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from dreamcoder.program import *
from dreamcoder.differentiation import *
import signal
class EvaluationTimeout(Exception):
pass
EVALUATIONTABLE = {}
class Task(object):
def __init__(self, name, request, examples, features=None, cache=False):
'''request: the type of this task
examples: list of tuples of (input, output). input should be a tuple, with one entry for each argument
cache: should program evaluations be cached?
features: list of floats.'''
self.cache = cache
self.features = features
self.request = request
self.name = name
self.examples = examples
if len(self.examples) > 0:
assert all(len(xs) == len(examples[0][0])
for xs, _ in examples), \
"(for task %s) FATAL: Number of arguments varies." % name
def __str__(self):
if self.supervision is None:
return self.name
else:
return self.name + " (%s)"%self.supervision
def __repr__(self):
return "Task(name={self.name}, request={self.request}, examples={self.examples}"\
.format(self=self)
def __eq__(self, o): return self.name == o.name
def __ne__(self, o): return not (self == o)
def __hash__(self): return hash(self.name)
def describe(self):
description = ["%s : %s" % (self.name, self.request)]
for xs, y in self.examples:
if len(xs) == 1:
description.append("f(%s) = %s" % (xs[0], y))
else:
description.append("f%s = %s" % (xs, y))
return "\n".join(description)
def predict(self, f, x):
for a in x:
f = f(a)
return f
@property
def supervision(self):
if not hasattr(self, 'supervisedSolution'): return None
return self.supervisedSolution
def check(self, e, timeout=None):
if timeout is not None:
def timeoutCallBack(_1, _2): raise EvaluationTimeout()
try:
signal.signal(signal.SIGVTALRM, timeoutCallBack)
signal.setitimer(signal.ITIMER_VIRTUAL, timeout)
try:
f = e.evaluate([])
except IndexError:
# free variable
return False
except Exception as e:
eprint("Exception during evaluation:", e)
return False
for x, y in self.examples:
if self.cache and (x, e) in EVALUATIONTABLE:
p = EVALUATIONTABLE[(x, e)]
else:
try:
p = self.predict(f, x)
except BaseException:
p = None
if self.cache:
EVALUATIONTABLE[(x, e)] = p
if p != y:
if timeout is not None:
signal.signal(signal.SIGVTALRM, lambda *_: None)
signal.setitimer(signal.ITIMER_VIRTUAL, 0)
return False
return True
# except e:
# eprint(e)
# assert(False)
except EvaluationTimeout:
eprint("Timed out while evaluating", e)
return False
finally:
if timeout is not None:
signal.signal(signal.SIGVTALRM, lambda *_: None)
signal.setitimer(signal.ITIMER_VIRTUAL, 0)
def logLikelihood(self, e, timeout=None):
if self.check(e, timeout):
return 0.0
else:
return NEGATIVEINFINITY
@staticmethod
def featureMeanAndStandardDeviation(tasks):
dimension = len(tasks[0].features)
averages = [sum(t.features[j] for t in tasks) / float(len(tasks))
for j in range(dimension)]
variances = [sum((t.features[j] -
averages[j])**2 for t in tasks) /
float(len(tasks)) for j in range(dimension)]
standardDeviations = [v**0.5 for v in variances]
for j, s in enumerate(standardDeviations):
if s == 0.:
eprint(
"WARNING: Feature %d is always %f" %
(j + 1, averages[j]))
return averages, standardDeviations
def as_json_dict(self):
return {
"name": self.name,
"request": str(self.request),
"examples": [{"inputs": x, "output": y} for x, y in self.examples]
}
class DifferentiableTask(Task):
def __init__(self, name, request, examples, _=None,
features=None, BIC=1., loss=None, likelihoodThreshold=None,
steps=50, restarts=300, lr=0.5, decay=0.5, grow=1.2, actualParameters=None,
temperature=1., maxParameters=None, clipLoss=None, clipOutput=None):
assert loss is not None
self.temperature = temperature
self.actualParameters = actualParameters
self.maxParameters = maxParameters
self.loss = loss
self.BIC = BIC
self.likelihoodThreshold = likelihoodThreshold
arguments = {"parameterPenalty": BIC * math.log(len(examples)),
"temperature": temperature,
"steps": steps, "restarts": restarts, "lr": lr, "decay": decay, "grow": grow,
"maxParameters": maxParameters,
"lossThreshold": -likelihoodThreshold}
if clipLoss is not None: arguments['clipLoss'] = float(clipLoss)
if clipOutput is not None: arguments['clipOutput'] = float(clipOutput)
if actualParameters is not None: arguments['actualParameters'] = int(actualParameters)
self.specialTask = ("differentiable",
arguments)
super(
DifferentiableTask,
self).__init__(
name,
request,
examples,
features,
cache=False)
def logLikelihood(self, e, timeout=None):
assert timeout is None, "timeout not implemented for differentiable tasks, but not for any good reason."
e, parameters = PlaceholderVisitor.execute(e)
if self.maxParameters is not None and len(
parameters) > self.maxParameters:
return NEGATIVEINFINITY
if self.actualParameters is not None and len(
parameters) > self.actualParameters:
return NEGATIVEINFINITY
f = e.evaluate([])
loss = sum(self.loss(self.predict(f, xs), y)
for xs, y in self.examples) / float(len(self.examples))
if isinstance(loss, DN):
try:
loss = loss.restartingOptimize(
parameters,
lr=self.specialTask[1]["lr"],
steps=self.specialTask[1]["steps"],
decay=self.specialTask[1]["decay"],
grow=self.specialTask[1]["grow"],
attempts=self.specialTask[1]["restarts"],
update=None)
except InvalidLoss:
loss = POSITIVEINFINITY
# BIC penalty
penalty = self.BIC * len(parameters) * math.log(len(self.examples))
if self.likelihoodThreshold is not None:
if loss > -self.likelihoodThreshold:
return NEGATIVEINFINITY
else:
return -penalty
else:
return -loss / self.temperature - penalty
def squaredErrorLoss(prediction, target):
d = prediction - target
return d * d
def l1loss(prediction, target):
return abs(prediction - target)
class PlaceholderVisitor(object):
def __init__(self): self.parameters = []
def primitive(self, e):
if e.name == 'REAL':
placeholder = Placeholder.named("REAL_", random.random())
self.parameters.append(placeholder)
return Primitive(e.name, e.tp, placeholder)
return e
def invented(self, e): return e.body.visit(self)
def abstraction(self, e): return Abstraction(e.body.visit(self))
def application(self, e):
return Application(e.f.visit(self), e.x.visit(self))
def index(self, e): return e
@staticmethod
def execute(e):
v = PlaceholderVisitor()
e = e.visit(v)
return e, v.parameters
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