def __init__(self, config):
super(History, self).__init__()
self.config = config
self.evals = 0
self.minSolution = None
self.minScore = np.inf
self.maxSolution = None
self.maxScore = -np.inf
self._empty = True
if not hasattr(self, 'cache'):
self.cache = LRUCache() # 10,000 items by default
self.updates = 0
#how often to print generation report
self.printEvery = config.printEvery or 1000000000000L
self.attrs = set([
"evals", "minSolution", "minScore", "maxScore", "attrs",
"minSolution", "maxSolution", "_empty", "cache", "updates",
"printEvery", "useCache"
])
def __init__(self, config):
super(History, self).__init__()
self.config = config
self.evals = 0
self.minSolution = None
self.minScore = np.inf
self.maxSolution = None
self.maxScore = -np.inf
self._empty = True
if not hasattr(self, 'cache'):
self.cache = LRUCache() # 10,000 items by default
self.updates = 0
#how often to print generation report
self.printEvery = config.printEvery or 1000000000000L
self.attrs = set(["evals","minSolution","minScore","maxScore","attrs",
"minSolution", "maxSolution","_empty","cache",
"updates","printEvery", "useCache"])
class BayesianDirichletScorer(object):
def __init__(self):
self.cache = {}
self.varCache = LRUCache()
def matchesPrior(self, data, configuration):
return 1.0
def matches(self, data, config):
"""count number of instances of configuration in data"""
cnt = 0L
for x in data:
if config <= x:
cnt += 1
return cnt
def __call__(self, network, data):
network.computeEdgeStatistics()
total = 0.0
total -= network.edgeRatio * len(data) * 10
for variable in network.variables:
varKey = str(variable.index) + str(variable.parents)
if self.varCache.has_key(varKey):
total += self.varCache[varKey]
continue
start = total
for configuration in variable.configurations():
prior = self.matchesPrior(data, configuration)
total += gammaln(prior)
total -= gammaln(prior + self.matches(data, configuration))
for val in variable.values():
priorVal = self.matchesPrior(data, configuration + val)
total -= gammaln(priorVal)
total += gammaln(priorVal +
self.matches(data, configuration + val))
self.varCache[varKey] = total - start
return total / len(data)
class BayesianDirichletScorer(object):
def __init__(self):
self.cache = {}
self.varCache = LRUCache()
def matchesPrior(self, data, configuration):
return 1.0
def matches(self, data, config):
"""count number of instances of configuration in data"""
cnt = 0L
for x in data:
if config <= x:
cnt += 1
return cnt
def __call__(self, network, data):
network.computeEdgeStatistics()
total = 0.0
total -= network.edgeRatio * len(data) * 10
for variable in network.variables:
varKey = str(variable.index) + str(variable.parents)
if self.varCache.has_key(varKey):
total += self.varCache[varKey]
continue
start = total
for configuration in variable.configurations():
prior = self.matchesPrior(data, configuration)
total += gammaln(prior)
total -= gammaln(prior + self.matches(data, configuration))
for val in variable.values():
priorVal = self.matchesPrior(data, configuration + val)
total -= gammaln(priorVal)
total += gammaln(priorVal + self.matches(data, configuration + val))
self.varCache[varKey] = total - start
return total / len(data)
def __init__(self):
self.cache = {}
self.varCache = LRUCache()
def __init__(self):
self.cache = {}
self.varCache = LRUCache()
class BayesNet(Distribution):
counter = 0
created = 0
config = Config(numVariables=None,
branchFactor=10,
variableGenerator=BayesVariable,
structureGenerator=GreedyStructureSearch(
10, BayesianInformationCriterion()),
sampler=DAGSampler(),
randomizer=lambda net: None)
densityCache = LRUCache()
weightCache = LRUCache()
likelihoodCache = LRUCache()
def __init__(self, **kwargs):
config = BayesNet.config.merge(Config(**kwargs))
super(BayesNet, self).__init__(**config.__properties__)
self.numVariables = self.config.numVariables
self.variableGenerator = self.config.variableGenerator
self.structureGenerator = self.config.structureGenerator
self.randomizer = self.config.randomizer
self.sampler = self.config.sampler
self.variables = []
for i in xrange(self.numVariables):
self.variables.append(self.variableGenerator(i, self.config))
self.decay = 1
self.dirty = False
self.acyclic = True
self.edges = []
self.edgeRatio = 0.0
self.edgeTuples = None
self.cacheKeys = dict([(v.index, v.cacheKey) for v in self.variables])
self.edgeMap = {}
self.binary = zeros(len(self.variables)**2)
self.deferred = False
self.deferredWeights = False
self.edgeRep = None
self.densityStored = None
self.cacheHits = 0
self.cacheTries = 0
self.changed = {}
self.last = {}
self.__class__.counter += 1
self.__class__.created += 1
def __del__(self):
for variable in self.variables:
del variable
self.__class__.counter -= 1
def __copy__(self):
return self.__class__.parse(str(self), self.config)
@checkDeferred
def get(self, index):
for var in self.variables:
if var.index == index:
return var
return None
@checkDeferred
def sort(self):
self.variables = sorted(self.variables, key=lambda x: len(x.parents))
visited = {}
skipped = {}
#print "before: ", [v.index for v in self.variables]
vars = LinkedList()
for var in self.variables:
vars.append(var)
current = vars.first
while current is not None:
v = current.value
advance = True
for idx, p in v.parents.iteritems():
if not visited.has_key(idx):
before = current.before
vars.remove(current)
vars.append(v)
if before is not None:
current = before.after
advance = False
skipped[v.index] = True
break
if not advance and skipped.has_key(current.value.index):
break
if advance:
current = current.after
skipped = {}
visited[v.index] = True
#print i, visited, v.index, v.parents
current = vars.first
self.variables = []
while current is not None:
self.variables.append(current.value)
current = current.after
#print "after: ", [v.index for v in self.variables]
return len(skipped) == 0
@checkDeferred
def decompose(self):
acyclic = self.sort()
#self.variables is now ordered in a way to allow sampling
clusters = []
for var in self.variables:
# is the parent in one of the clusters?
member = False
for c in clusters:
for v in c:
if var.parents.has_key(v):
member = True
break
if member:
c.append(var.index)
break
if not member:
clusters.append([var.index])
return clusters
@checkDeferredWeights
def distribution(self, index, x):
var = None
for v in self.variables:
if v.index == index:
var = v
return var.distribution(x)
@checkDeferredWeights
def randomize(self):
return self.randomizer(self)
@checkDeferredWeights
def conditionalLikelihood(self, index, data):
sum = 0.0
var = None
for v in self.variables:
if v.index == index:
var = v
break
for x in data:
key = self.cacheKeys[var.index]
key += str(x[var.index])
if not BayesNet.densityCache.has_key(key):
BayesNet.densityCache[key] = var.density(x)
sum += log(BayesNet.densityCache[key])
return sum
@checkDeferredWeights
def likelihood(self, data):
prod = 0.0
for v in self.variables:
vprod = 0.0
for x in data:
key = self.cacheKeys[v.index]
key += str(x[v.index])
if not BayesNet.densityCache.has_key(key):
BayesNet.densityCache[key] = v.density(x)
vprod += log(BayesNet.densityCache[key])
if isinstance(vprod, np.ndarray):
vprod = vprod[0]
self.last[v.index] = vprod
prod += vprod
self.densityStored = prod
return prod
@checkDeferredWeights
def likelihoodChanged(self, data, storeChange=False):
diff = 0.0
olddiff = 0.0
total = 0.0
for v in self.variables:
if self.changed.has_key(v.index) and self.changed[v.index]:
inner = 0.0
for x in data:
key = self.cacheKeys[v.index]
key += str(x[v.index])
if not BayesNet.densityCache.has_key(key):
BayesNet.densityCache[key] = v.density(x)
inner += log(BayesNet.densityCache[key])
total += inner
if storeChange:
self.last[v.index] = inner
else:
total += self.last[v.index]
return total
@checkDeferredWeights
def marginal(self, cmpIdx, data):
var = None
for v in self.variables:
if v.index == cmpIdx:
var = v
break
total = 0.0
for t in data:
total += var.marginalDensity(t, self)
return total / len(data)
@checkDeferredWeights
def map(self, cmpIdx, data):
var = None
for v in self.variables:
if v.index == cmpIdx:
var = v
break
total = 0.0
for t in data:
z = var.map(t)
if z[cmpIdx] == t[cmpIdx]:
total += 1.0
return total / len(data)
@checkDeferredWeights
def density(self, x):
self.computeEdgeStatistics()
prod = 1.0
for variable in self.variables:
key = self.cacheKeys[variable.index]
key += str(x[variable.index])
if not BayesNet.densityCache.has_key(key):
BayesNet.densityCache[key] = variable.density(x)
#print key, " - ", BayesNet.densityCache[key], " - ", variable.density(x)
prod *= BayesNet.densityCache[key]
return prod
@checkDeferredWeights
def __call__(self):
"""sample the network"""
return self.sampler(self)
@checkDeferredWeights
def sample(self):
return self.__call__()
@checkDeferredWeights
def batch(self, num):
return [self.__call__() for i in xrange(num)]
@checkDeferred
def numFreeParameters(self):
total = 0
for variable in self.variables:
total += variable.numFreeParameters()
return total
@checkDeferred
def update(self, epoch, data):
self.computeEdgeStatistics()
for variable in self.variables:
self.updateVar(variable, data)
@checkDeferred
def updateVar(self, variable, data):
self.computeEdgeStatistics()
key = self.cacheKeys[variable.index]
if not BayesNet.weightCache.has_key(key):
variable.update(data)
BayesNet.weightCache[key] = variable.getComputedState()
else:
variable.restoreComputedState(BayesNet.weightCache[key])
@checkDeferred
def merge(self, other, data):
return self.structureGenerator.merge(self, other, data)
@checkDeferred
def cross(self, other, data):
return self.structureGenerator.cross(self, other, data)
@checkDeferred
def hasEdge(self, frm, t):
"""Whether the network has an edge from the parent with index 'from'
to the child with index 'to'
"""
return (frm, t) in self.edges
#try:
# toNode = [variable for variable in self.variables if variable.index == t][0]
# fromNode = [parent for l,parent in fromNode.parents.iteritems() if parent.index == frm][0]
# return True
#except Exception:
# return False
@checkDeferred
def isAcyclic(self):
"""Is the network a DAG?"""
if self.dirty:
self.computeEdgeStatistics()
return self.acyclic
"""
tested = set([])
for variable in self.variables:
if len(set(variable.parents) - tested) > 0:
self.acyclic = False
return False
tested = set(list(tested) + [variable])
self.acyclic = True
return True
"""
"""
for variable in self.variables:
if variable in variable.parents:
return False
tested = set([])
while len(tested) < len(self.variables):
added = False
for variable in self.variables:
if variable in tested:
continue
if len(set(variable.parents) - tested) == 0:
tested = set(list(tested) + [variable])
added = True
break
if not added:
return False
return True
"""
@checkDeferred
def structureSearch(self, data):
return self.structureGenerator.search(self, data)
@checkDeferredWeights
def getComputedState(self):
state = {}
self.computeEdgeStatistics()
state['acyclic'] = self.acyclic
state['edges'] = self.edges
state['edgeRatio'] = self.edgeRatio
state['edgeTuples'] = self.edgeTuples
state['cacheKeys'] = self.cacheKeys
varstate = {}
varorder = {}
for i, v in enumerate(self.variables):
varstate[v.index] = v.getComputedState()
varorder[i] = v.index
state['varstate'] = varstate
state['varorder'] = varorder
return state
def restoreComputedState(self, state):
self.dirty = False
self.acyclic = state['acyclic']
self.edges = state['edges']
self.edgeRatio = state['edgeRatio']
self.edgeTuples = state['edgeTuples']
self.cacheKeys = state['cacheKeys']
varorder = state['varorder']
varstate = state['varstate']
self.variables = sorted(self.variables,
key=lambda v: varorder[v.index])
for v in self.variables:
v.restoreComputedState(varstate[v.index])
@checkDeferred
def computeEdgeStatistics(self):
if not self.dirty: return
self.acyclic = self.sort()
if self.edges is not None: del self.edges
self.edges = []
for variable in self.variables:
for l, variable2 in variable.parents.iteritems():
self.edges.append((variable2, variable))
self.edges = sorted(self.edges, key=lambda e: (e[0].index, e[1].index))
self.edgeRatio = len(self.edges) / (1e-10 + (len(self.variables)**2))
self.edgeTuples = [(frm.index, to.index) for frm, to in self.edges]
self.cacheKeys = dict([(v.index, v.cacheKey) for v in self.variables])
self.dirty = False
self.densityStored = None
@checkDeferred
def getChildren(self, variable):
children = []
self.computeEdgeStatistics()
for variable2 in self.variables:
if variable2.parents.has_key(variable.index):
children.append(variable2)
return children
@checkDeferred
def updateVariables(self, data):
for variable in self.variables:
variable.update(data)
@checkDeferred
def __getitem__(self, index):
if self.edgeMap.has_key(index):
return self.edgeMap[index]
frmidx = index % self.numVariables
toidx = index / self.numVariables
for l, v in self.variables[toidx].parents.iteritems():
if v.index == frmidx:
self.edgeMap[index] = True
return True
self.edgeMap[index] = False
return False
def __len__(self):
return self.numVariables**2
def __getstate__(self):
return {
'v': self.variables,
'r': self.randomizer,
's': self.sampler,
'sg': self.structureGenerator,
}
def __setstate__(self, state):
self.dirty = True
self.variables = state['v']
self.numVariables = len(self.variables)
self.randomizer = state['r']
self.structureGenerator = state['sg']
self.sampler = state['s']
indexMap = {}
for variable in self.variables:
indexMap[variable.index] = variable
for variable in self.variables:
variable.parents = {}
for i in variable.parentIndices:
variable.addParent(indexMap[i])
self.changed = {}
self.last = {}
self.deferred = False
self.deferredWeights = True
self.edgeRep = None
self.edgeMap = {}
self.edgeTuples = None
self.edges = None
self.binary = None
self.densityStored = None
self.computeEdgeStatistics()
@checkDeferred
def __str__(self):
"""pickle the object"""
self.computeEdgeStatistics()
return cPickle.dumps(len(self.variables)) + cPickle.dumps(
self.edgeTuples)
def initialize(net):
for frm, to in net.edgeRep:
net.variables[to].addParent(net.variables[frm])
net.dirty = True
net.deferred = False
net.edgeRep = None
net.computeEdgeStatistics()
@checkDeferred
def estimate(net):
for variable in net.variables:
net.updateVar(variable, net.config.data)
net.deferredWeights = False
@classmethod
def parse(cls, rep, cfg):
io = StringIO(rep)
numVars = cPickle.load(io)
if cfg is None:
cfg = BayesNet.config
net = cls(**cfg.__properties__)
edges = cPickle.load(io)
net.edgeRep = edges
net.deferred = True
net.deferredWeights = True
return net
class History(object):
"""A History used to track the progress of an optimization algorithm.
Different algorithms should extend this class in order to define
the minimal amount of history that needs to be stored in order for the
algorthm to operate.
"""
useCache = True
attrs = set()
sorted = False
root = True # whether the current history is the top level root for
# all histories of this algorithm
def __init__(self, config):
super(History, self).__init__()
self.config = config
self.evals = 0
self.minSolution = None
self.minScore = np.inf
self.maxSolution = None
self.maxScore = -np.inf
self._empty = True
if not hasattr(self, 'cache'):
self.cache = LRUCache() # 10,000 items by default
self.updates = 0
#how often to print generation report
self.printEvery = config.printEvery or 1000000000000L
self.attrs = set([
"evals", "minSolution", "minScore", "maxScore", "attrs",
"minSolution", "maxSolution", "_empty", "cache", "updates",
"printEvery", "useCache"
])
def __getstate__(self):
"""Used by :class:`CheckpointedHistory`
and :class:`CheckpointedMultipleHistory` to checkpoint a history
so it can be rolled back after updates.
Should return all objects in the history in a dictionary, sensitive
to the fact that object references may need to be copied.
:returns: A dictionary with the state of the history
"""
state = {}
for attr in self.attrs:
val = getattr(self, attr)
if isinstance(val, list):
val = [x for x in val]
state[attr] = val
state['cfg'] = self.config.__properties__
return state
def __setstate__(self, state):
for attr in self.attrs:
val = state[attr]
setattr(self, attr, val)
import pyec.config
self.config = pyec.config.Config(**state['cfg'])
def empty(self):
"""Whether the history has been used or not."""
return self._empty
def better(self, score1, score2):
"""Return whether one score is better than another.
Uses ``config.minimize`` to decide whether lesser or greater
numbers are better.
:param score1: the score in question (floating point number)
:type score1: ``float``
:param score2: the score being compared
:type score2: ``float``
:returns: whether ``score1`` is better than ``score2``
"""
if self.config.minimize:
return score1 < score2
else:
return score1 > score2
def best(self):
"""Get the best solution, whether minimizing or maximizing.
Same as ``optimal``
"""
if self.config.minimize:
return self.minimal()
else:
return self.maximal()
optimal = best
def minimal(self):
"""Get the minimal solution and its score.
:returns: A tuple of two item with the solution object first
and the score of that item second
"""
return self.minSolution, self.minScore
def maximal(self):
"""Get the maximal solution and its score.
:returns: A tuple of two item with the solution object first
and the score of that item second
"""
return self.maxSolution, self.maxScore
def num_evaluations(self):
"""Get the number of function evaluations performed in this
history object.
:returns: An integer representing the number of times the fitness
function or objective has been evaluated
"""
return self.evals
def update(self, population, fitness, space, opt):
"""
Update the state of the :class:`History` with the latest population
and its fitness scores. Subclasses should probably override
``internalUpdate`` rather than ``update``, unless they want to
change how the min/max are tracked.
Returns the history for use in continuations.
If ``population`` is ``None``, then this method does nothing; this
is so that you can set up a loop like to run an optimizer like::
p = None
f = lambda x:
t = History()
o = some_optimizer
s = o.config.space
for i in xrange(generations):
p = some_optimizer[t.update(p,f,s,0), f]()
t.update(p,f,s,o)
:params population: The previous population.
:type population: list of points in the search domain
:params fitness: The fitness / cost / objective function
:type fitness: Any callable object
:params space: The search domain
:type space: :class:`Space`
:params opt: The optimizer reporting this population
:type opt: :class:`PopulationDistribution`
:returns: The history (``self``), for continuations
"""
if population is None:
return
#self.config.stats.start(repr(self) + "history.update.all")
self._empty = False
self.evals += len(population)
self.updates += 1
#self.config.stats.start(repr(self) + "history.update.scoreall")
# score the sample
pop = population
scored = [(x, self.score(x, fitness, space)) for x in pop]
#self.config.stats.stop(repr(self) + "history.update.scoreall")
#self.config.stats.start(repr(self) + "history.update.findbest")
if self.root and self.config.observer is not None:
self.config.observer.report(opt, scored)
for x, s in scored:
if s > self.maxScore:
self.maxScore = s
self.maxSolution = x
if s < self.minScore:
self.minScore = s
self.minSolution = x
#self.config.stats.stop(repr(self) + "history.update.findbest")
if not (self.updates % self.printEvery):
genmin = min([s for x, s in scored])
genmax = max([s for x, s in scored])
genavg = np.average([s for x, s in scored])
print self.updates, ": min", self.minScore, " max", self.maxScore,
print " this generation (min, avg, max): ", genmin, genavg, genmax
#self.config.stats.start(repr(self) + "history.update.internal")
self.internalUpdate(scored)
#self.config.stats.stop(repr(self) + "history.update.internal")
#self.config.stats.stop(repr(self) + "history.update.all")
return self
def internalUpdate(self, population):
"""
Update the state of the :class:`History` with the latest population
and its fitness scores. This is an internal call intended for
overridden by subclasses. One of the important functions is to
delete points no longer needed by the history.
:params population: The previous population with its fitness scores.
:type population: list of (point, score) tuples
"""
pass
def score(self, point, fitness, space):
"""Get the fitness score, caching where possible.
:param point: A valid point in the space
:type point: Must match ``space.type``
:param fitness: The fitness function
:type fitness: Any callable
:param space: The space to which the point belongs
:type space: :class:`Space`
:returns: The fitness value, cached if possible
"""
if fitness is None:
return None
#self.config.stats.start("history.score")
if self.useCache:
try:
hashed = space.hash(point)
if self.cache.has_key(hashed):
ret = self.cache[hashed]
#self.config.stats.stop("history.score")
return ret
except Exception:
pass
if not space.in_bounds(point):
# use NaN so that the result is less than nor greater than
# any other score, and therefore NEVER optimal
s = np.inf - np.inf
else:
try:
s = fitness(space.convert(point))
except ValueError:
s = np.inf - np.inf
if self.useCache:
try:
hashed = space.hash(point)
self.cache[hashed] = s
except Exception:
pass
#self.config.stats.stop("history.score")
return s
def setCache(self, cache):
self.cache = cache
class History(object):
"""A History used to track the progress of an optimization algorithm.
Different algorithms should extend this class in order to define
the minimal amount of history that needs to be stored in order for the
algorthm to operate.
"""
useCache = True
attrs = set()
sorted = False
root = True # whether the current history is the top level root for
# all histories of this algorithm
def __init__(self, config):
super(History, self).__init__()
self.config = config
self.evals = 0
self.minSolution = None
self.minScore = np.inf
self.maxSolution = None
self.maxScore = -np.inf
self._empty = True
if not hasattr(self, 'cache'):
self.cache = LRUCache() # 10,000 items by default
self.updates = 0
#how often to print generation report
self.printEvery = config.printEvery or 1000000000000L
self.attrs = set(["evals","minSolution","minScore","maxScore","attrs",
"minSolution", "maxSolution","_empty","cache",
"updates","printEvery", "useCache"])
def __getstate__(self):
"""Used by :class:`CheckpointedHistory`
and :class:`CheckpointedMultipleHistory` to checkpoint a history
so it can be rolled back after updates.
Should return all objects in the history in a dictionary, sensitive
to the fact that object references may need to be copied.
:returns: A dictionary with the state of the history
"""
state = {}
for attr in self.attrs:
val = getattr(self, attr)
if isinstance(val, list):
val = [x for x in val]
state[attr] = val
state['cfg'] = self.config.__properties__
return state
def __setstate__(self, state):
for attr in self.attrs:
val = state[attr]
setattr(self, attr, val)
import pyec.config
self.config = pyec.config.Config(**state['cfg'])
def empty(self):
"""Whether the history has been used or not."""
return self._empty
def better(self, score1, score2):
"""Return whether one score is better than another.
Uses ``config.minimize`` to decide whether lesser or greater
numbers are better.
:param score1: the score in question (floating point number)
:type score1: ``float``
:param score2: the score being compared
:type score2: ``float``
:returns: whether ``score1`` is better than ``score2``
"""
if self.config.minimize:
return score1 < score2
else:
return score1 > score2
def best(self):
"""Get the best solution, whether minimizing or maximizing.
Same as ``optimal``
"""
if self.config.minimize:
return self.minimal()
else:
return self.maximal()
optimal = best
def minimal(self):
"""Get the minimal solution and its score.
:returns: A tuple of two item with the solution object first
and the score of that item second
"""
return self.minSolution, self.minScore
def maximal(self):
"""Get the maximal solution and its score.
:returns: A tuple of two item with the solution object first
and the score of that item second
"""
return self.maxSolution, self.maxScore
def num_evaluations(self):
"""Get the number of function evaluations performed in this
history object.
:returns: An integer representing the number of times the fitness
function or objective has been evaluated
"""
return self.evals
def update(self, population, fitness, space, opt):
"""
Update the state of the :class:`History` with the latest population
and its fitness scores. Subclasses should probably override
``internalUpdate`` rather than ``update``, unless they want to
change how the min/max are tracked.
Returns the history for use in continuations.
If ``population`` is ``None``, then this method does nothing; this
is so that you can set up a loop like to run an optimizer like::
p = None
f = lambda x:
t = History()
o = some_optimizer
s = o.config.space
for i in xrange(generations):
p = some_optimizer[t.update(p,f,s,0), f]()
t.update(p,f,s,o)
:params population: The previous population.
:type population: list of points in the search domain
:params fitness: The fitness / cost / objective function
:type fitness: Any callable object
:params space: The search domain
:type space: :class:`Space`
:params opt: The optimizer reporting this population
:type opt: :class:`PopulationDistribution`
:returns: The history (``self``), for continuations
"""
if population is None:
return
#self.config.stats.start(repr(self) + "history.update.all")
self._empty = False
self.evals += len(population)
self.updates += 1
#self.config.stats.start(repr(self) + "history.update.scoreall")
# score the sample
pop = population
scored = [(x, self.score(x, fitness, space)) for x in pop]
#self.config.stats.stop(repr(self) + "history.update.scoreall")
#self.config.stats.start(repr(self) + "history.update.findbest")
if self.root and self.config.observer is not None:
self.config.observer.report(opt, scored)
for x,s in scored:
if s > self.maxScore:
self.maxScore = s
self.maxSolution = x
if s < self.minScore:
self.minScore = s
self.minSolution = x
#self.config.stats.stop(repr(self) + "history.update.findbest")
if not (self.updates % self.printEvery):
genmin = min([s for x,s in scored])
genmax = max([s for x,s in scored])
genavg = np.average([s for x,s in scored])
print self.updates, ": min", self.minScore, " max", self.maxScore,
print " this generation (min, avg, max): ", genmin, genavg, genmax
#self.config.stats.start(repr(self) + "history.update.internal")
self.internalUpdate(scored)
#self.config.stats.stop(repr(self) + "history.update.internal")
#self.config.stats.stop(repr(self) + "history.update.all")
return self
def internalUpdate(self, population):
"""
Update the state of the :class:`History` with the latest population
and its fitness scores. This is an internal call intended for
overridden by subclasses. One of the important functions is to
delete points no longer needed by the history.
:params population: The previous population with its fitness scores.
:type population: list of (point, score) tuples
"""
pass
def score(self, point, fitness, space):
"""Get the fitness score, caching where possible.
:param point: A valid point in the space
:type point: Must match ``space.type``
:param fitness: The fitness function
:type fitness: Any callable
:param space: The space to which the point belongs
:type space: :class:`Space`
:returns: The fitness value, cached if possible
"""
if fitness is None:
return None
#self.config.stats.start("history.score")
if self.useCache:
try:
hashed = space.hash(point)
if self.cache.has_key(hashed):
ret = self.cache[hashed]
#self.config.stats.stop("history.score")
return ret
except Exception:
pass
if not space.in_bounds(point):
# use NaN so that the result is less than nor greater than
# any other score, and therefore NEVER optimal
s = np.inf - np.inf
else:
try:
s = fitness(space.convert(point))
except ValueError:
s = np.inf - np.inf
if self.useCache:
try:
hashed = space.hash(point)
self.cache[hashed] = s
except Exception:
pass
#self.config.stats.stop("history.score")
return s
def setCache(self, cache):
self.cache = cache