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Refactor backend classes to facilitate adding more #347

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Refactor backend classes to facilitate adding more #347

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8e96993
[backends] Make backends inherit from common base class
DavidMStraub Jun 25, 2020
3346472
[backends] Make HDF inherit from file backend
DavidMStraub Jun 25, 2020
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177 changes: 27 additions & 150 deletions src/emcee/backends/backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,16 @@

from .. import autocorr
from ..state import State
from .base import BackendBase

__all__ = ["Backend"]


class Backend(object):
class Backend(BackendBase):
"""A simple default backend that stores the chain in memory"""

def __init__(self, dtype=None):
self.initialized = False
self._initialized = False
if dtype is None:
dtype = np.float
self.dtype = dtype
Expand All @@ -27,26 +28,34 @@ def reset(self, nwalkers, ndim):
"""
self.nwalkers = int(nwalkers)
self.ndim = int(ndim)
self.iteration = 0
self._iteration = 0
self.accepted = np.zeros(self.nwalkers, dtype=self.dtype)
self.chain = np.empty((0, self.nwalkers, self.ndim), dtype=self.dtype)
self.log_prob = np.empty((0, self.nwalkers), dtype=self.dtype)
self.blobs = None
self.random_state = None
self.initialized = True
self._random_state = None
self._initialized = True

def has_blobs(self):
"""Returns ``True`` if the model includes blobs"""
return self.blobs is not None

def get_value(self, name, flat=False, thin=1, discard=0):
if self.iteration <= 0:
raise AttributeError(
"you must run the sampler with "
"'store == True' before accessing the "
"results"
)
@property
def iteration(self):
"""Return the iteration number."""
return self._iteration

@property
def initialized(self):
"""Return true if backend has been initialized."""
return self._initialized

@property
def shape(self):
"""The dimensions of the ensemble ``(nwalkers, ndim)``"""
return self.nwalkers, self.ndim

def _get_value(self, name, flat, thin, discard):
if name == "blobs" and not self.has_blobs():
return None

Expand All @@ -57,110 +66,6 @@ def get_value(self, name, flat=False, thin=1, discard=0):
return v.reshape(s)
return v

def get_chain(self, **kwargs):
"""Get the stored chain of MCMC samples

Args:
flat (Optional[bool]): Flatten the chain across the ensemble.
(default: ``False``)
thin (Optional[int]): Take only every ``thin`` steps from the
chain. (default: ``1``)
discard (Optional[int]): Discard the first ``discard`` steps in
the chain as burn-in. (default: ``0``)

Returns:
array[..., nwalkers, ndim]: The MCMC samples.

"""
return self.get_value("chain", **kwargs)

def get_blobs(self, **kwargs):
"""Get the chain of blobs for each sample in the chain

Args:
flat (Optional[bool]): Flatten the chain across the ensemble.
(default: ``False``)
thin (Optional[int]): Take only every ``thin`` steps from the
chain. (default: ``1``)
discard (Optional[int]): Discard the first ``discard`` steps in
the chain as burn-in. (default: ``0``)

Returns:
array[..., nwalkers]: The chain of blobs.

"""
return self.get_value("blobs", **kwargs)

def get_log_prob(self, **kwargs):
"""Get the chain of log probabilities evaluated at the MCMC samples

Args:
flat (Optional[bool]): Flatten the chain across the ensemble.
(default: ``False``)
thin (Optional[int]): Take only every ``thin`` steps from the
chain. (default: ``1``)
discard (Optional[int]): Discard the first ``discard`` steps in
the chain as burn-in. (default: ``0``)

Returns:
array[..., nwalkers]: The chain of log probabilities.

"""
return self.get_value("log_prob", **kwargs)

def get_last_sample(self):
"""Access the most recent sample in the chain"""
if (not self.initialized) or self.iteration <= 0:
raise AttributeError(
"you must run the sampler with "
"'store == True' before accessing the "
"results"
)
it = self.iteration
blobs = self.get_blobs(discard=it - 1)
if blobs is not None:
blobs = blobs[0]
return State(
self.get_chain(discard=it - 1)[0],
log_prob=self.get_log_prob(discard=it - 1)[0],
blobs=blobs,
random_state=self.random_state,
)

def get_autocorr_time(self, discard=0, thin=1, **kwargs):
"""Compute an estimate of the autocorrelation time for each parameter

Args:
thin (Optional[int]): Use only every ``thin`` steps from the
chain. The returned estimate is multiplied by ``thin`` so the
estimated time is in units of steps, not thinned steps.
(default: ``1``)
discard (Optional[int]): Discard the first ``discard`` steps in
the chain as burn-in. (default: ``0``)

Other arguments are passed directly to
:func:`emcee.autocorr.integrated_time`.

Returns:
array[ndim]: The integrated autocorrelation time estimate for the
chain for each parameter.

"""
x = self.get_chain(discard=discard, thin=thin)
return thin * autocorr.integrated_time(x, **kwargs)

@property
def shape(self):
"""The dimensions of the ensemble ``(nwalkers, ndim)``"""
return self.nwalkers, self.ndim

def _check_blobs(self, blobs):
has_blobs = self.has_blobs()
if has_blobs and blobs is None:
raise ValueError("inconsistent use of blobs")
if self.iteration > 0 and blobs is not None and not has_blobs:
raise ValueError("inconsistent use of blobs")

def grow(self, ngrow, blobs):
"""Expand the storage space by some number of samples

Expand All @@ -184,33 +89,6 @@ def grow(self, ngrow, blobs):
else:
self.blobs = np.concatenate((self.blobs, a), axis=0)

def _check(self, state, accepted):
self._check_blobs(state.blobs)
nwalkers, ndim = self.shape
has_blobs = self.has_blobs()
if state.coords.shape != (nwalkers, ndim):
raise ValueError(
"invalid coordinate dimensions; expected {0}".format(
(nwalkers, ndim)
)
)
if state.log_prob.shape != (nwalkers,):
raise ValueError(
"invalid log probability size; expected {0}".format(nwalkers)
)
if state.blobs is not None and not has_blobs:
raise ValueError("unexpected blobs")
if state.blobs is None and has_blobs:
raise ValueError("expected blobs, but none were given")
if state.blobs is not None and len(state.blobs) != nwalkers:
raise ValueError(
"invalid blobs size; expected {0}".format(nwalkers)
)
if accepted.shape != (nwalkers,):
raise ValueError(
"invalid acceptance size; expected {0}".format(nwalkers)
)

def save_step(self, state, accepted):
"""Save a step to the backend

Expand All @@ -227,11 +105,10 @@ def save_step(self, state, accepted):
if state.blobs is not None:
self.blobs[self.iteration, :] = state.blobs
self.accepted += accepted
self.random_state = state.random_state
self.iteration += 1
self._random_state = state.random_state
self._iteration += 1

def __enter__(self):
return self

def __exit__(self, exception_type, exception_value, traceback):
pass
@property
def random_state(self):
"""Return the random state."""
return self._random_state
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