'''
Base classes for Grond's problem definition and the model history container.
Common behaviour of all source models offered by Grond is implemented here.
Source model specific details are implemented in the respective submodules.
'''
import numpy as num
import math
import copy
import logging
import os.path as op
import os
import time
from pyrocko import gf, util, guts
from pyrocko.guts import Object, String, List, Dict, Int
from grond.meta import ADict, Parameter, GrondError, xjoin, Forbidden, \
StringID, has_get_plot_classes
from ..targets import MisfitResult, MisfitTarget, TargetGroup, \
WaveformMisfitTarget, SatelliteMisfitTarget, GNSSCampaignMisfitTarget
from grond import stats
from grond.version import __version__
guts_prefix = 'grond'
logger = logging.getLogger('grond.problems.base')
km = 1e3
as_km = dict(scale_factor=km, scale_unit='km')
g_rstate = num.random.RandomState()
def nextpow2(i):
return 2**int(math.ceil(math.log(i)/math.log(2.)))
def correlated_weights(values, weight_matrix):
'''
Applies correlated weights to values
The resulting weighed values have to be squared! Check out
:meth:`Problem.combine_misfits` for more information.
:param values: Misfits or norms as :class:`numpy.Array`
:param weight: Weight matrix, commonly the inverse of covariance matrix
:returns: :class:`numpy.Array` weighted values
'''
return num.matmul(values, weight_matrix)
[docs]
class ProblemConfig(Object):
'''
Base class for config section defining the objective function setup.
Factory for :py:class:`Problem` objects.
'''
name_template = String.T()
norm_exponent = Int.T(default=2)
nthreads = Int.T(
default=1,
optional=True,
help='Deprecated: use command line argument or global config to set '
'number of allowed threads.')
def check_deprecations(self):
if self.nthreads != 1:
logger.warn(
'The `nthreads` parameter in `ProblemConfig` has been '
'deprecated and is ignored now. Please use the `--nthreads` '
'command line option to set it.')
[docs]
def get_problem(self, event, target_groups, targets):
'''
Instantiate the problem with a given event and targets.
:returns: :py:class:`Problem` object
'''
self.check_deprecations()
raise NotImplementedError
[docs]
@has_get_plot_classes
class Problem(Object):
'''
Base class for objective function setup.
Defines the *problem* to be solved by the optimiser.
'''
name = String.T()
ranges = Dict.T(String.T(), gf.Range.T())
dependants = List.T(Parameter.T())
norm_exponent = Int.T(default=2)
base_source = gf.Source.T(optional=True)
targets = List.T(MisfitTarget.T())
target_groups = List.T(TargetGroup.T())
grond_version = String.T(optional=True)
nthreads = Int.T(
default=1,
optional=True,
help='Deprecated: use command line argument or global config to set '
'number of allowed threads.')
def __init__(self, **kwargs):
Object.__init__(self, **kwargs)
if self.grond_version is None:
self.grond_version = __version__
self._target_weights = None
self._engine = None
self._family_mask = None
if hasattr(self, 'problem_waveform_parameters') and self.has_waveforms:
self.problem_parameters =\
self.problem_parameters + self.problem_waveform_parameters
unused_parameters = []
for p in self.problem_parameters:
if p.optional and p._name not in self.ranges.keys():
unused_parameters.append(p)
for p in unused_parameters:
self.problem_parameters.remove(p)
self.check()
@classmethod
def get_plot_classes(cls):
from . import plot
return plot.get_plot_classes()
def check(self):
paths = set()
for grp in self.target_groups:
if grp.path == 'all':
continue
if grp.path in paths:
raise ValueError('Path %s defined more than once! In %s'
% (grp.path, grp.__class__.__name__))
paths.add(grp.path)
logger.debug('TargetGroup check OK.')
def copy(self):
o = copy.copy(self)
o._target_weights = None
return o
def set_target_parameter_values(self, x):
nprob = len(self.problem_parameters)
for target in self.targets:
target.set_parameter_values(x[nprob:nprob+target.nparameters])
nprob += target.nparameters
def get_parameter_dict(self, model, group=None):
params = []
for ip, p in enumerate(self.parameters):
if group in p.groups or group is None:
params.append((p.name, model[ip]))
return ADict(params)
def get_parameter_array(self, d):
arr = num.zeros(self.nparameters, dtype=float)
for ip, p in enumerate(self.parameters):
if p.name in d.keys():
arr[ip] = d[p.name]
return arr
def dump_problem_info(self, dirname):
fn = op.join(dirname, 'problem.yaml')
util.ensuredirs(fn)
guts.dump(self, filename=fn)
def dump_problem_data(
self, dirname, x, misfits, chains=None,
sampler_context=None):
fn = op.join(dirname, 'models')
if not isinstance(x, num.ndarray):
x = num.array(x)
with open(fn, 'ab') as f:
x.astype('<f8').tofile(f)
fn = op.join(dirname, 'misfits')
with open(fn, 'ab') as f:
misfits.astype('<f8').tofile(f)
if chains is not None:
fn = op.join(dirname, 'chains')
with open(fn, 'ab') as f:
chains.astype('<f8').tofile(f)
if sampler_context is not None:
fn = op.join(dirname, 'choices')
with open(fn, 'ab') as f:
num.array(sampler_context, dtype='<i8').tofile(f)
def name_to_index(self, name):
pnames = [p.name for p in self.combined]
return pnames.index(name)
@property
def parameters(self):
target_parameters = []
for target in self.targets:
target_parameters.extend(target.target_parameters)
return self.problem_parameters + target_parameters
@property
def parameter_names(self):
return [p.name for p in self.combined]
@property
def dependant_names(self):
return [p.name for p in self.dependants]
@property
def nparameters(self):
return len(self.parameters)
@property
def ntargets(self):
return len(self.targets)
@property
def nwaveform_targets(self):
return len(self.waveform_targets)
@property
def nsatellite_targets(self):
return len(self.satellite_targets)
@property
def ngnss_targets(self):
return len(self.gnss_targets)
@property
def nmisfits(self):
nmisfits = 0
for target in self.targets:
nmisfits += target.nmisfits
return nmisfits
@property
def ndependants(self):
return len(self.dependants)
@property
def ncombined(self):
return len(self.parameters) + len(self.dependants)
@property
def combined(self):
return self.parameters + self.dependants
@property
def satellite_targets(self):
return [t for t in self.targets
if isinstance(t, SatelliteMisfitTarget)]
@property
def gnss_targets(self):
return [t for t in self.targets
if isinstance(t, GNSSCampaignMisfitTarget)]
@property
def waveform_targets(self):
return [t for t in self.targets
if isinstance(t, WaveformMisfitTarget)]
@property
def has_satellite(self):
if self.satellite_targets:
return True
return False
@property
def has_waveforms(self):
if self.waveform_targets:
return True
return False
def set_engine(self, engine):
self._engine = engine
def get_engine(self):
return self._engine
def get_gf_store(self, target):
if self.get_engine() is None:
raise GrondError('Cannot get GF Store, modelling is not set up.')
return self.get_engine().get_store(target.store_id)
def random_uniform(self, xbounds, rstate, fixed_magnitude=None):
if fixed_magnitude is not None:
raise GrondError(
'Setting fixed magnitude in random model generation not '
'supported for this type of problem.')
x = rstate.uniform(0., 1., self.nparameters)
x *= (xbounds[:, 1] - xbounds[:, 0])
x += xbounds[:, 0]
return x
def preconstrain(self, x):
return x
def extract(self, xs, i):
if xs.ndim == 1:
return self.extract(xs[num.newaxis, :], i)[0]
if i < self.nparameters:
return xs[:, i]
else:
return self.make_dependant(
xs, self.dependants[i-self.nparameters].name)
def get_target_weights(self):
if self._target_weights is None:
self._target_weights = num.concatenate(
[target.get_combined_weight() for target in self.targets])
return self._target_weights
def get_target_residuals(self):
pass
def inter_family_weights(self, ns):
exp, root = self.get_norm_functions()
family, nfamilies = self.get_family_mask()
ws = num.zeros(self.nmisfits)
for ifamily in range(nfamilies):
mask = family == ifamily
ws[mask] = 1.0 / root(num.nansum(exp(ns[mask])))
return ws
[docs]
def inter_family_weights2(self, ns):
'''
:param ns: 2D array with normalization factors ``ns[imodel, itarget]``
:returns: 2D array ``weights[imodel, itarget]``
'''
exp, root = self.get_norm_functions()
family, nfamilies = self.get_family_mask()
ws = num.zeros(ns.shape)
for ifamily in range(nfamilies):
mask = family == ifamily
ws[:, mask] = (1.0 / root(
num.nansum(exp(ns[:, mask]), axis=1)))[:, num.newaxis]
return ws
def get_reference_model(self):
model = num.zeros(self.nparameters)
model_source_params = self.pack(self.base_source)
model[:model_source_params.size] = model_source_params
return model
def get_parameter_bounds(self):
out = []
for p in self.problem_parameters:
r = self.ranges[p.name]
out.append((r.start, r.stop))
for target in self.targets:
for p in target.target_parameters:
r = target.target_ranges[p.name_nogroups]
out.append((r.start, r.stop))
return num.array(out, dtype=float)
def get_dependant_bounds(self):
return num.zeros((0, 2))
def get_combined_bounds(self):
return num.vstack((
self.get_parameter_bounds(),
self.get_dependant_bounds()))
def raise_invalid_norm_exponent(self):
raise GrondError('Invalid norm exponent: %f' % self.norm_exponent)
def get_norm_functions(self):
if self.norm_exponent == 2:
def sqr(x):
return x**2
return sqr, num.sqrt
elif self.norm_exponent == 1:
def noop(x):
return x
return noop, num.abs
else:
self.raise_invalid_norm_exponent()
[docs]
def combine_misfits(
self, misfits,
extra_weights=None,
extra_residuals=None,
extra_correlated_weights=dict(),
get_contributions=False):
'''
Combine misfit contributions (residuals) to global or bootstrap misfits
:param misfits: 3D array ``misfits[imodel, iresidual, 0]`` are the
misfit contributions (residuals) ``misfits[imodel, iresidual, 1]``
are the normalisation contributions. It is also possible to give
the misfit and normalisation contributions for a single model as
``misfits[iresidual, 0]`` and misfits[iresidual, 1]`` in which
case, the first dimension (imodel) of the result will be stipped
off.
:param extra_weights: if given, 2D array of extra weights to be applied
to the contributions, indexed as
``extra_weights[ibootstrap, iresidual]``.
:param extra_residuals: if given, 2D array of perturbations to be added
to the residuals, indexed as
``extra_residuals[ibootstrap, iresidual]``.
:param extra_correlated_weights: if a dictionary of
``imisfit: correlated weight matrix`` is passed a correlated
weight matrix is applied to the misfit and normalisation values.
`imisfit` is the starting index in the misfits vector the
correlated weight matrix applies to.
:param get_contributions: get the weighted and perturbed contributions
(don't do the sum).
:returns: if no *extra_weights* or *extra_residuals* are given, a 1D
array indexed as ``misfits[imodel]`` containing the global misfit
for each model is returned, otherwise a 2D array
``misfits[imodel, ibootstrap]`` with the misfit for every model and
weighting/residual set is returned.
'''
if misfits.ndim == 2:
misfits = misfits[num.newaxis, :, :]
return self.combine_misfits(
misfits, extra_weights, extra_residuals,
extra_correlated_weights, get_contributions)[0, ...]
if extra_weights is None and extra_residuals is None:
return self.combine_misfits(
misfits, False, False,
extra_correlated_weights, get_contributions)[:, 0]
assert misfits.ndim == 3
assert not num.any(extra_weights) or extra_weights.ndim == 2
assert not num.any(extra_residuals) or extra_residuals.ndim == 2
if self.norm_exponent != 2 and extra_correlated_weights:
raise GrondError('Correlated weights can only be used '
' with norm_exponent=2')
exp, root = self.get_norm_functions()
nmodels = misfits.shape[0]
nmisfits = misfits.shape[1] # noqa
mf = misfits[:, num.newaxis, :, :].copy()
if num.any(extra_residuals):
mf = mf + extra_residuals[num.newaxis, :, :, num.newaxis]
res = mf[..., 0]
norms = mf[..., 1]
for imisfit, corr_weight_mat in extra_correlated_weights.items():
jmisfit = imisfit + corr_weight_mat.shape[0]
for imodel in range(nmodels):
corr_res = res[imodel, :, imisfit:jmisfit]
corr_norms = norms[imodel, :, imisfit:jmisfit]
res[imodel, :, imisfit:jmisfit] = \
correlated_weights(corr_res, corr_weight_mat)
norms[imodel, :, imisfit:jmisfit] = \
correlated_weights(corr_norms, corr_weight_mat)
# Apply normalization family weights (these weights depend on
# on just calculated correlated norms!)
weights_fam = \
self.inter_family_weights2(norms[:, 0, :])[:, num.newaxis, :]
weights_fam = exp(weights_fam)
res = exp(res)
norms = exp(norms)
res *= weights_fam
norms *= weights_fam
weights_tar = self.get_target_weights()[num.newaxis, num.newaxis, :]
if num.any(extra_weights):
weights_tar = weights_tar * extra_weights[num.newaxis, :, :]
weights_tar = exp(weights_tar)
res = res * weights_tar
norms = norms * weights_tar
if get_contributions:
return res / num.nansum(norms, axis=2)[:, :, num.newaxis]
result = root(
num.nansum(res, axis=2) /
num.nansum(norms, axis=2))
assert result[result < 0].size == 0
return result
def make_family_mask(self):
family_names = set()
families = num.zeros(self.nmisfits, dtype=int)
idx = 0
for itarget, target in enumerate(self.targets):
family_names.add(target.normalisation_family)
families[idx:idx + target.nmisfits] = len(family_names) - 1
idx += target.nmisfits
return families, len(family_names)
def get_family_mask(self):
if self._family_mask is None:
self._family_mask = self.make_family_mask()
return self._family_mask
def evaluate(self, x, mask=None, result_mode='full', targets=None):
source = self.get_source(x)
engine = self.get_engine()
self.set_target_parameter_values(x)
if mask is not None and targets is not None:
raise ValueError('Mask cannot be defined with targets set.')
targets = targets if targets is not None else self.targets
for target in targets:
target.set_result_mode(result_mode)
modelling_targets = []
t2m_map = {}
for itarget, target in enumerate(targets):
t2m_map[target] = target.prepare_modelling(engine, source, targets)
if mask is None or mask[itarget]:
modelling_targets.extend(t2m_map[target])
u2m_map = {}
for imtarget, mtarget in enumerate(modelling_targets):
if mtarget not in u2m_map:
u2m_map[mtarget] = []
u2m_map[mtarget].append(imtarget)
modelling_targets_unique = list(u2m_map.keys())
resp = engine.process(source, modelling_targets_unique)
modelling_results_unique = list(resp.results_list[0])
modelling_results = [None] * len(modelling_targets)
for mtarget, mresult in zip(
modelling_targets_unique, modelling_results_unique):
for itarget in u2m_map[mtarget]:
modelling_results[itarget] = mresult
imt = 0
results = []
for itarget, target in enumerate(targets):
nmt_this = len(t2m_map[target])
if mask is None or mask[itarget]:
result = target.finalize_modelling(
engine, source,
t2m_map[target],
modelling_results[imt:imt+nmt_this])
imt += nmt_this
else:
result = gf.SeismosizerError(
'target was excluded from modelling')
results.append(result)
return results
def misfits(self, x, mask=None):
results = self.evaluate(x, mask=mask, result_mode='sparse')
misfits = num.full((self.nmisfits, 2), num.nan)
imisfit = 0
for target, result in zip(self.targets, results):
if isinstance(result, MisfitResult):
misfits[imisfit:imisfit+target.nmisfits, :] = result.misfits
imisfit += target.nmisfits
return misfits
def forward(self, x):
source = self.get_source(x)
engine = self.get_engine()
plain_targets = []
for target in self.targets:
plain_targets.extend(target.get_plain_targets(engine, source))
resp = engine.process(source, plain_targets)
results = []
for target, result in zip(plain_targets, resp.results_list[0]):
if isinstance(result, gf.SeismosizerError):
logger.debug(
'%s.%s.%s.%s: %s' % (target.codes + (str(result),)))
else:
results.append(result)
return results
def get_random_model(self, ntries_limit=100):
xbounds = self.get_parameter_bounds()
for _ in range(ntries_limit):
x = self.random_uniform(xbounds, rstate=g_rstate)
try:
return self.preconstrain(x)
except Forbidden:
pass
raise GrondError(
'Could not find any suitable candidate sample within %i tries' % (
ntries_limit))
[docs]
class ProblemInfoNotAvailable(GrondError):
pass
[docs]
class ProblemDataNotAvailable(GrondError):
pass
[docs]
class NoSuchAttribute(GrondError):
pass
[docs]
class InvalidAttributeName(GrondError):
pass
[docs]
class ModelHistory(object):
'''
Write, read and follow sequences of models produced in an optimisation run.
:param problem: :class:`grond.Problem` instance
:param path: path to rundir, defaults to None
:type path: str, optional
:param mode: open mode, 'r': read, 'w': write
:type mode: str, optional
'''
nmodels_capacity_min = 1024
def __init__(self, problem, nchains=None, path=None, mode='r'):
self.mode = mode
self.problem = problem
self.path = path
self.nchains = nchains
self._models_buffer = None
self._misfits_buffer = None
self._bootstraps_buffer = None
self._sample_contexts_buffer = None
self._sorted_misfit_idx = {}
self.models = None
self.misfits = None
self.bootstrap_misfits = None
self.sampler_contexts = None
self.nmodels_capacity = self.nmodels_capacity_min
self.listeners = []
self._attributes = {}
if mode == 'r':
self.load()
@staticmethod
def verify_rundir(rundir):
_rundir_files = ('misfits', 'models')
if not op.exists(rundir):
raise ProblemDataNotAvailable(
'Directory does not exist: %s' % rundir)
for f in _rundir_files:
if not op.exists(op.join(rundir, f)):
raise ProblemDataNotAvailable('File not found: %s' % f)
[docs]
@classmethod
def follow(cls, path, nchains=None, wait=20.):
'''
Start following a rundir (constructor).
:param path: the path to follow, a grond rundir
:type path: str, optional
:param wait: wait time until the folder become alive
:type wait: number in seconds, optional
:returns: A :py:class:`ModelHistory` instance
'''
start_watch = time.time()
while (time.time() - start_watch) < wait:
try:
cls.verify_rundir(path)
problem = load_problem_info(path)
return cls(problem, nchains=nchains, path=path, mode='r')
except (ProblemDataNotAvailable, OSError):
time.sleep(.25)
@property
def nmodels(self):
if self.models is None:
return 0
else:
return self.models.shape[0]
@nmodels.setter
def nmodels(self, nmodels_new):
assert 0 <= nmodels_new <= self.nmodels
self.models = self._models_buffer[:nmodels_new, :]
self.misfits = self._misfits_buffer[:nmodels_new, :, :]
if self.nchains is not None:
self.bootstrap_misfits = self._bootstraps_buffer[:nmodels_new, :, :] # noqa
if self._sample_contexts_buffer is not None:
self.sampler_contexts = self._sample_contexts_buffer[:nmodels_new, :] # noqa
@property
def nmodels_capacity(self):
if self._models_buffer is None:
return 0
else:
return self._models_buffer.shape[0]
@nmodels_capacity.setter
def nmodels_capacity(self, nmodels_capacity_new):
if self.nmodels_capacity != nmodels_capacity_new:
models_buffer = num.zeros(
(nmodels_capacity_new, self.problem.nparameters),
dtype=float)
misfits_buffer = num.zeros(
(nmodels_capacity_new, self.problem.nmisfits, 2),
dtype=float)
sample_contexts_buffer = num.zeros(
(nmodels_capacity_new, 4),
dtype=int)
sample_contexts_buffer.fill(-1)
if self.nchains is not None:
bootstraps_buffer = num.zeros(
(nmodels_capacity_new, self.nchains),
dtype=float)
ncopy = min(self.nmodels, nmodels_capacity_new)
if self._models_buffer is not None:
models_buffer[:ncopy, :] = \
self._models_buffer[:ncopy, :]
misfits_buffer[:ncopy, :, :] = \
self._misfits_buffer[:ncopy, :, :]
sample_contexts_buffer[:ncopy, :] = \
self._sample_contexts_buffer[:ncopy, :]
self._models_buffer = models_buffer
self._misfits_buffer = misfits_buffer
self._sample_contexts_buffer = sample_contexts_buffer
if self.nchains is not None:
if self._bootstraps_buffer is not None:
bootstraps_buffer[:ncopy, :] = \
self._bootstraps_buffer[:ncopy, :]
self._bootstraps_buffer = bootstraps_buffer
def clear(self):
assert self.mode != 'r', 'History is read-only, cannot clear.'
self.nmodels = 0
self.nmodels_capacity = self.nmodels_capacity_min
def extend(
self, models, misfits,
bootstrap_misfits=None,
sampler_contexts=None):
nmodels = self.nmodels
n = models.shape[0]
nmodels_capacity_want = max(
self.nmodels_capacity_min, nextpow2(nmodels + n))
if nmodels_capacity_want != self.nmodels_capacity:
self.nmodels_capacity = nmodels_capacity_want
self._models_buffer[nmodels:nmodels+n, :] = models
self._misfits_buffer[nmodels:nmodels+n, :, :] = misfits
self.models = self._models_buffer[:nmodels+n, :]
self.misfits = self._misfits_buffer[:nmodels+n, :, :]
if bootstrap_misfits is not None:
self._bootstraps_buffer[nmodels:nmodels+n, :] = bootstrap_misfits
self.bootstrap_misfits = self._bootstraps_buffer[:nmodels+n, :]
if sampler_contexts is not None:
self._sample_contexts_buffer[nmodels:nmodels+n, :] \
= sampler_contexts
self.sampler_contexts = self._sample_contexts_buffer[:nmodels+n, :]
if self.path and self.mode == 'w':
for i in range(n):
self.problem.dump_problem_data(
self.path, models[i, :], misfits[i, :, :],
bootstrap_misfits[i, :]
if bootstrap_misfits is not None else None,
sampler_contexts[i, :]
if sampler_contexts is not None else None)
self._sorted_misfit_idx.clear()
self.emit('extend', nmodels, n, models, misfits, sampler_contexts)
def append(
self, model, misfits,
bootstrap_misfits=None,
sampler_context=None):
if bootstrap_misfits is not None:
bootstrap_misfits = bootstrap_misfits[num.newaxis, :]
if sampler_context is not None:
sampler_context = sampler_context[num.newaxis, :]
return self.extend(
model[num.newaxis, :], misfits[num.newaxis, :, :],
bootstrap_misfits, sampler_context)
def load(self):
self.mode = 'r'
self.verify_rundir(self.path)
models, misfits, bootstraps, sampler_contexts = load_problem_data(
self.path, self.problem, nchains=self.nchains)
self.extend(models, misfits, bootstraps, sampler_contexts)
[docs]
def update(self):
''' Update history from path '''
nmodels_available = get_nmodels(self.path, self.problem)
if self.nmodels == nmodels_available:
return
try:
new_models, new_misfits, new_bootstraps, new_sampler_contexts = \
load_problem_data(
self.path,
self.problem,
nmodels_skip=self.nmodels,
nchains=self.nchains)
except ValueError:
return
self.extend(
new_models,
new_misfits,
new_bootstraps,
new_sampler_contexts)
[docs]
def add_listener(self, listener):
''' Add a listener to the history
The listening class can implement the following methods:
* ``extend``
'''
self.listeners.append(listener)
def emit(self, event_name, *args, **kwargs):
for listener in self.listeners:
slot = getattr(listener, event_name, None)
if callable(slot):
slot(*args, **kwargs)
@property
def attribute_names(self):
apath = op.join(self.path, 'attributes')
if not os.path.exists(apath):
return []
return [fn for fn in os.listdir(apath)
if StringID.regex.match(fn)]
def get_attribute(self, name):
if name not in self._attributes:
if name not in self.attribute_names:
raise NoSuchAttribute(name)
path = op.join(self.path, 'attributes', name)
with open(path, 'rb') as f:
self._attributes[name] = num.fromfile(
f, dtype='<i4',
count=self.nmodels).astype(int)
assert self._attributes[name].shape == (self.nmodels,)
return self._attributes[name]
def set_attribute(self, name, attribute):
if not StringID.regex.match(name):
raise InvalidAttributeName(name)
attribute = attribute.astype(int)
assert attribute.shape == (self.nmodels,)
apath = op.join(self.path, 'attributes')
if not os.path.exists(apath):
os.mkdir(apath)
path = op.join(apath, name)
with open(path, 'wb') as f:
attribute.astype('<i4').tofile(f)
self._attributes[name] = attribute
def ensure_bootstrap_misfits(self, optimiser):
if self.bootstrap_misfits is None:
problem = self.problem
self.bootstrap_misfits = problem.combine_misfits(
self.misfits,
extra_weights=optimiser.get_bootstrap_weights(problem),
extra_residuals=optimiser.get_bootstrap_residuals(problem))
def imodels_by_cluster(self, cluster_attribute):
if cluster_attribute is None:
return [(-1, 100.0, num.arange(self.nmodels))]
by_cluster = []
try:
iclusters = self.get_attribute(cluster_attribute)
iclusters_avail = num.unique(iclusters)
for icluster in iclusters_avail:
imodels = num.where(iclusters == icluster)[0]
by_cluster.append(
(icluster,
(100.0 * imodels.size) / self.nmodels,
imodels))
if by_cluster and by_cluster[0][0] == -1:
by_cluster.append(by_cluster.pop(0))
except NoSuchAttribute:
logger.warning(
'Attribute %s not set in run %s.\n'
' Skipping model retrieval by clusters.' % (
cluster_attribute, self.problem.name))
return by_cluster
def models_by_cluster(self, cluster_attribute):
if cluster_attribute is None:
return [(-1, 100.0, self.models)]
return [
(icluster, percentage, self.models[imodels])
for (icluster, percentage, imodels)
in self.imodels_by_cluster(cluster_attribute)]
def mean_sources_by_cluster(self, cluster_attribute):
return [
(icluster, percentage, stats.get_mean_source(self.problem, models))
for (icluster, percentage, models)
in self.models_by_cluster(cluster_attribute)]
def get_sorted_misfits_idx(self, chain=0):
if chain not in self._sorted_misfit_idx.keys():
self._sorted_misfit_idx[chain] = num.argsort(
self.bootstrap_misfits[:, chain])
return self._sorted_misfit_idx[chain]
def get_sorted_misfits(self, chain=0):
isort = self.get_sorted_misfits_idx(chain)
return self.bootstrap_misfits[:, chain][isort]
def get_sorted_models(self, chain=0):
isort = self.get_sorted_misfits_idx(chain=0)
return self.models[isort, :]
def get_sorted_primary_misfits(self):
return self.get_sorted_misfits(chain=0)
def get_sorted_primary_models(self):
return self.get_sorted_models(chain=0)
def get_best_model(self, chain=0):
return self.get_sorted_models(chain)[0, ...]
def get_best_misfit(self, chain=0):
return self.get_sorted_misfits(chain)[0]
def get_mean_model(self):
return num.mean(self.models, axis=0)
def get_mean_misfit(self, chain=0):
return num.mean(self.bootstrap_misfits[:, chain])
def get_best_source(self, chain=0):
return self.problem.get_source(self.get_best_model(chain))
def get_mean_source(self, chain=0):
return self.problem.get_source(self.get_mean_model())
def get_chain_misfits(self, chain=0):
return self.bootstrap_misfits[:, chain]
def get_primary_chain_misfits(self):
return self.get_chain_misfits(chain=0)
def get_nmodels(dirname, problem):
fn = op.join(dirname, 'models')
with open(fn, 'r') as f:
nmodels1 = os.fstat(f.fileno()).st_size // (problem.nparameters * 8)
fn = op.join(dirname, 'misfits')
with open(fn, 'r') as f:
nmodels2 = os.fstat(f.fileno()).st_size // (problem.nmisfits * 2 * 8)
return min(nmodels1, nmodels2)
def load_problem_info_and_data(dirname, subset=None, nchains=None):
problem = load_problem_info(dirname)
models, misfits, bootstraps, sampler_contexts = load_problem_data(
xjoin(dirname, subset), problem, nchains=nchains)
return problem, models, misfits, bootstraps, sampler_contexts
def load_optimiser_info(dirname):
fn = op.join(dirname, 'optimiser.yaml')
return guts.load(filename=fn)
def load_problem_info(dirname):
try:
fn = op.join(dirname, 'problem.yaml')
return guts.load(filename=fn)
except OSError as e:
logger.debug(e)
raise ProblemInfoNotAvailable(
'No problem info available (%s).' % dirname)
def load_problem_data(dirname, problem, nmodels_skip=0, nchains=None):
def get_chains_fn():
for fn in (op.join(dirname, 'bootstraps'),
op.join(dirname, 'chains')):
if op.exists(fn):
return fn
return False
try:
nmodels = get_nmodels(dirname, problem) - nmodels_skip
fn = op.join(dirname, 'models')
with open(fn, 'r') as f:
f.seek(nmodels_skip * problem.nparameters * 8)
models = num.fromfile(
f, dtype='<f8',
count=nmodels * problem.nparameters)\
.astype(float)
models = models.reshape((nmodels, problem.nparameters))
fn = op.join(dirname, 'misfits')
with open(fn, 'r') as f:
f.seek(nmodels_skip * problem.nmisfits * 2 * 8)
misfits = num.fromfile(
f, dtype='<f8',
count=nmodels*problem.nmisfits*2)\
.astype(float)
misfits = misfits.reshape((nmodels, problem.nmisfits, 2))
chains = None
fn = get_chains_fn()
if fn and nchains is not None:
with open(fn, 'r') as f:
f.seek(nmodels_skip * nchains * 8)
chains = num.fromfile(
f, dtype='<f8',
count=nmodels*nchains)\
.astype(float)
chains = chains.reshape((nmodels, nchains))
sampler_contexts = None
fn = op.join(dirname, 'choices')
if op.exists(fn):
with open(fn, 'r') as f:
f.seek(nmodels_skip * 4 * 8)
sampler_contexts = num.fromfile(
f, dtype='<i8',
count=nmodels*4).astype(int)
sampler_contexts = sampler_contexts.reshape((nmodels, 4))
except OSError as e:
logger.debug(str(e))
raise ProblemDataNotAvailable(
'No problem data available (%s).' % dirname)
return models, misfits, chains, sampler_contexts
__all__ = '''
ProblemConfig
Problem
ModelHistory
ProblemInfoNotAvailable
ProblemDataNotAvailable
load_problem_info
load_problem_info_and_data
InvalidAttributeName
NoSuchAttribute
'''.split()
