def evalxc(self,xc,input):
inferenceFun = xc.inferenceFunction('predict/io')
rawPred = inferenceFun(input)
# trim small numbers to zero
pred = mutil.mapData(lambda d:np.clip((d - 1e-5),0.00,9999.99), rawPred)
pred.eliminate_zeros()
return pred
def _setFeatureWeightsForTypelessDB(self,epsilon=1.0):
"""Initialize each feature used in the feature part of a rule, i.e.,
for all rules annotated by "{foo(F):...}", declare 'foo/1' to
be a parameter, and initialize it to something plausible. The
'something plausible' is based on looking at how the variables
defining foo are defined, eg for something like "p(X,Y):-
... {posWeight(F):hasWord(X,F)}" a constant sparse vector with
non-zero weights for all second arguments of hasWord will be
used to initialize posWeight. The constant will be epsilon.
"""
for paramName,domainModes in self.paramDomains.items():
# we also need to infer a type for the parameter....
def typeOfWeights(mode):
for i in range(mode.arity):
if mode.isInput(i):
return self.db.schema.getArgType(mode.functor,mode.arity,i)
assert False
weights = self.db.matrixPreimage(domainModes[0])
weightType = typeOfWeights(domainModes[0])
for mode in domainModes[1:]:
weights = weights + self.db.matrixPreimage(mode)
assert typeOfWeights(mode)==weightType, 'feature weights have incompatible types: derived from %s and %s' % (mode,domainModes[0])
weights = weights * 1.0/len(domainModes)
weights = mutil.mapData(lambda d:np.clip(d,0.0,1.0), weights)
self.db.setParameter(paramName,1,weights*epsilon)
for (paramName,arity) in self.getParamList():
if not self.db.parameterIsInitialized(paramName,arity):
logging.warn("Parameter %s could not be set automatically")
logging.debug('total parameter size: %d', self.db.parameterSize())
def setRuleWeights(self,weights=None,epsilon=1.0,ruleIdPred=None):
"""Set the db predicate 'weighted/1' as a parameter, and initialize it
to the given vector. If no vector 'weights' is given, default
to a constant vector of epsilon for each rule. 'weighted/1'
is the default parameter used to weight rule-ids features,
e.g., "r" in p(X,Y):-... {r}. You can also specify the
ruleIds with the name of a unary db relation that holds all
the rule ids.
"""
if len(self.ruleIds)==0:
pass
elif ruleIdPred is not None:
# TODO check this stuff and add type inference!
assert (ruleIdPred,1) in self.db.matEncoding,'there is no unary predicate called %s' % ruleIdPred
self.db.markAsParameter("weighted",1)
self.db.setParameter("weighted",1,self.db.vector(declare.asMode('%s(o)' % ruleIdPred)) * epsilon)
else:
assert self.db.isTypeless(), 'cannot setRuleWeights for db with declared types unless ruleIdPred is given'
self.db.markAsParameter("weighted",1)
if weights==None:
weights = self.db.onehot(self.ruleIds[0])
for rid in self.ruleIds[1:]:
weights = weights + self.db.onehot(rid)
weights = mutil.mapData(lambda d:np.clip(d,0.0,1.0), weights)
self.db.setParameter("weighted",1,weights*epsilon)
def applyUpdate(self,paramGrads,rate):
"""Add each gradient to the appropriate param, after scaling by rate,
and clip negative parameters to zero.
"""
paramGrads.fitParameterShapes()
for (functor,arity),delta in paramGrads.items():
m0 = self.prog.db.getParameter(functor,arity)
m1 = m0 + rate * delta
m2 = mutil.mapData(lambda d:NP.clip(d,0.0,NP.finfo('float32').max), m1)
self.prog.db.setParameter(functor,arity,m2)
def meanUpdate(self,functor,arity,delta,n,totalN=0):
#clip the delta vector to avoid exploding gradients
delta = mutil.mapData(lambda d:NP.clip(d,conf.minGradient,conf.maxGradient), delta)
if arity==1:
#for a parameter that is a row-vector, we have one
#gradient per example and we will take the mean
compensation = 1.0 if totalN==0 else float(n)/totalN
return mutil.mean(delta)*compensation
else:
#for a parameter that is a matrix, we have one gradient for the whole matrix
compensation = (1.0/n) if totalN==0 else (1.0/totalN)
return delta*compensation
def accuracy(Y,P):
"""Evaluate accuracy of predictions P versus labels Y."""
#TODO surely there's a better way of doing this
def allZerosButArgmax(d):
result = NP.zeros_like(d)
result[d.argmax()] = 1.0
return result
n = mutil.numRows(P)
ok = 0.0
for i in range(n):
pi = P.getrow(i)
yi = Y.getrow(i)
ti = mutil.mapData(allZerosButArgmax,pi)
ok += yi.multiply(ti).sum()
return ok/n
def mapData(self,mapFun):
"""Apply some function to every gradient in the accumulator (in place)."""
result = GradAccumulator()
for k,m in self.items():
result.accum(k, mutil.mapData(mapFun,m))
return result
def crossEntropy(Y,P,perExample=False):
"""Compute cross entropy some predications relative to some labels."""
logP = mutil.mapData(NP.log,P)
result = -(Y.multiply(logP).sum())
return result/mutil.numRows(Y) if perExample else result
def train(self, dset):
modes = dset.modesToLearn()
trainStartTime = time.time()
sumSquareGrads = learn.GradAccumulator()
for i in range(self.epochs):
logging.info("starting epoch %d" % i)
startTime = time.time()
#generate the tasks
miniBatches = list(
dset.minibatchIterator(batchSize=self.miniBatchSize))
bpInputs = map(
lambda (k, b): ParallelFixedRateGDLearner.miniBatchToTask(
b, i, k, startTime), enumerate(miniBatches))
totalN = self.totalNumExamples(miniBatches)
#generate gradients - in parallel
bpOutputs = self.pool.map(_doBackpropTask, bpInputs)
# accumulate to sumSquareGrads
totalGradient = learn.GradAccumulator()
for (n, paramGrads) in bpOutputs:
for (functor, arity), grad in paramGrads.items():
totalGradient.accum(
(functor, arity),
self.meanUpdate(functor, arity, grad, n, totalN))
sumSquareGrads = sumSquareGrads.addedTo(
totalGradient.mapData(NP.square))
#compute gradient-specific rate
ratePerParam = sumSquareGrads.mapData(lambda d: d + 1e-1).mapData(
NP.sqrt).mapData(NP.reciprocal)
# scale down totalGradient by per-feature weight
for (functor, arity), grad in totalGradient.items():
totalGradient[(functor,
arity)] = grad.multiply(ratePerParam[(functor,
arity)])
self.regularizer.regularizeParams(self.prog, totalN)
for (functor, arity) in self.prog.db.paramList:
m = self.prog.db.getParameter(functor, arity)
print 'reg', functor, '/', arity, 'm shape', m.shape
if (functor, arity) in totalGradient.keys():
print 'vs totalGradient shape', totalGradient[(
functor, arity)].shape
else:
print 'not in totalGradient'
#cannot use process gradients because I've already scaled them down,
# need to just add and clip
for (functor, arity), grad in totalGradient.items():
m0 = self.prog.db.getParameter(functor, arity)
m1 = m0 + self.rate * grad
m = mutil.mapData(
lambda d: NP.clip(d, 0.0,
NP.finfo('float32').max), m1)
self.prog.db.setParameter(functor, arity, m)
# send params to workers
self.broadcastParameters()
# status updates
epochCounter = learn.GradAccumulator.mergeCounters(
map(lambda (n, grads): grads.counter, bpOutputs))
self.epochTracer(self, epochCounter, i=i, startTime=trainStartTime)
def _doBackprop(self, delta, gradAccum, pad):
newDelta = mutil.mapData(lambda d: numpy.reciprocal(d + 1),
self.inner).multiply(delta)
return self.fun.backprop(newDelta, gradAccum)
def _doEval(self, db, values, pad):
self.inner = self.fun.eval(db, values, pad)
return mutil.mapData(lambda d: numpy.log1p(d.clip(0, d)), self.inner)
