def test_localsearch_friends(self):
"""When var flip decreases energy."""
allvar = select_localsearch_adversaries(self.notb, {
'a': 1,
'b': 1,
'c': 1
},
min_gain=None)
self.assertEqual(allvar, ['c', 'a', 'b'])
neutral = select_localsearch_adversaries(self.notb, {
'a': 1,
'b': 1,
'c': 1
},
min_gain=0.0)
self.assertEqual(neutral, ['c', 'a'])
nonevar = select_localsearch_adversaries(self.notb, {
'a': 1,
'b': 1,
'c': 1
},
min_gain=1.0)
self.assertEqual(nonevar, [])
friends = select_localsearch_adversaries(self.notb, {
'a': 1,
'b': 1,
'c': 1
},
min_gain=-10)
self.assertEqual(friends, ['c', 'a', 'b'])
def next(self, state):
bqm = state.problem
if self.max_size > len(bqm):
raise ValueError(
"subproblem size cannot be greater than the problem size")
# select a new subset of `max_size` variables, making sure they differ
# from previous iteration by at least `min_diff` variables
sample = state.samples.change_vartype(bqm.vartype).first.sample
variables = select_localsearch_adversaries(bqm,
sample,
min_gain=self.min_gain)
# TODO: soft fail strategy? skip one iteration or relax vars selection?
if len(variables) < self.min_diff:
raise ValueError("less than min_diff variables identified as"
" contributors to min_gain energy increase")
offset = 0
next_vars = set(variables[offset:offset + self.max_size])
while len(next_vars ^ self._prev_vars) < self.min_diff:
offset += self.stride
next_vars = set(variables[offset:offset + self.max_size])
logger.debug("Select variables: %r (diff from prev = %r)", next_vars,
next_vars ^ self._prev_vars)
self._prev_vars = next_vars
# induce sub-bqm based on selected variables and global sample
subbqm = bqm_induced_by(bqm, next_vars, sample)
return state.updated(subproblem=subbqm)
def test_localsearch_adversaries(self):
"""When var flip increases energy."""
defvar = select_localsearch_adversaries(self.notall, {'a': 1, 'b': 1, 'c': -1})
self.assertEqual(defvar, ['c', 'b', 'a'])
allvar = select_localsearch_adversaries(self.notall, {'a': 1, 'b': 1, 'c': -1}, max_n=3, min_gain=-1)
self.assertEqual(allvar, ['c', 'b', 'a'])
subvar = select_localsearch_adversaries(self.notall, {'a': 1, 'b': 1, 'c': -1}, max_n=2, min_gain=-1)
self.assertEqual(subvar, ['c', 'b'])
subvar = select_localsearch_adversaries(self.notall, {'a': 1, 'b': 1, 'c': -1}, min_gain=1)
self.assertEqual(subvar, ['c'])
nonevar = select_localsearch_adversaries(self.notall, {'a': 1, 'b': 1, 'c': -1}, min_gain=10)
self.assertEqual(nonevar, [])
def next(self, state):
bqm = state.problem
sample = state.samples.change_vartype(bqm.vartype).first.sample
size = self.size
if self.size > len(bqm):
logger.debug(
"subproblem size greater than the problem size, adapting to problem size"
)
size = len(bqm)
bqm_changed = bqm != self._rolling_bqm
sample_changed = sample != self._prev_sample
if bqm_changed:
self._rewind_rolling(state)
if sample_changed:
self._prev_sample = sample
if bqm_changed or sample_changed or not self._variables:
self._variables = select_localsearch_adversaries(
bqm, sample, min_gain=self.min_gain)
if self.rolling:
if len(self._unrolled_vars) >= self.rolling_history * len(bqm):
logger.debug("Rolling reset at unrolled history size = %d",
len(self._unrolled_vars))
self._rewind_rolling(state)
# reset before exception, to be ready on a subsequent call
if not self.silent_rewind:
raise EndOfStream
novel_vars = [
v for v in self._variables if v not in self._unrolled_vars
]
next_vars = novel_vars[:size]
logger.debug("Selected %d subproblem variables: %r", len(next_vars),
next_vars)
if self.rolling:
self._unrolled_vars.update(next_vars)
# induce sub-bqm based on selected variables and global sample
subbqm = bqm_induced_by(bqm, next_vars, sample)
return state.updated(subproblem=subbqm)
def next(self, state):
bqm = state.problem
if bqm != self._rolling_bqm:
self._reset_rolling(state)
if self.max_size > len(bqm):
raise ValueError(
"subproblem size cannot be greater than the problem size")
sample = state.samples.change_vartype(bqm.vartype).first.sample
variables = select_localsearch_adversaries(bqm,
sample,
min_gain=self.min_gain)
if self.rolling and len(
self._unrolled_vars
) + self.max_size > self.rolling_history * len(bqm):
logger.debug("rolling reset at unrolled history size = %d",
len(self._unrolled_vars))
# reset before exception, to be ready on a subsequent call
self._reset_rolling(state)
if not self.silent_reset:
raise EndOfStream
novel_vars = [v for v in variables if v not in self._unrolled_vars]
next_vars = novel_vars[:self.max_size]
logger.debug("Selected %d subproblem variables: %r", len(next_vars),
next_vars)
if self.rolling:
self._unrolled_vars.update(next_vars)
# induce sub-bqm based on selected variables and global sample
subbqm = bqm_induced_by(bqm, next_vars, sample)
return state.updated(subproblem=subbqm)
