iiter < niter + phase.niterations:
return iphase, phase, iiter - niter
niter += phase.niterations
assert False, 'sample out of bounds'
def log_progress(self, problem, iiter, niter, phase, iiter_phase):
t = time.time()
if self._tlog_last < t - 10. \
or iiter_phase == 0 \
or iiter_phase == phase.niterations - 1:
logger.info(
'%s at %i/%i (%s, %i/%i)' % (
problem.name,
iiter+1, niter,
phase.__class__.__name__, iiter_phase, phase.niterations))
self._tlog_last = t
def optimise(self, problem, rundir=None):
if rundir is not None:
self.dump(filename=op.join(rundir, 'optimiser.yaml'))
history = ModelHistory(problem,
nchains=self.nchains,
path=rundir, mode='w')
chains = self.chains(problem, history)
niter = self.niterations
isbad_mask = None
self._tlog_last = 0
for iiter in range(niter):
iphase, phase, iiter_phase = self.get_sampler_phase(iiter)
self.log_progress(problem, iiter, niter, phase, iiter_phase)
sample = phase.get_sample(problem, iiter_phase, chains)
sample.iphase = iphase
if isbad_mask is not None and num.any(isbad_mask):
isok_mask = num.logical_not(isbad_mask)
else:
isok_mask = None
misfits = problem.misfits(
sample.model, mask=isok_mask, nthreads=self._nthreads)
bootstrap_misfits = problem.combine_misfits(
misfits,
extra_weights=self.get_bootstrap_weights(problem),
extra_residuals=self.get_bootstrap_residuals(problem),
extra_correlated_weights=self.get_correlated_weights(problem))
isbad_mask_new = num.isnan(misfits[:, 0])
if isbad_mask is not None and num.any(
isbad_mask != isbad_mask_new):
errmess = [
'problem %s: inconsistency in data availability'
' at iteration %i' %
(problem.name, iiter)]
for target, isbad_new, isbad in zip(
problem.targets, isbad_mask_new, isbad_mask):
if isbad_new != isbad:
errmess.append(' %s, %s -> %s' % (
target.string_id(), isbad, isbad_new))
raise BadProblem('\n'.join(errmess))
isbad_mask = isbad_mask_new
if num.all(isbad_mask):
raise BadProblem(
'Problem %s: all target misfit values are NaN.'
% problem.name)
history.append(
sample.model, misfits,
bootstrap_misfits,
sample.pack_context())
@property
def niterations(self):
return sum([ph.niterations for ph in self.sampler_phases])
def get_status(self, history):
if self._status_chains is None:
self._status_chains = self.chains(history.problem, history)
self._status_chains.goto(history.nmodels)
chains = self._status_chains
problem = history.problem
row_names = [p.name_nogroups for p in problem.parameters]
row_names.append('Misfit')
def colum_array(data):
arr = num.full(len(row_names), fill_value=num.nan)
arr[:data.size] = data
return arr
phase = self.get_sampler_phase(history.nmodels-1)[1]
bs_mean = colum_array(chains.mean_model(ichain=None))
bs_std = colum_array(chains.standard_deviation_models(
ichain=None, estimator='standard_deviation_all_chains'))
glob_mean = colum_array(chains.mean_model(ichain=0))
glob_mean[-1] = num.mean(chains.misfits(ichain=0))
glob_std = colum_array(chains.standard_deviation_models(
ichain=0, estimator='standard_deviation_single_chain'))
glob_std[-1] = num.std(chains.misfits(ichain=0))
glob_best = colum_array(chains.best_model(ichain=0))
glob_best[-1] = chains.best_model_misfit()
glob_misfits = chains.misfits(ichain=0)
acceptance_latest = chains.acceptance_history[
:, -min(chains.acceptance_history.shape[1], self.ACCEPTANCE_AVG_LEN):] # noqa
acceptance_avg = acceptance_latest.mean(axis=1)
def spark_plot(data, bins):
hist, _ = num.histogram(data, bins)
hist_max = num.max(hist)
if hist_max == 0.0:
hist_max = 1.0
hist = hist / hist_max
vec = num.digitize(hist, num.linspace(0., 1., len(self.SPARKS)))
return ''.join([self.SPARKS[b-1] for b in vec])
return OptimiserStatus(
row_names=row_names,
column_data=OrderedDict(
zip(['BS mean', 'BS std',
'Glob mean', 'Glob std', 'Glob best'],
[bs_mean, bs_std, glob_mean, glob_std, glob_best])),
extra_header= # noqa
u'Optimiser phase: {phase}, exploring {nchains} BS chains\n' # noqa
u'Global chain misfit distribution: \u2080{mf_dist}\xb9\n'
u'Acceptance rate distribution: \u2080{acceptance}'
u'\u2081\u2080\u2080\ufe6a (Median {acceptance_med:.1f}%)'
.format(
phase=phase.__class__.__name__,
nchains=chains.nchains,
mf_dist=spark_plot(
glob_misfits, num.linspace(0., 1., 25)),
acceptance=spark_plot(
acceptance_avg,
num.linspace(0., 1., 25)),
acceptance_med=num.median(acceptance_avg) * 100.
))
def get_movie_maker(
self, problem, history, xpar_name, ypar_name, movie_filename):
from . import plot
return plot.HighScoreOptimiserPlot(
self, problem, history, xpar_name, ypar_name, movie_filename)
@classmethod
def get_plot_classes(cls):
from .plot import HighScoreAcceptancePlot
plots = Optimiser.get_plot_classes()
plots.append(HighScoreAcceptancePlot)
return plots
[docs]class HighScoreOptimiserConfig(OptimiserConfig):
sampler_phases = List.T(
SamplerPhase.T(),
default=[UniformSamplerPhase(niterations=1000),
DirectedSamplerPhase(niterations=5000)],
help='Stages of the sampler: Start with uniform sampling of the model'
' model space and narrow down through directed sampling.')
chain_length_factor = Float.T(
default=8.,
help='Controls the length of each chain: '
'chain_length_factor * nparameters + 1')
nbootstrap = Int.T(
default=100,
help='Number of bootstrap realisations to be tracked simultaneously in'
' the optimisation.')
def get_optimiser(self):
return HighScoreOptimiser(
sampler_phases=list(self.sampler_phases),
chain_length_factor=self.chain_length_factor,
nbootstrap=self.nbootstrap)
def load_optimiser_history(dirname, problem):
fn = op.join(dirname, 'accepted')
with open(fn, 'r') as f:
nmodels = os.fstat(f.fileno()).st_size // (problem.nbootstrap+1)
data1 = num.fromfile(
f,
dtype='<i1',
count=nmodels*(problem.nbootstrap+1)).astype(num.bool)
accepted = data1.reshape((nmodels, problem.nbootstrap+1))
fn = op.join(dirname, 'choices')
with open(fn, 'r') as f:
data2 = num.fromfile(
f,
dtype='<i8',
count=nmodels*2).astype(num.int64)
ibootstrap_choices, imodel_choices = data2.reshape((nmodels, 2)).T
return ibootstrap_choices, imodel_choices, accepted
__all__ = '''
SamplerDistributionChoice
StandardDeviationEstimatorChoice
SamplerPhase
InjectionSamplerPhase
UniformSamplerPhase
DirectedSamplerPhase
Chains
HighScoreOptimiserConfig
HighScoreOptimiser
'''.split()
