def _termDictsFromContext(self, context, symbol):
vecs = NameToDictMap()
allNgrams = OccurrenceCounter()
context.neighbours.append(context.origin)
for neighbour in context.neighbours:
nOcc = neighbour.nOccurrences
location = neighbour.location
expressions = self.treeToExprConverter.getExpressionsForSymbol(location, symbol)
# expressions.append('@+$_+@')
# expressions.append('@+EXPR@+$_+@+@')
# print 'FOO %s: %s: %s' % (symbol, location, expressions)
neighbour.setExpressions(expressions)
# add null-vector for function if it does not contain expressions
if len(expressions) == 0:
vecs.add(None, location)
for expr in expressions:
# vecs.add(expr, location, 1.0/nOcc)
# vecs.add(expr, location, 1.0)
vecs.setItem(expr, location, 1.0)
allNgrams.add(expr)
context.neighbours.pop()
if len(vecs.d) == 0 or len(allNgrams.d) == 0:
return None
return (vecs, allNgrams)
class SinkMatrixCreator:
def __init__(self, projectRoot):
self.projectRoot = projectRoot
self.programDir = '/'.join(self.projectRoot.split('/')[:-3]) + '/'
self.sinkUserProvider = SinkUserProvider(self.projectRoot + '../../')
def createMatrixForSink(self, sinkName):
(unused, callsToSink) = self.sinkUserProvider.getSinkByName(sinkName)
functionNames = self.uniq(
['%s%s' % (self.programDir, c[1]) for c in callsToSink])
return self.createMatrixForFunctionNames(functionNames)
"""
This operation looses TF-IDF. I don't think that's the way to go.
"""
def createMatrixForFunctionNames(self, functionNames):
self._loadFunc2SubtreesMap()
self.nameToDictMap = NameToDictMap()
self.allSymbolsDict = OccurrenceCounter()
nameDictMapToMatrix = NameDictMapToMatrix()
functions = [(doc, self.func2SubtreesMap.d[doc])
for doc in functionNames]
for (doc, func) in functions:
for (ngram, nOccurrences) in func.iteritems():
for unused in xrange(nOccurrences):
self.nameToDictMap.add(ngram, doc)
self.allSymbolsDict.add(ngram)
nameDictMapToMatrix.convertFromDicts(self.nameToDictMap,
self.allSymbolsDict)
newTermDocMatrix = nameDictMapToMatrix.termDocumentMatrix
return newTermDocMatrix
def _loadFunc2SubtreesMap(self):
filename = self.projectRoot + 'func2SubtreesMap.pickl'
self.func2SubtreesMap = pickle.load(file(filename))
def uniq(self, seq, idfun=None):
# order preserving
if idfun is None:
def idfun(x):
return x
seen = {}
result = []
for item in seq:
marker = idfun(item)
if marker in seen: continue
seen[marker] = 1
result.append(item)
return result
class SinkMatrixCreator:
def __init__(self, projectRoot):
self.projectRoot = projectRoot
self.programDir = '/'.join(self.projectRoot.split('/')[:-3]) + '/'
self.sinkUserProvider = SinkUserProvider(self.projectRoot + '../../')
def createMatrixForSink(self, sinkName):
(unused, callsToSink) = self.sinkUserProvider.getSinkByName(sinkName)
functionNames = self.uniq([ '%s%s' % (self.programDir, c[1]) for c in callsToSink])
return self.createMatrixForFunctionNames(functionNames)
"""
This operation looses TF-IDF. I don't think that's the way to go.
"""
def createMatrixForFunctionNames(self, functionNames):
self._loadFunc2SubtreesMap()
self.nameToDictMap = NameToDictMap()
self.allSymbolsDict = OccurrenceCounter()
nameDictMapToMatrix = NameDictMapToMatrix()
functions = [(doc ,self.func2SubtreesMap.d[doc]) for doc in functionNames]
for (doc, func) in functions:
for (ngram, nOccurrences) in func.iteritems():
for unused in xrange(nOccurrences):
self.nameToDictMap.add(ngram, doc)
self.allSymbolsDict.add(ngram)
nameDictMapToMatrix.convertFromDicts(self.nameToDictMap, self.allSymbolsDict)
newTermDocMatrix = nameDictMapToMatrix.termDocumentMatrix
return newTermDocMatrix
def _loadFunc2SubtreesMap(self):
filename = self.projectRoot + 'func2SubtreesMap.pickl'
self.func2SubtreesMap = pickle.load(file(filename))
def uniq(self, seq, idfun=None):
# order preserving
if idfun is None:
def idfun(x): return x
seen = {}
result = []
for item in seq:
marker = idfun(item)
if marker in seen: continue
seen[marker] = 1
result.append(item)
return result
def calculateCheckVectors(WFuncs, CFuncs, F, binary=True, alpha=1, weighByF = False):
WDict = NameToDictMap()
for (functionLocation, symbols) in WFuncs.d.iteritems():
if not functionLocation in CFuncs.d:
# The function does not contain any check,
# thus, projected onto the check-space, it's
# the NULL-vector
WDict.d[functionLocation] = {}
continue
CFunc = CFuncs.d[functionLocation]
for (s,occurrences) in symbols.iteritems():
if binary: occurrences = 1
if (not s in F):
# This symbol is never checked
WDict.setItem(s, functionLocation, 0)
elif (s in CFunc):
w = 1.0
if weighByF: w = F[s]
nChecks = CFunc[s]
if binary: nChecks = 1
WDict.setItem(s, functionLocation, (occurrences - alpha*nChecks)*w)
else:
w = 1.0
if weighByF: w = F[s]
WDict.setItem(s, functionLocation, occurrences*w)
return WDict
def relevancyWeighting(checkVectors, featureDir):
k = 20
termDocMatrix = pickle.load(file(featureDir + 'termDocMatrix.pickl'))
functionLocations = termDocMatrix.index2Doc
# it doesn't make much sense that we use euclidean distances here,
# should be L1, but I can't calculate L1 on the sparse matrices for now.
from scipy.spatial.distance import squareform
D = squareform(pickle.load(file(featureDir + 'D_euclidean.pickl')))
anomalyCalculator = AnomalyCalculator()
(NNV, NNI) = anomalyCalculator.calculateNearestNeighbours(k, D)
WDict = NameToDictMap()
for i in xrange(len(functionLocations)):
location = functionLocations[i]
if not location in checkVectors.d:
continue
WDict.d[location] = checkVectors.d[location]
indices = NNI[:, i]
gamma = float(numpy.sum(NNV[:, i])) / k
locations = [functionLocations[j] for j in indices]
V = [checkVectors.d[l] for l in locations if l in checkVectors.d]
distances = [
NNV[j, i] for j in xrange(len(locations))
if locations[j] in checkVectors.d
]
# len(V) may be unequal to k if at least one of the nearest neighbours has no checks.
# It is then a null-vector, so we're implicitly adding it in mean-calculation
meanVector = {}
for (v, d) in zip(V, distances):
for (name, score) in v.iteritems():
try:
meanVector[name] += (1 - d) * (float(score) / k)
except KeyError:
meanVector[name] = (1 - d) * (float(score) / k)
for (name, score) in checkVectors.d[location].iteritems():
if meanVector.has_key(name):
score -= meanVector[name]
if score < 0: score = 0
WDict.setItem(name, location, score)
return WDict
def relevancyWeighting(checkVectors, featureDir):
k = 20
termDocMatrix = pickle.load(file(featureDir + 'termDocMatrix.pickl'))
functionLocations = termDocMatrix.index2Doc
# it doesn't make much sense that we use euclidean distances here,
# should be L1, but I can't calculate L1 on the sparse matrices for now.
from scipy.spatial.distance import squareform
D = squareform(pickle.load(file(featureDir + 'D_euclidean.pickl')))
anomalyCalculator = AnomalyCalculator()
(NNV, NNI) = anomalyCalculator.calculateNearestNeighbours(k, D)
WDict = NameToDictMap()
for i in xrange(len(functionLocations)):
location = functionLocations[i]
if not location in checkVectors.d:
continue
WDict.d[location] = checkVectors.d[location]
indices = NNI[:,i]
gamma = float(numpy.sum(NNV[:,i]))/k
locations = [functionLocations[j] for j in indices]
V = [checkVectors.d[l] for l in locations if l in checkVectors.d]
distances = [NNV[j,i] for j in xrange(len(locations)) if locations[j] in checkVectors.d]
# len(V) may be unequal to k if at least one of the nearest neighbours has no checks.
# It is then a null-vector, so we're implicitly adding it in mean-calculation
meanVector = {}
for (v,d) in zip(V,distances):
for (name, score) in v.iteritems():
try:
meanVector[name] += (1-d)* (float(score)/k)
except KeyError:
meanVector[name] = (1-d)* (float(score)/k)
for (name, score) in checkVectors.d[location].iteritems():
if meanVector.has_key(name):
score -= meanVector[name]
if score < 0: score = 0
WDict.setItem(name, location, score)
return WDict
class FeatureArray(object):
def __init__(self):
self.vecs = NameToDictMap()
self.allSymbols = OccurrenceCounter()
def add(self, label, items):
if len(items) == 0:
self.vecs.add(None, label)
return
for item in items:
itemStr = str(item)
self.vecs.add(itemStr, label)
self.allSymbols.add(itemStr)
def __iter__(self):
for x in self.vecs.iteritems():
yield x
def createMatrixForFunctionNames(self, functionNames):
self._loadFunc2SubtreesMap()
self.nameToDictMap = NameToDictMap()
self.allSymbolsDict = OccurrenceCounter()
nameDictMapToMatrix = NameDictMapToMatrix()
functions = [(doc, self.func2SubtreesMap.d[doc])
for doc in functionNames]
for (doc, func) in functions:
for (ngram, nOccurrences) in func.iteritems():
for unused in xrange(nOccurrences):
self.nameToDictMap.add(ngram, doc)
self.allSymbolsDict.add(ngram)
nameDictMapToMatrix.convertFromDicts(self.nameToDictMap,
self.allSymbolsDict)
newTermDocMatrix = nameDictMapToMatrix.termDocumentMatrix
return newTermDocMatrix
def _termDictsFromContext(self, context, symbol):
vecs = NameToDictMap()
allNgrams = OccurrenceCounter()
context.neighbours.append(context.origin)
for neighbour in context.neighbours:
nOcc = neighbour.nOccurrences
location = neighbour.location
expressions = self.treeToExprConverter.getExpressionsForSymbol(
location, symbol)
# expressions.append('@+$_+@')
# expressions.append('@+EXPR@+$_+@+@')
# print 'FOO %s: %s: %s' % (symbol, location, expressions)
neighbour.setExpressions(expressions)
# add null-vector for function if it does not contain expressions
if len(expressions) == 0:
vecs.add(None, location)
for expr in expressions:
# vecs.add(expr, location, 1.0/nOcc)
# vecs.add(expr, location, 1.0)
vecs.setItem(expr, location, 1.0)
allNgrams.add(expr)
context.neighbours.pop()
if len(vecs.d) == 0 or len(allNgrams.d) == 0:
return None
return (vecs, allNgrams)
def calculateCheckVectors(WFuncs,
CFuncs,
F,
binary=True,
alpha=1,
weighByF=False):
WDict = NameToDictMap()
for (functionLocation, symbols) in WFuncs.d.iteritems():
if not functionLocation in CFuncs.d:
# The function does not contain any check,
# thus, projected onto the check-space, it's
# the NULL-vector
WDict.d[functionLocation] = {}
continue
CFunc = CFuncs.d[functionLocation]
for (s, occurrences) in symbols.iteritems():
if binary: occurrences = 1
if (not s in F):
# This symbol is never checked
WDict.setItem(s, functionLocation, 0)
elif (s in CFunc):
w = 1.0
if weighByF: w = F[s]
nChecks = CFunc[s]
if binary: nChecks = 1
WDict.setItem(s, functionLocation,
(occurrences - alpha * nChecks) * w)
else:
w = 1.0
if weighByF: w = F[s]
WDict.setItem(s, functionLocation, occurrences * w)
return WDict
def createMatrixForFunctionNames(self, functionNames):
self._loadFunc2SubtreesMap()
self.nameToDictMap = NameToDictMap()
self.allSymbolsDict = OccurrenceCounter()
nameDictMapToMatrix = NameDictMapToMatrix()
functions = [(doc ,self.func2SubtreesMap.d[doc]) for doc in functionNames]
for (doc, func) in functions:
for (ngram, nOccurrences) in func.iteritems():
for unused in xrange(nOccurrences):
self.nameToDictMap.add(ngram, doc)
self.allSymbolsDict.add(ngram)
nameDictMapToMatrix.convertFromDicts(self.nameToDictMap, self.allSymbolsDict)
newTermDocMatrix = nameDictMapToMatrix.termDocumentMatrix
return newTermDocMatrix
def __init__(self):
self.vecs = NameToDictMap()
self.allSymbols = OccurrenceCounter()
