def sampleNext(self, actionSequence, depth, availableActionIDs,
usedActionIDs):
#print spansPath.keys()
activations1 = self.applyGeoManipulation(self.activations,
actionSequence)
(newClass, newConfident) = self.predictWithActivations(activations1)
#print euclideanDistance(self.activations,activations1), newConfident, newClass
(distMethod, distVal) = controlledSearch
if distMethod == "euclidean":
dist = 1 - euclideanDistance(activations1, self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "L1":
dist = 1 - l1Distance(activations1, self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "Percentage":
dist = 1 - diffPercent(activations1, self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "NumDiffs":
dist = self.activations.size - diffPercent(
activations1, self.activations) * self.activations.size
termValue = 0.0
termByDist = dist < self.activations.size - distVal
if newClass != self.originalClass:
print("sampling a path ends in a terminal node with depth %s... " %
depth)
self.decisionTree.addOnePath(dist, actionSequence)
self.re_training.addDatum(activations1, self.originalClass)
if self.bestCase[0] < dist:
self.bestCase = (dist, spansPath, numSpansPath)
return (depth == 0, dist)
elif termByDist == True:
print(
"sampling a path ends by controlled search with depth %s ... "
% depth)
return (depth == 0, termValue)
else:
#print("continue sampling node ... ")
#allChildren = initialisePixelSets(self.model,self.activations,spansPath.keys())
randomActionIndex = random.choice(
list(set(availableActionIDs) - set(usedActionIDs))
) #random.randint(0, len(allChildren)-1)
action = self.actions[randomActionIndex]
availableActionIDs.remove(randomActionIndex)
usedActionIDs.append(randomActionIndex)
newActSeq = actionSequence + [actioin]
return self.sampleNext(newActSeq, depth + 1, availableActionIDs,
usedActionIDs)
def terminatedByControlledSearch(self,index):
activations1 = applyManipulation(self.activations,self.spans[index],self.numSpans[index])
(distMethod,distVal) = controlledSearch
if distMethod == "euclidean":
dist = euclideanDistance(activations1,self.activations)
elif distMethod == "L1":
dist = l1Distance(activations1,self.activations)
elif distMethod == "Percentage":
dist = diffPercent(activations1,self.activations)
elif distMethod == "NumDiffs":
dist = diffPercent(activations1,self.activations)
nprint("terminated by controlled search")
return dist > distVal
def diffPercent(self, index):
activations1 = self.applyGeoManipulation(self.activations,
self.actSeq[index])
return diffPercent(self.activations, activations1)
def diffPercent(self,index):
activations1 = applyManipulation(self.activations,self.spans[index],self.numSpans[index])
return diffPercent(self.activations,activations1)
def sampleNext(self):
activations1 = applyManipulation(self.activations,self.spansPath,self.numSpansPath)
(newClass,newConfident) = self.predictWithActivations(activations1)
(distMethod,distVal) = controlledSearch
if distMethod == "euclidean":
dist = 1 - euclideanDistance(activations1,self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "L1":
dist = 1 - l1Distance(activations1,self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "Percentage":
dist = 1 - diffPercent(activations1,self.activations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "NumDiffs":
dist = self.activations.size - diffPercent(activations1,self.activations) * self.activations.size
termValue = 0.0
termByDist = dist < self.activations.size - distVal
#if termByDist == False and newConfident < 0.5 and self.depth <= 3:
# termByDist = True
if newClass != self.originalClass and newConfident > effectiveConfidenceWhenChanging:
# and newClass == dataBasics.next_index(self.originalClass,self.originalClass):
nprint("sampling a path ends in a terminal node with self.depth %s... "%self.depth)
(self.spansPath,self.numSpansPath) = self.scrutinizePath(self.spansPath,self.numSpansPath,newClass)
self.decisionTree.addOnePath(dist,self.spansPath,self.numSpansPath)
self.numAdv += 1
self.analyseAdv.addAdv(activations1)
self.getUsefulPixels(self.accDims,self.d)
self.re_training.addDatum(activations1,self.originalClass)
if self.bestCase[0] < dist: self.bestCase = (dist,self.spansPath,self.numSpansPath)
return (self.depth == 0, dist)
elif termByDist == True:
nprint("sampling a path ends by controlled search with self.depth %s ... "%self.depth)
self.re_training.addDatum(activations1,self.originalClass)
return (self.depth == 0, termValue)
elif list(set(self.availableActionIDs)-set(self.usedActionIDs)) == []:
nprint("sampling a path ends with self.depth %s because no more actions can be taken ... "%self.depth)
return (self.depth == 0, termValue)
else:
#print("continue sampling node ... ")
#allChildren = initialisePixelSets(self.model,self.activations,self.spansPath.keys())
randomActionIndex = random.choice(list(set(self.availableActionIDs)-set(self.usedActionIDs))) #random.randint(0, len(allChildren)-1)
(span,numSpan,_) = self.actions[randomActionIndex]
self.availableActionIDs.remove(randomActionIndex)
self.usedActionIDs.append(randomActionIndex)
newSpanPath = self.mergeSpan(self.spansPath,span)
newNumSpanPath = self.mergeNumSpan(self.numSpansPath,numSpan)
activations2 = applyManipulation(self.activations,newSpanPath,newNumSpanPath)
(newClass2,newConfident2) = self.predictWithActivations(activations2)
confGap2 = newConfident - newConfident2
if newClass2 == newClass:
self.accDims.append((randomActionIndex,confGap2))
else: self.accDims.append((randomActionIndex,1.0))
self.spansPath = newSpanPath
self.numSpansPath = newNumSpanPath
self.depth = self.depth+1
self.availableActionIDs = self.availableActionIDs
self.usedActionIDs = self.usedActionIDs
self.accDims = self.accDims
self.d = self.d
return self.sampleNext()
def sampleNextSndRound(self, spansPath, numSpansPath, depth,
availableActionIDs, usedActionIDs, accDims, d):
activations1 = applyManipulation(self.activations, spansPath,
numSpansPath)
(newClass, newConfident) = self.predictWithActivations(activations1)
(distMethod, distVal) = controlledSearch
distVal = distVal / float(2)
print("second depth-first search in %s" % (distVal))
if distMethod == "euclidean":
dist = 1 - euclideanDistance(activations1, self.pocketActivations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "L1":
dist = 1 - l1Distance(activations1, self.pocketActivations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "Percentage":
dist = 1 - diffPercent(activations1, self.pocketActivations)
termValue = 0.0
termByDist = dist < 1 - distVal
elif distMethod == "NumDiffs":
dist = self.activations.size - diffPercent(
activations1,
self.pocketActivations) * self.pocketActivations.size
termValue = 0.0
termByDist = dist < self.pocketActivations.size - distVal
if newClass == self.originalClass:
# and newClass == dataBasics.next_index(self.originalClass,self.originalClass):
nprint(
"sampling a path ends in a terminal node with depth %s... " %
depth)
self.pocketTree.addOnePath(dist, spansPath, numSpansPath)
print("found a pocket!")
return (depth == 0, dist)
elif termByDist == True:
nprint(
"sampling a path ends by controlled search with depth %s ... "
% depth)
return (depth == 0, termValue)
elif list(set(availableActionIDs) - set(usedActionIDs)) == []:
nprint(
"sampling a path ends with depth %s because no more actions can be taken ... "
% depth)
return (depth == 0, termValue)
else:
#print("continue sampling node ... ")
#allChildren = initialisePixelSets(self.model,self.activations,spansPath.keys())
randomActionIndex = random.choice(
list(set(availableActionIDs) - set(usedActionIDs))
) #random.randint(0, len(allChildren)-1)
(span, numSpan, _) = self.actions[randomActionIndex]
availableActionIDs.remove(randomActionIndex)
usedActionIDs.append(randomActionIndex)
newSpanPath = self.mergeSpan(spansPath, span)
newNumSpanPath = self.mergeNumSpan(numSpansPath, numSpan)
activations2 = applyManipulation(self.activations, newSpanPath,
newNumSpanPath)
(newClass2,
newConfident2) = self.predictWithActivations(activations2)
confGap2 = newConfident - newConfident2
if newClass2 == newClass:
accDims.append((randomActionIndex, confGap2))
else:
accDims.append((randomActionIndex, 1.0))
return self.sampleNextSndRound(newSpanPath, newNumSpanPath,
depth + 1, availableActionIDs,
usedActionIDs, accDims, d)